{"paper_id":"43c720eb-fcd4-4c8b-af6e-0909a7f03ff9","body_text":"Correlating data from different sensors to increase... | F1000Research <!-- --> <!-- --> <!-- This is commented out to fix display problems on mobile devices. We may use it again once we implement a responsive design that supports native device resolutions. --> \"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;f<d;f++)s[f].apply(a,r);var l=T()[c[e]];return l&&l.push([p,e,r,a]),a}}function b(e,t){n[e]=w(e).concat(t)}function y(e,t){var r=n[e];if(r)for(var i=0;i {r.d(t,{E:()=>n,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/1-45\" }, \"headline\": \"Correlating data from different sensors to increase the positive predictive value of alarms: an empiric...\", \"datePublished\": \"2012-11-08T17:06:57\", \"dateModified\": \"2012-11-08T17:06:57\", \"author\": [ { \"@type\": \"Person\", \"name\": \"Yuval Bitan\" }, { \"@type\": \"Person\", \"name\": \"Michael F O’Connor\" } ], \"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\": \"Objectives: Alarm fatigue from high false alarm rate is a well described phenomenon in the intensive care unit (ICU). Progress to further reduce false alarms must employ a new strategy. Highly sensitive alarms invariably have a very high false alarm rate. Clinically useful alarms have a high Positive-Predictive Value. Our goal is to demonstrate one approach to suppressing false alarms using an algorithm that correlates information across sensors and replicates the ways that human evaluators discriminate artifact from real signal.Methods: After obtaining IRB approval and waiver of informed consent, a set of definitions, (hypovolemia, left ventricular shock, tamponade, hemodynamically significant ventricular tachycardia, and hemodynamically significant supraventricular tachycardia), were installed in the monitors in a 10 bed cardiothoracic ICU and evaluated over an 85 day study period. The logic of the algorithms was intended to replicate the logic of practitioners, and correlated information across sensors in a way similar to that used by practitioners. The performance of the alarms was evaluated via a daily interview with the ICU attending and review of the tracings recorded over the previous 24 hours in the monitor. True alarms and false alarms were identified by an expert clinician, and the performance of the algorithms evaluated using the standard definitions of sensitivity, specificity, positive predictive value, and negative predictive value.Results: Between 1 and 221 instances of defined events occurred over the duration of the study, and the positive predictive value of the definitions varied between 4.1% and 84%.Conclusions: Correlation of information across alarms can suppress artifact, increase the positive predictive value of alarms, and can employ more sophisticated definitions of alarm events than present single-sensor based systems.\" } { \"@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/1-45\", \"name\": \"Correlating data from different sensors to increase the positive predictive...\" } } ] } Home Browse Correlating data from different sensors to increase the positive predictive... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Bitan Y and O’Connor MF. Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.12688/f1000research.1-45.v1 ) 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 ▬ ✚ Research Article Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] Yuval Bitan 1 , Michael F O’Connor 2 Yuval Bitan 1 , Michael F O’Connor 2 PUBLISHED 08 Nov 2012 Author details Author details 1 Cognitive Technologies Laboratory, The University of Chicago, Chicago, IL, USA 2 Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL, USA OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Objectives: Alarm fatigue from high false alarm rate is a well described phenomenon in the intensive care unit (ICU). Progress to further reduce false alarms must employ a new strategy. Highly sensitive alarms invariably have a very high false alarm rate. Clinically useful alarms have a high Positive-Predictive Value. Our goal is to demonstrate one approach to suppressing false alarms using an algorithm that correlates information across sensors and replicates the ways that human evaluators discriminate artifact from real signal. Methods: After obtaining IRB approval and waiver of informed consent, a set of definitions, (hypovolemia, left ventricular shock, tamponade, hemodynamically significant ventricular tachycardia, and hemodynamically significant supraventricular tachycardia), were installed in the monitors in a 10 bed cardiothoracic ICU and evaluated over an 85 day study period. The logic of the algorithms was intended to replicate the logic of practitioners, and correlated information across sensors in a way similar to that used by practitioners. The performance of the alarms was evaluated via a daily interview with the ICU attending and review of the tracings recorded over the previous 24 hours in the monitor. True alarms and false alarms were identified by an expert clinician, and the performance of the algorithms evaluated using the standard definitions of sensitivity, specificity, positive predictive value, and negative predictive value. Results: Between 1 and 221 instances of defined events occurred over the duration of the study, and the positive predictive value of the definitions varied between 4.1% and 84%. Conclusions: Correlation of information across alarms can suppress artifact, increase the positive predictive value of alarms, and can employ more sophisticated definitions of alarm events than present single-sensor based systems. READ ALL READ LESS Corresponding Author(s) Yuval Bitan ( [email protected] ) Close Corresponding author: Yuval Bitan Competing interests: No competing interests were disclosed. Grant information: Philips Medical installed event surveillance software on the monitors employed for this study, installed the study definitions for the investigators, and provided salary support for the study technician who collected the data for analysis. Philips Medical also provided travel expenses to present the work at the Human Factors Conference 2012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2012 Bitan Y and O’Connor MF. 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. Data associated with the article are available under the terms of the Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication). How to cite: Bitan Y and O’Connor MF. Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.12688/f1000research.1-45.v1 ) First published: 08 Nov 2012, 1 :45 ( https://doi.org/10.12688/f1000research.1-45.v1 ) Latest published: 08 Nov 2012, 1 :45 ( https://doi.org/10.12688/f1000research.1-45.v1 ) Introduction Historically, desire for high performance and concern over legal liability has motivated the design of alarm systems in clinical medicine that are highly sensitive, but which also have a very high false positive rate 1 . False positive alarms have multiple causes, including ‘low threshold’ settings, motion interference, and false signals generated from a variety of clinical activities. Paradoxically, the high rate of false positive (80–99%) alarms trains practitioners to ignore alarms 2 , 3 . Alarm fatigue is a phenomenon where practitioners come to ignore alarms 3 . In many ICUs, the audible signals from the alarms built into their bedside monitors are disabled or silenced. This strategy has reduced the noise pollution associated with these systems without obviously decreasing their performance. Previous literature 4 points towards the need to reduce the total number of alarms that occur in working environments such as the ICU. One strategy to increase the clinical utility of such alarms is to specify alarm definitions that are less sensitive, but have a high positive predictive value (PPV). Based on Signal Detection Theory 5 strategies to accomplish this could include higher thresholds for alarm conditions, and advanced alarms that might be less likely to be triggered by either artifact or clinical activity. Higher thresholds would alarm less often, but would also alert caregivers later in the course of a patient’s decompensation. Importantly, setting the threshold for an alarm at a higher value may not substantially change the rate of false alarms from artifacts. Alarms with a higher positive predictive value would be triggered less often, and would be much more likely to summon bedside caregivers to respond appropriately. The greatest risk from this strategy is that an alarm might not sound when a life threatening condition is present. Another strategy to reduce the rate of false alarms is to increase the sophistication of the alarm software 6 , in effect, making the monitor analyze data across sensors to verify the alarm condition. For example, when a patient moves, she can disturb her EKG electrodes and produce an EKG signal that appears to be ventricular fibrillation. In this instance, the EKG alarms 'V fib'! Frequently, however, other sensors are generating information that could be used to suppress that false alarm. The correlation of information across sensors may be especially effective in reducing artifact related false alarms. For example, either an arterial line or a pulse oximeter might detect a pulse in the above patient, which is impossible in the setting of V fib. By comparing information across sensors, smarter monitors might decrease the rate of false alarms and facilitate the early detection of other clinical problems. Similarly, a patient who is tachycardic should have a high heart rate on their EKG, pulse-oximeter, and arterial line (if one is present). Simply correlating information from these different sensors is likely to decrease the rate of false alarms without reducing sensitivity to a clinically important degree. The presence of alarms triggered by a single sensor is an artifact of device history, not deliberate design. Advanced software could be programmed to replicate the logic that caregivers utilize to discriminate real conditions from artifact. Another strategy to increase response to alarms is to assess parameters that are clinically important in the context of the abnormal parameter. For example, tachycardia associated with a precipitous decline in blood pressure is almost always clinically more significant than tachycardia associated with no change or an increase in blood pressure. Advanced alarms which alert bedside caregivers to important patterns of change (clinical correlations) are far more likely to generate the desired clinical response than monitors that continually alarm for situations that represent little or no danger. Such alarms would have a high PPV, lower rate of false alarm, and are likely to elicit more purposeful responses from caregivers. In this study, we utilized Philip’s Event Monitoring software to define alarm conditions that correlated information across sensors, and which were prospectively intended to have a high positive predictive value. The software being studied in this trial is intended to serve both of these purposes, and the data collected during this trial will inform its refinement. The Clinical Study of the Event Surveillance Software/Event Alarming usability and functionality is a feedback collection and comparative multi-center study of the recently released Philips' D. O. software for Intellivue Monitors (MP70/90). The software was designed to detect scenarios that are either harmful or might predict a critical situation for the ICU patient. Methods Cardiac surgery patients in a 10 bed Intensive Care Unit were eligible for Intellivue monitor data capture for the purpose of determining the incidence of true positive events as compared with false positive events. IRB approval was obtained and waiver of consent was granted. Event Surveillance software was installed into every monitor in the ICU, and operational in parallel with the institutional default alarms settings. Five clinically important alarm scenarios (‘smart alarms’) were programmed into the bedside monitors using the Event Surveillance software (Table 1) . Table 1. Clinical alarm scenarios that were programmed into the bedside monitors. Detected Scenarios Parameters Limits/Trigger Time (scenario name) (detect what?) (maximum of four) (lower & upper violation for x seconds or relative triggers in % over a defined time in sec/min) SVT + BP onset of paroxysmal atrial fibrillation HR (Pulse) ART sys Pulse (HR) +40% within 59 sec -15% within 59 sec >110 bpm for 20 sec Vtach + BP Vtach with low blood pressure HR (Pulse) PVC ARTsys Pulse (HR) +30 bpm within 20 sec ***Vtach -30% within 20 sec >110 bpm for 10 sec LV Shock left ventricular shock ARTsys CVPmean PAPdia Perf <78 mmHg for 300 sec <16 mmHg for 300 sec >16 mmHg for 300 sec <1.2 for 300 sec TPX & TPND tamponade (obstructive shock) ARTsys CVPmean Perf PAPdia <78 mmHg for 180 sec >16 mmHg for 180 sec -20% within 3 min >16 mmHg for 180 sec Hypovl hypovolemia ARTmean CVP Perf NIBPm <50 mmHg for 300 sec <5 mmHg for 300 sec -20% within 120 sec/10 min <55 mmHg for 300 sec Notes on names in Table 1 1. SVT + BP – Supraventricular Tachycardia and Blood Pressure – This is intended to indicate high heart rate with low blood pressure, as frequently occurs in patients with Atrial fibrillation and a rapid ventricular rate. Tachycardia associated with hypertension, as commonly occurs with light sedation, would not trigger this alarm. 2. VTACH + BP – This is intended to indicate ventricular tachycardia with low blood pressure. This definition would be much less likely to be triggered by motion artifact than the EKG alarm is. 3. LV SHOCK – This is intended to detect Left ventricular failure (cardiogenic shock). 4. TPX & TPND – This is intended to detect either tamponade or tension pneumothorax. 5. HYPOVL – This is intended to indicate low blood pressure from hypovolemia. The first two (SVT+BP and Vtach+BP) definitions required the presence of an arterial line and EKG. The third and fourth (LV shock and tamponade) required a pulmonary artery catheter and an arterial line. Hypovolemia required the presence of a CVP monitor, and could be triggered by a blood pressure from either the arterial line or a non-invasive blood pressure cuff. If the requisite sensors were not present in a patient, then events and definitions related to that event were not analyzed for the purposes of this study. For example, if atrial fibrillation happened in a patient without an arterial line, it was ignored for the purposes of this study. When any alarm (factory installed or event surveillance software) is triggered, a log of monitor data from the event is stored in the central monitoring station. Every day, the log file of events from the previous 24 hours was reviewed with the ICU physician (attending or fellow), and all events were classified (Table 2) . Table 2. Events’ classifications. Abbreviation Explanation TPRE True Positive Real Event TP Predict True Positive Predictive FP Art False Positive Artifact (e.g. CVP 200 mmHg or Arterial pressure -10 mmHg) FP Ins Dif False Positive Insufficient Definition (e.g. patient on LVAD with Vtach or atrial fibrillation) FN Th False Negative threat or late (definition failure) FN No Th False negative non-threat (e.g. atrial fibrillation without significant hypotension). FN Sens Off False Negative sensor off (e.g. atrial fibrillation that occurred while RN was positioning patient and EKG was disconnected). TN Time Int Time Interval. These were the patients for which no events were registered during the time period of the observation. Results Events were recorded for 85 days from Mid-May 2007 until Mid-November 2007 (Table 3) . In total 564 patient days monitored were monitored. Table 3. Number of true positive, false positive and false negative events, together with the positive predictive value for each clinical alarm scenario using Event Surveillance software. Scenario # Events True Positives (#Patients) False Positive Artifact False Positive Insufficient definition Positive Predictive Value False Negative SVT+BP 221 170(10) 17 22 0.8 9(7) Vtach+BP 1 1(1) 0 0 1.0 0 LV shock 42 34(6) 8 0 0.81 1 Tamponade 24 1(1) 23 0 0.04 1 Hypovolemia 29 8 21 0 0.27 2 For SVT + BP there were a total of 221 events over 35 patient days. There were 529 patient days where this event did not occur (i.e., no alarm and no false negative occurred). Out of the 221 events, 170 were True Positive events and 1 was a TP predict event (see Table 2 for abbreviations). 19 were FP Artifact and 22 were FP Insufficient Definition. Thus, out of a total of 221 alarms, 171 were true positive, for a PPV of 0.807. The 171 TP events were concentrated on 10 patients (patient IDs: 31, 1, 22, 11, 10, 32, 19, 17, 8, 4). The 9 FN events happened to 7 patients. Ventricular Tachycardia with hypotension occurred only in one patient during the 564 recorded patient days, and there were no FP or FN events. Left Ventricular (LV) Shock occurred in 42 of the 564 patient days and among 6 patients in total. There were 8 FP Artifact events and only 1 FN with threat. Thus, the PPV here was 0.81. Tamponade had only one TP event, and 23 FP events (for 13 patient days), as well as 1 Non-threatening FN event in a total of 564 patient days. The PPV was therefore 0.04. Hypovolemia had 8 TP events, as well as 21 FP events (for 10 patients) and 2 FN events. For Hypovolemia the PPV was 0.27. Discussion No alarm system in use or under development can perform perfectly. Hence, practitioners are compelled to trade-off among the kinds of failures that are acceptable to them. While there is ample literature that demonstrates that simple monitors generate vastly more false alarms than real alarms, the regulatory environment of most medical practice has generated regulations that require these alarms to be activated. In the current study, the data we have collected thus far suggest that the SVT+BP trigger group is likely to be a useful alarm in clinical practice. The evidence is not quite as strong, but is encouraging for LV shock as well. The other events we were surveying for, tamponade, hypovolemic shock, and Vtach+BP were all sufficiently rare (by our definition) that we remain unable to evaluate the positive predictive performance of these trigger groups. While LV shock is commonplace in the ICU where this study was conducted, most patients were actively managed by their caregivers and rarely met the definition for LV shock we employed. Importantly, the absolute rate of false positive alarms for these groups was low (29%) compared to the approximately 80% rate reported in other studies 2 , consistent with our hypothesis that correlating information across sensors might decrease the rate of false positive alarms. Correlating information across sensors and simultaneously probing for important deflections from other sensors produced a dramatic improvement in alarm performance in this study. The most important limitation to this approach is that event surveillance software utilizing multiple sensors requires that those sensors be present, operational, and free of artifact. There were multiple episodes of atrial fibrillation that occurred in patients who did not have an arterial line, and were hence not captured by event surveillance software, and not eligible for inclusion in this analysis. Dampening of the arterial waveform produced a situation in which the criterion for hypotension was satisfied in event surveillance software. This was principally a problem with the SVT+BP and hypovolemia definitions, but would confound any definition that relies upon accurate data from an arterial catheter. Another important failure came from artifact in the CVP. Failure to level can produce artifactually high or low values in the CVP. Infusions consistently produce artifactually elevated CVP measurements. These artifacts generated most of the false positives in the hypovolemia and tamponade definitions. The software used to conduct this study did not allow any parameter from a sensor to be used more than once in any definition, which precluded screening for these artifacts by excluding extreme values (e.g. CVP of 60mm Hg or -20 mmHg). The ability to examine a parameter more than once would have prevented many of the false positive activations of these definitions. The failure rate of definitions that require data from different sensors will be at least the sum of the artifact rate of those sensors. Logic that replicates how human operators process alarms can be employed using Event Surveillance software and similar software, and has the potential to significantly improve the performance of bedside monitors. The event surveillance software employed in the present study could not access all of the information generated from all of the sensors in the monitor, which severely constrained the events that could be surveyed and the definitions that were generated. Successive generations of software, if they incorporate expanded ability to capture information, might be used to generate definitions that will be more useful than most of those used for the current study. The most important limitation of the present study is that we were unable to deploy an independent observer in the ICU continuously, and thus had to depend upon bedside RNs and resident physicians to report episodes of the events we sought to capture. It is unlikely that we missed a large number of significant events, but precise estimation of the performance of these definitions would require this more reliable database. We hope that we will be able to obtain the resources to perform a successor study of this design at multiple sites. If all of the output from the clinical devices was recorded into a single massive database, that database could then be used to iteratively evaluate and refine different alarm definitions. Event surveillance software utilizes the same audible and visible signals as the other alarms built into these monitors. Hence, study definitions with a very high true positive alarm rate were mixed in with the high rate of false alarms generated by the factory settings for each sensor. The number of false alarms from the individual sensors substantially outnumbers the alarms generated by event surveillance software. Until such time as different audible and visual alarms are utilized, it may be difficult or impossible to demonstrate an important difference in the response of bedside caregivers. Conclusion Correlation of information across sensors can be used to detect and suppress artifact in a manner similar to how human operators analyze data. Such simple algorithms can generate alarms with a much higher positive predictive value than the simple alarms associated with any of the individual sensors. Additionally, the ability to correlate information across sensors allows the monitor to process clinical information in a manner similar to human operators. The most important limitation to the correlation of information across sensors is that the failure rate becomes at least the sum of the artifact rate of the individual sensors. Nevertheless, these two approaches have the potential to significantly reduce false alarms, increase the positive predictive value of alarms, and make some progress reducing the ubiquitous problem of alarm fatigue in the ICU. Author contributions M. O’Connor and Y. Bitan conceived the study. M. O’Connor executed the study and gathered the data. Dr. Bitan analyzed the data and prepared the manuscript. Competing interests Both authors declare they have no competing interests. Grant information Philips Medical installed event surveillance software on the monitors employed for this study, installed the study definitions for the investigators, and provided salary support for the study technician who collected the data for analysis. Philips Medical also provided travel expenses to present the work at the Human Factors Conference 2012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgment This work was performed at the Department of Anesthesia and Critical Care, The University of Chicago, Chicago, Illinois. The authors wish to thank Joachim Meyer for his insightful comments during the preparation of this paper. The authors would also like to thanks Berndt Duller for his help in analyzing the results of this study, and the technical support provided in installing the alarm definitions into the ICU monitors. The authors would also like to thank Leah Karl for her efforts on behalf of the study and Philips for supporting this study. References 1. Kerr JH, Hayes B: An \"alarming\" situation in the intensive therapy unit. Intensive Care Med. 1983; 9 : 103–4. PubMed Abstract 2. Schmid F, Goepfert MS, Kuhnt D, et al. : The Wolf is Crying in the Operating Room: Patient Monitor and Anesthesia Workstation Alarming Patterns During Cardiac Surgery. Anesth Analg. 2011; 112 : 78–83. PubMed Abstract | Publisher Full Text 3. Lawless ST: Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994; 22 : 981–5. PubMed Abstract 4. Bitan Y, Meyer J, Shinar D, et al. : Nurses’ reactions to alarms in the neonatal intensive care unit. Cogn Tech Work. 2004; 6 : 239–46. Publisher Full Text 5. Green DM, Swets JA: Signal Detection Theory and Psychophysics. New York: Wiley, 1966. Reference Source 6. Tsien CL, Fackler JC: Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997; 25 : 614–9. PubMed Abstract Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 08 Nov 2012 ADD YOUR COMMENT Comment Author details Author details 1 Cognitive Technologies Laboratory, The University of Chicago, Chicago, IL, USA 2 Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL, USA Competing interests No competing interests were disclosed. Grant information Philips Medical installed event surveillance software on the monitors employed for this study, installed the study definitions for the investigators, and provided salary support for the study technician who collected the data for analysis. Philips Medical also provided travel expenses to present the work at the Human Factors Conference 2012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 08 Nov 2012, 1:45 https://doi.org/10.12688/f1000research.1-45.v1 Copyright © 2012 Bitan Y and O’Connor MF. 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. Data associated with the article are available under the terms of the Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication). 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 Bitan Y and O’Connor MF. Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.12688/f1000research.1-45.v1 ) 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 1 VERSION 1 PUBLISHED 08 Nov 2012 Views 0 Cite How to cite this report: Voga G. Reviewer Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r360 ) The direct URL for this report is: https://f1000research.com/articles/1-45/v1#referee-response-360 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 27 Nov 2012 Gorazd Voga , Medical ICU, General Hospital Celje, Celje, Slovenia Approved VIEWS 0 https://doi.org/10.5256/f1000research.220.r360 The ideology behind the research of this article is good and relevant. Despite the article having a ... Continue reading READ ALL The ideology behind the research of this article is good and relevant. Despite the article having a few flaws, the work presented highlights an important topic that is worthy of further discussion. Competing Interests: No competing interests were disclosed. 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 Voga G. Reviewer Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r360 ) The direct URL for this report is: https://f1000research.com/articles/1-45/v1#referee-response-360 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 Views 0 Cite How to cite this report: Wright M. Reviewer Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r358 ) The direct URL for this report is: https://f1000research.com/articles/1-45/v1#referee-response-358 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 19 Nov 2012 Melanie Wright , Trinity Health System, Boise, ID, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.220.r358 The scope and depth of the work is appropriate as something that would be presented as an abstract or pilot work, as the study is a collection of baseline data. There are no comparisons of other methods used to monitor patients, ... Continue reading READ ALL The scope and depth of the work is appropriate as something that would be presented as an abstract or pilot work, as the study is a collection of baseline data. There are no comparisons of other methods used to monitor patients, for example, did the authors turn off the single sensor alarms whilst performing this study? The authors also compare their presumed false alarm rates to rates presented in other studies, rather than actually capturing single sensor false alarm rates in this setting, and it is difficult to understand how one might place the use of the correlating data (for example SVT + BP to detect atrial fibrillation) within the context of other conditions that low BP and/or high HR/pulse might predict. How did they determine false negatives? Expert review of alarm logs does not instill me with confidence that they captured events that may have been missed. I think the limitations, appropriately described within the document, are great enough to question whether this research is yet at a level that is meaningful for a wide audience. However, the writing is good and the findings may be meaningful for others working in this developing area of research. Competing Interests: No competing interests were disclosed. 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 Wright M. Reviewer Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r358 ) The direct URL for this report is: https://f1000research.com/articles/1-45/v1#referee-response-358 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 Views 0 Cite How to cite this report: Xiao Y. Reviewer Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r357 ) The direct URL for this report is: https://f1000research.com/articles/1-45/v1#referee-response-357 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 15 Nov 2012 Yan Xiao , Office of Patient Safety, Baylor University Medical Center at Dallas, Dallas, TX, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.220.r357 I confirm that I have read this submission and believe that I have an ... Continue reading READ ALL Competing Interests: No competing interests were disclosed. 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 Xiao Y. Reviewer Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r357 ) The direct URL for this report is: https://f1000research.com/articles/1-45/v1#referee-response-357 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 Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 08 Nov 2012 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 3 Version 1 08 Nov 12 read read read Yan Xiao , Baylor University Medical Center at Dallas, Dallas, TX, USA Melanie Wright , Trinity Health System, Boise, ID, USA Gorazd Voga , General Hospital Celje, Celje, Slovenia 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 © 2012 Voga G. 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. 27 Nov 2012 | for Version 1 Gorazd Voga , Medical ICU, General Hospital Celje, Celje, Slovenia 0 Views copyright © 2012 Voga G. 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 The ideology behind the research of this article is good and relevant. Despite the article having a few flaws, the work presented highlights an important topic that is worthy of further discussion. Competing Interests No competing interests were disclosed. 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) Voga G. Peer Review Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r360) 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/1-45/v1#referee-response-360 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2012 Wright M. 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. 19 Nov 2012 | for Version 1 Melanie Wright , Trinity Health System, Boise, ID, USA 0 Views copyright © 2012 Wright M. 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 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 The scope and depth of the work is appropriate as something that would be presented as an abstract or pilot work, as the study is a collection of baseline data. There are no comparisons of other methods used to monitor patients, for example, did the authors turn off the single sensor alarms whilst performing this study? The authors also compare their presumed false alarm rates to rates presented in other studies, rather than actually capturing single sensor false alarm rates in this setting, and it is difficult to understand how one might place the use of the correlating data (for example SVT + BP to detect atrial fibrillation) within the context of other conditions that low BP and/or high HR/pulse might predict. How did they determine false negatives? Expert review of alarm logs does not instill me with confidence that they captured events that may have been missed. I think the limitations, appropriately described within the document, are great enough to question whether this research is yet at a level that is meaningful for a wide audience. However, the writing is good and the findings may be meaningful for others working in this developing area of research. Competing Interests No competing interests were disclosed. 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 (0) Wright M. Peer Review Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r358) 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/1-45/v1#referee-response-358 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2012 Xiao Y. 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. 15 Nov 2012 | for Version 1 Yan Xiao , Office of Patient Safety, Baylor University Medical Center at Dallas, Dallas, TX, USA 0 Views copyright © 2012 Xiao Y. 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 Competing Interests No competing interests were disclosed. 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) Xiao Y. Peer Review Report For: Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2012, 1 :45 ( https://doi.org/10.5256/f1000research.220.r357) 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/1-45/v1#referee-response-357 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 = \"Correlating data from different sensors to...\".replace(\"'\", ''); var linkedInUrl = \"http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/1-45/v1\" + \"&title=\" + encodeURIComponent(lTitle) + \"&summary=\" + encodeURIComponent('Read the article by '); var deliciousUrl = \"https://del.icio.us/post?url=https://f1000research.com/articles/1-45/v1&title=\" + encodeURIComponent(lTitle); var redditUrl = \"http://reddit.com/submit?url=https://f1000research.com/articles/1-45/v1\" + \"&title=\" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Bitan Y and O’Connor MF'); 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/1-45/v1/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/1-45\", templates : { twitter : \"Correlating data from different sensors to increase the positive.... Bitan Y and O’Connor MF, published by \" + \"@F1000Research\" + \", https://f1000research.com/articles/1-45/v1\" } }; 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/220/220\") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay(\"articles\", \"article\", \"220\"); $(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 = { \"357\": 17, \"358\": 15, \"360\": 13, }; $(\".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 = \"b790ccf7-e72f-44ed-af04-3a544807c011\"; 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: \"research@f1000.com\", infoEmail: \"info@f1000.com\", 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 --> 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(\",\",\"/\")); } }); });","source_license":"CC-BY-4.0","license_restricted":false}