Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review

Development, validation and use of... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/12-1598" }, "headline": "Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills...", "datePublished": "2023-12-18T09:39:51", "dateModified": "2026-04-24T08:31:53", "author": [ { "@type": "Person", "name": "Joel Figueroa-Quiñones" }, { "@type": "Person", "name": "Juan Ipanaque-Neyra" }, { "@type": "Person", "name": "Heber Gómez Hurtado" }, { "@type": "Person", "name": "Oscar Bazo-Alvarez" }, { "@type": "Person", "name": "Juan Carlos Bazo-Alvarez" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background In basic motor skills evaluation, two observers can eventually mark the same child’s performance differently. When systematic, this brings serious noise to the assessment. New motion sensing and tracking technologies offer more precise measures of these children’s capabilities. We aimed to review current development, validation and use of artificial intelligence-related technologies that assess basic motor skills in children aged 3 to 6 years old. Methods We performed a scoping review in Medline, EBSCO, IEEE and Web of Science databases. PRISMA Extension recommendations for scoping reviews were applied for the full review, whereas the COSMIN criteria for diagnostic instruments helped to evaluate the validation of the artificial intelligence (AI)-related measurements. Results We found 672 studies, from which 12 were finally selected, 7 related to development and validation and 5 related to use. From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. Studies on AI-related technologies have prioritized development and technological features. The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children’s motor development. They also did not consider the technical characteristics that are typically assessed in psychometric instruments designed to assess motor skills in children (e.g., the Consensus-based Standards for the Selection of Health Measurement Instruments “COSMIN”). Therefore, the use of these AI-related technologies in scientific research is still limited. Conclusion Clinical measurement standards have not been integrated into the development of AI-related technologies for measuring basic motor skills in children. This compromises the validity, reliability and practical utility of these tools, so future improvement in this type of research is needed. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/12-1598/v3", "name": " Development, validation and use of artificial-intelligence-related..." } } ] } Home Browse Development, validation and use of artificial-intelligence-related... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Figueroa-Quiñones J, Ipanaque-Neyra J, Gómez Hurtado H et al. Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.12688/f1000research.138616.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Revised Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] Joel Figueroa-Quiñones https://orcid.org/0000-0003-3907-7606 1 , Juan Ipanaque-Neyra 2 , Heber Gómez Hurtado https://orcid.org/0000-0002-7259-7817 2,3 , Oscar Bazo-Alvarez 2,4 , Juan Carlos Bazo-Alvarez 5,6 Joel Figueroa-Quiñones https://orcid.org/0000-0003-3907-7606 1 , Juan Ipanaque-Neyra 2 , [...] Heber Gómez Hurtado https://orcid.org/0000-0002-7259-7817 2,3 , Oscar Bazo-Alvarez 2,4 , Juan Carlos Bazo-Alvarez 5,6 PUBLISHED 24 Apr 2026 Author details Author details 1 Universidad Autonoma de Ica, Ica, Peru 2 Instituto de Investigación, Capacitación y Desarrollo Psicosocial y Educativo (PSYCOPERU), Lima, Peru 3 Ingeniería de Sistemas e Informática, Universidad Tecnológica del Perú, Lima, Peru 4 School of Medicine, Universidad San Juan Bautista, Lima, Peru 5 Research Department of Primary Care and Population Health, University College London, London, UK 6 MedFam Group, School of Medicine, Universidad Cesar Vallejo, Trujillo, Peru Joel Figueroa-Quiñones Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Juan Ipanaque-Neyra Roles: Data Curation, Formal Analysis, Resources, Validation, Visualization, Writing – Original Draft Preparation Heber Gómez Hurtado Roles: Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation Oscar Bazo-Alvarez Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Review & Editing Juan Carlos Bazo-Alvarez Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Background In basic motor skills evaluation, two observers can eventually mark the same child’s performance differently. When systematic, this brings serious noise to the assessment. New motion sensing and tracking technologies offer more precise measures of these children’s capabilities. We aimed to review current development, validation and use of artificial intelligence-related technologies that assess basic motor skills in children aged 3 to 6 years old. Methods We performed a scoping review in Medline, EBSCO, IEEE and Web of Science databases. PRISMA Extension recommendations for scoping reviews were applied for the full review, whereas the COSMIN criteria for diagnostic instruments helped to evaluate the validation of the artificial intelligence (AI)-related measurements. Results We found 672 studies, from which 12 were finally selected, 7 related to development and validation and 5 related to use. From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. Studies on AI-related technologies have prioritized development and technological features. The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children’s motor development. They also did not consider the technical characteristics that are typically assessed in psychometric instruments designed to assess motor skills in children (e.g., the Consensus-based Standards for the Selection of Health Measurement Instruments “COSMIN”). Therefore, the use of these AI-related technologies in scientific research is still limited. Conclusion Clinical measurement standards have not been integrated into the development of AI-related technologies for measuring basic motor skills in children. This compromises the validity, reliability and practical utility of these tools, so future improvement in this type of research is needed. READ ALL READ LESS Keywords Basic motor skills, fundamental movements, machine learning, motion detection, prediction techniques Corresponding Author(s) Joel Figueroa-Quiñones ( [email protected] ) Close Corresponding author: Joel Figueroa-Quiñones Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Figueroa-Quiñones J et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Figueroa-Quiñones J, Ipanaque-Neyra J, Gómez Hurtado H et al. Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.12688/f1000research.138616.3 ) First published: 18 Dec 2023, 12 :1598 ( https://doi.org/10.12688/f1000research.138616.1 ) Latest published: 24 Apr 2026, 12 :1598 ( https://doi.org/10.12688/f1000research.138616.3 ) Revised Amendments from Version 2 In this third version of the manuscript, we introduced targeted revisions in response to reviewer comments to improve the clarity, methodological framing, and interpretation of the review. The title, author list, figures, and underlying data remain unchanged. The abstract was revised to explicitly report the literature search cutoff date. The Introduction was refined to strengthen the background on basic motor skills and to better acknowledge contemporary AI approaches, including deep learning-based pipelines, while maintaining the clinical and assessment-oriented focus of the review. In the Methods and Data Analysis sections, we clarified the rationale for using COSMIN standards, explaining that COSMIN was applied from a measurement perspective to assess evidence relevant to the use of these technologies as assessment tools, rather than to evaluate algorithmic performance itself. In line with this, the Results and Discussion sections were revised to better distinguish between engineering validation and clinical/psychometric validation, and to clarify the interpretation of the COSMIN findings. We also corrected wording, consistency, and formatting issues in the text and tables. In addition, the Strengths and Limitations section was revised to explicitly acknowledge that the literature search was conducted up to January 30, 2023, and that the findings should therefore be interpreted within that temporal scope. Finally, the implications section was strengthened to highlight the need for more interdisciplinary and standardized validation frameworks for AI-related technologies used in children’s motor skill assessment. In this third version of the manuscript, we introduced targeted revisions in response to reviewer comments to improve the clarity, methodological framing, and interpretation of the review. The title, author list, figures, and underlying data remain unchanged. The abstract was revised to explicitly report the literature search cutoff date. The Introduction was refined to strengthen the background on basic motor skills and to better acknowledge contemporary AI approaches, including deep learning-based pipelines, while maintaining the clinical and assessment-oriented focus of the review. In the Methods and Data Analysis sections, we clarified the rationale for using COSMIN standards, explaining that COSMIN was applied from a measurement perspective to assess evidence relevant to the use of these technologies as assessment tools, rather than to evaluate algorithmic performance itself. In line with this, the Results and Discussion sections were revised to better distinguish between engineering validation and clinical/psychometric validation, and to clarify the interpretation of the COSMIN findings. We also corrected wording, consistency, and formatting issues in the text and tables. In addition, the Strengths and Limitations section was revised to explicitly acknowledge that the literature search was conducted up to January 30, 2023, and that the findings should therefore be interpreted within that temporal scope. Finally, the implications section was strengthened to highlight the need for more interdisciplinary and standardized validation frameworks for AI-related technologies used in children’s motor skill assessment. See the authors' detailed response to the review by Abraham M. Joshua and Ashish John Prabhakar See the authors' detailed response to the review by Dhahbi Wissem READ REVIEWER RESPONSES Introduction The development of basic motor skills (BMS) in children aged 3 to 6 years is critical, as this is a period of rapid motor growth, where children acquire physical skills that allow them to participate in a variety of activities. 1 , 2 At this age, children experience significant improvements in gross motor control, allowing them to perform movements such as running, jumping, and manipulating objects with greater precision. 3 , 4 The acquisition of these motor skills is essential for physical, cognitive and emotional development, as BMS are strongly linked to general well-being, self-esteem and social integration. 5 Early BMS competence may also have long-term biomechanical relevance, as it supports more efficient movement patterns, later participation in physical activity, and potentially lower injury risk across development. 49 For example, children with BMS stimulation tend to participate more in physical activities (e.g., school games and sports), suggesting socioemotional and health benefits such as early prevention of obesity. 6 Likewise, some studies have designed, implemented, and recommended early interventions to promote healthy BMS development in preschool children. 7 To evaluate the efficacy of these interventions and to monitor the optimal development of BMS in children, valid and reliable measurement tools are needed. Typically, BMS assessment relies on trained professionals who observe, record, and score children’s performance on specific motor tasks. 8 , 9 However, a major challenge in this approach is observer bias. Even when raters receive standardized training, small differences in scoring can introduce variability in BMS measurements. This variability reduces the accuracy of the assessment and can lead to misinterpretations. For example, two children with similar motor skills may receive different scores depending on the assessor, resulting in inconsistent results. When these inconsistencies follow a systematic pattern, they contribute to observer bias, a well-documented source of measurement error. 10 , 11 Earlier reviews in behavioral research documented substantial shortcomings in the reporting of interobserver reliability and procedures to minimize observer bias. 12 Similarly, another review on child development found that the quality of reporting on the use of assessors in these studies was poor and that variability in assessor performance may obscure the true developmental status of children, compromising complex and costly clinical decisions. 13 AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment. 14 For example, for motion capture and analysis, computer vision tools such as OpenPose, MediaPipe and DeepLabCut enable pose estimation and tracking of key points of the human body with high accuracy. 15 In addition, deep learning techniques, such as Convolutional Neural Networks (CNN) and vision-specialized Transformer Models (ViT), have proven to be effective in classifying motion sequences in videos. 16 In that sense, these AI-related technologies for recognizing and classifying human motion patterns consist of several steps ( Figure 1 ). 17 First, sensor or video devices capture data on human movement. Then, these data undergo pre-processing to reduce noise and enhance relevant features. This step often involves filtering techniques, such as Fast Fourier Transformation (which helps separate important movement signals from background noise) or wavelet transforms. Additionally, to simplify complex data and highlight key movement patterns, methods like principal components analysis (which reduces data dimensions while preserving essential information) or linear discriminant analysis (which enhances the distinction between movement categories) are applied. 17 Next, feature selection methods come into play, determining a subset of features from the initial set that is highly suitable for subsequent classification while adhering to various optimisation criteria. Among the efficient methods for feature selection are Sequential Forward Selection, which starts with an empty set and iteratively adds the feature that best meets the optimisation criterion, and Backward Selection, which involves removing features from the set in a repetitive manner. However, many contemporary AI pipelines rely on end-to-end deep learning approaches, such as CNNs or vision transformers, which can learn relevant representations directly from images, videos, or pose sequences with limited or no manual feature extraction. Finally, AI or machine learning classifiers are required to identify the corresponding class of motion, in our case, a class that reflects the BMS development of a child ( e.g. , delayed, normal or advanced for its age group). Machine learning tools include binary classification trees, decision engines, Bayes classifiers, k-Nearest Neighbour, rule-based approaches, linear discriminant classifiers and Support Vector Machines. More sophisticated deep learning tools, such as neural networks, are also used. From here onwards, we indistinctly use the expression ‘AI-related technology’ for referring to the full process described in Figure 1 or just the classification tools. Figure 1. Process of recognition and classification of human motion patterns performed by artificial intelligence (AI)-related technologies. The application of AI-related technology in physical performance assessment is rapidly increasing. 18 For example, machine learning techniques have been used to assess physical activity intensity in adults. 19 , 20 A recent review identified at least 53 studies on motion detection using deep learning or machine learning, with 75% of these studies published since 2015. 21 AI has also been applied to detect gait abnormalities 22 and diagnose health conditions related to walking patterns, 23 , 24 as well as to identify early motor skill impairments linked to neurodevelopmental disorders. 25 Other AI-based algorithms have been implemented to evaluate psychomotor learning performance in students. 26 , 27 However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Moreover, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools. In this study, we aimed to perform a scoping review on studies related to the development and use of AI-related technologies to assess BMS in children. Our objectives were to: 1) determine the general characteristics of the studies; 2) describe the engineering of the AI technologies designed to assess BMS in preschoolers; 3) determine the substantive validation performed on the AI technologies identified, and 4) describe the current use of these AI technologies in applied research. Methods The protocol for this review is available here . 28 The PRISMA Extension recommendations for scoping reviews were applied for the full review, whereas the COSMIN guidelines were applied for objective 2. 29 , 30 The checklists of these guidelines can be found here . 31 Target studies We were interested in published studies focused on engineering, substantive validation, or the use of AI-related technologies developed to evaluate BMS in children. A study was focused on engineering when it was strictly dedicated to developing algorithms for motor skills recognition and classification. A study was focused on substantive validation when the validity and reliability of the AI-related technology were evaluated following psychometric international standards. 32 A study only used AI-related technology when it did not include engineering or validation; in other words, it just used the technology developed by someone else. We also defined the following criteria for the search: 1) studies in preschool-aged children (3 to 6 years), 2) studies in which the motor ability (motor or play skills) of the child was assessed using AI-related technologies for motion detection, and 3) studies in which at least one of the basic motor skills described in the literature (running, jumping, kicking, throwing, or catching a ball) was measured. In addition, we excluded 1) studies that did not clearly describe the AI-related technology used or developed, 2) opinion articles, editorials, or narrative reviews without empirical data and 3) grey literature (e.g. theses, dissertations, or non-peer-reviewed reports). Search strategy We searched for studies published before January 30, 2023 in the target publications in Medline (SCR_002185), Web of Science (SCR_022706), IEEE (SCR_008314), and EBSCO (SCR_022707). These databases were selected because they specialize in biomedical, engineering, and multidisciplinary research, ensuring that we captured relevant studies in health sciences, AI applications, and motion analysis. Search terms included keyword combinations such as “child,” “preschool,” “basic motor skills,” “artificial intelligence,” “motion sensing,” and “calibration,” along with related terms and synonyms identified through a preliminary literature review (keywords) and controlled vocabulary (MeSH terms). The full search strategy and complete list of search terms are available here . 33 The search formulas were applied to the databases and all the files were exported in RIS format. Then, to ensure an objective selection process, these identified files were uploaded to the Rayyan platform which facilitated blind selection by the reviewers and expedited the identification of duplicates. The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator. Additionally, we mapped those studies that updated or used the AI-related technology identified as engineered and validated in the previous step, by exploring the citations/references reported in the latter. Data extraction Data extraction was performed in a structured manner using a pre-designed form . 34 To reduce errors and improve the accuracy of the extracted data, one peer reviewer performed the initial extraction and a second peer independently verified the information. Any discrepancies in the extraction were reviewed jointly and/or, with the intervention of the principal investigator. Cross-checks were implemented to ensure the consistency of the information collected. The form included data about the general characteristics of the studies, the engineering of the AI-related technologies, the substantive validation of these technologies, and their current use for BMS assessment in children. 1. General information: First author of the study, country of the study, year of publication, number and sex of participants, health condition ( e.g. , children with a medical condition that could influence their motor skills). 2. Engineering: Motion capture interface type, basic composition of technologies, system used for motion capture, type of programming language used for system development or modelling, and technology accessibility. 3. Substantive validation: Type of technology developed and validated, validation method, data collection methods, data for COSMIN (see next section), feasibility and usability of the technology. 4. Use: Type of technology used, training of the evaluation team, reported technology reliability, limitations during the technology use, advantages of the technology application, complementary tools, reference to a publication on the technology used. Data analysis All data collected were summarised as categorical variables, organised and presented in tables, using descriptive statistics such as simple frequencies and percentages. Since this was a scoping review, a narrative synthesis was used to summarize the findings of the studies, focusing on the characteristics and psychometric properties evaluated according to COSMIN standards. The COSMIN standards were applied to assess the technical quality of the substantive validation of the AI-related technologies for BMS evaluation. 27 In practice, these technologies ( e.g. , algorithms) work like psychometric tests ( e.g. , producing similar BMS measurements); thus, the former can be ‘substantively validated’ as the latter usually are. In this review, COSMIN was applied from a measurement perspective to examine evidence relevant to assessment use, rather than to evaluate algorithmic performance per se. COSMIN is an international standard for reviewing the technical quality of validation studies of psychometric tools ( e.g. , tests for measuring BMS). To perform the COSMIN assessment, two investigators independently assessed and scored eight psychometric properties or indicators (content validity, internal consistency, structural validity, reliability, measurement error, criterion validity, construct validity, and responsiveness). Each indicator was evaluated according to the checklist proposed by Mokkink et al. 35 For this study, we scored as follows: 1 = N. A, 2 = inadequate, 3 = doubtful, 4 = adequate and 5 = very good. A total score was calculated for each indicator, keeping similar levels for interpretations (very good, adequate, doubtful, inadequate, N.A.). All results from COSMIN assessment were presented in a table. Results We identified 672 studies in the first search step, from which 12 studies were finally selected. Among these studies, five were focused on AI-related technology use, while seven were focused on AI-related technology engineering and/or validation ( Figure 2 ). Figure 2. PRISMA diagram for the scoping review. During the last decade, most studies were performed in Asian and European countries (n=9/12, 74.9%) ( Table 1 ). Almost all studies were carried out in children of both sexes (n=9/12, 75%), and only one was focused on children with some type of motor problem. Table 1. General characteristics. Characteristics of the studies N=12 Continent Asia 5 (41.6) Europe 4 (33.3) Latin America 1 (8.33) North America 1 (8.33) Oceania 1 (8.33) Year of publication 2011-2021 10 (83.3) ≤2010 2 (16.7) Participant gender Boys only 1 (8.33) Girls only 1 (8.33) Both boys and girls 9 (75.0) Not report 1 (8.33) Population type Children without health problems 9 (75.0) Children with attention and concentration problems 1 (8.3) Children with some delay in motor development 1 (8.3) Obese children 1 (8.3) To capture the child’s movement, researchers mostly used simple devices such as digital video cameras (n=5/7, 71.4%) ( Table 2 ). More sophisticated devices were less common, such as sensors attached to the body (n=2/7, 28.6%) or multimedia devices connected to personal computers (n=2/7, 28.6%). The software used for each device was different for each study. The most common type of AI-related technology was machine learning tools for movement pattern recognition (n=4/7, 57.1%), while deep learning algorithms were rarely used (n=1/7, 14.3%). Only a few of these tools are free-access (n=2/7, 28.6%). Most codes were implemented in Python (SCR_008394) and supported by libraries such as OpenGL (which produces 2D and 3D graphics) 36 – 38 and Numpy (SCR_008633) (which creates vectors and matrices, and mathematical functions) (45) that helps to process images that are captured in real-time and obtain an accurate representation of the movement. Table 2. Engineering characteristics of studies that developed artificial intelligence (AI)-related technologies. Characteristics N=7 Motion capture device Digital cameras 5 (71.4) Smartphones application 1 (14.3) iPod touch 1 (14.3) Other motion capture devices Tracker, marker or movement sensor 2(28.6) Multimedia devices 2(28.6) Both of them 3 (42.2) System used for motion capture Microsoft Kinect 1 (14.3) myoMOTION 1 (14.3) OptiTrack Arena 1 (14.3) ActiGraph GT3X 1 (14.3) ProReflex-MCU 240; QualisysMedical AB 1 (14.3) Acceleration recorder 1 (14.3) iPod touch (operative system) 1 (14.3) Type of AI-related tool Machine learning for movement patterns recognition 4 (57.1) Kinematic analysis 2(28.6) Deep learning and neural networks 1 (14.3) Accessibility to technology or codes Free or open source 2(28.6) Paid/does not report 5 (71.4) For the COSMIN evaluation, we considered seven studies that developed a substantive validation of AI technologies ( Table 3 ). More than half of the studies reported the evaluation of content validity (n=4/7, 57.1%), reliability (n=1/7, 14.2%), and construct validity (n=1/7, 14.2%) with an adequate level. However, other measurement properties, such as structural validity, measurement error and responsiveness, were inadequately or not evaluated in all studies, according to COSMIN standards (n=5/8, 62.5%). These ratings should be interpreted as reflecting limited psychometric validation or reporting, rather than poor algorithmic performance per se, since many included studies primarily focused on engineering development and technical performance. In traditional motor assessment, reliability usually requires evidence of stable scores under similar testing conditions, whereas responsiveness requires evidence that the tool can detect meaningful changes over time or after intervention. Such evidence was rarely reported in the studies included in this review. It was not unusual for studies to declare the evaluation of a psychometric property (e.g., reliability) without reporting the corresponding final results. Table 3. Studies that developed substantive validation of artificial intelligence (AI)-related technology (n = 7) COSMIN Standards. MEASUREMENT PROPERTY Study 1 Study 2 Study 3 Study 4 Study 5 Study 6 Study 7 Shengyan Li (2017) Santiago Ramos (2014) Yukie Amemiya (2018) Hsun-Ying Mao (2014) Satoshi Suzuki (2019) Matthew N. Ahmadi (2020) Parvinpour, S. (2019) CONTENT VALIDITY Relevance INADEQUATE DOUBTFUL DOUBTFUL INADEQUATE DOUBTFUL INADEQUATE DOUBTFUL Comprehensiveness DOUBTFUL ADEQUATE ADEQUATE DOUBTFUL ADEQUATE DOUBTFUL ADEQUATE Comprehensibility DOUBTFUL ADEQUATE ADEQUATE DOUBTFUL ADEQUATE DOUBTFUL ADEQUATE INTERNAL CONSISTENCY INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE STRUCTURAL VALIDITY INADEQUATE DOUBTFUL DOUBTFUL DOUBTFUL DOUBTFUL INADEQUATE INADEQUATE RELIABILITY DOUBTFUL DOUBTFUL DOUBTFUL ADEQUATE DOUBTFUL DOUBTFUL DOUBTFUL (ICC=0,67) MEASUREMENT ERROR INADEQUATE DOUBTFUL DOUBTFUL DOUBTFUL INADEQUATE DOUBTFUL INADEQUATE CRITERION VALIDITY INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE CONSTRUCT VALIDITY Convergent validity INADEQUATE INADEQUATE ADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE Discriminative validity INADEQUATE INADEQUATE DOUBTFUL DOUBTFUL DOUBTFUL DOUBTFUL DOUBTFUL RESPONSIVENESS INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE INADEQUATE In studies using AI-related technology, the children’s movements were captured by trained personnel (n=2/5, 40%) using digital cameras or camcorders (n=4/5, 80%) ( Table 4 ). In addition, some supporting technologies that provide high-quality video motion capture, such as “Quintic Biomechanics software”, was also reported. Users reported some advantages of these technologies; for example, the short-term evaluation needed and precise and consistent measures that allow a detailed analysis of motor skills. However, no formal generalization of the conclusions to larger populations was reported as a technical limitation. Table 4. Current use of studies that used artificial intelligence (AI)-related technology. Characteristics N=5 Motion capture device Digital camera/camcorder 4 (80.0) Haptic interface 1 (20.0) Training for the evaluation team Yes 2 (40.0) No/not reported 3 (60.0) Reliability of AI-related technology Inter- and intra-rater reliability 2 (40.0) Not reported 3 (60.0) Limitations reported while using technology Yes 1 (20.0) No/not reported 4 (80.0) Advantages reported while using technology Yes 5 (100.0) No/not reported 0 (0.0) Complementary tools or technology Laptop 1 (20.0) Quintic biomechanical analysis software 1 (20.0) Portable DVD 1 (20.0) Panasonic AG-7350 recorder, a Sony PVM-1341 monitor and a microcomputer 1 (20.0) Not reported 1 (20.0) Used technology reference Published 0 (0.0) Manual 5 (100.0) We identified 10 studies that updated and/or applied the exact AI-related technology reported in Tables 2 and 3 (Table III, supplemental material). Among those studies, 7/10, (70%) were used for the assessment of motor skills; and 3/10, (30%) were updated and used ( i.e. , a new version of the technology). Discussion We performed a scoping review of AI-related technologies developed and used to assess motor skills in children. Engineering work and technological features have been prioritized in these studies, such as the use of advanced motion-capture systems and the training of sophisticated machine-learning algorithms for movement pattern recognition. Validation was predominantly conducted using engineering criteria focused on technical performance, which is appropriate within algorithm-development contexts. However, when these technologies are intended to function as assessment tools for children’s motor skills, additional evidence on measurement properties becomes relevant from a clinical and psychometric perspective. Therefore, our findings suggest an interdisciplinary gap between technical validation and assessment-oriented validation, rather than a deficiency of engineering approaches alone. The use of these AI-related technologies in scientific research is still limited. Most studies on AI technologies engineering ignored the standard psychometric validation process ( i. e. , COSMIN standards). Although many of them complied with the good practices in the development of image processing-oriented software, none of them integrated a substantive validation. AI-related technology is good for identifying movement patterns that are rare in children or patterns that children of a certain age should show, and they are not. This capacity has enormous value for clinical and educative purposes. However, for these AI measures to be integrated into a formal clinical evaluation, some technical features must be confirmed. For example, the measurement error estimate is essential for evaluating individuals from the target population, allowing the definition of critical ranges ( i.e. , minimum and maximum values) to contrast individual measures and conclude an advantaged, normal or sub-normal motor skill development. Another important psychometric characteristic is responsiveness, which reveals whether any change seen between within-individual AI measurements performed before and after an intervention corresponds to true changes in motor skills (smallest detectable changes), which is linked to investigating when these changes are clinically relevant (minimal important changes). A previous review of AI technologies for evaluating motor skills in paediatric populations warns that the validation of these tools is limited. 39 As we do here, they concluded that this limitation has practical implications in the assessment precision and applicability in clinical contexts. Without a standard psychometric validation process, AI developers do not collect the correct and sufficient evidence to ensure the minimal validity and reliability required for this kind of measurement. For example, one of our reviewed studies reported that the AI algorithm was reliable and valid because it was based on a test previously declared reliable by its original author. 40 Differences between the population for which the original test was created and the sample used to develop the AI version can seriously compromise the reliability of the measures and their clinical interpretation criterion due to cultural/ethnic, linguistic, social, economic and age differences. 10 In practice, clinical interpretation is an essential component of measurement validity and usually requires evidence beyond the standard qualification norm. For example, the recent study reported a new video-based technology that was based on a classical motor skill test ( i.e. , that needs paper, pencil and evaluator’s criteria), showing concurrent validity against another measure of motor skills. 41 Contrasting AI measurements against external independent criteria is essential, not only to confirm that the algorithm is measuring what we intend to but also to connect these measurements with other signs and symptoms clinically relevant. In this way, AI measurements become more informative and useful for a full evaluation of a children’s healthy development. 42 There are some factors explaining the limited production of AI-related technologies for evaluating motor skills in children. There is a priority for using AI to assess other health problems in this and other populations. During the last two decades, most AI for health has been developed for the diagnosis and follow-up of physical problems such as cancer, cardiovascular diseases, or neurodegenerative disorders in adult subjects. 18 , 43 High costs slow the production of these AI-related technologies, 44 , 45 especially in low-and-middle-income countries. Rich countries promote the investment of significant amounts of money for developing new cutting-edge technology, 46 although for a wide range of purposes. In low- and middle-income countries, AI development suffers from some extra limitations, such as insufficient economic and human resources, limited data, non-transparent AI algorithm sharing, and scarce collaboration between technological institutions. 47 The use of AI-related technologies in scientific research is also limited, and this is linked to other factors. As expected, developers focused on engineering and not research to facilitate the use of their technologies. For example, only one of our reviewed studies performed a usability and feasibility analysis, 48 which is important to make the technology friendlier and more accessible to future users. 40 This can be explained, in part, because most of them is still developed within the academia, and not yet in the private sector and for commercial purposes. However, considering how they can improve the speed and precision of BMS evaluation of children for doctors and teachers, these AI-related technologies have great commercial potential in the educative and clinical contexts. Strengths and Limitations This is the first scoping review emphasising the substantive validation processes of AI-related technologies produced to assess motor skills in preschool children. The databases consulted during the identification and selection of studies were specialised and extensive; thus, there was a limited loss of relevant information. Also, although this review was based on COSMIN standards to assess the psychometric quality of AI-related technologies, due to the heterogeneity observed in the included studies, no specific adjustments were made to control for possible confounding variables. Therefore, the conclusions need to be interpreted with caution. It is recommended that future research address these factors and use control methods to provide more generalizable conclusions. Furthermore, feasibility and usability were extracted only if the reviewed studies explicitly reported having done so in their analysis of AI-related technologies. Therefore, further studies should evaluate these analyses using a standardized framework. This review did not aim to analyze associations between variables; however, variability in sample sizes, age ranges, and types of AI-based technologies used across studies may affect the comparability and generalizability of the findings. These differences should be considered when interpreting the results and highlight the need for more standardized approaches in future research. The literature search was conducted up to January 30, 2023. Given the rapid evolution of artificial intelligence and computer vision, more recent studies may not have been captured. Therefore, the findings should be interpreted as reflecting the evidence available up to that date rather than the most current state of the field. Implications To facilitate use, developers could conduct studies that evaluate the acceptance, ease of use, cost-effectiveness, and accessibility of these technologies. For example, most technologies rely on sensors and monitors that, while accurate, can be costly, require specialized training, and can be difficult to implement in real-world settings for physicians, teachers, therapists, or practitioners unfamiliar with these tools. In addition, disparities in access to advanced technologies may limit their widespread adoption, particularly in low-resource settings. Also, these types of technologies may be closer to more universal and cost-effective devices, such as video cameras, smartphones, and tablets, that can assess and report motor skills in real time. However, addressing these challenges requires a collaborative and interdisciplinary approach. At present, there is no universal standardized assessment protocol that adequately bridges clinical psychometric requirements with algorithmic and sensor-based evaluation for AI-related technologies used in BMS assessment. Therefore, future research should move toward an interdisciplinary framework that defines common standards for both measurement properties (e.g., validity, reliability, measurement error, responsiveness) and technical procedures (e.g., data acquisition, calibration, synchronization, and reporting), in order to improve comparability, scalability, and practical implementation across settings. 50 Future validation studies should involve experts from multiple fields, including engineers, healthcare professionals, educators and policy makers, to ensure that these technologies are not only accurate, but also practical, scalable and accessible to diverse populations. New validation studies of these technologies should include validation standards for BMS tests, prioritizing key psychometric properties such as construct validity, criterion validity, reliability, measurement error, among others. To make this possible, engineering teams could incorporate specialists in psychometrics, developmental therapy and medicine to work collaboratively. This multidisciplinary approach will facilitate the integration of medical knowledge and psychometric standards into future software releases, improving both measurement accuracy and practical usability. Finally, developers should consider providing open source code or detailed methodological documentation, which will allow for further refinement, replication, and clinical adaptation of these technologies in future research and real-world applications. Conclusions Engineering work and technological features have been prioritized in the studies about AI-related technologies. The validation of these algorithms was strictly based on engineering criteria; it means, no substantive knowledge of the medical or psychological aspects of motor skills was integrated into the validation process. Technical features typically evaluated in psychometric instruments designed for assessing motor skills in children were also ignored ( e.g. , COSMIN criteria). The use of these AI-related technologies in scientific research is still limited. Data availability Extended data Zenodo: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review, https://doi.org/10.5281/zenodo.8056742 33 This project contains the following extended data: • Appendix 1. Supplementary Tables • Appendix 2. Search formulas Also in Zenodo: Figueroa-Quiñones, Joel. (2023). date extension. Zenodo. https://doi.org/10.5281/zenodo.8190823 . 34 It’s found: • Information extraction form Finally, in Zenodo: Joel. (2023). data extension [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8253201 . 31 This project contains the following extended data: • 1. COSMIN checklist • 2. Scoping Reviews (PRISMA-ScR) Checklist • 3. Flowchart Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements This report is independent research supported by the National Institute for Health and Care Research ARC North Thames. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care. We thank Miguel Moscoso for his help in the initial stage of this project. References 1. Gallahue DL, Ozmun JC, Goodway J: Understanding motor development: infants, children, adolescents, adults. New York: McGraw-Hill; 2012. 2. Kit BK, Akinbami LJ, Isfahani NS, et al. : Gross Motor Development in Children Aged 3–5 Years, United States 2012. Matern. Child Health J. 2017; 21 : 1573–1580. PubMed Abstract | Publisher Full Text | Free Full Text 3. Figueroa R, An R: Motor Skill Competence and Physical Activity in Preschoolers: A Review. Matern. Child Health J. 2017; 21 (1): 136–146. Publisher Full Text 4. Jiang G-P, Jiao X-B, Wu S-K, et al. : Balance, proprioception, and gross motor development of Chinese children aged 3 to 6 years.J. Mot. Behav.2018; 50 (3): 343–352. PubMed Abstract | Publisher Full Text 5. Gandotra A, Kotyuk E, Bizonics R, et al. : An exploratory study of the relationship between motor skills and indicators of cognitive and socio-emotional development in preschoolers.Eur. J. Dev. Psychol.2023; 20 (1): 50–65. Publisher Full Text 6. Bremer E, Cairney J: Fundamental Movement Skills and Health-Related Outcomes: A Narrative Review of Longitudinal and Intervention Studies Targeting Typically Developing Children. Am. J. Lifestyle Med. 2018; 12 (2): 148–159. Publisher Full Text 7. Eddy LH, Wood ML, Shire KA, et al. : A systematic review of randomized and case-controlled trials investigating the effectiveness of school-based motor skill interventions in 3- to 12-year-old children. Child Care Health Dev. 2019; 45 (6): 773–790. PubMed Abstract | Publisher Full Text 8. Connolly KJ, Forssberg H: Neurophysiology and Neuropsychology of Motor Development. Cambridge University Press; 1997; 400. 9. Manoel EJ, Connolly KJ: Variability and the development of skilled actions. Int. J. Psychophysiol. 1995; 19 : 129–147. Publisher Full Text 10. American Educational Research Association: American Psychological Association, National Council on Measurement in Education. Standards for educational and psychological testing. Washington, DC: AERA Publications Sales; 2014. 11. Hatfield BD, Landers DM: Observer Expectancy Effects upon Appraisal of Gross Motor Performance. Res. Q. Am. Alliance Health Phys. Educ. Recreat. 1978; 49 (1): 53–61. PubMed Abstract | Publisher Full Text 12. Burghardt GM, Bartmess-LeVasseur JN, Browning SA, et al. : Perspectives - Minimizing Observer Bias in Behavioral Studies: A Review and Recommendations. Ethology. 2012; 118 (6): 511–517. Publisher Full Text 13. Khalid R, Willatts P, Williams FLR: Do research studies in the UK reporting child neurodevelopment adjust for the variability of assessors: a systematic review. Dev. Med. Child Neurol. 2015; 58 (2): 131–137. PubMed Abstract | Publisher Full Text 14. Bossavit B, Arnedillo-Sánchez I: Designing Digital Activities to Screen Locomotor Skills in Developing Children.Alario-Hoyos C, Rodríguez-Triana MJ, Scheffel M, et al. , editors. Addressing Global Challenges and Quality Education. Cham: Springer International Publishing; 2020; p. 416–420. (Lecture Notes in Computer Science). 15. Roggio F, Trovato B, Sortino M, et al. : A comprehensive analysis of the machine learning pose estimation models used in human movement and posture analyses: A narrative review.Heliyon.2024; 10 (21): e39977. PubMed Abstract | Publisher Full Text | Free Full Text 16. Mao M, Lee A, Hong M: Deep learning innovations in video classification: A survey on techniques and dataset evaluations. Electronics (Basel). 2024; 13 (14): 2732. Publisher Full Text 17. Baca A: Methods for Recognition and Classification of Human Motion Patterns – A Prerequisite for Intelligent Devices Assisting in Sports Activities. IFAC Proc. Vol. 2012; 45 (2). Publisher Full Text 18. Jiang F, Jiang Y, Zhi H, et al. : Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2017; 2 (4): 230–243. PubMed Abstract | Publisher Full Text | Free Full Text 19. Farrahi V, Niemelä M, Tjurin P, et al. : Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data. IEEE J. Biomed. Health Inform. Jan. 2020; 24 (1): 27–38. PubMed Abstract | Publisher Full Text 20. Alsareii SA, Awais M, Alamri AM, et al. : Physical activity monitoring and classification using machine learning techniques. Life (Basel). 2022; 12 (8): 1103. PubMed Abstract | Publisher Full Text | Free Full Text 21. Cust EE, Sweeting AJ, Ball K, et al. : Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance. J. Sports Sci. 2019; 37 (5): 568–600. PubMed Abstract | Publisher Full Text 22. Tang Y-M, Wang Y-H, Feng X-Y, et al. : Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities. Gait. Posture. 2022; 91 : 205–211. PubMed Abstract | Publisher Full Text 23. Butt AH, Rovini E, Dolciotti C, et al. : Leap motion evaluation for assessment of upper limb motor skills in Parkinson’s disease. 2017 International Conference on Rehabilitation Robotics (ICORR). 2017; pp. 116–121. Publisher Full Text 24. Pogorelc B, Bosnić Z, Gams M: Automatic recognition of gait-related health problems in the elderly using machine learning. Multimed. Tools Appl. 2012; 58 (2): 333–354. Publisher Full Text 25. Bertoncelli CM, Altamura P, Vieira ER, et al. : Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy. Neuropediatrics. 2019; 50 (3): 178–187. Publisher Full Text 26. Santos OC: Artificial Intelligence in Psychomotor Learning: Modeling Human Motion from Inertial Sensor Data. Int. J. Artif. Intell. Tools. 2019; 28 (04): 1940006. Publisher Full Text 27. Santos OC: Beyond cognitive and affective issues: Designing smart learning environments for psychomotor personalized learning. En: Learning, Design, and Technology. Cham: Springer International Publishing; 2023; pp. 3309–3332. Publisher Full Text 28. JC: Protocol for a scoping review. Zenodo. 2023. Publisher Full Text 29. Tricco AC, Lillie E, Zarin W, et al. : PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 4 de septiembre de 2018; 169 (7): 467–473. PubMed Abstract | Publisher Full Text 30. Prinsen CAC, Mokkink LB, Bouter LM, et al. : COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual. Life Res. 2018; 27 (5): 1147–1157. PubMed Abstract | Publisher Full Text | Free Full Text 31. Joel: data extension. [Data set]. Zenodo. 2023. Publisher Full Text 32. Mokkink LB, de Vet HCW , Prinsen CAC, et al. : COSMIN risk of bias checklist for systematic reviews of patient-reported outcome measures. Qual. Life Res. 2018; 27 (5): 1171–1179. PubMed Abstract | Publisher Full Text | Free Full Text 33. Figueroa-Quiñones J, Ipanaque-Neyra J, Hurtado HG, et al. : Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review (Last version). [Data set]. Zenodo. 2023. Publisher Full Text 34. Figueroa-Quiñones J: data extension. Zenodo. 2023. Publisher Full Text 35. Mokkink LB, Terwee CB, Patrick DL, et al. : The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual. Life Res. 2010; 19 : 539–549. PubMed Abstract | Publisher Full Text | Free Full Text 36. Shengyan LI, Bin LI, Shixiong ZHANG, et al. : A Markerless Visual-motor Tracking System for Behavior Monitoring in DCD Assessment. Proceedings of APSIPA Annual Summit and Conference. 2017; 774–777. Publisher Full Text 37. Mao HY, Kuo LC, Yang AL, et al. : Balance in children with attention deficit hyperactivity disorder-combined type. Res. Dev. Disabil. 2014; 35 : 1252–1258. PubMed Abstract | Publisher Full Text 38. Parvinpour S, Shafizadeh M, Balali M, et al. : Effects of Developmental Task Constraints on Kinematic Synergies during Catching in Children with Developmental Delays. J. Mot. Behav. 2020; 52 : 527–543. PubMed Abstract | Publisher Full Text 39. Redd CB, Karunanithi M, Boyd RN, et al. : Technology-assisted quantification of movement to predict infants at high risk of motor disability: A systematic review. Res. Dev. Disabil. 2021; 118 : 104071. PubMed Abstract | Publisher Full Text 40. Yu K-H, Beam AL, Kohane IS: Artificial intelligence in healthcare. Nat. Biomed. Eng. 2018; 2 (10): 719–731. Publisher Full Text 41. Monje MHG, Domínguez S, Vera-Olmos J, et al. : Remote Evaluation of Parkinson’s Disease Using a Conventional Webcam and Artificial Intelligence. Front. Neurol. 2021; 12 : 742654. PubMed Abstract | Publisher Full Text | Free Full Text 42. Suzuki S, Amemiya Y, Sato M: Enhancement of gross-motor action recognition for children by CNN with OpenPose. IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal. 2019; pp. 5382–5387. Publisher Full Text 43. Oung QW, Muthusamy H, Lee HL, et al. : Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review. Sensors. 2015; 15 (9): 21710–21745. PubMed Abstract | Publisher Full Text | Free Full Text 44. Belić M, Bobić V, Badža M, et al. : Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review. Clin. Neurol. Neurosurg. 2019; 184 : 105442. Publisher Full Text 45. Michalski SC, Szpak A, Loetscher T: Using Virtual Environments to Improve Real-World Motor Skills in Sports: A Systematic Review. Front. Psychol. 2019; 10 . PubMed Abstract | Publisher Full Text | Free Full Text 46. Bredt S: Artificial Intelligence (AI) in the Financial Sector—Potential and Public Strategies. Front. Artif. Intell. 2019; 2 : 16. PubMed Abstract | Publisher Full Text | Free Full Text 47. UNESCO: Artificial intelligence for sustainable development: challenges and opportunities for UNESCO’s science and engineering programmes.2019. Reference Source 48. Amemiya Y, Suzuki S, Satoh M: A Support System for Gross Motor Assessment of Preschool Children. En: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. 2018; 4251–4256. Publisher Full Text 49. Dhahbi W, Suzuki S, Satoh M: Editorial: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. Front. Sports Act. Living 2025; 7 :1556024 Publisher Full Text 50. Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. J Strength Cond Res. 2017; 31 (7): e74. Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 18 Dec 2023 ADD YOUR COMMENT Comment Author details Author details 1 Universidad Autonoma de Ica, Ica, Peru 2 Instituto de Investigación, Capacitación y Desarrollo Psicosocial y Educativo (PSYCOPERU), Lima, Peru 3 Ingeniería de Sistemas e Informática, Universidad Tecnológica del Perú, Lima, Peru 4 School of Medicine, Universidad San Juan Bautista, Lima, Peru 5 Research Department of Primary Care and Population Health, University College London, London, UK 6 MedFam Group, School of Medicine, Universidad Cesar Vallejo, Trujillo, Peru Joel Figueroa-Quiñones Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Juan Ipanaque-Neyra Roles: Data Curation, Formal Analysis, Resources, Validation, Visualization, Writing – Original Draft Preparation Heber Gómez Hurtado Roles: Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation Oscar Bazo-Alvarez Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Review & Editing Juan Carlos Bazo-Alvarez Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 24 Apr 2026, 12:1598 https://doi.org/10.12688/f1000research.138616.3 version 2 Revised Published: 02 Sep 2025, 12:1598 https://doi.org/10.12688/f1000research.138616.2 version 1 Published: 18 Dec 2023, 12:1598 https://doi.org/10.12688/f1000research.138616.1 Copyright © 2026 Figueroa-Quiñones J et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Figueroa-Quiñones J, Ipanaque-Neyra J, Gómez Hurtado H et al. Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.12688/f1000research.138616.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 24 Apr 2026 Revised Views 0 Cite How to cite this report: Wissem D. Reviewer Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.198263.r478798 ) The direct URL for this report is: https://f1000research.com/articles/12-1598/v3#referee-response-478798 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Apr 2026 Dhahbi Wissem , University of Jendouba, Kef, Tunisia Approved VIEWS 0 https://doi.org/10.5256/f1000research.198263.r478798 General Comments This third version of the scoping review significantly improves upon previous iterations by addressing the interdisciplinary gap between engineering validation and clinical psychometric standards. The authors have successfully refined the manuscript to clarify that the application ... Continue reading READ ALL General Comments This third version of the scoping review significantly improves upon previous iterations by addressing the interdisciplinary gap between engineering validation and clinical psychometric standards. The authors have successfully refined the manuscript to clarify that the application of COSMIN standards was intended as a measurement-oriented appraisal rather than a critique of algorithmic efficiency. While the temporal limitation of the literature search (cutoff January 30, 2023) remains a constraint, the authors have appropriately framed this as a historical mapping of evidence available at the time of original submission. The manuscript now provides a coherent narrative that bridges the technical capabilities of AI-related technologies with the practical requirements of pediatric motor assessment. Specific Comments 1. Methodological Framing and COSMIN Application The authors have clarified the rationale for using COSMIN guidelines, explicitly stating they were applied to examine evidence relevant to assessment use rather than to evaluate algorithmic performance per se. The distinction between engineering validation (focused on accuracy and technical performance) and clinical/psychometric validation (focused on reliability and responsiveness) is now well-defined in the Results and Discussion sections. 2. Introduction and Contextual Background The Introduction now effectively connects early basic motor skill (BMS) competency with long-term biomechanical implications, such as movement efficiency and potentially lower injury risk. The revised description of AI pipelines acknowledges modern deep learning-based pipelines and vision transformers, providing a more balanced view of contemporary computational approaches. 3. Addressing Limitations and Bias The authors have explicitly acknowledged the search cutoff date in the Strengths and Limitations section, providing necessary context for the reader to interpret the results within that specific temporal scope. Typographical and formatting errors identified in previous versions, such as the "Participant gender" categorization and geographical misspellings, have been corrected. 4. Implications for Future Research The implications section has been strengthened to highlight the urgent need for a universal standardized assessment protocol that bridges the gap between engineering and clinical disciplines. The recommendation for interdisciplinary collaboration involving engineers, healthcare professionals, and educators is a critical addition for the practical implementation of these technologies. Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI and Sports Sciences I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wissem D. Reviewer Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.198263.r478798 ) The direct URL for this report is: https://f1000research.com/articles/12-1598/v3#referee-response-478798 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 02 Sep 2025 Revised Views 0 Cite How to cite this report: Wissem D. Reviewer Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.186910.r469885 ) The direct URL for this report is: https://f1000research.com/articles/12-1598/v2#referee-response-469885 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Mar 2026 Dhahbi Wissem , University of Jendouba, Kef, Tunisia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.186910.r469885 General Comments The manuscript presents a scoping review evaluating the development, validation, and use of AI-related technologies for assessing basic motor skills (BMS) in children aged 3 to 6. While the objective of bridging engineering validation ... Continue reading READ ALL General Comments The manuscript presents a scoping review evaluating the development, validation, and use of AI-related technologies for assessing basic motor skills (BMS) in children aged 3 to 6. While the objective of bridging engineering validation with clinical psychometric standards is highly relevant, the paper suffers from critical methodological and temporal flaws. Major Weaknesses: Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes References 1. Dhahbi W., Chamari K., Chèze L., Behm D.G., et al.: External responsiveness and intrasession reliability of the rope-climbing test. The Journal of Strength & Conditioning Research, 30(10), 2952–2958 . 2016. 2. Dhahbi W., Chamari K.: The Algorithmic Athlete: A Call to Standardize Assessment of Sensor Technologies and Artificial Intelligence. International Journal of Sports Physiology and Performance, 1(aop), 1–2 . 2026. Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI and Sports Sciences I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wissem D. Reviewer Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.186910.r469885 ) The direct URL for this report is: https://f1000research.com/articles/12-1598/v2#referee-response-469885 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 24 Apr 2026 Joel Figueroa-Quiñones , Universidad Autonoma de Ica, Chiclayo, Peru 24 Apr 2026 Author Response Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving ... Continue reading Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. We thank the reviewer for this important observation. However, this search date corresponded to the evidence available at the time of the manuscript’s original submission. The extended interval between submission and the present revision was due to the editorial and peer-review process, which was beyond the authors’ control. Therefore, the review should be interpreted as a mapping of the literature available up to January 30, 2023, rather than as a fully updated representation of the field at the time of revision. We have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that more recent studies, including potentially a greater number of deep learning applications, may not have been captured. This issue has been highlighted to ensure an appropriate interpretation of the findings. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. We respectfully disagree with the view that the COSMIN framework was misapplied in the present review. Our use of COSMIN was not intended to evaluate the engineering performance of artificial intelligence algorithms per se, nor to replace standard computer science benchmarks such as precision, recall, F1-score, or positional error metrics. Rather, COSMIN was used because the focus of this review was on AI-related technologies insofar as they are proposed and used as assessment tools for basic motor skills in children. From this perspective, the key question is not only whether an algorithm performs well computationally, but also whether the resulting tool demonstrates adequate measurement properties for assessment purposes, such as validity, reliability, and interpretability within clinical, educational, or psychomotor contexts. In other words, benchmark metrics may demonstrate technical performance, but they do not by themselves establish that a tool is suitable as a measurement instrument in applied settings. For this reason, we considered COSMIN an appropriate framework to appraise the extent to which the included studies provided evidence relevant to measurement quality. We agree, however, that this involves an interdisciplinary interface between engineering validation and clinical/psychometric evaluation. To avoid misunderstanding, we have clarified in the revised manuscript that COSMIN was applied from a measurement and clinical assessment perspective, not as a substitute for engineering model validation standards. We have also made explicit that the absence of COSMIN-related evidence in some studies should not be interpreted as poor algorithmic quality, but rather as limited reporting of measurement properties relevant to assessment use. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. We respectfully disagree that the inclusion of 12 studies inherently undermines the contribution of the review. The purpose of a scoping review is not to generate statistically generalizable conclusions about an entire field, but rather to map, characterize, and synthesize the nature and extent of the available evidence on a given topic. In this sense, the limited number of eligible studies should not be interpreted solely as a weakness of the review, but also as a reflection of the current state of the literature at the intersection of artificial intelligence and pediatric basic motor skill assessment. The restricted yield suggests that this remains an emerging and still relatively fragmented area of research, particularly when applying predefined eligibility criteria focused on development, validation, and practical use in children. Importantly, our manuscript does not intend to overgeneralize the evidence base. Instead, the conclusions are framed to reflect the scope and volume of the studies identified, highlighting trends, methodological characteristics, and existing gaps in the literature. Therefore, the small number of included studies is itself a relevant finding, as it underscores the need for further research in this field. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. We acknowledge that some typographical and formatting inconsistencies remained in the revised version. In response, we carefully reviewed the entire manuscript and corrected the identified errors in the text and tables to improve clarity, consistency, and overall presentation. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. We agree that recent advances in artificial intelligence for pose estimation have increasingly been driven by deep learning approaches, including convolutional neural networks and, more recently, transformer-based architectures. However, the purpose of the Introduction was not to provide an exhaustive technical review of the full evolution of AI pipelines, but rather to offer a general conceptual background for readers from clinical, developmental, and assessment-oriented fields. In this context, we described traditional signal processing and classical machine learning approaches because they remain part of the methodological foundation of the field and are also represented in the body of studies included in our review. Moreover, the emphasis in the manuscript is on the application of AI-related technologies for the assessment of basic motor skills in children, rather than on benchmarking the latest computer vision architectures. Therefore, the introductory section was intentionally oriented toward framing the field in accessible and applied terms. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. We agree that the literature search cutoff date is a critical parameter in a scoping review and should be explicitly stated in the abstract. In response, we revised the Methods section of the abstract to indicate that the search included studies published up to January 30, 2023. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” We agree that the long-term biomechanical relevance of early basic motor skill (BMS) development could be stated more explicitly in the Introduction. Although the present review focuses on the assessment of BMS in preschool children rather than on athletic performance per se, early motor competence may have broader biomechanical implications across the life course, including movement efficiency, later participation in physical and sport activities, and potentially injury-risk mitigation through the development of more coordinated and stable movement patterns. In response, we revised the first paragraph of the Introduction to better connect early BMS acquisition with its longer-term biomechanical relevance and to acknowledge recent perspectives highlighting the role of biomechanics in performance optimization, injury-risk reduction, and rehabilitation. Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. We thank the reviewer for this observation. We acknowledge that the cited prevalence figures were derived from earlier reviews and may not reflect current reporting practices. However, these references were retained because they provide direct quantitative evidence of longstanding concerns regarding interobserver reliability and observer-related bias in behavioral research. To avoid implying that these figures represent the current prevalence, we revised the text to frame them as historical evidence of a methodological problem rather than as a contemporary estimate. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. We agree that the original wording of this paragraph placed greater emphasis on classical machine-learning pipelines, which may not fully reflect the diversity of contemporary AI approaches. However, our intention was not to suggest that all current motion-analysis systems rely on manual feature extraction, but rather to provide a broad and accessible description of computational steps that may be involved in some AI-related technologies for human movement assessment. To address this concern, we revised the paragraph to clarify that, although some approaches still involve preprocessing, feature extraction, and conventional classification procedures, many contemporary pipelines rely on end-to-end deep learning models, such as convolutional neural networks and vision transformers, which can learn relevant representations directly from images, videos, or pose sequences with limited or no manual feature extraction. This clarification has been incorporated to better reflect the current methodological landscape. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. We appreciate the reviewer’s concern and agree that the pace of development in AI-related research is particularly rapid. The literature search in this review ended on January 30, 2023 because this date corresponded to the original submission stage of the manuscript. The prolonged interval before the current revision was largely attributable to the editorial and peer-review timeline, which was outside the authors’ control. Accordingly, the review is intended to represent the evidence available up to that date, rather than the current state of the art at the time of revision. To ensure appropriate interpretation, we have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that newer studies on computer vision models applied to human movement assessment may not have been captured. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. We agree that engineering studies commonly evaluate systems using discipline-specific performance metrics rather than clinical reliability indices. To clarify our methodological approach, we added a brief statement in the Data analysis section indicating that COSMIN was applied from a measurement perspective to examine evidence relevant to assessment use, rather than to evaluate algorithmic performance per se. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". The word was updated Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. We agree that the category “Just kids” was unclear and inappropriate under the variable “Participant gender.” This was a wording error. We revised Table 1 to use clearer and logically consistent categories: “Boys only,” “Girls only,” “Both boys and girls,” and “Not reported.” Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” We agree that device-related factors such as calibration procedures, sampling rate, and synchronization may influence motion analysis results. However, the purpose of Table 2 was to provide a descriptive summary of the motion-capture devices and AI-related tools reported in the included studies, rather than to conduct a device-specific technical appraisal of biomechanical sensor performance. We respectfully note that the reference suggested by the reviewer addresses methodological issues related to force plates in the assessment of push-up power, which corresponds to a different biomechanical and performance-testing context from the present review, focused on AI-related technologies for assessing basic motor skills in children. For this reason, we did not incorporate that reference directly into Table 2. Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. We agree that deep learning approaches have become increasingly prominent in the broader pose estimation literature. However, we respectfully note that the proportion reported in Table 2 reflects only the characteristics of the studies included in this review, based on the predefined eligibility criteria and review period, rather than the overall prevalence of deep learning approaches across the wider field. Therefore, the finding should be interpreted within the specific scope of this scoping review and not as a comprehensive representation of the current pose estimation landscape. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. We agree that the predominance of “Inadequate” and “Doubtful” ratings in Table 3 requires careful interpretation. These ratings were intended to reflect the extent to which the included studies reported psychometric or measurement-related evidence according to COSMIN standards, rather than their algorithmic or engineering quality per se. In many cases, the included papers primarily focused on technical or algorithmic development, which likely explains the predominance of lower COSMIN ratings. We clarified this interpretive point in the text accompanying Table 3. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 We agree that the original text did not sufficiently clarify what adequate evidence of reliability and responsiveness would look like when these AI-related technologies are intended to function as assessment tools. In traditional physical performance and functional motor assessment, reliability is typically supported by evidence such as test–retest or intrasession stability, together with indicators of measurement consistency and error. Likewise, responsiveness requires evidence that an instrument can detect meaningful changes in performance over time or after intervention. In contrast, many of the studies included in our review primarily emphasized algorithmic or technical performance and did not report this type of measurement-oriented evidence. We have clarified this point in the manuscript to better explain that the COSMIN-related gap does not simply refer to missing psychometric terminology, but to the limited demonstration of whether these AI-related tools can provide stable and change-sensitive measurements comparable to those expected from established motor assessment instruments. Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. We agree that the original wording of this paragraph may have overstated the interpretation of the COSMIN findings. Our intention was not to suggest that the included studies failed simply because they did not follow psychometric validation standards, but rather to highlight an interdisciplinary gap between engineering-oriented validation and clinical or assessment-oriented validation. In many of the included studies, validation was primarily conducted using engineering criteria focused on technical performance. This is appropriate within computer science and algorithm-development contexts. However, when these technologies are proposed as tools for assessing motor skills in children, additional evidence on measurement properties becomes relevant from a clinical and psychometric perspective. We have revised the paragraph to better acknowledge that both forms of validation are important and complementary, and that the absence of psychometric evidence should not be interpreted as poor engineering quality per se. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. We agree that the search cutoff date represents an important limitation in a rapidly evolving field such as artificial intelligence and computer vision. In response, we explicitly added this issue to the Strengths and Limitations section, stating that the literature search was conducted up to January 30, 2023 and that, consequently, more recent studies may not have been captured. We also clarified that the findings should be interpreted as reflecting the evidence available up to that date rather than the most current state of the field. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 We agree that the implications section should more explicitly state the current absence of a universal standardized protocol integrating clinical psychometrics with algorithmic and sensor-based evaluation. In response, we revised the implications section to emphasize that future research should move toward a standardized interdisciplinary framework specifying not only psychometric requirements (e.g., validity, reliability, measurement error, responsiveness) but also minimum technical requirements for sensor- and algorithm-based systems, such as data acquisition procedures, calibration, synchronization, and reporting standards. We also strengthened the call for collaboration across engineering, clinical, educational, and psychometric disciplines so that AI-related technologies for BMS assessment can be developed and validated under a more unified and transferable framework. Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. We thank the reviewer for this important observation. However, this search date corresponded to the evidence available at the time of the manuscript’s original submission. The extended interval between submission and the present revision was due to the editorial and peer-review process, which was beyond the authors’ control. Therefore, the review should be interpreted as a mapping of the literature available up to January 30, 2023, rather than as a fully updated representation of the field at the time of revision. We have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that more recent studies, including potentially a greater number of deep learning applications, may not have been captured. This issue has been highlighted to ensure an appropriate interpretation of the findings. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. We respectfully disagree with the view that the COSMIN framework was misapplied in the present review. Our use of COSMIN was not intended to evaluate the engineering performance of artificial intelligence algorithms per se, nor to replace standard computer science benchmarks such as precision, recall, F1-score, or positional error metrics. Rather, COSMIN was used because the focus of this review was on AI-related technologies insofar as they are proposed and used as assessment tools for basic motor skills in children. From this perspective, the key question is not only whether an algorithm performs well computationally, but also whether the resulting tool demonstrates adequate measurement properties for assessment purposes, such as validity, reliability, and interpretability within clinical, educational, or psychomotor contexts. In other words, benchmark metrics may demonstrate technical performance, but they do not by themselves establish that a tool is suitable as a measurement instrument in applied settings. For this reason, we considered COSMIN an appropriate framework to appraise the extent to which the included studies provided evidence relevant to measurement quality. We agree, however, that this involves an interdisciplinary interface between engineering validation and clinical/psychometric evaluation. To avoid misunderstanding, we have clarified in the revised manuscript that COSMIN was applied from a measurement and clinical assessment perspective, not as a substitute for engineering model validation standards. We have also made explicit that the absence of COSMIN-related evidence in some studies should not be interpreted as poor algorithmic quality, but rather as limited reporting of measurement properties relevant to assessment use. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. We respectfully disagree that the inclusion of 12 studies inherently undermines the contribution of the review. The purpose of a scoping review is not to generate statistically generalizable conclusions about an entire field, but rather to map, characterize, and synthesize the nature and extent of the available evidence on a given topic. In this sense, the limited number of eligible studies should not be interpreted solely as a weakness of the review, but also as a reflection of the current state of the literature at the intersection of artificial intelligence and pediatric basic motor skill assessment. The restricted yield suggests that this remains an emerging and still relatively fragmented area of research, particularly when applying predefined eligibility criteria focused on development, validation, and practical use in children. Importantly, our manuscript does not intend to overgeneralize the evidence base. Instead, the conclusions are framed to reflect the scope and volume of the studies identified, highlighting trends, methodological characteristics, and existing gaps in the literature. Therefore, the small number of included studies is itself a relevant finding, as it underscores the need for further research in this field. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. We acknowledge that some typographical and formatting inconsistencies remained in the revised version. In response, we carefully reviewed the entire manuscript and corrected the identified errors in the text and tables to improve clarity, consistency, and overall presentation. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. We agree that recent advances in artificial intelligence for pose estimation have increasingly been driven by deep learning approaches, including convolutional neural networks and, more recently, transformer-based architectures. However, the purpose of the Introduction was not to provide an exhaustive technical review of the full evolution of AI pipelines, but rather to offer a general conceptual background for readers from clinical, developmental, and assessment-oriented fields. In this context, we described traditional signal processing and classical machine learning approaches because they remain part of the methodological foundation of the field and are also represented in the body of studies included in our review. Moreover, the emphasis in the manuscript is on the application of AI-related technologies for the assessment of basic motor skills in children, rather than on benchmarking the latest computer vision architectures. Therefore, the introductory section was intentionally oriented toward framing the field in accessible and applied terms. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. We agree that the literature search cutoff date is a critical parameter in a scoping review and should be explicitly stated in the abstract. In response, we revised the Methods section of the abstract to indicate that the search included studies published up to January 30, 2023. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” We agree that the long-term biomechanical relevance of early basic motor skill (BMS) development could be stated more explicitly in the Introduction. Although the present review focuses on the assessment of BMS in preschool children rather than on athletic performance per se, early motor competence may have broader biomechanical implications across the life course, including movement efficiency, later participation in physical and sport activities, and potentially injury-risk mitigation through the development of more coordinated and stable movement patterns. In response, we revised the first paragraph of the Introduction to better connect early BMS acquisition with its longer-term biomechanical relevance and to acknowledge recent perspectives highlighting the role of biomechanics in performance optimization, injury-risk reduction, and rehabilitation. Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. We thank the reviewer for this observation. We acknowledge that the cited prevalence figures were derived from earlier reviews and may not reflect current reporting practices. However, these references were retained because they provide direct quantitative evidence of longstanding concerns regarding interobserver reliability and observer-related bias in behavioral research. To avoid implying that these figures represent the current prevalence, we revised the text to frame them as historical evidence of a methodological problem rather than as a contemporary estimate. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. We agree that the original wording of this paragraph placed greater emphasis on classical machine-learning pipelines, which may not fully reflect the diversity of contemporary AI approaches. However, our intention was not to suggest that all current motion-analysis systems rely on manual feature extraction, but rather to provide a broad and accessible description of computational steps that may be involved in some AI-related technologies for human movement assessment. To address this concern, we revised the paragraph to clarify that, although some approaches still involve preprocessing, feature extraction, and conventional classification procedures, many contemporary pipelines rely on end-to-end deep learning models, such as convolutional neural networks and vision transformers, which can learn relevant representations directly from images, videos, or pose sequences with limited or no manual feature extraction. This clarification has been incorporated to better reflect the current methodological landscape. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. We appreciate the reviewer’s concern and agree that the pace of development in AI-related research is particularly rapid. The literature search in this review ended on January 30, 2023 because this date corresponded to the original submission stage of the manuscript. The prolonged interval before the current revision was largely attributable to the editorial and peer-review timeline, which was outside the authors’ control. Accordingly, the review is intended to represent the evidence available up to that date, rather than the current state of the art at the time of revision. To ensure appropriate interpretation, we have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that newer studies on computer vision models applied to human movement assessment may not have been captured. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. We agree that engineering studies commonly evaluate systems using discipline-specific performance metrics rather than clinical reliability indices. To clarify our methodological approach, we added a brief statement in the Data analysis section indicating that COSMIN was applied from a measurement perspective to examine evidence relevant to assessment use, rather than to evaluate algorithmic performance per se. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". The word was updated Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. We agree that the category “Just kids” was unclear and inappropriate under the variable “Participant gender.” This was a wording error. We revised Table 1 to use clearer and logically consistent categories: “Boys only,” “Girls only,” “Both boys and girls,” and “Not reported.” Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” We agree that device-related factors such as calibration procedures, sampling rate, and synchronization may influence motion analysis results. However, the purpose of Table 2 was to provide a descriptive summary of the motion-capture devices and AI-related tools reported in the included studies, rather than to conduct a device-specific technical appraisal of biomechanical sensor performance. We respectfully note that the reference suggested by the reviewer addresses methodological issues related to force plates in the assessment of push-up power, which corresponds to a different biomechanical and performance-testing context from the present review, focused on AI-related technologies for assessing basic motor skills in children. For this reason, we did not incorporate that reference directly into Table 2. Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. We agree that deep learning approaches have become increasingly prominent in the broader pose estimation literature. However, we respectfully note that the proportion reported in Table 2 reflects only the characteristics of the studies included in this review, based on the predefined eligibility criteria and review period, rather than the overall prevalence of deep learning approaches across the wider field. Therefore, the finding should be interpreted within the specific scope of this scoping review and not as a comprehensive representation of the current pose estimation landscape. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. We agree that the predominance of “Inadequate” and “Doubtful” ratings in Table 3 requires careful interpretation. These ratings were intended to reflect the extent to which the included studies reported psychometric or measurement-related evidence according to COSMIN standards, rather than their algorithmic or engineering quality per se. In many cases, the included papers primarily focused on technical or algorithmic development, which likely explains the predominance of lower COSMIN ratings. We clarified this interpretive point in the text accompanying Table 3. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 We agree that the original text did not sufficiently clarify what adequate evidence of reliability and responsiveness would look like when these AI-related technologies are intended to function as assessment tools. In traditional physical performance and functional motor assessment, reliability is typically supported by evidence such as test–retest or intrasession stability, together with indicators of measurement consistency and error. Likewise, responsiveness requires evidence that an instrument can detect meaningful changes in performance over time or after intervention. In contrast, many of the studies included in our review primarily emphasized algorithmic or technical performance and did not report this type of measurement-oriented evidence. We have clarified this point in the manuscript to better explain that the COSMIN-related gap does not simply refer to missing psychometric terminology, but to the limited demonstration of whether these AI-related tools can provide stable and change-sensitive measurements comparable to those expected from established motor assessment instruments. Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. We agree that the original wording of this paragraph may have overstated the interpretation of the COSMIN findings. Our intention was not to suggest that the included studies failed simply because they did not follow psychometric validation standards, but rather to highlight an interdisciplinary gap between engineering-oriented validation and clinical or assessment-oriented validation. In many of the included studies, validation was primarily conducted using engineering criteria focused on technical performance. This is appropriate within computer science and algorithm-development contexts. However, when these technologies are proposed as tools for assessing motor skills in children, additional evidence on measurement properties becomes relevant from a clinical and psychometric perspective. We have revised the paragraph to better acknowledge that both forms of validation are important and complementary, and that the absence of psychometric evidence should not be interpreted as poor engineering quality per se. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. We agree that the search cutoff date represents an important limitation in a rapidly evolving field such as artificial intelligence and computer vision. In response, we explicitly added this issue to the Strengths and Limitations section, stating that the literature search was conducted up to January 30, 2023 and that, consequently, more recent studies may not have been captured. We also clarified that the findings should be interpreted as reflecting the evidence available up to that date rather than the most current state of the field. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 We agree that the implications section should more explicitly state the current absence of a universal standardized protocol integrating clinical psychometrics with algorithmic and sensor-based evaluation. In response, we revised the implications section to emphasize that future research should move toward a standardized interdisciplinary framework specifying not only psychometric requirements (e.g., validity, reliability, measurement error, responsiveness) but also minimum technical requirements for sensor- and algorithm-based systems, such as data acquisition procedures, calibration, synchronization, and reporting standards. We also strengthened the call for collaboration across engineering, clinical, educational, and psychometric disciplines so that AI-related technologies for BMS assessment can be developed and validated under a more unified and transferable framework. Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 24 Apr 2026 Joel Figueroa-Quiñones , Universidad Autonoma de Ica, Chiclayo, Peru 24 Apr 2026 Author Response Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving ... Continue reading Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. We thank the reviewer for this important observation. However, this search date corresponded to the evidence available at the time of the manuscript’s original submission. The extended interval between submission and the present revision was due to the editorial and peer-review process, which was beyond the authors’ control. Therefore, the review should be interpreted as a mapping of the literature available up to January 30, 2023, rather than as a fully updated representation of the field at the time of revision. We have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that more recent studies, including potentially a greater number of deep learning applications, may not have been captured. This issue has been highlighted to ensure an appropriate interpretation of the findings. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. We respectfully disagree with the view that the COSMIN framework was misapplied in the present review. Our use of COSMIN was not intended to evaluate the engineering performance of artificial intelligence algorithms per se, nor to replace standard computer science benchmarks such as precision, recall, F1-score, or positional error metrics. Rather, COSMIN was used because the focus of this review was on AI-related technologies insofar as they are proposed and used as assessment tools for basic motor skills in children. From this perspective, the key question is not only whether an algorithm performs well computationally, but also whether the resulting tool demonstrates adequate measurement properties for assessment purposes, such as validity, reliability, and interpretability within clinical, educational, or psychomotor contexts. In other words, benchmark metrics may demonstrate technical performance, but they do not by themselves establish that a tool is suitable as a measurement instrument in applied settings. For this reason, we considered COSMIN an appropriate framework to appraise the extent to which the included studies provided evidence relevant to measurement quality. We agree, however, that this involves an interdisciplinary interface between engineering validation and clinical/psychometric evaluation. To avoid misunderstanding, we have clarified in the revised manuscript that COSMIN was applied from a measurement and clinical assessment perspective, not as a substitute for engineering model validation standards. We have also made explicit that the absence of COSMIN-related evidence in some studies should not be interpreted as poor algorithmic quality, but rather as limited reporting of measurement properties relevant to assessment use. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. We respectfully disagree that the inclusion of 12 studies inherently undermines the contribution of the review. The purpose of a scoping review is not to generate statistically generalizable conclusions about an entire field, but rather to map, characterize, and synthesize the nature and extent of the available evidence on a given topic. In this sense, the limited number of eligible studies should not be interpreted solely as a weakness of the review, but also as a reflection of the current state of the literature at the intersection of artificial intelligence and pediatric basic motor skill assessment. The restricted yield suggests that this remains an emerging and still relatively fragmented area of research, particularly when applying predefined eligibility criteria focused on development, validation, and practical use in children. Importantly, our manuscript does not intend to overgeneralize the evidence base. Instead, the conclusions are framed to reflect the scope and volume of the studies identified, highlighting trends, methodological characteristics, and existing gaps in the literature. Therefore, the small number of included studies is itself a relevant finding, as it underscores the need for further research in this field. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. We acknowledge that some typographical and formatting inconsistencies remained in the revised version. In response, we carefully reviewed the entire manuscript and corrected the identified errors in the text and tables to improve clarity, consistency, and overall presentation. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. We agree that recent advances in artificial intelligence for pose estimation have increasingly been driven by deep learning approaches, including convolutional neural networks and, more recently, transformer-based architectures. However, the purpose of the Introduction was not to provide an exhaustive technical review of the full evolution of AI pipelines, but rather to offer a general conceptual background for readers from clinical, developmental, and assessment-oriented fields. In this context, we described traditional signal processing and classical machine learning approaches because they remain part of the methodological foundation of the field and are also represented in the body of studies included in our review. Moreover, the emphasis in the manuscript is on the application of AI-related technologies for the assessment of basic motor skills in children, rather than on benchmarking the latest computer vision architectures. Therefore, the introductory section was intentionally oriented toward framing the field in accessible and applied terms. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. We agree that the literature search cutoff date is a critical parameter in a scoping review and should be explicitly stated in the abstract. In response, we revised the Methods section of the abstract to indicate that the search included studies published up to January 30, 2023. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” We agree that the long-term biomechanical relevance of early basic motor skill (BMS) development could be stated more explicitly in the Introduction. Although the present review focuses on the assessment of BMS in preschool children rather than on athletic performance per se, early motor competence may have broader biomechanical implications across the life course, including movement efficiency, later participation in physical and sport activities, and potentially injury-risk mitigation through the development of more coordinated and stable movement patterns. In response, we revised the first paragraph of the Introduction to better connect early BMS acquisition with its longer-term biomechanical relevance and to acknowledge recent perspectives highlighting the role of biomechanics in performance optimization, injury-risk reduction, and rehabilitation. Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. We thank the reviewer for this observation. We acknowledge that the cited prevalence figures were derived from earlier reviews and may not reflect current reporting practices. However, these references were retained because they provide direct quantitative evidence of longstanding concerns regarding interobserver reliability and observer-related bias in behavioral research. To avoid implying that these figures represent the current prevalence, we revised the text to frame them as historical evidence of a methodological problem rather than as a contemporary estimate. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. We agree that the original wording of this paragraph placed greater emphasis on classical machine-learning pipelines, which may not fully reflect the diversity of contemporary AI approaches. However, our intention was not to suggest that all current motion-analysis systems rely on manual feature extraction, but rather to provide a broad and accessible description of computational steps that may be involved in some AI-related technologies for human movement assessment. To address this concern, we revised the paragraph to clarify that, although some approaches still involve preprocessing, feature extraction, and conventional classification procedures, many contemporary pipelines rely on end-to-end deep learning models, such as convolutional neural networks and vision transformers, which can learn relevant representations directly from images, videos, or pose sequences with limited or no manual feature extraction. This clarification has been incorporated to better reflect the current methodological landscape. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. We appreciate the reviewer’s concern and agree that the pace of development in AI-related research is particularly rapid. The literature search in this review ended on January 30, 2023 because this date corresponded to the original submission stage of the manuscript. The prolonged interval before the current revision was largely attributable to the editorial and peer-review timeline, which was outside the authors’ control. Accordingly, the review is intended to represent the evidence available up to that date, rather than the current state of the art at the time of revision. To ensure appropriate interpretation, we have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that newer studies on computer vision models applied to human movement assessment may not have been captured. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. We agree that engineering studies commonly evaluate systems using discipline-specific performance metrics rather than clinical reliability indices. To clarify our methodological approach, we added a brief statement in the Data analysis section indicating that COSMIN was applied from a measurement perspective to examine evidence relevant to assessment use, rather than to evaluate algorithmic performance per se. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". The word was updated Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. We agree that the category “Just kids” was unclear and inappropriate under the variable “Participant gender.” This was a wording error. We revised Table 1 to use clearer and logically consistent categories: “Boys only,” “Girls only,” “Both boys and girls,” and “Not reported.” Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” We agree that device-related factors such as calibration procedures, sampling rate, and synchronization may influence motion analysis results. However, the purpose of Table 2 was to provide a descriptive summary of the motion-capture devices and AI-related tools reported in the included studies, rather than to conduct a device-specific technical appraisal of biomechanical sensor performance. We respectfully note that the reference suggested by the reviewer addresses methodological issues related to force plates in the assessment of push-up power, which corresponds to a different biomechanical and performance-testing context from the present review, focused on AI-related technologies for assessing basic motor skills in children. For this reason, we did not incorporate that reference directly into Table 2. Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. We agree that deep learning approaches have become increasingly prominent in the broader pose estimation literature. However, we respectfully note that the proportion reported in Table 2 reflects only the characteristics of the studies included in this review, based on the predefined eligibility criteria and review period, rather than the overall prevalence of deep learning approaches across the wider field. Therefore, the finding should be interpreted within the specific scope of this scoping review and not as a comprehensive representation of the current pose estimation landscape. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. We agree that the predominance of “Inadequate” and “Doubtful” ratings in Table 3 requires careful interpretation. These ratings were intended to reflect the extent to which the included studies reported psychometric or measurement-related evidence according to COSMIN standards, rather than their algorithmic or engineering quality per se. In many cases, the included papers primarily focused on technical or algorithmic development, which likely explains the predominance of lower COSMIN ratings. We clarified this interpretive point in the text accompanying Table 3. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 We agree that the original text did not sufficiently clarify what adequate evidence of reliability and responsiveness would look like when these AI-related technologies are intended to function as assessment tools. In traditional physical performance and functional motor assessment, reliability is typically supported by evidence such as test–retest or intrasession stability, together with indicators of measurement consistency and error. Likewise, responsiveness requires evidence that an instrument can detect meaningful changes in performance over time or after intervention. In contrast, many of the studies included in our review primarily emphasized algorithmic or technical performance and did not report this type of measurement-oriented evidence. We have clarified this point in the manuscript to better explain that the COSMIN-related gap does not simply refer to missing psychometric terminology, but to the limited demonstration of whether these AI-related tools can provide stable and change-sensitive measurements comparable to those expected from established motor assessment instruments. Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. We agree that the original wording of this paragraph may have overstated the interpretation of the COSMIN findings. Our intention was not to suggest that the included studies failed simply because they did not follow psychometric validation standards, but rather to highlight an interdisciplinary gap between engineering-oriented validation and clinical or assessment-oriented validation. In many of the included studies, validation was primarily conducted using engineering criteria focused on technical performance. This is appropriate within computer science and algorithm-development contexts. However, when these technologies are proposed as tools for assessing motor skills in children, additional evidence on measurement properties becomes relevant from a clinical and psychometric perspective. We have revised the paragraph to better acknowledge that both forms of validation are important and complementary, and that the absence of psychometric evidence should not be interpreted as poor engineering quality per se. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. We agree that the search cutoff date represents an important limitation in a rapidly evolving field such as artificial intelligence and computer vision. In response, we explicitly added this issue to the Strengths and Limitations section, stating that the literature search was conducted up to January 30, 2023 and that, consequently, more recent studies may not have been captured. We also clarified that the findings should be interpreted as reflecting the evidence available up to that date rather than the most current state of the field. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 We agree that the implications section should more explicitly state the current absence of a universal standardized protocol integrating clinical psychometrics with algorithmic and sensor-based evaluation. In response, we revised the implications section to emphasize that future research should move toward a standardized interdisciplinary framework specifying not only psychometric requirements (e.g., validity, reliability, measurement error, responsiveness) but also minimum technical requirements for sensor- and algorithm-based systems, such as data acquisition procedures, calibration, synchronization, and reporting standards. We also strengthened the call for collaboration across engineering, clinical, educational, and psychometric disciplines so that AI-related technologies for BMS assessment can be developed and validated under a more unified and transferable framework. Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. We thank the reviewer for this important observation. However, this search date corresponded to the evidence available at the time of the manuscript’s original submission. The extended interval between submission and the present revision was due to the editorial and peer-review process, which was beyond the authors’ control. Therefore, the review should be interpreted as a mapping of the literature available up to January 30, 2023, rather than as a fully updated representation of the field at the time of revision. We have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that more recent studies, including potentially a greater number of deep learning applications, may not have been captured. This issue has been highlighted to ensure an appropriate interpretation of the findings. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. We respectfully disagree with the view that the COSMIN framework was misapplied in the present review. Our use of COSMIN was not intended to evaluate the engineering performance of artificial intelligence algorithms per se, nor to replace standard computer science benchmarks such as precision, recall, F1-score, or positional error metrics. Rather, COSMIN was used because the focus of this review was on AI-related technologies insofar as they are proposed and used as assessment tools for basic motor skills in children. From this perspective, the key question is not only whether an algorithm performs well computationally, but also whether the resulting tool demonstrates adequate measurement properties for assessment purposes, such as validity, reliability, and interpretability within clinical, educational, or psychomotor contexts. In other words, benchmark metrics may demonstrate technical performance, but they do not by themselves establish that a tool is suitable as a measurement instrument in applied settings. For this reason, we considered COSMIN an appropriate framework to appraise the extent to which the included studies provided evidence relevant to measurement quality. We agree, however, that this involves an interdisciplinary interface between engineering validation and clinical/psychometric evaluation. To avoid misunderstanding, we have clarified in the revised manuscript that COSMIN was applied from a measurement and clinical assessment perspective, not as a substitute for engineering model validation standards. We have also made explicit that the absence of COSMIN-related evidence in some studies should not be interpreted as poor algorithmic quality, but rather as limited reporting of measurement properties relevant to assessment use. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. We respectfully disagree that the inclusion of 12 studies inherently undermines the contribution of the review. The purpose of a scoping review is not to generate statistically generalizable conclusions about an entire field, but rather to map, characterize, and synthesize the nature and extent of the available evidence on a given topic. In this sense, the limited number of eligible studies should not be interpreted solely as a weakness of the review, but also as a reflection of the current state of the literature at the intersection of artificial intelligence and pediatric basic motor skill assessment. The restricted yield suggests that this remains an emerging and still relatively fragmented area of research, particularly when applying predefined eligibility criteria focused on development, validation, and practical use in children. Importantly, our manuscript does not intend to overgeneralize the evidence base. Instead, the conclusions are framed to reflect the scope and volume of the studies identified, highlighting trends, methodological characteristics, and existing gaps in the literature. Therefore, the small number of included studies is itself a relevant finding, as it underscores the need for further research in this field. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. We acknowledge that some typographical and formatting inconsistencies remained in the revised version. In response, we carefully reviewed the entire manuscript and corrected the identified errors in the text and tables to improve clarity, consistency, and overall presentation. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. We agree that recent advances in artificial intelligence for pose estimation have increasingly been driven by deep learning approaches, including convolutional neural networks and, more recently, transformer-based architectures. However, the purpose of the Introduction was not to provide an exhaustive technical review of the full evolution of AI pipelines, but rather to offer a general conceptual background for readers from clinical, developmental, and assessment-oriented fields. In this context, we described traditional signal processing and classical machine learning approaches because they remain part of the methodological foundation of the field and are also represented in the body of studies included in our review. Moreover, the emphasis in the manuscript is on the application of AI-related technologies for the assessment of basic motor skills in children, rather than on benchmarking the latest computer vision architectures. Therefore, the introductory section was intentionally oriented toward framing the field in accessible and applied terms. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. We agree that the literature search cutoff date is a critical parameter in a scoping review and should be explicitly stated in the abstract. In response, we revised the Methods section of the abstract to indicate that the search included studies published up to January 30, 2023. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” We agree that the long-term biomechanical relevance of early basic motor skill (BMS) development could be stated more explicitly in the Introduction. Although the present review focuses on the assessment of BMS in preschool children rather than on athletic performance per se, early motor competence may have broader biomechanical implications across the life course, including movement efficiency, later participation in physical and sport activities, and potentially injury-risk mitigation through the development of more coordinated and stable movement patterns. In response, we revised the first paragraph of the Introduction to better connect early BMS acquisition with its longer-term biomechanical relevance and to acknowledge recent perspectives highlighting the role of biomechanics in performance optimization, injury-risk reduction, and rehabilitation. Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. We thank the reviewer for this observation. We acknowledge that the cited prevalence figures were derived from earlier reviews and may not reflect current reporting practices. However, these references were retained because they provide direct quantitative evidence of longstanding concerns regarding interobserver reliability and observer-related bias in behavioral research. To avoid implying that these figures represent the current prevalence, we revised the text to frame them as historical evidence of a methodological problem rather than as a contemporary estimate. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. We agree that the original wording of this paragraph placed greater emphasis on classical machine-learning pipelines, which may not fully reflect the diversity of contemporary AI approaches. However, our intention was not to suggest that all current motion-analysis systems rely on manual feature extraction, but rather to provide a broad and accessible description of computational steps that may be involved in some AI-related technologies for human movement assessment. To address this concern, we revised the paragraph to clarify that, although some approaches still involve preprocessing, feature extraction, and conventional classification procedures, many contemporary pipelines rely on end-to-end deep learning models, such as convolutional neural networks and vision transformers, which can learn relevant representations directly from images, videos, or pose sequences with limited or no manual feature extraction. This clarification has been incorporated to better reflect the current methodological landscape. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. We appreciate the reviewer’s concern and agree that the pace of development in AI-related research is particularly rapid. The literature search in this review ended on January 30, 2023 because this date corresponded to the original submission stage of the manuscript. The prolonged interval before the current revision was largely attributable to the editorial and peer-review timeline, which was outside the authors’ control. Accordingly, the review is intended to represent the evidence available up to that date, rather than the current state of the art at the time of revision. To ensure appropriate interpretation, we have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that newer studies on computer vision models applied to human movement assessment may not have been captured. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. We agree that engineering studies commonly evaluate systems using discipline-specific performance metrics rather than clinical reliability indices. To clarify our methodological approach, we added a brief statement in the Data analysis section indicating that COSMIN was applied from a measurement perspective to examine evidence relevant to assessment use, rather than to evaluate algorithmic performance per se. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". The word was updated Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. We agree that the category “Just kids” was unclear and inappropriate under the variable “Participant gender.” This was a wording error. We revised Table 1 to use clearer and logically consistent categories: “Boys only,” “Girls only,” “Both boys and girls,” and “Not reported.” Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” We agree that device-related factors such as calibration procedures, sampling rate, and synchronization may influence motion analysis results. However, the purpose of Table 2 was to provide a descriptive summary of the motion-capture devices and AI-related tools reported in the included studies, rather than to conduct a device-specific technical appraisal of biomechanical sensor performance. We respectfully note that the reference suggested by the reviewer addresses methodological issues related to force plates in the assessment of push-up power, which corresponds to a different biomechanical and performance-testing context from the present review, focused on AI-related technologies for assessing basic motor skills in children. For this reason, we did not incorporate that reference directly into Table 2. Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. We agree that deep learning approaches have become increasingly prominent in the broader pose estimation literature. However, we respectfully note that the proportion reported in Table 2 reflects only the characteristics of the studies included in this review, based on the predefined eligibility criteria and review period, rather than the overall prevalence of deep learning approaches across the wider field. Therefore, the finding should be interpreted within the specific scope of this scoping review and not as a comprehensive representation of the current pose estimation landscape. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. We agree that the predominance of “Inadequate” and “Doubtful” ratings in Table 3 requires careful interpretation. These ratings were intended to reflect the extent to which the included studies reported psychometric or measurement-related evidence according to COSMIN standards, rather than their algorithmic or engineering quality per se. In many cases, the included papers primarily focused on technical or algorithmic development, which likely explains the predominance of lower COSMIN ratings. We clarified this interpretive point in the text accompanying Table 3. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 We agree that the original text did not sufficiently clarify what adequate evidence of reliability and responsiveness would look like when these AI-related technologies are intended to function as assessment tools. In traditional physical performance and functional motor assessment, reliability is typically supported by evidence such as test–retest or intrasession stability, together with indicators of measurement consistency and error. Likewise, responsiveness requires evidence that an instrument can detect meaningful changes in performance over time or after intervention. In contrast, many of the studies included in our review primarily emphasized algorithmic or technical performance and did not report this type of measurement-oriented evidence. We have clarified this point in the manuscript to better explain that the COSMIN-related gap does not simply refer to missing psychometric terminology, but to the limited demonstration of whether these AI-related tools can provide stable and change-sensitive measurements comparable to those expected from established motor assessment instruments. Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. We agree that the original wording of this paragraph may have overstated the interpretation of the COSMIN findings. Our intention was not to suggest that the included studies failed simply because they did not follow psychometric validation standards, but rather to highlight an interdisciplinary gap between engineering-oriented validation and clinical or assessment-oriented validation. In many of the included studies, validation was primarily conducted using engineering criteria focused on technical performance. This is appropriate within computer science and algorithm-development contexts. However, when these technologies are proposed as tools for assessing motor skills in children, additional evidence on measurement properties becomes relevant from a clinical and psychometric perspective. We have revised the paragraph to better acknowledge that both forms of validation are important and complementary, and that the absence of psychometric evidence should not be interpreted as poor engineering quality per se. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. We agree that the search cutoff date represents an important limitation in a rapidly evolving field such as artificial intelligence and computer vision. In response, we explicitly added this issue to the Strengths and Limitations section, stating that the literature search was conducted up to January 30, 2023 and that, consequently, more recent studies may not have been captured. We also clarified that the findings should be interpreted as reflecting the evidence available up to that date rather than the most current state of the field. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 We agree that the implications section should more explicitly state the current absence of a universal standardized protocol integrating clinical psychometrics with algorithmic and sensor-based evaluation. In response, we revised the implications section to emphasize that future research should move toward a standardized interdisciplinary framework specifying not only psychometric requirements (e.g., validity, reliability, measurement error, responsiveness) but also minimum technical requirements for sensor- and algorithm-based systems, such as data acquisition procedures, calibration, synchronization, and reporting standards. We also strengthened the call for collaboration across engineering, clinical, educational, and psychometric disciplines so that AI-related technologies for BMS assessment can be developed and validated under a more unified and transferable framework. Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 18 Dec 2023 Views 0 Cite How to cite this report: Joshua AM and Prabhakar AJ. Reviewer Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.151828.r349533 ) The direct URL for this report is: https://f1000research.com/articles/12-1598/v1#referee-response-349533 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Jan 2025 Abraham M. Joshua , Department of Physiotherapy, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India Ashish John Prabhakar , Department of Physiotherapy, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education (Ringgold ID: 76793), Manipal, Karnataka, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.151828.r349533 ABSTRACT Background - Redundant phrasing: "two observersers" is misspelled and repetitive. Results - Tracking of "10 other publications" is unclear and vague. - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). ... Continue reading READ ALL ABSTRACT Background - Redundant phrasing: "two observersers" is misspelled and repetitive. Results - Tracking of "10 other publications" is unclear and vague. - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study Introduction: suggestions 1. Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion : Simplify the explanation or provide brief definitions or context for these terms. 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion : Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion : Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion : Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion : Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion : Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion : Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion : Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion : Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion : Complete the sentence and ensure that all points are concluded before introducing the research objectives. Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? 3. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Suggestion: Provide more detail on how Rayyan was used throughout the review process. How were disagreements between the two independent groups resolved, and what role did the principal investigator play in the final decisions? 4. Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? 6. Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? 7. Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Neurorehabilitation, Upper limb functional evaluation, AI for rehabilitation, Exercise therapy, Balance training We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Joshua AM and Prabhakar AJ. Reviewer Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.151828.r349533 ) The direct URL for this report is: https://f1000research.com/articles/12-1598/v1#referee-response-349533 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Sep 2025 Joel Figueroa-Quiñones , Lambayeque, Universidad Señor de Sipán, Chiclayo, Peru 10 Sep 2025 Author Response Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background ... Continue reading Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background section. The revised sentence now reads: “In basic motor skills evaluation, two observers may rate the same child’s performance differently, introducing variability in the assessment” Results - Tracking of "10 other publications" is unclear and vague. We revised the sentence in the Results section to provide a more precise description of the process. The updated text now reads: “From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. ” - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). We revised the sentence to reduce technical jargon and make it more accessible to general readers. In the corresponding section, the updated sentence now reads: “The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children's motor development.” Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study We have updated the list of keywords to better reflect the title and content of the study. The following terms have been added: “assessment tools,” “functional abilities,” and “gross motor.” Introduction: suggestions Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion: Simplify the explanation or provide brief definitions or context for these terms. We have revised the third paragraph of the Introduction to provide brief definitions and clearer context for the mentioned techniques. Specifically, we now explain the role of Fast Fourier Transformation, principal components analysis, and linear discriminant analysis in simpler terms to aid reader understanding. The revised sentence reads as follows: “Then, these data undergo pre-processing to reduce noise and enhance relevant features. This step often involves filtering techniques, such as Fast Fourier Transformation (which helps separate important movement signals from background noise) or wavelet transforms. Additionally, to simplify complex data and highlight key movement patterns, methods like principal components analysis (which reduces data dimensions while preserving essential information) or linear discriminant analysis (which enhances the distinction between movement categories) are applied (17).” 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion: Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. We agree that the connection between BMS, traditional measurement tools, observer bias, and artificial intelligence (AI)-related technology could be more explicit. In response to your suggestion, we have made adjustments to the third paragraph of the Introduction to improve the flow and strengthen the connection between these topics. Specifically, we added a clearer explanation of how AI can directly address observer bias and improve accuracy in BMS assessment. We now describe how AI, by automating the motion classification process, reduces human subjectivity and enables more objective and accurate evaluations. In this way, we aim to reinforce the argument that AI-related technology not only offers an alternative to traditional methods but effectively addresses the limitations of current BMS assessment approaches. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion: Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. We have strengthened the second paragraph of the Introduction by incorporating concrete data that illustrate the prevalence and real-world consequences of this issue. Specifically, we now include evidence from two reviews: one indicating that, among 960 behavioral studies, only 3.2% reported interobserver reliability measures and only 1.9% met rigorous criteria for minimizing bias (12); and another highlighting that in child development research, poor reporting and variability in assessor performance may obscure children’s true developmental status, potentially compromising clinical decisions (13). These additions aim to clarify the extent and significance of observer bias, reinforcing the necessity of adopting more objective tools—such as AI-based technologies—for accurate and reliable BMS assessment. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion: Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. We have revised the beginning of the third paragraph of the Introduction to explicitly define “AI-related technologies” and specify the tools most relevant for BMS assessment. The revised text reads as follows: “AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14). For example, for motion capture and analysis, computer vision tools such as OpenPose, MediaPipe and DeepLabCut enable pose estimation and tracking of key points of the human body with high accuracy (15). In addition, deep learning techniques, such as Convolutional Neural Networks (CNN) and vision-specialized Transformer Models (ViT), have proven to be effective in classifying motion sequences in videos (16). In that sense, these AI-related technologies for recognizing and classifying human motion patterns consist of several steps (Figure 1) (17).” 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion: Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. We appreciate your feedback on the flow and structure of the manuscript. Our team has improved the text to facilitate smooth transitions between sections. Specifically, the first part of the paragraph now reads: " Typically, BMS assessment relies on trained professionals who observe, record, and score children's performance on specific motor tasks (8,9). However, a major challenge in this approach is observer bias. Even when raters receive standardized training, small differences in scoring can introduce variability in BMS measurements. This variability reduces the accuracy of the assessment and can lead to misinterpretations. For example, two children with similar motor skills may receive different scores depending on the assessor, resulting in inconsistent results. When these inconsistencies follow a systematic pattern, they contribute to observer bias, a well-documented source of measurement error (10,11) .". We have also added a transitional paragraph at the end of the second paragraph, linking the problem of observer bias to the introduction of AI technologies as a solution. This paragraph highlights how observer bias can affect BMS assessment results, and the following paragraph addresses how AI-related technologies can mitigate this problem, offering a more objective and accurate alternative. The text reads: "In fact, one review reported that of 960 behavioral studies, only 3.2% reported measures of interobserver reliability, and only 1.9% met rigorous criteria for minimizing bias (12). Similarly, another review on child development found that the quality of reporting on the use of assessors in these studies was poor and that variability in assessor performance may obscure the true developmental status of children, compromising complex and costly clinical decisions (13)”. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion: Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. We have added a brief explanation in the first paragraph of the introduction that highlights why this age group is crucial for BMS development. Specifically, we have pointed out that during this period of rapid physical and motor development, children refine fundamental skills that are essential for later complex activities, which directly impacts their cognitive, emotional, and social development. In addition, we underscored the relevance of early assessment to identify potential delays and facilitate timely interventions. Now the text says the following: "The development of basic motor skills (BMS) in children aged 3 to 6 years is critical, as this is a period of rapid motor growth, where children acquire physical skills that allow them to participate in a variety of activities (1,2). At this age, children experience significant improvements in gross motor control, allowing them to perform movements such as running, jumping, and manipulating objects with greater precision (3,4). The acquisition of these motor skills is essential for physical, cognitive and emotional development, as BMS are strongly linked to general well-being, self-esteem and social integration (5)." 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion: Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. We have carefully reviewed the claims about AI applications and ensured that each is supported by an appropriate reference. In addition, we have revised the general claims to improve their accuracy with the cited literature. Specifically, we have updated citations 4, 5, 6, and 7 with their respective references: 4. Jiang G-P, Jiao X-B, Wu S-K, Ji Z-Q, Liu W-T, Chen X, et al. Balance, proprioception, and gross motor development of Chinese children aged 3 to 6 years. J Mot Behav. 2018;50(3):343–52. Available at: http://dx.doi.org/10.1080/00222895.2017.1363694 5. Gandotra A, Kotyuk E, Bizonics R, Khan I, Petánszki M, Kiss L, et al. An exploratory study of the relationship between motor skills and indicators of cognitive and socio-emotional development in preschoolers. Eur J Dev Psychol. 2023;20(1):50–65. Available at: http://dx.doi.org/10.1080/17405629.2022.2028617 6. Bremer E, Cairney J. Fundamental Movement Skills and Health-Related Outcomes: A Narrative Review of Longitudinal and Intervention Studies Targeting Typically Developing Children. Am J Lifestyle Med. 2018;12(2):148-59. https://doi.org/10.1177/1559827616640196 7. Eddy LH, Wood ML, Shire KA, Bingham DD, Bonnick E, Creaser A, et al. A systematic review of randomized and case-controlled trials investigating the effectiveness of school-based motor skill interventions in 3- to 12-year-old children. Child Care Health Dev. 2019;45(6):773-90. https://doi.org/10.1111/cch.12712 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion: Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. We have revised the last two final sentences of the introduction to highlight the research gap in the use of AI to accurately assess BMS in children and the need for this scoping review. Specifically, we now emphasize the lack of a comprehensive review of AI applications for assessing basic motor skills (BMS) in preschool children, as well as the need to systematize the scope, limitations, and validity of these technologies. Specifically, we have added the following: "However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Furthermore, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools" 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion: Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. Our team has made sure to review and update the text to avoid redundancy of the term “AI-related technologies”. We have also decided to keep the term “AI-related technologies” consistently throughout the introduction to ensure clarity and consistency throughout the manuscript. In fact, in the third paragraph, we have included a clarification referring to the term “AI-related technologies”. Specifically, in the first three lines of the third paragraph in the introduction, we have added: "AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14)" 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion: Complete the sentence and ensure that all points are concluded before introducing the research objectives. We have reviewed and revised this section to ensure the idea is fully expressed and concluded before presenting the research objectives. Now, in the final lines of the fourth paragraph of the introduction, we have added the following: “However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Moreover, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools” Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? We have revised the manuscript and added more detailed inclusion and exclusion criteria. Specifically, in the third paragraph of methods, we added: "We also defined the following criteria for the search: 1) studies in preschool-aged children (3 to 6 years), 2) studies in which the motor ability (motor or play skills) of the child was assessed using AI-related technologies for motion detection, and 3) studies in which at least one of the basic motor skills described in the literature (running, jumping, kicking, throwing, or catching a ball) was measured. In addition, we excluded 1) studies that did not clearly describe the AI-related technology used or developed, 2) opinion articles, editorials, or narrative reviews without empirical data and 3) gray literature (e.g. theses, dissertations, or non-peer-reviewed reports)." 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? We've added clearer justification for our selection of databases and search terms. Specifically, we added in the methods section, subsection Search strategy, the following two paragraphs: " We searched for studies published before January 30, 2023 in the target publications in Medline (SCR_002185), Web of Science (SCR_022706), IEEE (SCR_008314), and EBSCO (SCR_022707). These databases were selected because they specialize in biomedical, engineering, and multidisciplinary research, ensuring that we captured relevant studies in health sciences, AI applications, and motion analysis. Search terms included keyword combinations such as “child,” “preschool,” “basic motor skills,” “artificial intelligence,” “motion sensing,” and “calibration,” along with related terms and synonyms identified through a preliminary literature review (keywords) and controlled vocabulary (MeSH terms). The full search strategy and complete list of search terms are available here (42)." Likewise, we cite the complete search strategy as number 42. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Our team has detailed how Rayyan was used during the review process. Specifically, in the methods section, subsection Search strategy, in the third, fourth and fifth paragraph, we add: "The search formulas were applied to the databases and all the files were exported in RIS format. Then, to ensure an objective selection process, these identified files were uploaded to the Rayyan platform which facilitated blind selection by the reviewers and expedited the identification of duplicates. The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? Our team has added information about the process to minimize bias during study selection. Specifically, in the methods section, Search Strategy subsection, in the fourth and fifth paragraphs, we add: "The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? We have updated the paragraph by adding information in the methods section, Data extraction subsection. Now the text says the following: "Data extraction was performed in a structured manner using a pre-designed form (43). To reduce errors and improve the accuracy of the extracted data, one peer reviewer performed the initial extraction and a second peer independently verified the information. Any discrepancies in the extraction were reviewed jointly and/or, with the intervention of the principal investigator. Cross-checks were implemented to ensure the consistency of the information collected." Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? Since our study was a scoping review, we did not conduct a meta-analysis. Instead, we opted for a narrative synthesis of the results, presented in tables of frequencies and percentages. We also used the COSMIN standards, whose scoring and rating process is described in their original report, which we also reference in our study. Specifically, our text, in the methods section, subsection "Data analysis," in its first paragraph, states the following: "All data collected were summarized as categorical variables, organized and presented in tables, using descriptive statistics such as simple frequencies and percentages. Since this was a scoping review, a narrative synthesis was used to summarize the findings of the studies, focusing on the characteristics and psychometric properties evaluated according to COSMIN standards." Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? We would like to clarify that, since our study is a scoping review focused on the mapping and characterization of AI-based technologies used to assess motor skills in children, we did not aim to establish causal or correlational relationships between variables. Therefore, the methodological approach does not involve controlling or statistically analyzing confounding variables, as would be expected in primary empirical studies. The purpose of this review was to identify and describe the technologies developed, their applications, and the reported psychometric dimensions. Nonetheless, we have incorporated a critical reflection on the differences in sample sizes, age ranges (expressed in both years and months), and the types of technologies used, acknowledging these as limitations that could influence the validity and generalizability of the findings reported by the included studies. Specifically, the last lines of the limitations section say the following: "This review did not aim to analyze associations between variables; however, variability in sample sizes, age ranges, and types of AI-based technologies used across studies may affect the comparability and generalizability of the findings. These differences should be considered when interpreting the results and highlight the need for more standardized approaches in future research." 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Since no standardized framework was used to assess feasibility and usability, this is an aspect that limits the interpretation and comparison of the results among the included studies. We state this information in the limitations. Specifically, we added to the beginning of the limitations:: "Also, although this review was based on COSMIN standards to assess the psychometric quality of AI-related technologies, due to the heterogeneity observed in the included studies, no specific adjustments were made to control for possible confounding variables. Therefore, the conclusions need to be interpreted with caution. It is recommended that future research address these factors and use control methods to provide more generalizable conclusions. Furthermore, feasibility and usability were extracted only if the reviewed studies explicitly reported having done so in their analysis of AI-related technologies. Therefore, further studies should evaluate these analyzes using a standardized framework." In response to comments on discussion, we have expanded this section on implications. Specifically, we added the following paragraphs in implications: "To facilitate use, developers could conduct studies that evaluate the acceptance, ease of use, cost-effectiveness, and accessibility of these technologies. For example, most technologies rely on sensors and monitors that, while accurate, can be costly, require specialized training, and can be difficult to implement in real-world settings for physicians, teachers, therapists, or practitioners unfamiliar with these tools. In addition, disparities in access to advanced technologies may limit their adoption, particularly in low-resource settings. Also, these types of technologies may be closer to more universal and cost-effective devices, such as video cameras, smartphones, and tablets, that can assess and report motor skills in real time. However, addressing these challenges requires a collaborative and interdisciplinary approach. Future validation studies should involve experts from multiple fields, including engineers, healthcare professionals, educators and policy makers, to ensure that these technologies are not only accurate, but also practical, scalable and accessible to diverse populations. New validation studies of these technologies should include validation standards for BMS tests, prioritizing key psychometric properties such as construct validity, criterion validity, reliability, measurement error, among others. To make this possible, engineering teams could incorporate specialists in psychometrics, developmental therapy and medicine to work collaboratively. This multidisciplinary approach will facilitate the integration of medical knowledge and psychometric standards into future software releases, improving both measurement accuracy and practical usability. Finally, developers should consider providing open source code or detailed methodological documentation, which will allow for further refinement, replication, and clinical adaptation of these technologies in future research and real-world applications." Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background section. The revised sentence now reads: “In basic motor skills evaluation, two observers may rate the same child’s performance differently, introducing variability in the assessment” Results - Tracking of "10 other publications" is unclear and vague. We revised the sentence in the Results section to provide a more precise description of the process. The updated text now reads: “From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. ” - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). We revised the sentence to reduce technical jargon and make it more accessible to general readers. In the corresponding section, the updated sentence now reads: “The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children's motor development.” Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study We have updated the list of keywords to better reflect the title and content of the study. The following terms have been added: “assessment tools,” “functional abilities,” and “gross motor.” Introduction: suggestions Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion: Simplify the explanation or provide brief definitions or context for these terms. We have revised the third paragraph of the Introduction to provide brief definitions and clearer context for the mentioned techniques. Specifically, we now explain the role of Fast Fourier Transformation, principal components analysis, and linear discriminant analysis in simpler terms to aid reader understanding. The revised sentence reads as follows: “Then, these data undergo pre-processing to reduce noise and enhance relevant features. This step often involves filtering techniques, such as Fast Fourier Transformation (which helps separate important movement signals from background noise) or wavelet transforms. Additionally, to simplify complex data and highlight key movement patterns, methods like principal components analysis (which reduces data dimensions while preserving essential information) or linear discriminant analysis (which enhances the distinction between movement categories) are applied (17).” 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion: Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. We agree that the connection between BMS, traditional measurement tools, observer bias, and artificial intelligence (AI)-related technology could be more explicit. In response to your suggestion, we have made adjustments to the third paragraph of the Introduction to improve the flow and strengthen the connection between these topics. Specifically, we added a clearer explanation of how AI can directly address observer bias and improve accuracy in BMS assessment. We now describe how AI, by automating the motion classification process, reduces human subjectivity and enables more objective and accurate evaluations. In this way, we aim to reinforce the argument that AI-related technology not only offers an alternative to traditional methods but effectively addresses the limitations of current BMS assessment approaches. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion: Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. We have strengthened the second paragraph of the Introduction by incorporating concrete data that illustrate the prevalence and real-world consequences of this issue. Specifically, we now include evidence from two reviews: one indicating that, among 960 behavioral studies, only 3.2% reported interobserver reliability measures and only 1.9% met rigorous criteria for minimizing bias (12); and another highlighting that in child development research, poor reporting and variability in assessor performance may obscure children’s true developmental status, potentially compromising clinical decisions (13). These additions aim to clarify the extent and significance of observer bias, reinforcing the necessity of adopting more objective tools—such as AI-based technologies—for accurate and reliable BMS assessment. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion: Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. We have revised the beginning of the third paragraph of the Introduction to explicitly define “AI-related technologies” and specify the tools most relevant for BMS assessment. The revised text reads as follows: “AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14). For example, for motion capture and analysis, computer vision tools such as OpenPose, MediaPipe and DeepLabCut enable pose estimation and tracking of key points of the human body with high accuracy (15). In addition, deep learning techniques, such as Convolutional Neural Networks (CNN) and vision-specialized Transformer Models (ViT), have proven to be effective in classifying motion sequences in videos (16). In that sense, these AI-related technologies for recognizing and classifying human motion patterns consist of several steps (Figure 1) (17).” 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion: Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. We appreciate your feedback on the flow and structure of the manuscript. Our team has improved the text to facilitate smooth transitions between sections. Specifically, the first part of the paragraph now reads: " Typically, BMS assessment relies on trained professionals who observe, record, and score children's performance on specific motor tasks (8,9). However, a major challenge in this approach is observer bias. Even when raters receive standardized training, small differences in scoring can introduce variability in BMS measurements. This variability reduces the accuracy of the assessment and can lead to misinterpretations. For example, two children with similar motor skills may receive different scores depending on the assessor, resulting in inconsistent results. When these inconsistencies follow a systematic pattern, they contribute to observer bias, a well-documented source of measurement error (10,11) .". We have also added a transitional paragraph at the end of the second paragraph, linking the problem of observer bias to the introduction of AI technologies as a solution. This paragraph highlights how observer bias can affect BMS assessment results, and the following paragraph addresses how AI-related technologies can mitigate this problem, offering a more objective and accurate alternative. The text reads: "In fact, one review reported that of 960 behavioral studies, only 3.2% reported measures of interobserver reliability, and only 1.9% met rigorous criteria for minimizing bias (12). Similarly, another review on child development found that the quality of reporting on the use of assessors in these studies was poor and that variability in assessor performance may obscure the true developmental status of children, compromising complex and costly clinical decisions (13)”. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion: Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. We have added a brief explanation in the first paragraph of the introduction that highlights why this age group is crucial for BMS development. Specifically, we have pointed out that during this period of rapid physical and motor development, children refine fundamental skills that are essential for later complex activities, which directly impacts their cognitive, emotional, and social development. In addition, we underscored the relevance of early assessment to identify potential delays and facilitate timely interventions. Now the text says the following: "The development of basic motor skills (BMS) in children aged 3 to 6 years is critical, as this is a period of rapid motor growth, where children acquire physical skills that allow them to participate in a variety of activities (1,2). At this age, children experience significant improvements in gross motor control, allowing them to perform movements such as running, jumping, and manipulating objects with greater precision (3,4). The acquisition of these motor skills is essential for physical, cognitive and emotional development, as BMS are strongly linked to general well-being, self-esteem and social integration (5)." 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion: Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. We have carefully reviewed the claims about AI applications and ensured that each is supported by an appropriate reference. In addition, we have revised the general claims to improve their accuracy with the cited literature. Specifically, we have updated citations 4, 5, 6, and 7 with their respective references: 4. Jiang G-P, Jiao X-B, Wu S-K, Ji Z-Q, Liu W-T, Chen X, et al. Balance, proprioception, and gross motor development of Chinese children aged 3 to 6 years. J Mot Behav. 2018;50(3):343–52. Available at: http://dx.doi.org/10.1080/00222895.2017.1363694 5. Gandotra A, Kotyuk E, Bizonics R, Khan I, Petánszki M, Kiss L, et al. An exploratory study of the relationship between motor skills and indicators of cognitive and socio-emotional development in preschoolers. Eur J Dev Psychol. 2023;20(1):50–65. Available at: http://dx.doi.org/10.1080/17405629.2022.2028617 6. Bremer E, Cairney J. Fundamental Movement Skills and Health-Related Outcomes: A Narrative Review of Longitudinal and Intervention Studies Targeting Typically Developing Children. Am J Lifestyle Med. 2018;12(2):148-59. https://doi.org/10.1177/1559827616640196 7. Eddy LH, Wood ML, Shire KA, Bingham DD, Bonnick E, Creaser A, et al. A systematic review of randomized and case-controlled trials investigating the effectiveness of school-based motor skill interventions in 3- to 12-year-old children. Child Care Health Dev. 2019;45(6):773-90. https://doi.org/10.1111/cch.12712 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion: Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. We have revised the last two final sentences of the introduction to highlight the research gap in the use of AI to accurately assess BMS in children and the need for this scoping review. Specifically, we now emphasize the lack of a comprehensive review of AI applications for assessing basic motor skills (BMS) in preschool children, as well as the need to systematize the scope, limitations, and validity of these technologies. Specifically, we have added the following: "However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Furthermore, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools" 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion: Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. Our team has made sure to review and update the text to avoid redundancy of the term “AI-related technologies”. We have also decided to keep the term “AI-related technologies” consistently throughout the introduction to ensure clarity and consistency throughout the manuscript. In fact, in the third paragraph, we have included a clarification referring to the term “AI-related technologies”. Specifically, in the first three lines of the third paragraph in the introduction, we have added: "AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14)" 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion: Complete the sentence and ensure that all points are concluded before introducing the research objectives. We have reviewed and revised this section to ensure the idea is fully expressed and concluded before presenting the research objectives. Now, in the final lines of the fourth paragraph of the introduction, we have added the following: “However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Moreover, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools” Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? We have revised the manuscript and added more detailed inclusion and exclusion criteria. Specifically, in the third paragraph of methods, we added: "We also defined the following criteria for the search: 1) studies in preschool-aged children (3 to 6 years), 2) studies in which the motor ability (motor or play skills) of the child was assessed using AI-related technologies for motion detection, and 3) studies in which at least one of the basic motor skills described in the literature (running, jumping, kicking, throwing, or catching a ball) was measured. In addition, we excluded 1) studies that did not clearly describe the AI-related technology used or developed, 2) opinion articles, editorials, or narrative reviews without empirical data and 3) gray literature (e.g. theses, dissertations, or non-peer-reviewed reports)." 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? We've added clearer justification for our selection of databases and search terms. Specifically, we added in the methods section, subsection Search strategy, the following two paragraphs: " We searched for studies published before January 30, 2023 in the target publications in Medline (SCR_002185), Web of Science (SCR_022706), IEEE (SCR_008314), and EBSCO (SCR_022707). These databases were selected because they specialize in biomedical, engineering, and multidisciplinary research, ensuring that we captured relevant studies in health sciences, AI applications, and motion analysis. Search terms included keyword combinations such as “child,” “preschool,” “basic motor skills,” “artificial intelligence,” “motion sensing,” and “calibration,” along with related terms and synonyms identified through a preliminary literature review (keywords) and controlled vocabulary (MeSH terms). The full search strategy and complete list of search terms are available here (42)." Likewise, we cite the complete search strategy as number 42. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Our team has detailed how Rayyan was used during the review process. Specifically, in the methods section, subsection Search strategy, in the third, fourth and fifth paragraph, we add: "The search formulas were applied to the databases and all the files were exported in RIS format. Then, to ensure an objective selection process, these identified files were uploaded to the Rayyan platform which facilitated blind selection by the reviewers and expedited the identification of duplicates. The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? Our team has added information about the process to minimize bias during study selection. Specifically, in the methods section, Search Strategy subsection, in the fourth and fifth paragraphs, we add: "The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? We have updated the paragraph by adding information in the methods section, Data extraction subsection. Now the text says the following: "Data extraction was performed in a structured manner using a pre-designed form (43). To reduce errors and improve the accuracy of the extracted data, one peer reviewer performed the initial extraction and a second peer independently verified the information. Any discrepancies in the extraction were reviewed jointly and/or, with the intervention of the principal investigator. Cross-checks were implemented to ensure the consistency of the information collected." Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? Since our study was a scoping review, we did not conduct a meta-analysis. Instead, we opted for a narrative synthesis of the results, presented in tables of frequencies and percentages. We also used the COSMIN standards, whose scoring and rating process is described in their original report, which we also reference in our study. Specifically, our text, in the methods section, subsection "Data analysis," in its first paragraph, states the following: "All data collected were summarized as categorical variables, organized and presented in tables, using descriptive statistics such as simple frequencies and percentages. Since this was a scoping review, a narrative synthesis was used to summarize the findings of the studies, focusing on the characteristics and psychometric properties evaluated according to COSMIN standards." Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? We would like to clarify that, since our study is a scoping review focused on the mapping and characterization of AI-based technologies used to assess motor skills in children, we did not aim to establish causal or correlational relationships between variables. Therefore, the methodological approach does not involve controlling or statistically analyzing confounding variables, as would be expected in primary empirical studies. The purpose of this review was to identify and describe the technologies developed, their applications, and the reported psychometric dimensions. Nonetheless, we have incorporated a critical reflection on the differences in sample sizes, age ranges (expressed in both years and months), and the types of technologies used, acknowledging these as limitations that could influence the validity and generalizability of the findings reported by the included studies. Specifically, the last lines of the limitations section say the following: "This review did not aim to analyze associations between variables; however, variability in sample sizes, age ranges, and types of AI-based technologies used across studies may affect the comparability and generalizability of the findings. These differences should be considered when interpreting the results and highlight the need for more standardized approaches in future research." 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Since no standardized framework was used to assess feasibility and usability, this is an aspect that limits the interpretation and comparison of the results among the included studies. We state this information in the limitations. Specifically, we added to the beginning of the limitations:: "Also, although this review was based on COSMIN standards to assess the psychometric quality of AI-related technologies, due to the heterogeneity observed in the included studies, no specific adjustments were made to control for possible confounding variables. Therefore, the conclusions need to be interpreted with caution. It is recommended that future research address these factors and use control methods to provide more generalizable conclusions. Furthermore, feasibility and usability were extracted only if the reviewed studies explicitly reported having done so in their analysis of AI-related technologies. Therefore, further studies should evaluate these analyzes using a standardized framework." In response to comments on discussion, we have expanded this section on implications. Specifically, we added the following paragraphs in implications: "To facilitate use, developers could conduct studies that evaluate the acceptance, ease of use, cost-effectiveness, and accessibility of these technologies. For example, most technologies rely on sensors and monitors that, while accurate, can be costly, require specialized training, and can be difficult to implement in real-world settings for physicians, teachers, therapists, or practitioners unfamiliar with these tools. In addition, disparities in access to advanced technologies may limit their adoption, particularly in low-resource settings. Also, these types of technologies may be closer to more universal and cost-effective devices, such as video cameras, smartphones, and tablets, that can assess and report motor skills in real time. However, addressing these challenges requires a collaborative and interdisciplinary approach. Future validation studies should involve experts from multiple fields, including engineers, healthcare professionals, educators and policy makers, to ensure that these technologies are not only accurate, but also practical, scalable and accessible to diverse populations. New validation studies of these technologies should include validation standards for BMS tests, prioritizing key psychometric properties such as construct validity, criterion validity, reliability, measurement error, among others. To make this possible, engineering teams could incorporate specialists in psychometrics, developmental therapy and medicine to work collaboratively. This multidisciplinary approach will facilitate the integration of medical knowledge and psychometric standards into future software releases, improving both measurement accuracy and practical usability. Finally, developers should consider providing open source code or detailed methodological documentation, which will allow for further refinement, replication, and clinical adaptation of these technologies in future research and real-world applications." Competing Interests: There is no conflict of interest Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Sep 2025 Joel Figueroa-Quiñones , Lambayeque, Universidad Señor de Sipán, Chiclayo, Peru 10 Sep 2025 Author Response Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background ... Continue reading Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background section. The revised sentence now reads: “In basic motor skills evaluation, two observers may rate the same child’s performance differently, introducing variability in the assessment” Results - Tracking of "10 other publications" is unclear and vague. We revised the sentence in the Results section to provide a more precise description of the process. The updated text now reads: “From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. ” - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). We revised the sentence to reduce technical jargon and make it more accessible to general readers. In the corresponding section, the updated sentence now reads: “The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children's motor development.” Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study We have updated the list of keywords to better reflect the title and content of the study. The following terms have been added: “assessment tools,” “functional abilities,” and “gross motor.” Introduction: suggestions Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion: Simplify the explanation or provide brief definitions or context for these terms. We have revised the third paragraph of the Introduction to provide brief definitions and clearer context for the mentioned techniques. Specifically, we now explain the role of Fast Fourier Transformation, principal components analysis, and linear discriminant analysis in simpler terms to aid reader understanding. The revised sentence reads as follows: “Then, these data undergo pre-processing to reduce noise and enhance relevant features. This step often involves filtering techniques, such as Fast Fourier Transformation (which helps separate important movement signals from background noise) or wavelet transforms. Additionally, to simplify complex data and highlight key movement patterns, methods like principal components analysis (which reduces data dimensions while preserving essential information) or linear discriminant analysis (which enhances the distinction between movement categories) are applied (17).” 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion: Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. We agree that the connection between BMS, traditional measurement tools, observer bias, and artificial intelligence (AI)-related technology could be more explicit. In response to your suggestion, we have made adjustments to the third paragraph of the Introduction to improve the flow and strengthen the connection between these topics. Specifically, we added a clearer explanation of how AI can directly address observer bias and improve accuracy in BMS assessment. We now describe how AI, by automating the motion classification process, reduces human subjectivity and enables more objective and accurate evaluations. In this way, we aim to reinforce the argument that AI-related technology not only offers an alternative to traditional methods but effectively addresses the limitations of current BMS assessment approaches. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion: Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. We have strengthened the second paragraph of the Introduction by incorporating concrete data that illustrate the prevalence and real-world consequences of this issue. Specifically, we now include evidence from two reviews: one indicating that, among 960 behavioral studies, only 3.2% reported interobserver reliability measures and only 1.9% met rigorous criteria for minimizing bias (12); and another highlighting that in child development research, poor reporting and variability in assessor performance may obscure children’s true developmental status, potentially compromising clinical decisions (13). These additions aim to clarify the extent and significance of observer bias, reinforcing the necessity of adopting more objective tools—such as AI-based technologies—for accurate and reliable BMS assessment. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion: Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. We have revised the beginning of the third paragraph of the Introduction to explicitly define “AI-related technologies” and specify the tools most relevant for BMS assessment. The revised text reads as follows: “AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14). For example, for motion capture and analysis, computer vision tools such as OpenPose, MediaPipe and DeepLabCut enable pose estimation and tracking of key points of the human body with high accuracy (15). In addition, deep learning techniques, such as Convolutional Neural Networks (CNN) and vision-specialized Transformer Models (ViT), have proven to be effective in classifying motion sequences in videos (16). In that sense, these AI-related technologies for recognizing and classifying human motion patterns consist of several steps (Figure 1) (17).” 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion: Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. We appreciate your feedback on the flow and structure of the manuscript. Our team has improved the text to facilitate smooth transitions between sections. Specifically, the first part of the paragraph now reads: " Typically, BMS assessment relies on trained professionals who observe, record, and score children's performance on specific motor tasks (8,9). However, a major challenge in this approach is observer bias. Even when raters receive standardized training, small differences in scoring can introduce variability in BMS measurements. This variability reduces the accuracy of the assessment and can lead to misinterpretations. For example, two children with similar motor skills may receive different scores depending on the assessor, resulting in inconsistent results. When these inconsistencies follow a systematic pattern, they contribute to observer bias, a well-documented source of measurement error (10,11) .". We have also added a transitional paragraph at the end of the second paragraph, linking the problem of observer bias to the introduction of AI technologies as a solution. This paragraph highlights how observer bias can affect BMS assessment results, and the following paragraph addresses how AI-related technologies can mitigate this problem, offering a more objective and accurate alternative. The text reads: "In fact, one review reported that of 960 behavioral studies, only 3.2% reported measures of interobserver reliability, and only 1.9% met rigorous criteria for minimizing bias (12). Similarly, another review on child development found that the quality of reporting on the use of assessors in these studies was poor and that variability in assessor performance may obscure the true developmental status of children, compromising complex and costly clinical decisions (13)”. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion: Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. We have added a brief explanation in the first paragraph of the introduction that highlights why this age group is crucial for BMS development. Specifically, we have pointed out that during this period of rapid physical and motor development, children refine fundamental skills that are essential for later complex activities, which directly impacts their cognitive, emotional, and social development. In addition, we underscored the relevance of early assessment to identify potential delays and facilitate timely interventions. Now the text says the following: "The development of basic motor skills (BMS) in children aged 3 to 6 years is critical, as this is a period of rapid motor growth, where children acquire physical skills that allow them to participate in a variety of activities (1,2). At this age, children experience significant improvements in gross motor control, allowing them to perform movements such as running, jumping, and manipulating objects with greater precision (3,4). The acquisition of these motor skills is essential for physical, cognitive and emotional development, as BMS are strongly linked to general well-being, self-esteem and social integration (5)." 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion: Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. We have carefully reviewed the claims about AI applications and ensured that each is supported by an appropriate reference. In addition, we have revised the general claims to improve their accuracy with the cited literature. Specifically, we have updated citations 4, 5, 6, and 7 with their respective references: 4. Jiang G-P, Jiao X-B, Wu S-K, Ji Z-Q, Liu W-T, Chen X, et al. Balance, proprioception, and gross motor development of Chinese children aged 3 to 6 years. J Mot Behav. 2018;50(3):343–52. Available at: http://dx.doi.org/10.1080/00222895.2017.1363694 5. Gandotra A, Kotyuk E, Bizonics R, Khan I, Petánszki M, Kiss L, et al. An exploratory study of the relationship between motor skills and indicators of cognitive and socio-emotional development in preschoolers. Eur J Dev Psychol. 2023;20(1):50–65. Available at: http://dx.doi.org/10.1080/17405629.2022.2028617 6. Bremer E, Cairney J. Fundamental Movement Skills and Health-Related Outcomes: A Narrative Review of Longitudinal and Intervention Studies Targeting Typically Developing Children. Am J Lifestyle Med. 2018;12(2):148-59. https://doi.org/10.1177/1559827616640196 7. Eddy LH, Wood ML, Shire KA, Bingham DD, Bonnick E, Creaser A, et al. A systematic review of randomized and case-controlled trials investigating the effectiveness of school-based motor skill interventions in 3- to 12-year-old children. Child Care Health Dev. 2019;45(6):773-90. https://doi.org/10.1111/cch.12712 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion: Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. We have revised the last two final sentences of the introduction to highlight the research gap in the use of AI to accurately assess BMS in children and the need for this scoping review. Specifically, we now emphasize the lack of a comprehensive review of AI applications for assessing basic motor skills (BMS) in preschool children, as well as the need to systematize the scope, limitations, and validity of these technologies. Specifically, we have added the following: "However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Furthermore, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools" 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion: Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. Our team has made sure to review and update the text to avoid redundancy of the term “AI-related technologies”. We have also decided to keep the term “AI-related technologies” consistently throughout the introduction to ensure clarity and consistency throughout the manuscript. In fact, in the third paragraph, we have included a clarification referring to the term “AI-related technologies”. Specifically, in the first three lines of the third paragraph in the introduction, we have added: "AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14)" 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion: Complete the sentence and ensure that all points are concluded before introducing the research objectives. We have reviewed and revised this section to ensure the idea is fully expressed and concluded before presenting the research objectives. Now, in the final lines of the fourth paragraph of the introduction, we have added the following: “However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Moreover, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools” Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? We have revised the manuscript and added more detailed inclusion and exclusion criteria. Specifically, in the third paragraph of methods, we added: "We also defined the following criteria for the search: 1) studies in preschool-aged children (3 to 6 years), 2) studies in which the motor ability (motor or play skills) of the child was assessed using AI-related technologies for motion detection, and 3) studies in which at least one of the basic motor skills described in the literature (running, jumping, kicking, throwing, or catching a ball) was measured. In addition, we excluded 1) studies that did not clearly describe the AI-related technology used or developed, 2) opinion articles, editorials, or narrative reviews without empirical data and 3) gray literature (e.g. theses, dissertations, or non-peer-reviewed reports)." 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? We've added clearer justification for our selection of databases and search terms. Specifically, we added in the methods section, subsection Search strategy, the following two paragraphs: " We searched for studies published before January 30, 2023 in the target publications in Medline (SCR_002185), Web of Science (SCR_022706), IEEE (SCR_008314), and EBSCO (SCR_022707). These databases were selected because they specialize in biomedical, engineering, and multidisciplinary research, ensuring that we captured relevant studies in health sciences, AI applications, and motion analysis. Search terms included keyword combinations such as “child,” “preschool,” “basic motor skills,” “artificial intelligence,” “motion sensing,” and “calibration,” along with related terms and synonyms identified through a preliminary literature review (keywords) and controlled vocabulary (MeSH terms). The full search strategy and complete list of search terms are available here (42)." Likewise, we cite the complete search strategy as number 42. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Our team has detailed how Rayyan was used during the review process. Specifically, in the methods section, subsection Search strategy, in the third, fourth and fifth paragraph, we add: "The search formulas were applied to the databases and all the files were exported in RIS format. Then, to ensure an objective selection process, these identified files were uploaded to the Rayyan platform which facilitated blind selection by the reviewers and expedited the identification of duplicates. The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? Our team has added information about the process to minimize bias during study selection. Specifically, in the methods section, Search Strategy subsection, in the fourth and fifth paragraphs, we add: "The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? We have updated the paragraph by adding information in the methods section, Data extraction subsection. Now the text says the following: "Data extraction was performed in a structured manner using a pre-designed form (43). To reduce errors and improve the accuracy of the extracted data, one peer reviewer performed the initial extraction and a second peer independently verified the information. Any discrepancies in the extraction were reviewed jointly and/or, with the intervention of the principal investigator. Cross-checks were implemented to ensure the consistency of the information collected." Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? Since our study was a scoping review, we did not conduct a meta-analysis. Instead, we opted for a narrative synthesis of the results, presented in tables of frequencies and percentages. We also used the COSMIN standards, whose scoring and rating process is described in their original report, which we also reference in our study. Specifically, our text, in the methods section, subsection "Data analysis," in its first paragraph, states the following: "All data collected were summarized as categorical variables, organized and presented in tables, using descriptive statistics such as simple frequencies and percentages. Since this was a scoping review, a narrative synthesis was used to summarize the findings of the studies, focusing on the characteristics and psychometric properties evaluated according to COSMIN standards." Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? We would like to clarify that, since our study is a scoping review focused on the mapping and characterization of AI-based technologies used to assess motor skills in children, we did not aim to establish causal or correlational relationships between variables. Therefore, the methodological approach does not involve controlling or statistically analyzing confounding variables, as would be expected in primary empirical studies. The purpose of this review was to identify and describe the technologies developed, their applications, and the reported psychometric dimensions. Nonetheless, we have incorporated a critical reflection on the differences in sample sizes, age ranges (expressed in both years and months), and the types of technologies used, acknowledging these as limitations that could influence the validity and generalizability of the findings reported by the included studies. Specifically, the last lines of the limitations section say the following: "This review did not aim to analyze associations between variables; however, variability in sample sizes, age ranges, and types of AI-based technologies used across studies may affect the comparability and generalizability of the findings. These differences should be considered when interpreting the results and highlight the need for more standardized approaches in future research." 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Since no standardized framework was used to assess feasibility and usability, this is an aspect that limits the interpretation and comparison of the results among the included studies. We state this information in the limitations. Specifically, we added to the beginning of the limitations:: "Also, although this review was based on COSMIN standards to assess the psychometric quality of AI-related technologies, due to the heterogeneity observed in the included studies, no specific adjustments were made to control for possible confounding variables. Therefore, the conclusions need to be interpreted with caution. It is recommended that future research address these factors and use control methods to provide more generalizable conclusions. Furthermore, feasibility and usability were extracted only if the reviewed studies explicitly reported having done so in their analysis of AI-related technologies. Therefore, further studies should evaluate these analyzes using a standardized framework." In response to comments on discussion, we have expanded this section on implications. Specifically, we added the following paragraphs in implications: "To facilitate use, developers could conduct studies that evaluate the acceptance, ease of use, cost-effectiveness, and accessibility of these technologies. For example, most technologies rely on sensors and monitors that, while accurate, can be costly, require specialized training, and can be difficult to implement in real-world settings for physicians, teachers, therapists, or practitioners unfamiliar with these tools. In addition, disparities in access to advanced technologies may limit their adoption, particularly in low-resource settings. Also, these types of technologies may be closer to more universal and cost-effective devices, such as video cameras, smartphones, and tablets, that can assess and report motor skills in real time. However, addressing these challenges requires a collaborative and interdisciplinary approach. Future validation studies should involve experts from multiple fields, including engineers, healthcare professionals, educators and policy makers, to ensure that these technologies are not only accurate, but also practical, scalable and accessible to diverse populations. New validation studies of these technologies should include validation standards for BMS tests, prioritizing key psychometric properties such as construct validity, criterion validity, reliability, measurement error, among others. To make this possible, engineering teams could incorporate specialists in psychometrics, developmental therapy and medicine to work collaboratively. This multidisciplinary approach will facilitate the integration of medical knowledge and psychometric standards into future software releases, improving both measurement accuracy and practical usability. Finally, developers should consider providing open source code or detailed methodological documentation, which will allow for further refinement, replication, and clinical adaptation of these technologies in future research and real-world applications." Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background section. The revised sentence now reads: “In basic motor skills evaluation, two observers may rate the same child’s performance differently, introducing variability in the assessment” Results - Tracking of "10 other publications" is unclear and vague. We revised the sentence in the Results section to provide a more precise description of the process. The updated text now reads: “From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. ” - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). We revised the sentence to reduce technical jargon and make it more accessible to general readers. In the corresponding section, the updated sentence now reads: “The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children's motor development.” Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study We have updated the list of keywords to better reflect the title and content of the study. The following terms have been added: “assessment tools,” “functional abilities,” and “gross motor.” Introduction: suggestions Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion: Simplify the explanation or provide brief definitions or context for these terms. We have revised the third paragraph of the Introduction to provide brief definitions and clearer context for the mentioned techniques. Specifically, we now explain the role of Fast Fourier Transformation, principal components analysis, and linear discriminant analysis in simpler terms to aid reader understanding. The revised sentence reads as follows: “Then, these data undergo pre-processing to reduce noise and enhance relevant features. This step often involves filtering techniques, such as Fast Fourier Transformation (which helps separate important movement signals from background noise) or wavelet transforms. Additionally, to simplify complex data and highlight key movement patterns, methods like principal components analysis (which reduces data dimensions while preserving essential information) or linear discriminant analysis (which enhances the distinction between movement categories) are applied (17).” 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion: Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. We agree that the connection between BMS, traditional measurement tools, observer bias, and artificial intelligence (AI)-related technology could be more explicit. In response to your suggestion, we have made adjustments to the third paragraph of the Introduction to improve the flow and strengthen the connection between these topics. Specifically, we added a clearer explanation of how AI can directly address observer bias and improve accuracy in BMS assessment. We now describe how AI, by automating the motion classification process, reduces human subjectivity and enables more objective and accurate evaluations. In this way, we aim to reinforce the argument that AI-related technology not only offers an alternative to traditional methods but effectively addresses the limitations of current BMS assessment approaches. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion: Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. We have strengthened the second paragraph of the Introduction by incorporating concrete data that illustrate the prevalence and real-world consequences of this issue. Specifically, we now include evidence from two reviews: one indicating that, among 960 behavioral studies, only 3.2% reported interobserver reliability measures and only 1.9% met rigorous criteria for minimizing bias (12); and another highlighting that in child development research, poor reporting and variability in assessor performance may obscure children’s true developmental status, potentially compromising clinical decisions (13). These additions aim to clarify the extent and significance of observer bias, reinforcing the necessity of adopting more objective tools—such as AI-based technologies—for accurate and reliable BMS assessment. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion: Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. We have revised the beginning of the third paragraph of the Introduction to explicitly define “AI-related technologies” and specify the tools most relevant for BMS assessment. The revised text reads as follows: “AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14). For example, for motion capture and analysis, computer vision tools such as OpenPose, MediaPipe and DeepLabCut enable pose estimation and tracking of key points of the human body with high accuracy (15). In addition, deep learning techniques, such as Convolutional Neural Networks (CNN) and vision-specialized Transformer Models (ViT), have proven to be effective in classifying motion sequences in videos (16). In that sense, these AI-related technologies for recognizing and classifying human motion patterns consist of several steps (Figure 1) (17).” 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion: Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. We appreciate your feedback on the flow and structure of the manuscript. Our team has improved the text to facilitate smooth transitions between sections. Specifically, the first part of the paragraph now reads: " Typically, BMS assessment relies on trained professionals who observe, record, and score children's performance on specific motor tasks (8,9). However, a major challenge in this approach is observer bias. Even when raters receive standardized training, small differences in scoring can introduce variability in BMS measurements. This variability reduces the accuracy of the assessment and can lead to misinterpretations. For example, two children with similar motor skills may receive different scores depending on the assessor, resulting in inconsistent results. When these inconsistencies follow a systematic pattern, they contribute to observer bias, a well-documented source of measurement error (10,11) .". We have also added a transitional paragraph at the end of the second paragraph, linking the problem of observer bias to the introduction of AI technologies as a solution. This paragraph highlights how observer bias can affect BMS assessment results, and the following paragraph addresses how AI-related technologies can mitigate this problem, offering a more objective and accurate alternative. The text reads: "In fact, one review reported that of 960 behavioral studies, only 3.2% reported measures of interobserver reliability, and only 1.9% met rigorous criteria for minimizing bias (12). Similarly, another review on child development found that the quality of reporting on the use of assessors in these studies was poor and that variability in assessor performance may obscure the true developmental status of children, compromising complex and costly clinical decisions (13)”. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion: Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. We have added a brief explanation in the first paragraph of the introduction that highlights why this age group is crucial for BMS development. Specifically, we have pointed out that during this period of rapid physical and motor development, children refine fundamental skills that are essential for later complex activities, which directly impacts their cognitive, emotional, and social development. In addition, we underscored the relevance of early assessment to identify potential delays and facilitate timely interventions. Now the text says the following: "The development of basic motor skills (BMS) in children aged 3 to 6 years is critical, as this is a period of rapid motor growth, where children acquire physical skills that allow them to participate in a variety of activities (1,2). At this age, children experience significant improvements in gross motor control, allowing them to perform movements such as running, jumping, and manipulating objects with greater precision (3,4). The acquisition of these motor skills is essential for physical, cognitive and emotional development, as BMS are strongly linked to general well-being, self-esteem and social integration (5)." 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion: Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. We have carefully reviewed the claims about AI applications and ensured that each is supported by an appropriate reference. In addition, we have revised the general claims to improve their accuracy with the cited literature. Specifically, we have updated citations 4, 5, 6, and 7 with their respective references: 4. Jiang G-P, Jiao X-B, Wu S-K, Ji Z-Q, Liu W-T, Chen X, et al. Balance, proprioception, and gross motor development of Chinese children aged 3 to 6 years. J Mot Behav. 2018;50(3):343–52. Available at: http://dx.doi.org/10.1080/00222895.2017.1363694 5. Gandotra A, Kotyuk E, Bizonics R, Khan I, Petánszki M, Kiss L, et al. An exploratory study of the relationship between motor skills and indicators of cognitive and socio-emotional development in preschoolers. Eur J Dev Psychol. 2023;20(1):50–65. Available at: http://dx.doi.org/10.1080/17405629.2022.2028617 6. Bremer E, Cairney J. Fundamental Movement Skills and Health-Related Outcomes: A Narrative Review of Longitudinal and Intervention Studies Targeting Typically Developing Children. Am J Lifestyle Med. 2018;12(2):148-59. https://doi.org/10.1177/1559827616640196 7. Eddy LH, Wood ML, Shire KA, Bingham DD, Bonnick E, Creaser A, et al. A systematic review of randomized and case-controlled trials investigating the effectiveness of school-based motor skill interventions in 3- to 12-year-old children. Child Care Health Dev. 2019;45(6):773-90. https://doi.org/10.1111/cch.12712 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion: Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. We have revised the last two final sentences of the introduction to highlight the research gap in the use of AI to accurately assess BMS in children and the need for this scoping review. Specifically, we now emphasize the lack of a comprehensive review of AI applications for assessing basic motor skills (BMS) in preschool children, as well as the need to systematize the scope, limitations, and validity of these technologies. Specifically, we have added the following: "However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Furthermore, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools" 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion: Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. Our team has made sure to review and update the text to avoid redundancy of the term “AI-related technologies”. We have also decided to keep the term “AI-related technologies” consistently throughout the introduction to ensure clarity and consistency throughout the manuscript. In fact, in the third paragraph, we have included a clarification referring to the term “AI-related technologies”. Specifically, in the first three lines of the third paragraph in the introduction, we have added: "AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14)" 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion: Complete the sentence and ensure that all points are concluded before introducing the research objectives. We have reviewed and revised this section to ensure the idea is fully expressed and concluded before presenting the research objectives. Now, in the final lines of the fourth paragraph of the introduction, we have added the following: “However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Moreover, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools” Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? We have revised the manuscript and added more detailed inclusion and exclusion criteria. Specifically, in the third paragraph of methods, we added: "We also defined the following criteria for the search: 1) studies in preschool-aged children (3 to 6 years), 2) studies in which the motor ability (motor or play skills) of the child was assessed using AI-related technologies for motion detection, and 3) studies in which at least one of the basic motor skills described in the literature (running, jumping, kicking, throwing, or catching a ball) was measured. In addition, we excluded 1) studies that did not clearly describe the AI-related technology used or developed, 2) opinion articles, editorials, or narrative reviews without empirical data and 3) gray literature (e.g. theses, dissertations, or non-peer-reviewed reports)." 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? We've added clearer justification for our selection of databases and search terms. Specifically, we added in the methods section, subsection Search strategy, the following two paragraphs: " We searched for studies published before January 30, 2023 in the target publications in Medline (SCR_002185), Web of Science (SCR_022706), IEEE (SCR_008314), and EBSCO (SCR_022707). These databases were selected because they specialize in biomedical, engineering, and multidisciplinary research, ensuring that we captured relevant studies in health sciences, AI applications, and motion analysis. Search terms included keyword combinations such as “child,” “preschool,” “basic motor skills,” “artificial intelligence,” “motion sensing,” and “calibration,” along with related terms and synonyms identified through a preliminary literature review (keywords) and controlled vocabulary (MeSH terms). The full search strategy and complete list of search terms are available here (42)." Likewise, we cite the complete search strategy as number 42. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Our team has detailed how Rayyan was used during the review process. Specifically, in the methods section, subsection Search strategy, in the third, fourth and fifth paragraph, we add: "The search formulas were applied to the databases and all the files were exported in RIS format. Then, to ensure an objective selection process, these identified files were uploaded to the Rayyan platform which facilitated blind selection by the reviewers and expedited the identification of duplicates. The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? Our team has added information about the process to minimize bias during study selection. Specifically, in the methods section, Search Strategy subsection, in the fourth and fifth paragraphs, we add: "The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? We have updated the paragraph by adding information in the methods section, Data extraction subsection. Now the text says the following: "Data extraction was performed in a structured manner using a pre-designed form (43). To reduce errors and improve the accuracy of the extracted data, one peer reviewer performed the initial extraction and a second peer independently verified the information. Any discrepancies in the extraction were reviewed jointly and/or, with the intervention of the principal investigator. Cross-checks were implemented to ensure the consistency of the information collected." Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? Since our study was a scoping review, we did not conduct a meta-analysis. Instead, we opted for a narrative synthesis of the results, presented in tables of frequencies and percentages. We also used the COSMIN standards, whose scoring and rating process is described in their original report, which we also reference in our study. Specifically, our text, in the methods section, subsection "Data analysis," in its first paragraph, states the following: "All data collected were summarized as categorical variables, organized and presented in tables, using descriptive statistics such as simple frequencies and percentages. Since this was a scoping review, a narrative synthesis was used to summarize the findings of the studies, focusing on the characteristics and psychometric properties evaluated according to COSMIN standards." Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? We would like to clarify that, since our study is a scoping review focused on the mapping and characterization of AI-based technologies used to assess motor skills in children, we did not aim to establish causal or correlational relationships between variables. Therefore, the methodological approach does not involve controlling or statistically analyzing confounding variables, as would be expected in primary empirical studies. The purpose of this review was to identify and describe the technologies developed, their applications, and the reported psychometric dimensions. Nonetheless, we have incorporated a critical reflection on the differences in sample sizes, age ranges (expressed in both years and months), and the types of technologies used, acknowledging these as limitations that could influence the validity and generalizability of the findings reported by the included studies. Specifically, the last lines of the limitations section say the following: "This review did not aim to analyze associations between variables; however, variability in sample sizes, age ranges, and types of AI-based technologies used across studies may affect the comparability and generalizability of the findings. These differences should be considered when interpreting the results and highlight the need for more standardized approaches in future research." 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Since no standardized framework was used to assess feasibility and usability, this is an aspect that limits the interpretation and comparison of the results among the included studies. We state this information in the limitations. Specifically, we added to the beginning of the limitations:: "Also, although this review was based on COSMIN standards to assess the psychometric quality of AI-related technologies, due to the heterogeneity observed in the included studies, no specific adjustments were made to control for possible confounding variables. Therefore, the conclusions need to be interpreted with caution. It is recommended that future research address these factors and use control methods to provide more generalizable conclusions. Furthermore, feasibility and usability were extracted only if the reviewed studies explicitly reported having done so in their analysis of AI-related technologies. Therefore, further studies should evaluate these analyzes using a standardized framework." In response to comments on discussion, we have expanded this section on implications. Specifically, we added the following paragraphs in implications: "To facilitate use, developers could conduct studies that evaluate the acceptance, ease of use, cost-effectiveness, and accessibility of these technologies. For example, most technologies rely on sensors and monitors that, while accurate, can be costly, require specialized training, and can be difficult to implement in real-world settings for physicians, teachers, therapists, or practitioners unfamiliar with these tools. In addition, disparities in access to advanced technologies may limit their adoption, particularly in low-resource settings. Also, these types of technologies may be closer to more universal and cost-effective devices, such as video cameras, smartphones, and tablets, that can assess and report motor skills in real time. However, addressing these challenges requires a collaborative and interdisciplinary approach. Future validation studies should involve experts from multiple fields, including engineers, healthcare professionals, educators and policy makers, to ensure that these technologies are not only accurate, but also practical, scalable and accessible to diverse populations. New validation studies of these technologies should include validation standards for BMS tests, prioritizing key psychometric properties such as construct validity, criterion validity, reliability, measurement error, among others. To make this possible, engineering teams could incorporate specialists in psychometrics, developmental therapy and medicine to work collaboratively. This multidisciplinary approach will facilitate the integration of medical knowledge and psychometric standards into future software releases, improving both measurement accuracy and practical usability. Finally, developers should consider providing open source code or detailed methodological documentation, which will allow for further refinement, replication, and clinical adaptation of these technologies in future research and real-world applications." Competing Interests: There is no conflict of interest Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 18 Dec 2023 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 3 (revision) 24 Apr 26 read Version 2 (revision) 02 Sep 25 read Version 1 18 Dec 23 read Abraham M. Joshua , Manipal Academy of Higher Education, Manipal, India Ashish John Prabhakar , Manipal Academy of Higher Education (Ringgold ID: 76793), Manipal, India Dhahbi Wissem , University of Jendouba, Kef, Tunisia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Wissem D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Apr 2026 | for Version 3 Dhahbi Wissem , University of Jendouba, Kef, Tunisia 0 Views copyright © 2026 Wissem D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions General Comments This third version of the scoping review significantly improves upon previous iterations by addressing the interdisciplinary gap between engineering validation and clinical psychometric standards. The authors have successfully refined the manuscript to clarify that the application of COSMIN standards was intended as a measurement-oriented appraisal rather than a critique of algorithmic efficiency. While the temporal limitation of the literature search (cutoff January 30, 2023) remains a constraint, the authors have appropriately framed this as a historical mapping of evidence available at the time of original submission. The manuscript now provides a coherent narrative that bridges the technical capabilities of AI-related technologies with the practical requirements of pediatric motor assessment. Specific Comments 1. Methodological Framing and COSMIN Application The authors have clarified the rationale for using COSMIN guidelines, explicitly stating they were applied to examine evidence relevant to assessment use rather than to evaluate algorithmic performance per se. The distinction between engineering validation (focused on accuracy and technical performance) and clinical/psychometric validation (focused on reliability and responsiveness) is now well-defined in the Results and Discussion sections. 2. Introduction and Contextual Background The Introduction now effectively connects early basic motor skill (BMS) competency with long-term biomechanical implications, such as movement efficiency and potentially lower injury risk. The revised description of AI pipelines acknowledges modern deep learning-based pipelines and vision transformers, providing a more balanced view of contemporary computational approaches. 3. Addressing Limitations and Bias The authors have explicitly acknowledged the search cutoff date in the Strengths and Limitations section, providing necessary context for the reader to interpret the results within that specific temporal scope. Typographical and formatting errors identified in previous versions, such as the "Participant gender" categorization and geographical misspellings, have been corrected. 4. Implications for Future Research The implications section has been strengthened to highlight the urgent need for a universal standardized assessment protocol that bridges the gap between engineering and clinical disciplines. The recommendation for interdisciplinary collaboration involving engineers, healthcare professionals, and educators is a critical addition for the practical implementation of these technologies. Competing Interests No competing interests were disclosed. Reviewer Expertise AI and Sports Sciences I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Wissem D. Peer Review Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.198263.r478798) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-1598/v3#referee-response-478798 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Wissem D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Mar 2026 | for Version 2 Dhahbi Wissem , University of Jendouba, Kef, Tunisia 0 Views copyright © 2026 Wissem D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions General Comments The manuscript presents a scoping review evaluating the development, validation, and use of AI-related technologies for assessing basic motor skills (BMS) in children aged 3 to 6. While the objective of bridging engineering validation with clinical psychometric standards is highly relevant, the paper suffers from critical methodological and temporal flaws. Major Weaknesses: Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes References 1. Dhahbi W., Chamari K., Chèze L., Behm D.G., et al.: External responsiveness and intrasession reliability of the rope-climbing test. The Journal of Strength & Conditioning Research, 30(10), 2952–2958 . 2016. 2. Dhahbi W., Chamari K.: The Algorithmic Athlete: A Call to Standardize Assessment of Sensor Technologies and Artificial Intelligence. International Journal of Sports Physiology and Performance, 1(aop), 1–2 . 2026. Competing Interests No competing interests were disclosed. Reviewer Expertise AI and Sports Sciences I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 24 Apr 2026 Joel Figueroa-Quiñones, Universidad Autonoma de Ica, Chiclayo, Peru Outdated Literature Search: The most severe flaw is the search cutoff date of January 30, 2023. For a manuscript undergoing revision in 2025, a >2.5-year gap in a rapidly evolving field like artificial intelligence and computer vision renders the findings almost obsolete. The low prevalence of deep learning applications identified in the results is likely a direct consequence of this outdated search, rather than a true reflection of the current state of the art. We thank the reviewer for this important observation. However, this search date corresponded to the evidence available at the time of the manuscript’s original submission. The extended interval between submission and the present revision was due to the editorial and peer-review process, which was beyond the authors’ control. Therefore, the review should be interpreted as a mapping of the literature available up to January 30, 2023, rather than as a fully updated representation of the field at the time of revision. We have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that more recent studies, including potentially a greater number of deep learning applications, may not have been captured. This issue has been highlighted to ensure an appropriate interpretation of the findings. Misapplication of Evaluation Frameworks: The authors apply the COSMIN checklist—a tool primarily designed for Patient-Reported Outcome Measures (PROMs) and adapted for clinician-reported outcomes—to engineering algorithms. While evaluating the clinical validity of these tools is necessary, computer scientists validate models using benchmark metrics (e.g., Mean Per Joint Position Error, precision, recall, F1-score). The manuscript penalizes engineering papers for not using clinical psychometrics without sufficiently addressing this disciplinary disconnect. We respectfully disagree with the view that the COSMIN framework was misapplied in the present review. Our use of COSMIN was not intended to evaluate the engineering performance of artificial intelligence algorithms per se, nor to replace standard computer science benchmarks such as precision, recall, F1-score, or positional error metrics. Rather, COSMIN was used because the focus of this review was on AI-related technologies insofar as they are proposed and used as assessment tools for basic motor skills in children. From this perspective, the key question is not only whether an algorithm performs well computationally, but also whether the resulting tool demonstrates adequate measurement properties for assessment purposes, such as validity, reliability, and interpretability within clinical, educational, or psychomotor contexts. In other words, benchmark metrics may demonstrate technical performance, but they do not by themselves establish that a tool is suitable as a measurement instrument in applied settings. For this reason, we considered COSMIN an appropriate framework to appraise the extent to which the included studies provided evidence relevant to measurement quality. We agree, however, that this involves an interdisciplinary interface between engineering validation and clinical/psychometric evaluation. To avoid misunderstanding, we have clarified in the revised manuscript that COSMIN was applied from a measurement and clinical assessment perspective, not as a substitute for engineering model validation standards. We have also made explicit that the absence of COSMIN-related evidence in some studies should not be interpreted as poor algorithmic quality, but rather as limited reporting of measurement properties relevant to assessment use. Limited Sample Size: The scoping review relies on a highly restricted yield of 12 studies (7 for development/validation, 5 for use). Drawing broad conclusions about the entire landscape of AI in pediatric motor assessment from such a limited pool undermines the manuscript's generalizability. We respectfully disagree that the inclusion of 12 studies inherently undermines the contribution of the review. The purpose of a scoping review is not to generate statistically generalizable conclusions about an entire field, but rather to map, characterize, and synthesize the nature and extent of the available evidence on a given topic. In this sense, the limited number of eligible studies should not be interpreted solely as a weakness of the review, but also as a reflection of the current state of the literature at the intersection of artificial intelligence and pediatric basic motor skill assessment. The restricted yield suggests that this remains an emerging and still relatively fragmented area of research, particularly when applying predefined eligibility criteria focused on development, validation, and practical use in children. Importantly, our manuscript does not intend to overgeneralize the evidence base. Instead, the conclusions are framed to reflect the scope and volume of the studies identified, highlighting trends, methodological characteristics, and existing gaps in the literature. Therefore, the small number of included studies is itself a relevant finding, as it underscores the need for further research in this field. Minor Weaknesses: Typographical and Formatting Errors: Despite being a revised version, several typographical errors persist in the tables and text. We acknowledge that some typographical and formatting inconsistencies remained in the revised version. In response, we carefully reviewed the entire manuscript and corrected the identified errors in the text and tables to improve clarity, consistency, and overall presentation. Over-simplification of AI Pipelines: The description of AI methodologies in the introduction leans heavily on traditional signal processing and classical machine learning, underrepresenting the current dominance of deep learning and vision transformers in pose estimation. We agree that recent advances in artificial intelligence for pose estimation have increasingly been driven by deep learning approaches, including convolutional neural networks and, more recently, transformer-based architectures. However, the purpose of the Introduction was not to provide an exhaustive technical review of the full evolution of AI pipelines, but rather to offer a general conceptual background for readers from clinical, developmental, and assessment-oriented fields. In this context, we described traditional signal processing and classical machine learning approaches because they remain part of the methodological foundation of the field and are also represented in the body of studies included in our review. Moreover, the emphasis in the manuscript is on the application of AI-related technologies for the assessment of basic motor skills in children, rather than on benchmarking the latest computer vision architectures. Therefore, the introductory section was intentionally oriented toward framing the field in accessible and applied terms. Specific Comments Page 1, Abstract, Methods: The abstract fails to state the cutoff date of the literature search. This is a critical parameter for a scoping review and must be explicitly declared in the abstract. We agree that the literature search cutoff date is a critical parameter in a scoping review and should be explicitly stated in the abstract. In response, we revised the Methods section of the abstract to indicate that the search included studies published up to January 30, 2023. Page 4, Introduction, Paragraph 1: The manuscript briefly mentions physical development but fails to connect early motor skill competency with long-term athletic optimization and injury risk mitigation. Expand on the long-term biomechanical implications of early BMS acquisition by referencing recent paradigms: “[Dhahbi W: Advancing biomechanics: enhancing sports performance, mitigating injury risks, and optimizing athlete rehabilitation. In., vol. 7: Frontiers Media SA; 2025: 1556024.]” We agree that the long-term biomechanical relevance of early basic motor skill (BMS) development could be stated more explicitly in the Introduction. Although the present review focuses on the assessment of BMS in preschool children rather than on athletic performance per se, early motor competence may have broader biomechanical implications across the life course, including movement efficiency, later participation in physical and sport activities, and potentially injury-risk mitigation through the development of more coordinated and stable movement patterns. In response, we revised the first paragraph of the Introduction to better connect early BMS acquisition with its longer-term biomechanical relevance and to acknowledge recent perspectives highlighting the role of biomechanics in performance optimization, injury-risk reduction, and rehabilitation. Page 4, Introduction, Paragraph 2: The statistics regarding the prevalence of observer bias (3.2% reporting interobserver reliability; 1.9% meeting rigorous criteria) are supported by citations from 2012 and 2015. Update these figures with contemporary evidence, as methodological reporting standards have evolved over the last decade. We thank the reviewer for this observation. We acknowledge that the cited prevalence figures were derived from earlier reviews and may not reflect current reporting practices. However, these references were retained because they provide direct quantitative evidence of longstanding concerns regarding interobserver reliability and observer-related bias in behavioral research. To avoid implying that these figures represent the current prevalence, we revised the text to frame them as historical evidence of a methodological problem rather than as a contemporary estimate. Page 4, Introduction, Paragraph 3: The explanation of AI pipelines emphasizes "Fast Fourier Transformation," "principal components analysis," and "linear discriminant analysis". These are classical machine learning techniques. While pose estimation (OpenPose) is mentioned, the technical description does not accurately reflect modern deep learning pipelines (e.g., convolutional neural networks or vision transformers) which bypass manual feature extraction entirely. We agree that the original wording of this paragraph placed greater emphasis on classical machine-learning pipelines, which may not fully reflect the diversity of contemporary AI approaches. However, our intention was not to suggest that all current motion-analysis systems rely on manual feature extraction, but rather to provide a broad and accessible description of computational steps that may be involved in some AI-related technologies for human movement assessment. To address this concern, we revised the paragraph to clarify that, although some approaches still involve preprocessing, feature extraction, and conventional classification procedures, many contemporary pipelines rely on end-to-end deep learning models, such as convolutional neural networks and vision transformers, which can learn relevant representations directly from images, videos, or pose sequences with limited or no manual feature extraction. This clarification has been incorporated to better reflect the current methodological landscape. Page 6, Search strategy, Paragraph 1: The literature search ended on "January 30, 2023". This is unacceptable for a manuscript published in 2025 focusing on AI. The search must be updated to at least late 2024 to capture the explosion of computer vision models applied to human kinematics. We appreciate the reviewer’s concern and agree that the pace of development in AI-related research is particularly rapid. The literature search in this review ended on January 30, 2023 because this date corresponded to the original submission stage of the manuscript. The prolonged interval before the current revision was largely attributable to the editorial and peer-review timeline, which was outside the authors’ control. Accordingly, the review is intended to represent the evidence available up to that date, rather than the current state of the art at the time of revision. To ensure appropriate interpretation, we have clarified this point in the manuscript and strengthened the limitations section, explicitly acknowledging that newer studies on computer vision models applied to human movement assessment may not have been captured. Page 7, Data analysis, Paragraph 2: The methodology relies exclusively on COSMIN to evaluate technical quality. The manuscript lacks a robust justification for applying a psychometric tool to algorithmic development papers. Add a methodological disclaimer addressing the fact that engineers evaluate systems using distinct metrics (e.g., accuracy, loss functions) rather than clinical reliability indices. We agree that engineering studies commonly evaluate systems using discipline-specific performance metrics rather than clinical reliability indices. To clarify our methodological approach, we added a brief statement in the Data analysis section indicating that COSMIN was applied from a measurement perspective to examine evidence relevant to assessment use, rather than to evaluate algorithmic performance per se. Page 8, Table 1: Correct the typographical error "Nort America" to "North America". The word was updated Page 8, Table 1: The category "Participant gender" lists "Both" but includes a sub-category "Just kids". This categorization is illogical and requires reclassification. We agree that the category “Just kids” was unclear and inappropriate under the variable “Participant gender.” This was a wording error. We revised Table 1 to use clearer and logically consistent categories: “Boys only,” “Girls only,” “Both boys and girls,” and “Not reported.” Page 9, Table 2: The reporting of "Motion capture devices" is overly broad and lacks methodological rigor. Detail the calibration and sampling rate issues inherent to combining visual data with biomechanical sensors. To improve the methodological robustness of sensor evaluation, incorporate findings on the methodological constraints of such devices: “[Dhahbi W, Chaouachi A, Cochrane J, Chèze L, Chamari K: Methodological issues associated with the use of force plates when assessing push-ups power. The Journal of Strength & Conditioning Research 2017, 31(7):e74.]” We agree that device-related factors such as calibration procedures, sampling rate, and synchronization may influence motion analysis results. However, the purpose of Table 2 was to provide a descriptive summary of the motion-capture devices and AI-related tools reported in the included studies, rather than to conduct a device-specific technical appraisal of biomechanical sensor performance. We respectfully note that the reference suggested by the reviewer addresses methodological issues related to force plates in the assessment of push-up power, which corresponds to a different biomechanical and performance-testing context from the present review, focused on AI-related technologies for assessing basic motor skills in children. For this reason, we did not incorporate that reference directly into Table 2. Page 9, Table 2: The table reports only 1 study (14.3%) utilizing "Deep learning and neural networks". This finding is highly suspect and strongly indicates that the search strategy or the 2023 cutoff date failed to capture the modern landscape of pose estimation technologies. We agree that deep learning approaches have become increasingly prominent in the broader pose estimation literature. However, we respectfully note that the proportion reported in Table 2 reflects only the characteristics of the studies included in this review, based on the predefined eligibility criteria and review period, rather than the overall prevalence of deep learning approaches across the wider field. Therefore, the finding should be interpreted within the specific scope of this scoping review and not as a comprehensive representation of the current pose estimation landscape. Page 10, Table 3: The application of the COSMIN standards results in almost uniform "Inadequate" or "Doubtful" ratings. Provide clarity in the text on whether these papers failed standard psychometric validation, or if they simply did not attempt it because their primary focus was algorithmic engineering. We agree that the predominance of “Inadequate” and “Doubtful” ratings in Table 3 requires careful interpretation. These ratings were intended to reflect the extent to which the included studies reported psychometric or measurement-related evidence according to COSMIN standards, rather than their algorithmic or engineering quality per se. In many cases, the included papers primarily focused on technical or algorithmic development, which likely explains the predominance of lower COSMIN ratings. We clarified this interpretive point in the text accompanying Table 3. Page 11, Paragraph 1: The manuscript identifies a lack of "responsiveness" and "reliability" reporting (per COSMIN standards) but fails to provide a concrete benchmark of what adequate reporting looks like in physical performance assessment. Clarify this gap by contrasting AI validation with the rigorous intrasession reliability and external responsiveness required in traditional functional motor tests: Refer to reference no. 1 We agree that the original text did not sufficiently clarify what adequate evidence of reliability and responsiveness would look like when these AI-related technologies are intended to function as assessment tools. In traditional physical performance and functional motor assessment, reliability is typically supported by evidence such as test–retest or intrasession stability, together with indicators of measurement consistency and error. Likewise, responsiveness requires evidence that an instrument can detect meaningful changes in performance over time or after intervention. In contrast, many of the studies included in our review primarily emphasized algorithmic or technical performance and did not report this type of measurement-oriented evidence. We have clarified this point in the manuscript to better explain that the COSMIN-related gap does not simply refer to missing psychometric terminology, but to the limited demonstration of whether these AI-related tools can provide stable and change-sensitive measurements comparable to those expected from established motor assessment instruments. Page 11, Paragraph 3: The discussion frames the lack of COSMIN adherence as a failure of the developers. Reframe this to acknowledge the interdisciplinary gap: computer science validates via dataset benchmarks, while clinical science requires psychometrics. Both are necessary, but judging one by the standards of the other without context is reductive. We agree that the original wording of this paragraph may have overstated the interpretation of the COSMIN findings. Our intention was not to suggest that the included studies failed simply because they did not follow psychometric validation standards, but rather to highlight an interdisciplinary gap between engineering-oriented validation and clinical or assessment-oriented validation. In many of the included studies, validation was primarily conducted using engineering criteria focused on technical performance. This is appropriate within computer science and algorithm-development contexts. However, when these technologies are proposed as tools for assessing motor skills in children, additional evidence on measurement properties becomes relevant from a clinical and psychometric perspective. We have revised the paragraph to better acknowledge that both forms of validation are important and complementary, and that the absence of psychometric evidence should not be interpreted as poor engineering quality per se. Page 12, Strengths and Limitations: The authors fail to list the outdated search date (Jan 2023) as a limitation. This must be explicitly stated as a severe threat to the currency and validity of the findings. We agree that the search cutoff date represents an important limitation in a rapidly evolving field such as artificial intelligence and computer vision. In response, we explicitly added this issue to the Strengths and Limitations section, stating that the literature search was conducted up to January 30, 2023 and that, consequently, more recent studies may not have been captured. We also clarified that the findings should be interpreted as reflecting the evidence available up to that date rather than the most current state of the field. Page 12, Implications: The recommendations for future multidisciplinary approaches are vague. Explicitly address the current absence of, and urgent need for, a universal standardized assessment protocol that bridges clinical psychometrics and algorithmic sensor evaluation. Integrate this call to action by referencing contemporary directives on standardizing AI and sensor technologies in performance assessment: Refer to reference no. 2 We agree that the implications section should more explicitly state the current absence of a universal standardized protocol integrating clinical psychometrics with algorithmic and sensor-based evaluation. In response, we revised the implications section to emphasize that future research should move toward a standardized interdisciplinary framework specifying not only psychometric requirements (e.g., validity, reliability, measurement error, responsiveness) but also minimum technical requirements for sensor- and algorithm-based systems, such as data acquisition procedures, calibration, synchronization, and reporting standards. We also strengthened the call for collaboration across engineering, clinical, educational, and psychometric disciplines so that AI-related technologies for BMS assessment can be developed and validated under a more unified and transferable framework. View more View less Competing Interests None reply Respond Report a concern Wissem D. Peer Review Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.186910.r469885) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-1598/v2#referee-response-469885 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Joshua A et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Jan 2025 | for Version 1 Abraham M. Joshua , Department of Physiotherapy, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India Ashish John Prabhakar , Department of Physiotherapy, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education (Ringgold ID: 76793), Manipal, Karnataka, India 0 Views copyright © 2025 Joshua A et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions ABSTRACT Background - Redundant phrasing: "two observersers" is misspelled and repetitive. Results - Tracking of "10 other publications" is unclear and vague. - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study Introduction: suggestions 1. Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion : Simplify the explanation or provide brief definitions or context for these terms. 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion : Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion : Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion : Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion : Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion : Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion : Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion : Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion : Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion : Complete the sentence and ensure that all points are concluded before introducing the research objectives. Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? 3. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Suggestion: Provide more detail on how Rayyan was used throughout the review process. How were disagreements between the two independent groups resolved, and what role did the principal investigator play in the final decisions? 4. Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? 6. Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? 7. Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Neurorehabilitation, Upper limb functional evaluation, AI for rehabilitation, Exercise therapy, Balance training We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Joel Figueroa-Quiñones, Lambayeque, Universidad Señor de Sipán, Chiclayo, Peru Reviewer # 1: Abstract Background - Redundant phrasing: "two observersers" is misspelled and repetitive. We corrected the typographical error and improved the phrasing for clarity in the Background section. The revised sentence now reads: “In basic motor skills evaluation, two observers may rate the same child’s performance differently, introducing variability in the assessment” Results - Tracking of "10 other publications" is unclear and vague. We revised the sentence in the Results section to provide a more precise description of the process. The updated text now reads: “From the 7 technology development studies, we examined their citation networks using Google Scholar and identified 10 subsequent peer-reviewed publications that either enhanced the original technologies or applied them in new research contexts. ” - Overuse of technical language without explanation for general readers (e.g., "engineering criteria"). We revised the sentence to reduce technical jargon and make it more accessible to general readers. In the corresponding section, the updated sentence now reads: “The validation of these algorithms was based on engineering standards, focusing on their accuracy and technical performance, but without integrating medical and psychological knowledge about children's motor development.” Keywords - Missing potential keywords like "assessment tools," “functional abilities,” and "gross motor” based on the ‘title and the contents of the study We have updated the list of keywords to better reflect the title and content of the study. The following terms have been added: “assessment tools,” “functional abilities,” and “gross motor.” Introduction: suggestions Overly Technical Language: While the introduction is generally informative, it becomes very technical in certain sections, especially when describing the AI process (e.g., "Fast Fourier Transformation," "principal components analysis," "linear discriminant analysis"). This might be difficult for readers unfamiliar with these terms or the AI field. Suggestion: Simplify the explanation or provide brief definitions or context for these terms. We have revised the third paragraph of the Introduction to provide brief definitions and clearer context for the mentioned techniques. Specifically, we now explain the role of Fast Fourier Transformation, principal components analysis, and linear discriminant analysis in simpler terms to aid reader understanding. The revised sentence reads as follows: “Then, these data undergo pre-processing to reduce noise and enhance relevant features. This step often involves filtering techniques, such as Fast Fourier Transformation (which helps separate important movement signals from background noise) or wavelet transforms. Additionally, to simplify complex data and highlight key movement patterns, methods like principal components analysis (which reduces data dimensions while preserving essential information) or linear discriminant analysis (which enhances the distinction between movement categories) are applied (17).” 2. Lack of Clear Connection Between BMS and AI: While the introduction discusses BMS, AI technologies, and their potential to address observer bias, the connection between these topics could be more explicit. The introduction seems to jump between BMS, traditional measurement tools, observer bias, and AI without a smooth flow that ties everything together. Suggestion: Strengthen the connection between the problems with current BMS assessment and how AI could specifically address them. A clearer explanation of how AI can solve observer bias and improve accuracy in BMS assessment would help make the argument more compelling. We agree that the connection between BMS, traditional measurement tools, observer bias, and artificial intelligence (AI)-related technology could be more explicit. In response to your suggestion, we have made adjustments to the third paragraph of the Introduction to improve the flow and strengthen the connection between these topics. Specifically, we added a clearer explanation of how AI can directly address observer bias and improve accuracy in BMS assessment. We now describe how AI, by automating the motion classification process, reduces human subjectivity and enables more objective and accurate evaluations. In this way, we aim to reinforce the argument that AI-related technology not only offers an alternative to traditional methods but effectively addresses the limitations of current BMS assessment approaches. 3. Limited Emphasis on the Scope of the Problem: The issue of observer bias and its impact on BMS assessment is raised, but it’s not explored in-depth. The extent of the problem (how often it happens, how significant the impact is) isn’t fully explained. Suggestion: Provide more concrete examples or data to highlight the real-world implications of observer bias in BMS assessments. This would help emphasize the need for better solutions like AI. We have strengthened the second paragraph of the Introduction by incorporating concrete data that illustrate the prevalence and real-world consequences of this issue. Specifically, we now include evidence from two reviews: one indicating that, among 960 behavioral studies, only 3.2% reported interobserver reliability measures and only 1.9% met rigorous criteria for minimizing bias (12); and another highlighting that in child development research, poor reporting and variability in assessor performance may obscure children’s true developmental status, potentially compromising clinical decisions (13). These additions aim to clarify the extent and significance of observer bias, reinforcing the necessity of adopting more objective tools—such as AI-based technologies—for accurate and reliable BMS assessment. 4. Vague Description of "AI-Related Technologies": The term “AI-related technologies” is introduced, but it remains somewhat vague. While the steps of motion recognition are detailed, it’s not entirely clear what specific AI tools or algorithms are most effective in this context or how these methods directly translate to motor skill assessment in children. Suggestion: Clarify the specific AI tools used or might be used in BMS assessment. This could make the introduction feel more grounded in practical applications. We have revised the beginning of the third paragraph of the Introduction to explicitly define “AI-related technologies” and specify the tools most relevant for BMS assessment. The revised text reads as follows: “AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14). For example, for motion capture and analysis, computer vision tools such as OpenPose, MediaPipe and DeepLabCut enable pose estimation and tracking of key points of the human body with high accuracy (15). In addition, deep learning techniques, such as Convolutional Neural Networks (CNN) and vision-specialized Transformer Models (ViT), have proven to be effective in classifying motion sequences in videos (16). In that sense, these AI-related technologies for recognizing and classifying human motion patterns consist of several steps (Figure 1) (17).” 5. Inconsistent Flow and Structure: The flow between sections could be smoother. For example, the transition from talking about BMS instruments (TGMD-2, BOT-2) to observer bias is a bit abrupt. Similarly, after discussing the issue of observer bias, the leap to the technicalities of AI feels disconnected. Suggestion: Reorganize the content so that each section builds more naturally on the previous one. Consider adding a paragraph that ties observer bias to the introduction of AI as a solution before jumping into the technical aspects. We appreciate your feedback on the flow and structure of the manuscript. Our team has improved the text to facilitate smooth transitions between sections. Specifically, the first part of the paragraph now reads: " Typically, BMS assessment relies on trained professionals who observe, record, and score children's performance on specific motor tasks (8,9). However, a major challenge in this approach is observer bias. Even when raters receive standardized training, small differences in scoring can introduce variability in BMS measurements. This variability reduces the accuracy of the assessment and can lead to misinterpretations. For example, two children with similar motor skills may receive different scores depending on the assessor, resulting in inconsistent results. When these inconsistencies follow a systematic pattern, they contribute to observer bias, a well-documented source of measurement error (10,11) .". We have also added a transitional paragraph at the end of the second paragraph, linking the problem of observer bias to the introduction of AI technologies as a solution. This paragraph highlights how observer bias can affect BMS assessment results, and the following paragraph addresses how AI-related technologies can mitigate this problem, offering a more objective and accurate alternative. The text reads: "In fact, one review reported that of 960 behavioral studies, only 3.2% reported measures of interobserver reliability, and only 1.9% met rigorous criteria for minimizing bias (12). Similarly, another review on child development found that the quality of reporting on the use of assessors in these studies was poor and that variability in assessor performance may obscure the true developmental status of children, compromising complex and costly clinical decisions (13)”. 6. Missing Emphasis on the Target Age Group (3-6 Years): The introduction discusses BMS in general terms but doesn’t emphasize the importance of assessing motor skills in children aged 3 to 6 years. Since this is a specific target group, more focus on why this age range is critical for BMS development would strengthen the relevance of the study. Suggestion: Provide a brief explanation of why this age group is the focus and what makes BMS development particularly crucial at this stage. We have added a brief explanation in the first paragraph of the introduction that highlights why this age group is crucial for BMS development. Specifically, we have pointed out that during this period of rapid physical and motor development, children refine fundamental skills that are essential for later complex activities, which directly impacts their cognitive, emotional, and social development. In addition, we underscored the relevance of early assessment to identify potential delays and facilitate timely interventions. Now the text says the following: "The development of basic motor skills (BMS) in children aged 3 to 6 years is critical, as this is a period of rapid motor growth, where children acquire physical skills that allow them to participate in a variety of activities (1,2). At this age, children experience significant improvements in gross motor control, allowing them to perform movements such as running, jumping, and manipulating objects with greater precision (3,4). The acquisition of these motor skills is essential for physical, cognitive and emotional development, as BMS are strongly linked to general well-being, self-esteem and social integration (5)." 7. Lack of Citations for Claims About AI in Other Fields: There are a few claims about AI applications in other fields (e.g., assessing physical activity in adults, recognizing walking alterations), but not all of these are fully supported by citations. The mention of “a recent review” and other general statements could be more precise. Suggestion: Provide specific references or clarify that the claims about AI in other fields are based on the literature review cited earlier. We have carefully reviewed the claims about AI applications and ensured that each is supported by an appropriate reference. In addition, we have revised the general claims to improve their accuracy with the cited literature. Specifically, we have updated citations 4, 5, 6, and 7 with their respective references: 4. Jiang G-P, Jiao X-B, Wu S-K, Ji Z-Q, Liu W-T, Chen X, et al. Balance, proprioception, and gross motor development of Chinese children aged 3 to 6 years. J Mot Behav. 2018;50(3):343–52. Available at: http://dx.doi.org/10.1080/00222895.2017.1363694 5. Gandotra A, Kotyuk E, Bizonics R, Khan I, Petánszki M, Kiss L, et al. An exploratory study of the relationship between motor skills and indicators of cognitive and socio-emotional development in preschoolers. Eur J Dev Psychol. 2023;20(1):50–65. Available at: http://dx.doi.org/10.1080/17405629.2022.2028617 6. Bremer E, Cairney J. Fundamental Movement Skills and Health-Related Outcomes: A Narrative Review of Longitudinal and Intervention Studies Targeting Typically Developing Children. Am J Lifestyle Med. 2018;12(2):148-59. https://doi.org/10.1177/1559827616640196 7. Eddy LH, Wood ML, Shire KA, Bingham DD, Bonnick E, Creaser A, et al. A systematic review of randomized and case-controlled trials investigating the effectiveness of school-based motor skill interventions in 3- to 12-year-old children. Child Care Health Dev. 2019;45(6):773-90. https://doi.org/10.1111/cch.12712 8. No Clear Statement of the Research Gap: While the introduction implies a gap in the literature (i.e., a lack of formal review of AI in BMS assessment for children), this could be more explicitly stated and emphasized. It could be clearer why this review is necessary and what new insights it might offer. Suggestion: Make the research gap more prominent by rephrasing the last couple of sentences to more forcefully explain why this scoping review is essential. We have revised the last two final sentences of the introduction to highlight the research gap in the use of AI to accurately assess BMS in children and the need for this scoping review. Specifically, we now emphasize the lack of a comprehensive review of AI applications for assessing basic motor skills (BMS) in preschool children, as well as the need to systematize the scope, limitations, and validity of these technologies. Specifically, we have added the following: "However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Furthermore, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools" 9. Repetitive Mention of AI: The introduction repeatedly mentions “AI-related technologies,” which could be streamlined. The term is used several times in close proximity without adding new information each time, which can feel redundant. Suggestion: Try to vary the phrasing (e.g., "machine learning tools," "AI classifiers") to keep the introduction engaging and avoid repetition. Our team has made sure to review and update the text to avoid redundancy of the term “AI-related technologies”. We have also decided to keep the term “AI-related technologies” consistently throughout the introduction to ensure clarity and consistency throughout the manuscript. In fact, in the third paragraph, we have included a clarification referring to the term “AI-related technologies”. Specifically, in the first three lines of the third paragraph in the introduction, we have added: "AI-related technologies (i.e., computational systems that use artificial intelligence to analyze, learn from, and interpret data) offer a promising alternative to minimize observer bias in BMS assessment (14)" 10. Unfinished Sentence in Objective Statement: The final sentence of the introduction (before the objectives) seems cut off. This makes the last thought feel incomplete and leaves the reader hanging. Suggestion: Complete the sentence and ensure that all points are concluded before introducing the research objectives. We have reviewed and revised this section to ensure the idea is fully expressed and concluded before presenting the research objectives. Now, in the final lines of the fourth paragraph of the introduction, we have added the following: “However, despite the increasing use of AI in motor performance assessment, there is no comprehensive review examining its specific application in the assessment of BMS in preschool children, being a crucial stage for early detection and intervention. Moreover, the scope, limitations and validity of AI-based technologies in this context are not yet clearly systematized. Therefore, it is required to synthesize existing knowledge and guide the development of more accurate and accessible assessment tools” Methods and discussion: 1. Lack of Detail in Study Selection Criteria While the article clearly defines the types of studies it targeted (engineering, substantive validation, and use of AI-related technologies), it doesn’t provide much detail on what specific inclusion and exclusion criteria were applied beyond these categories. For example, were studies excluded for reasons like sample size, study design, or methodological rigor? Suggestion: Provide more specific inclusion and exclusion criteria. Were only randomized controlled trials considered? Were there any restrictions based on the publication type (e.g., peer-reviewed articles only)? We have revised the manuscript and added more detailed inclusion and exclusion criteria. Specifically, in the third paragraph of methods, we added: "We also defined the following criteria for the search: 1) studies in preschool-aged children (3 to 6 years), 2) studies in which the motor ability (motor or play skills) of the child was assessed using AI-related technologies for motion detection, and 3) studies in which at least one of the basic motor skills described in the literature (running, jumping, kicking, throwing, or catching a ball) was measured. In addition, we excluded 1) studies that did not clearly describe the AI-related technology used or developed, 2) opinion articles, editorials, or narrative reviews without empirical data and 3) gray literature (e.g. theses, dissertations, or non-peer-reviewed reports)." 2. Vague Explanation of Search Strategy The search strategy mentions specific databases but does not explain the rationale for selecting these particular sources over others. Are there other databases relevant to the field that might have been excluded? How were the keywords selected, and were any synonyms or related terms considered? Suggestion: Provide a clearer justification for the selection of these particular databases and search terms. Did you consider any grey literature (e.g., theses, dissertations, reports from non-peer-reviewed sources) to broaden the search? We've added clearer justification for our selection of databases and search terms. Specifically, we added in the methods section, subsection Search strategy, the following two paragraphs: " We searched for studies published before January 30, 2023 in the target publications in Medline (SCR_002185), Web of Science (SCR_022706), IEEE (SCR_008314), and EBSCO (SCR_022707). These databases were selected because they specialize in biomedical, engineering, and multidisciplinary research, ensuring that we captured relevant studies in health sciences, AI applications, and motion analysis. Search terms included keyword combinations such as “child,” “preschool,” “basic motor skills,” “artificial intelligence,” “motion sensing,” and “calibration,” along with related terms and synonyms identified through a preliminary literature review (keywords) and controlled vocabulary (MeSH terms). The full search strategy and complete list of search terms are available here (42)." Likewise, we cite the complete search strategy as number 42. Details on the Rayyan Platform The mention of the Rayyan platform for managing studies is helpful, but the text doesn't clarify whether Rayyan was used for the full review process or just the initial screening. The review process appears to be manual in nature, but it could benefit from some details on how disagreements were handled between reviewers (e.g., was there a consensus meeting or did one reviewer have final say on the study’s inclusion?). Our team has detailed how Rayyan was used during the review process. Specifically, in the methods section, subsection Search strategy, in the third, fourth and fifth paragraph, we add: "The search formulas were applied to the databases and all the files were exported in RIS format. Then, to ensure an objective selection process, these identified files were uploaded to the Rayyan platform which facilitated blind selection by the reviewers and expedited the identification of duplicates. The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." Potential Bias in Study Selection The method states that the initial review of titles and abstracts was done by two independent groups, but there is no mention of any specific strategies to minimize bias during this phase. Given that the authors are likely familiar with the topic, it could be helpful to acknowledge how any biases in study selection (e.g., confirmation bias or publication bias) were minimized. Suggestion: Mention strategies to reduce bias in study selection. For example, were studies randomly assigned to reviewers, and was there a protocol to ensure that preconceived notions didn’t influence the selection process? Our team has added information about the process to minimize bias during study selection. Specifically, in the methods section, Search Strategy subsection, in the fourth and fifth paragraphs, we add: "The selection process consisted of two phases. In the first phase, titles and abstracts were reviewed by two independent groups (each consisting of two previously trained medical students). To minimize selection bias, the Rayyan blinding function was used, which prevented reviewers from identifying the decisions of the other reviewers until the final selection phase. In addition, allocation of studies to reviewers was randomized within each group to further reduce potential bias. In case of disagreement, a consensus discussion was held among the reviewers. If consensus could not be reached, the principal investigator made the final inclusion decision. In the second phase, a full-text review was performed following the same procedure, ensuring consistency and methodological rigor. The final set of studies was determined after resolving all discrepancies through consensus discussions and the intervention of the principal investigator." 5. Limited Details on Data Extraction Process While the data extraction form is outlined with categories such as general information, engineering, substantive validation, and use, there is little detail on how the data were extracted from the studies. For example, was any kind of reliability check conducted between reviewers, or were discrepancies resolved through discussion? Suggestion: Provide more details on the data extraction process, such as whether two independent reviewers performed the extraction and how discrepancies were resolved. Additionally, were any statistical methods used to assess inter-rater reliability in the extraction process? We have updated the paragraph by adding information in the methods section, Data extraction subsection. Now the text says the following: "Data extraction was performed in a structured manner using a pre-designed form (43). To reduce errors and improve the accuracy of the extracted data, one peer reviewer performed the initial extraction and a second peer independently verified the information. Any discrepancies in the extraction were reviewed jointly and/or, with the intervention of the principal investigator. Cross-checks were implemented to ensure the consistency of the information collected." Lack of Clarity on Data Analysis The description of data analysis mentions the use of descriptive statistics (frequencies and percentages) and the COSMIN standards for evaluating psychometric properties. However, there is no clear explanation of how the data were analyzed or how the results were synthesized. Were any meta-analysis or qualitative synthesis methods considered? How were the studies compared and summarized? Suggestion: Provide more clarity on how the data were synthesized. Was any quantitative or qualitative analysis beyond simple descriptive statistics performed, such as meta-analysis or thematic analysis? Did you perform a subgroup analysis for specific types of AI technologies or specific study characteristics? Since our study was a scoping review, we did not conduct a meta-analysis. Instead, we opted for a narrative synthesis of the results, presented in tables of frequencies and percentages. We also used the COSMIN standards, whose scoring and rating process is described in their original report, which we also reference in our study. Specifically, our text, in the methods section, subsection "Data analysis," in its first paragraph, states the following: "All data collected were summarized as categorical variables, organized and presented in tables, using descriptive statistics such as simple frequencies and percentages. Since this was a scoping review, a narrative synthesis was used to summarize the findings of the studies, focusing on the characteristics and psychometric properties evaluated according to COSMIN standards." Lack of Discussion on Potential Confounding Variables There is a brief mention of the use of psychometric standards (COSMIN) to evaluate AI technologies, but the article doesn't discuss how confounding factors (e.g., differences in sample size, age groups, or types of technology used) might impact the validity of the conclusions. Were these factors taken into consideration in the evaluation process? Suggestion: Acknowledge potential confounding variables and how they might affect the validity of the studies reviewed. How did the authors control for these variables, if at all, in the synthesis process? We would like to clarify that, since our study is a scoping review focused on the mapping and characterization of AI-based technologies used to assess motor skills in children, we did not aim to establish causal or correlational relationships between variables. Therefore, the methodological approach does not involve controlling or statistically analyzing confounding variables, as would be expected in primary empirical studies. The purpose of this review was to identify and describe the technologies developed, their applications, and the reported psychometric dimensions. Nonetheless, we have incorporated a critical reflection on the differences in sample sizes, age ranges (expressed in both years and months), and the types of technologies used, acknowledging these as limitations that could influence the validity and generalizability of the findings reported by the included studies. Specifically, the last lines of the limitations section say the following: "This review did not aim to analyze associations between variables; however, variability in sample sizes, age ranges, and types of AI-based technologies used across studies may affect the comparability and generalizability of the findings. These differences should be considered when interpreting the results and highlight the need for more standardized approaches in future research." 8. Unclear Rationale for Data Collection Methods In the section about data extraction, the mention of "feasibility and usability" as part of the data form is important but lacks further context. Was there a specific framework used to evaluate these factors? Were the usability and feasibility criteria standardized across the included studies? Suggestion: Provide more information on the frameworks or criteria used to assess usability and feasibility. Were these evaluations subjective or based on standardized measures? Clarifying this would help readers understand how the data extraction process assessed these aspects. Discussion - Discuss usability, cost-effectiveness, and accessibility challenges. -Emphasize the need for interdisciplinary collaboration in future validation studies. -Propose specific psychometric properties (e.g., construct validity) to prioritize future AI validation research. - Add practical recommendations for improving AI tools (e.g., integrating psychometric standards, improving usability for non-specialists). Since no standardized framework was used to assess feasibility and usability, this is an aspect that limits the interpretation and comparison of the results among the included studies. We state this information in the limitations. Specifically, we added to the beginning of the limitations:: "Also, although this review was based on COSMIN standards to assess the psychometric quality of AI-related technologies, due to the heterogeneity observed in the included studies, no specific adjustments were made to control for possible confounding variables. Therefore, the conclusions need to be interpreted with caution. It is recommended that future research address these factors and use control methods to provide more generalizable conclusions. Furthermore, feasibility and usability were extracted only if the reviewed studies explicitly reported having done so in their analysis of AI-related technologies. Therefore, further studies should evaluate these analyzes using a standardized framework." In response to comments on discussion, we have expanded this section on implications. Specifically, we added the following paragraphs in implications: "To facilitate use, developers could conduct studies that evaluate the acceptance, ease of use, cost-effectiveness, and accessibility of these technologies. For example, most technologies rely on sensors and monitors that, while accurate, can be costly, require specialized training, and can be difficult to implement in real-world settings for physicians, teachers, therapists, or practitioners unfamiliar with these tools. In addition, disparities in access to advanced technologies may limit their adoption, particularly in low-resource settings. Also, these types of technologies may be closer to more universal and cost-effective devices, such as video cameras, smartphones, and tablets, that can assess and report motor skills in real time. However, addressing these challenges requires a collaborative and interdisciplinary approach. Future validation studies should involve experts from multiple fields, including engineers, healthcare professionals, educators and policy makers, to ensure that these technologies are not only accurate, but also practical, scalable and accessible to diverse populations. New validation studies of these technologies should include validation standards for BMS tests, prioritizing key psychometric properties such as construct validity, criterion validity, reliability, measurement error, among others. To make this possible, engineering teams could incorporate specialists in psychometrics, developmental therapy and medicine to work collaboratively. This multidisciplinary approach will facilitate the integration of medical knowledge and psychometric standards into future software releases, improving both measurement accuracy and practical usability. Finally, developers should consider providing open source code or detailed methodological documentation, which will allow for further refinement, replication, and clinical adaptation of these technologies in future research and real-world applications." View more View less Competing Interests There is no conflict of interest reply Respond Report a concern Joshua AM and Prabhakar AJ. Peer Review Report For: Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review [version 3; peer review: 1 approved, 1 approved with reservations] . F1000Research 2026, 12 :1598 ( https://doi.org/10.5256/f1000research.151828.r349533) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-1598/v1#referee-response-349533 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Development, validation and use of artificial-intelligence-related...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/12-1598/v3" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/12-1598/v3&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/12-1598/v3" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Figueroa-Quiñones J et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/12-1598/v3/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/12-1598", templates : { twitter : "Development, validation and use of artificial-intelligence-related.... Figueroa-Quiñones J et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/12-1598/v3" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/138616/198263") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "198263"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "369683": 0, "264749": 0, "264750": 0, "264751": 0, "264756": 0, "264757": 0, "264758": 0, "264752": 0, "264753": 0, "264754": 0, "264755": 0, "343103": 0, "343102": 0, "343109": 0, "343108": 0, "343111": 0, "343110": 0, "343105": 0, "343104": 0, "343107": 0, "343106": 0, "411214": 0, "478799": 0, "478798": 5, "277586": 0, "277587": 0, "277598": 0, "242786": 0, "242789": 0, "242795": 0, "277612": 0, "242792": 0, "242798": 0, "277609": 0, "277611": 0, "242801": 0, "277623": 0, "242807": 0, "242804": 0, "242810": 0, "277624": 0, "277625": 0, "242813": 0, "289927": 0, "289928": 0, "289929": 0, "427670": 0, "427671": 0, "427668": 0, "427669": 0, "427666": 0, "427667": 0, "427665": 0, "427674": 0, "277656": 0, "427672": 0, "427673": 0, "419516": 0, "419517": 0, "419514": 0, "419515": 0, "419512": 0, "419513": 0, "412866": 0, "412864": 0, "419530": 0, "419531": 0, "419528": 0, "419529": 0, "403174": 0, "403175": 0, "403172": 0, "403173": 0, "403170": 0, "403171": 0, "403169": 0, "403178": 0, "403176": 0, "403177": 0, "471807": 0, "471806": 0, "471805": 0, "471804": 0, "471813": 0, "471812": 0, "471811": 0, "471810": 0, "471809": 0, "471808": 0, "412950": 0, "412951": 0, "412948": 0, "412949": 0, "412947": 0, "412954": 0, "412952": 0, "412953": 0, "416086": 0, "349525": 0, "416087": 0, "416084": 0, "349527": 0, "416085": 0, "349526": 0, "416083": 0, "349533": 20, "349532": 0, "416092": 0, "416093": 0, "349534": 0, "416090": 0, "349529": 0, "416091": 0, "349528": 0, "416088": 0, "349531": 0, "416089": 0, "349530": 0, "429926": 0, "429927": 0, "429924": 0, "429925": 0, "429922": 0, "429923": 0, "429930": 0, "429931": 0, "429928": 0, "429929": 0, "248179": 0, "248182": 0, "232308": 0, "469887": 0, "232315": 0, "469886": 0, "469885": 5, "469884": 0, "248184": 0, "248190": 0, "469893": 0, "232321": 0, "469892": 0, "469891": 0, "232327": 0, "469890": 0, "469889": 0, "248197": 0, "469888": 0, "232324": 0, "248206": 0, "232332": 0, "232336": 0, "248214": 0, "248212": 0, "248217": 0, "232344": 0, "248220": 0, "423334": 0, "423335": 0, "423332": 0, "232353": 0, "423333": 0, "423330": 0, "423331": 0, "423328": 0, "423329": 0, "232363": 0, "483247": 0, "483246": 0, "483245": 0, "423336": 0, "423337": 0, "467895": 0, "467894": 0, "483254": 0, "467893": 0, "483253": 0, "467892": 0, "483252": 0, "467891": 0, "483251": 0, "467890": 0, "483250": 0, "467889": 0, "483249": 0, "467888": 0, "483248": 0, "332735": 0, "467897": 0, "467896": 0, "479687": 0, "479686": 0, "479685": 0, "235968": 0, "479684": 0, "235975": 0, "479683": 0, "235974": 0, "332736": 0, "479682": 0, "235973": 0, "479681": 0, "479680": 0, "235979": 0, "235978": 0, "235977": 0, "235976": 0, "235981": 0, "479689": 0, "235980": 0, "479688": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "fda9c3e1-ce05-4015-a5b3-7c7d33834397"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });