Using Exercise Intensity to Predict a Minimal Clinically Important Difference in the Six-Minute Walk Test in People with Chronic Stroke

Using Exercise Intensity to Predict a Minimal Clinically Important Difference in the Six-Minute Walk Test in People with Chronic Stroke | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Using Exercise Intensity to Predict a Minimal Clinically Important Difference in the Six-Minute Walk Test in People with Chronic Stroke View ORCID Profile Kiersten M. McCartney , View ORCID Profile Pierce Boyne , View ORCID Profile Ryan T. Pohlig , View ORCID Profile Susanne M. Morton , View ORCID Profile Darcy S. Reisman doi: https://doi.org/10.1101/2025.04.15.25325882 Kiersten M. McCartney 1 Department of Physical Therapy, University of Delaware , Newark, DE, USA 2 Biomechanics and Movement Science Program, University of Delaware , Newark, DE, USA PT, DPT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kiersten M. McCartney Pierce Boyne 3 Department of Rehabilitation, Exercise and Nutrition Sciences, University of Cincinnati , Cincinnati, OH, USA PT, DPT, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pierce Boyne Ryan T. Pohlig 4 Biostatistics Core, Epidemiology Department, University of Delaware , Newark, DE, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ryan T. Pohlig Susanne M. Morton 1 Department of Physical Therapy, University of Delaware , Newark, DE, USA 2 Biomechanics and Movement Science Program, University of Delaware , Newark, DE, USA PT, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Susanne M. Morton Darcy S. Reisman 1 Department of Physical Therapy, University of Delaware , Newark, DE, USA 2 Biomechanics and Movement Science Program, University of Delaware , Newark, DE, USA PT, PhD, FAPTA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Darcy S. Reisman For correspondence: dreisman{at}udel.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background People with chronic stroke have significant impairments in their walking capacity. Minimal clinically important differences (MCIDs) can be used to interpret changes in patient outcomes following interventions. There is significant variability in the response to moderate-to-high walking interventions in people with chronic stroke. One reason for this response variability could be the lack of understanding of the threshold exercise dose needed to achieve an MCID. The purpose of this analysis was to determine the threshold of exercise training speed most predictive of a small ( > 20m) or moderate ( > 50m) clinically important difference in 6MWT in people with chronic stroke. Materials and Methods Participants with chronic stroke with a walking speed of 0.3-1.0m/s were randomized into a 12-week (1) fast-walking training or (2) fast-walking training and step-activity monitoring intervention. This analysis included participants ( n = 129; age: 63.1 ± 12.5, 46% female) with complete pre- and post-intervention data. Exercise intensity was quantified as average training speed. Results Receiver operating characteristic curves analyzed whether training speed is predictive of attaining a clinically important difference in the 6MWT. Training speed had poor, non-significant accuracy of predicting a small (AUC [95% CI] = 0.584 [0.475 - 0.693], p = 0.131) or moderate (AUC [95% CI] = 0.597 [0.498 - 0.696], p = 0.056) change in 6MWT. Discussion The average walking training speed during this high-intensity walking intervention did not accurately predict which people with chronic stroke would attain a small or moderate clinically meaningful change in 6MWT distance. INTRODUCTION In clinical practice, minimal clinically important differences (MCIDs) can be used to interpret changes in patient outcomes following interventions. MCIDs provide the smallest threshold value on a specific outcome (e.g., walking endurance) which indicate a patient-perceived beneficial change in that outcome was met. 1 - 3 Meeting or exceeding an MCID is one factor clinicians can use to assess if the outcome of their intervention(s) were meaningful to their patient. People with chronic stroke have significant impairments in their walking capacity and commonly cite improving walking as a priority for rehabilitation. 4 , 5 MCIDs have been proposed for meaningful changes in walking endurance, measured by the six-minute walk test (6MWT), in people with stroke. 6 - 8 There is no consensus on a singular MCID value for changes in 6MWT in those with stroke. However, this literature does provide a basis for reasonable MCID selection when trying to understand changes in 6MWT following intervention. With this in mind, a recent meta-analysis examined changes in the 6MWT following moderate-to-high intensity walking exercise interventions, which have been shown to improve walking endurance in people with stroke. 9 This analysis found high levels of variability in response to these walking interventions. 10 It was estimated there is a 68% probability people with chronic stroke will have at least a small clinically important difference in walking endurance following moderate-to-vigorous intensity interventions, and only a 36% probability of having a moderate clinically important difference in walking endurance. 10 One reason for this response variability could be the lack of understanding of the threshold exercise dose needed to achieve an MCID with these types of intervention. 11 , 12 Cumulative evidence on exercise dose for walking training after stroke has demonstrated exercise intensity is a key ingredient to achieving greater changes in walking capacity. 9 , 13 , 14 In most research interventions and the associated clinical translation of moderate-to-high walking interventions, training heart rate has been used as the measure of intensity. 9 However, training speed, when considered as the metric of exercise intensity, is currently the most significant predictor of walking capacity outcomes in people with chronic stroke following high intensity interventions. 11 , 12 It is unknown if there is a threshold training speed which would best predict an individual with stroke attaining a meaningful change in walking outcomes. Determining this threshold dose could assist clinicians by providing a more precise exercise prescription to reduce the exercise response variability. The purpose of this analysis was to determine the threshold of exercise training speed that is most predictive of a small ( > 20m) or moderate ( > 50m) clinically important difference in 6MWT in people with chronic stroke. 7 We hypothesized that higher average training speeds would be required to attain a moderate change compared to a small change in 6MWT distance following a fast-walking training intervention. METHODS Participants This is a secondary analysis from a larger randomized controlled trial “ Promoting Recovery Optimization of Walking Activity in Stroke” (“PROWALKS”; NIH1R01HD086362; NCT02835313 ). The full protocol and primary results have previously been published. 15 , 16 Participants signed an informed consent prior to enrollment with all study procedures approved by the University of Delaware Institutional Review Board. The funders played no role in the design, conduct, or reporting of this study and the authors report there are no competing interests to declare. Participants had their most recent stroke at least 6 months prior to enrollment, were 21-85 years old with self-selected walking speeds of 0.3-1.0m/s and could ambulate without the assistance of another person. Of the total 250 participants randomized to the PROWALKS clinical trial, this secondary analysis included the 169 participants randomized into the (1) fast-walking training (FAST, n = 89) or (2) fast-walking training and step activity monitoring combined intervention (FAST + SAM, n = 80). 17 The 81 participants randomized to the step activity monitoring (SAM) intervention were excluded from this analysis as they did not undergo any high-intensity treadmill training. Clinical Evaluation Prior to randomization, all participants underwent a baseline clinical evaluation which included collecting demographic and medical information. Demographic information included age and biological sex, and medical information included time since their most recent stroke (TSS), the Charlson Comorbidity Index (CCI), and the Activities-specific Balance Confidence Scale (ABC). 18 - 21 The previously published protocol outlines all measures collected in the parent trial. 16 Participants completed baseline measures of walking capacity, defined as what a person can do as assessed by standardized tests conducted in structured environments such as a clinic or laboratory. 22 The six-minute walk test (6MWT) and 10-meter walk test (10mWT) are psychometrically strong measures and are recommended to quantify walking capacity in those with chronic stroke. 23 , 24 The 6MWT may represent an individual’s walking endurance and can be influenced by their cardiovascular fitness and/or neuromotor function. 23 , 25 - 27 The 10mWT can capture individuals self-selected (SSWS) and fastest walking speeds (FWS) over a short distance. 23 , 28 Participants were instructed to “ walk at your normal pace ” (SSWS) or “ walk at your fastest pace ” (FWS) on the 10mWT and completed three trials at each speed, with the average of the trials recorded. 16 The primary outcome of this analysis was the change in 6MWT distance. Exercise Intervention Participants randomized to the FAST or FAST+SAM intervention received up to 36, 30-minute sessions of treadmill training over 12 weeks. 15 , 16 The intervention protocol was designed to promote continuous walking at high (70-80% heart rate reserve) cardiovascular intensities. 16 Training physical therapists were free to manipulate treadmill speed to achieve the target heart rate zone. All participants wore a safety harness which did not provide any body weight support. The exercise session was terminated if a participant’s response violated the guidelines set forth by the American College of Sports Medicine (ACSM) for individuals in phase III or IV cardiac rehabilitation. 29 Heart rate was monitored continuously (Polar H10 chest-straps), with heart rate and treadmill speed recorded each minute. Exercise Intensity For this analysis exercise intensity was quantified as the average training speed attained across the entire intervention. Values were averaged across every minute of training from the intervention. Treadmill speeds were converted from miles per hour to meters per second. Statistical Analyses Receiver operating characteristic (ROC) curves are a graphical representation of whether a test or measure can accurately detect if an outcome of interest is attained (e.g., a clinically meaningful change in walking distance ). 30 , 31 If the area under the curve (AUC) of the ROC curve demonstrates appropriate prediction accuracy, Youden’s Index is a summary measure which identifies an optimal cutoff point by identifying the point at which sensitivity and specificity are maximized. 32 , 33 This type of analysis could provide the threshold training speed required for people with chronic stroke to attain a meaningful change in 6MWT distance. Two separate receiver operating characteristic (ROC) curves were used to analyze if walking training speed is predictive of individuals attaining a (1) small or (2) moderate change in the 6MWT. 34 To complete these analyses, a binary outcome was used to classify the response of each individual as either “meeting” or “not meeting” the respective change in 6MWT. There is currently no consensus on the best approach to determining an MCID for medical interventions, including rehabilitation measures. 1 , 35 Therefore, for this analysis, the MCID thresholds were chosen after reviewing the available literature on previously determined MCIDs for a change in 6MWT distance in people with stroke. 6 - 8 To meet a small change in the 6MWT, participants had to have a change of at least 20m, and to meet a moderate change, participants had to have a change of at least 50m. 7 The area under the curve (AUC) metric assessed the accuracy of each ROC curve, with AUC values of > 0.9 indicating “excellent”, 0.8-0.9 indicating “good”, 0.7-0.8 indicating “fair/acceptable”, and 0.7, indicating possible clinical utility, 36 Youden’s Index was used to determine the threshold average training speed required to attain a small or moderate change in 6MWT. 32 Youden’s Index is a summary measure of the ROC curve which identifies an optimal cutoff point for the variable of choice by identifying the point at which sensitivity and specificity are maximized. 32 , 33 Using the same methodology, two additional ROC curves were used to analyze if walking training speed is predictive of individuals with chronic stroke attaining a (1) small or (2) moderate change in FWS (see Supplement). RESULTS One-hundred twenty-nine participants randomized to the FAST ( n = 68) or FAST+SAM ( n = 61) intervention completed both baseline and post-intervention walking capacity measures. Participants were (mean±SD) 63±13 years old, 46% female, were 45±56 months (∼3.75 years) post-stroke ( Table 1 ), and had on average a 44m increase in 6MWT distance ( Table 2 ). Across the intervention, these participants attended 29 training sessions and walked for an average of 28 minutes per session at a training speed of 0.74 m/s ( Table 3 ). There were no differences in baseline characteristics or training fidelity metrics between participants in the FAST or FAST+SAM interventions ( Table 3 , 4). View this table: View inline View popup Download powerpoint Table 1: Participant Characteristics View this table: View inline View popup Download powerpoint Table 2: Changes in Walking Capacity Outcomes View this table: View inline View popup Download powerpoint Table 3: Exercise Dose Parameters Small Change in 6MWT Distance Ninety-one (71.1%) participants had a change of at least 20m in 6MWT distance. Average training speed had poor, non-significant accuracy (AUC [95% CI] = 0.584 [0.475 - 0.693], p = 0.131) of predicting a small change in 6MWT ( Figure 1 ). Download figure Open in new tab Figure 1. 6MWT = Six Minute Walk Test; small change = a pre-to-post change in 6MWT distance of > 20 meters . Moderate Change in 6MWT Distance Forty-nine (38.3%) participants had a change of at least 50m in 6MWT distance. Average training speed had poor, non-significant accuracy (AUC [95% CI] = 0.597 [0.498 - 0.696], p = 0.056) of predicting a moderate change in 6MWT ( Figure 2 ). Download figure Open in new tab Figure 2. 6MWT = Six Minute Walk Test; moderate change = a pre-to-post change in 6MWT distance of > 50 meters . DISCUSSION The average walking training speed during this high intensity walking intervention did not accurately predict which people with chronic stroke would attain a small or moderate clinically meaningful change in 6MWT distance. Since individuals with chronic stroke prioritize walking recovery in rehabilitation, 9 , 37 - 41 and only 38.7% of American physical therapists feel confident prescribing aerobic exercise for this group, 42 this analysis was an important step toward developing a precise exercise prescription to improve walking capacity in people with chronic stroke. Previous studies of walking interventions in people with stroke have explored (1) what parameters of exercise dose are key or “active” ingredients in walking interventions, 43 - 49 and most recently, (2) what metric of exercise intensity is the strongest predictor to achieving changes in walking capacity outcomes. 9 , 11 , 12 Collectively the results of this research have determined that intensity is a key ingredient for walking interventions, and training speed is a significant predictor of changes in walking endurance in people with chronic stroke. 11 , 12 A next step in optimizing exercise dose for clinical application is to decipher the therapeutic dose required to achieve clinically meaningful changes, which was the motivation for this analysis. However, the results found here demonstrate that training speed alone was not sufficient to identify a specific threshold exercise dose needed to achieve meaningful changes in walking capacity outcomes in people with chronic stroke. Training speed during a walking intervention is only one parameter of overall exercise dose. Exercise dose includes multiple parameters including the intervention duration, duration of each session, frequency of sessions, and intensity of training within each session, making dosing within an exercise intervention highly complex. 35 , 36 There is a confluence of evidence pointing to intensity being a key factor in people with chronic stroke achieving changes in their walking capacity following moderate-to-high intensity walking intervention. 9 , 12 , 13 However, the current results suggest one exercise dose parameter may not be sufficient to predict a dichotomized response of a small or moderate change in a walking capacity outcome. Future studies could attempt to determine an exercise dose metric which incorporates multiple parameters. Prior work in our lab, however, has demonstrated how coupling parameters of exercise dose can lead to ambiguity on how the parameters are interacting with one another (McCartney et al., 2025, in press JNPT ). Coupling parameters of exercise dose can reduce the ability to provide guidelines on what dose is occurring. This is because the combined arbitrary dosing metric could be due to a high volume/low intensity combination or a low volume/high intensity. Further work is needed to determine if there is a metric with improved prediction accuracy to determine which individuals with chronic stroke will meet an MCID. While minimal clinically important differences (MCID) can be used to interpret whether a patient achieved a “meaningful change”, it is important to consider (1) the method used to calculate the MCID, and (2) the specific characteristics of the population used to calculate the MCID. 1 , 35 , 50 , 51 As there is no consensus on the best approach to determining an MCID for rehabilitation measures, 1 , 35 the previously defined MCID thresholds for a change in 6MWT distance in people with stroke were thoroughly assessed and are briefly reviewed here. 6 - 8 Perera et al., used 3 different methods to determine initial estimates for a small or moderate change in the 6MWT in 100 individuals with stroke. 7 When assessing the results across these methods, the recommended criterion was 20m for a small meaningful change and 50m for a substantial meaningful change. 7 Tang et al., used a correlation analysis to assess the relationship between the measured change in 6MWT distance and perceived change in walking ability in 22 individuals with chronic stroke. 6 The difference in 6MWT change between the groups was 34.4m [95% CI = 17.2-51.6]. 7 While the Tang et al. study had a much smaller sample size, and less rigorous analytical techniques, the 95% confidence interval is very similar to the recommended MCIDs from Perera et al. Lastly, Fulk & He used two-different anchors to determine MCID values for the 6MWT. 8 However, the ROC curve analysis for both anchors (modified Rankin Scale, Stroke Impact Scale) had poor prediction accuracy (AUC = 0.66, 0.59, respectively). 8 Previous literature has demonstrated how MCIDs can significantly differ based on the analysis used to derive it. 50 Using 17-unique analyses, Franceschini et al., found extreme heterogeneity in the MCIDs calculated, including up to a 14-time difference in the threshold value determined between analyses. 50 This calls into question if MCIDs can reliably account for a “meaningful” change. Furthermore, dichotomizing a previously continuous outcome ( such as a change in 6MWT ) can lead to a substantial loss of information. 52 While this is an unavoidable limitation of ROC analyses, this loss of information in combination with the current uncertainty of MCID thresholds for the 6MWT in people with chronic stroke is likely contributing to the inability to determine the threshold training speed required to accurately predict a small or moderate meaningful change. Conclusions While ROC curves have been useful in other areas of medicine to determine thresholds for diagnosis, exercise training speed had unreliable accuracy for predicting a small or moderate change in 6MWT in people with chronic stroke. There is current ambiguity in determining MCID thresholds for rehabilitation outcomes and uncertainty about whether these thresholds accurately assess a meaningful change in walking endurance in people with chronic stroke. Collectively, these analytical and methodological considerations, paired with the complexity of exercise dosing, make it difficult to determine the threshold exercise dose required to meet a small or moderate change in 6MWT in people with chronic stroke. This area of study is worthy of continued exploration as elucidating the threshold exercise dose required to achieve a meaningful change in walking capacity has the potential to reduce response variability to walking interventions and improve rehabilitation outcomes for people with chronic stroke. Data Availability Deidentified data and relevant documents from the parent clinical trial are available via the NICHD DASH repository (https://dash.nichd.nih.gov/) upon reasonable request approved by the repository. https://dash.nichd.nih.gov/study/425019 Appendix Appendix Additional Analyses Two separate receiver operating characteristic (ROC) curves were used to analyze if exercise training speed is predictive of individuals attaining a (1) small or (2) moderate change in the FWS. 34 A small FWS change was defined as a change of 0.1m/s with a moderate change defined as a change of 0.2m/s. Results Small Change in FWS Seventy-nine (61.2%) participants had a change of at least 0.1m/s in FWS. Average training speed had poor, significant accuracy (AUC [95% CI] = 0.671 [0.577 - 0.765], p < 0.001) of predicting a small change in FWS ( Supplementary Figure 1 ). Moderate Change in FWS Forty-nine (38.3%) participants had a change of at least 0.2m/s in FWS. Average training speed had poor, significant accuracy (AUC [95% CI] = 0.697 [0.605-0.790], p < 0.001) of predicting a moderate change in FWS ( Supplementary Figure 2 ). Download figure Open in new tab Supplemental Figure 1. FWS = Fastest Walking Speed; small change = a pre-to-post change in FWS distance of > 0 . 1 meters/second . Download figure Open in new tab Supplemental Figure 2. FWS = Fastest Walking Speed; moderate change = a pre-to-post change in FWS distance of > 0 . 2 meters/second . Footnotes Funding: This work was supported by the NIH/NICHD under grant 1R01HD086362; NIH/NICHD under grant T32HD007490; NIH NICHD/NCMRR under grant R25HD105583; and partially supported by the Foundation for Physical Therapy Research under a Promotion of Doctoral Studies Award. Data Availability: Deidentified data and relevant documents from the parent clinical trial are available via the NICHD DASH repository ( https://dash.nichd.nih.gov/ ) upon reasonable request approved by the repository. References 1. ↵ Malec JF , Ketchum JM . A Standard Method for Determining the Minimal Clinically Important Difference for Rehabilitation Measures . Arch Phys Med Rehabil . Jun 2020 ; 101 ( 6 ): 1090 – 1094 . doi: 10.1016/j.apmr.2019.12.008 OpenUrl CrossRef PubMed 2. Coon CD , Cook KF . Moving from significance to real-world meaning: methods for interpreting change in clinical outcome assessment scores . Qual Life Res . Jan 2018 ; 27 ( 1 ): 33 – 40 . doi: 10.1007/s11136-017-1616-3 OpenUrl CrossRef PubMed 3. ↵ Jaeschke R , Singer J , Guyatt GH . Measurement of health status. Ascertaining the minimal clinically important difference . Control Clin Trials . Dec 1989 ; 10 ( 4 ): 407 – 15 . doi: 10.1016/0197-2456(89)90005-6 OpenUrl CrossRef PubMed Web of Science 4. ↵ Tsao CW , Aday AW , Almarzooq ZI , et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association . Circulation . Feb 22 2022 ; 145 ( 8 ): e153 – e639 . doi: 10.1161/cir.0000000000001052 OpenUrl CrossRef PubMed 5. ↵ Rudberg AS , Berge E , Laska AC , et al. Stroke survivors’ priorities for research related to life after stroke . Top Stroke Rehabil . Mar 2021 ; 28 ( 2 ): 153 – 158 . doi: 10.1080/10749357.2020.1789829 OpenUrl CrossRef 6. ↵ Tang A , Eng JJ , Rand D. Relationship between perceived and measured changes in walking after stroke . J Neurol Phys Ther . Sep 2012 ; 36 ( 3 ): 115 – 21 . doi: 10.1097/NPT.0b013e318262dbd0 OpenUrl CrossRef PubMed 7. ↵ Perera S , Mody SH , Woodman RC , Studenski SA . Meaningful change and responsiveness in common physical performance measures in older adults . J Am Geriatr Soc . May 2006 ; 54 ( 5 ): 743 – 9 . doi: 10.1111/j.1532-5415.2006.00701.x OpenUrl CrossRef PubMed Web of Science 8. ↵ Fulk GD , He Y. Minimal Clinically Important Difference of the 6-Minute Walk Test in People With Stroke . J Neurol Phys Ther . Oct 2018 ; 42 ( 4 ): 235 – 240 . doi: 10.1097/npt.0000000000000236 OpenUrl CrossRef PubMed 9. ↵ Hornby TG , Reisman DS , Ward IG , et al. Clinical Practice Guideline to Improve Locomotor Function Following Chronic Stroke, Incomplete Spinal Cord Injury, and Brain Injury . J Neurol Phys Ther . Jan 2020 ; 44 ( 1 ): 49 – 100 . doi: 10.1097/npt.0000000000000303 OpenUrl CrossRef PubMed 10. ↵ Boyne P , Miller A , Kubalak O , Mink C , Reisman DS , Fulk G. Moderate to Vigorous Intensity Locomotor Training After Stroke: A Systematic Review and Meta-analysis of Mean Effects and Response Variability . J Neurol Phys Ther . Jan 1 2024 ; 48 ( 1 ): 15 – 26 . doi: 10.1097/npt.0000000000000456 OpenUrl CrossRef 11. ↵ McCartney KM , Boyne P , Pohlig R , Morton S , Reisman D. Quantifying Exercise Intensity to Predict Changes in Walking Capacity in People with Chronic Stroke . medRxiv . 2025 :2025.03.04.25323345. doi: 10.1101/2025.03.04.25323345 OpenUrl Abstract / FREE Full Text 12. ↵ Boyne P , Miller A , Schwab-Farrell SM , et al. Training Parameters and Adaptations that Mediate Walking Capacity Gains from High-Intensity Gait Training Post-Stroke . Med Sci Sports Exerc . Mar 3 2025 ; doi: 10.1249/mss.0000000000003691 OpenUrl CrossRef 13. ↵ Boyne P , Billinger SA , Reisman DS , et al. Optimal Intensity and Duration of Walking Rehabilitation in Patients With Chronic Stroke: A Randomized Clinical Trial . JAMA Neurology . 2023 ; doi: 10.1001/jamaneurol.2023.0033 OpenUrl CrossRef PubMed 14. ↵ Boyne P , Scholl V , Doren S , et al. Locomotor training intensity after stroke: Effects of interval type and mode . Top Stroke Rehabil . Oct 2020 ; 27 ( 7 ): 483 – 493 . doi: 10.1080/10749357.2020.1728953 OpenUrl CrossRef PubMed 15. ↵ Thompson ED , Pohlig RT , McCartney KM , et al. Increasing Activity After Stroke: A Randomized Controlled Trial of High-Intensity Walking and Step Activity Intervention . Stroke . Jan 2024 ; 55 ( 1 ): 5 – 13 . doi: 10.1161/strokeaha.123.044596 OpenUrl CrossRef PubMed 16. ↵ Wright H , Wright T , Pohlig RT , Kasner SE , Raser-Schramm J , Reisman D. Protocol for promoting recovery optimization of walking activity in stroke (PROWALKS): a randomized controlled trial . BMC Neurol. Apr 12 2018 ; 18 ( 1 ): 39 . doi: 10.1186/s12883-018-1044-1 OpenUrl CrossRef PubMed 17. ↵ Thompson ED , Pohlig RT , Hornby TG , et al. Trial of high-intensity gait training and step monitoring for stroke . medRxiv . 2023 :2023.03.11.23287111. doi: 10.1101/2023.03.11.23287111 OpenUrl Abstract / FREE Full Text 18. ↵ D’Hoore W , Sicotte C , Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index . Methods Inf Med . Nov 1993 ; 32 ( 5 ): 382 – 7 . OpenUrl PubMed Web of Science 19. Charlson ME , Pompei P , Ales KL , MacKenzie CR . A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation . Journal of Chronic Diseases . 1987/01/01/ 1987 ; 40 ( 5 ): 373 – 383 . doi: 10.1016/0021-9681(87)90171-8 OpenUrl CrossRef PubMed Web of Science 20. Powell LE , Myers AM . The Activities-specific Balance Confidence (ABC) Scale . J Gerontol A Biol Sci Med Sci . Jan 1995 ; 50a ( 1 ): M28 – 34 . doi: 10.1093/gerona/50a.1.m28 OpenUrl CrossRef 21. ↵ Botner EM , Miller WC , Eng JJ . Measurement properties of the Activities-specific Balance Confidence Scale among individuals with stroke . Disabil Rehabil . Feb 18 2005 ; 27 ( 4 ): 156 – 63 . doi: 10.1080/09638280400008982 OpenUrl CrossRef PubMed Web of Science 22. ↵ Lang CE , Holleran CL , Strube MJ , et al. Improvement in the Capacity for Activity Versus Improvement in Performance of Activity in Daily Life During Outpatient Rehabilitation . Journal of Neurologic Physical Therapy . 2023 ; 47 ( 1 ): 16 – 25 . doi: 10.1097/npt.0000000000000413 OpenUrl CrossRef 23. ↵ Moore JL , Potter K , Blankshain K , Kaplan SL , O’Dwyer LC , Sullivan JE . A Core Set of Outcome Measures for Adults With Neurologic Conditions Undergoing Rehabilitation: A CLINICAL PRACTICE GUIDELINE . Journal of Neurologic Physical Therapy . 2018 ; 42 ( 3 ): 174 – 220 . doi: 10.1097/npt.0000000000000229 OpenUrl CrossRef PubMed 24. ↵ Tyson S , Connell L. The psychometric properties and clinical utility of measures of walking and mobility in neurological conditions: a systematic review . Clin Rehabil . Nov 2009 ; 23 ( 11 ): 1018 – 33 . doi: 10.1177/0269215509339004 OpenUrl CrossRef PubMed 25. ↵ Winstein CJ , Stein J , Arena R , et al. Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association . Stroke . Jun 2016 ; 47 ( 6 ): e98 – e169 . doi: 10.1161/str.0000000000000098 OpenUrl Abstract / FREE Full Text 26. Pohl PS , Duncan PW , Perera S , et al. Influence of stroke-related impairments on performance in 6-minute walk test . J Rehabil Res Dev . Jul -Aug 2002 ; 39 ( 4 ): 439 – 44 . OpenUrl PubMed Web of Science 27. ↵ Peniche PDC , Aguiar LT , Reis M , Oliveira DMG , Scalzo PL , Faria C. The Distance Covered in Field Tests is more Explained by Walking Capacity than by Cardiorespiratory Fitness after Stroke . J Stroke Cerebrovasc Dis . Sep 2021 ; 30 ( 9 ): 105995 . doi: 10.1016/j.jstrokecerebrovasdis.2021.105995 OpenUrl CrossRef PubMed 28. ↵ Sullivan JE , Crowner BE , Kluding PM , et al. Outcome measures for individuals with stroke: process and recommendations from the American Physical Therapy Association neurology section task force . Phys Ther . Oct 2013 ; 93 ( 10 ): 1383 – 96 . doi: 10.2522/ptj.20120492 OpenUrl Abstract / FREE Full Text 29. ↵ Ehrman JK LG , Magal M & Riebe D. ACSM’s Guidelines for Exercise Testing and Prescription . vol 10 th Edition. Wolters Kluwer; 2018 . 30. ↵ Akobeng AK . Understanding diagnostic tests 3: receiver operating characteristic curves . Acta Paediatrica . 2007 ; 96 ( 5 ): 644 – 647 . doi: 10.1111/j.1651-2227.2006.00178.x OpenUrl CrossRef PubMed Web of Science 31. ↵ Çorbacioglu S K , Aksel G. Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value . Turk J Emerg Med . Oct -Dec 2023 ; 23 ( 4 ): 195 – 198 . doi: 10.4103/tjem.tjem_182_23 OpenUrl CrossRef PubMed 32. ↵ Fluss R , Faraggi D , Reiser B. Estimation of the Youden Index and its Associated Cutoff Point . Biometrical Journal . 2005 ; 47 ( 4 ): 458 – 472 . doi: 10.1002/bimj.200410135 OpenUrl CrossRef PubMed Web of Science 33. ↵ Perkins NJ , Schisterman EF . The Youden Index and the Optimal Cut-Point Corrected for Measurement Error . Biometrical Journal . 2005 ; 47 ( 4 ): 428 – 441 . doi: 10.1002/bimj.200410133 OpenUrl CrossRef PubMed Web of Science 34. ↵ Nahm FS . Receiver operating characteristic curve: overview and practical use for clinicians . Korean J Anesthesiol . Feb 2022 ; 75 ( 1 ): 25 – 36 . doi: 10.4097/kja.21209 OpenUrl CrossRef PubMed 35. ↵ Copay AG , Subach BR , Glassman SD , Polly DW , Jr . ., Schuler TC . Understanding the minimum clinically important difference: a review of concepts and methods . Spine J . Sep -Oct 2007 ; 7 ( 5 ): 541 – 6 . doi: 10.1016/j.spinee.2007.01.008 OpenUrl CrossRef PubMed Web of Science 36. ↵ Fischer JE , Bachmann LM , Jaeschke R. A readers’ guide to the interpretation of diagnostic test properties: clinical example of sepsis . Intensive Care Medicine . 2003/07/01 2003 ; 29 ( 7 ): 1043 – 1051 . doi: 10.1007/s00134-003-1761-8 OpenUrl CrossRef PubMed Web of Science 37. ↵ Virani SS , Alonso A , Benjamin EJ , et al. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association . Circulation . Mar 3 2020 ; 141 ( 9 ): e139 – e596 . doi: 10.1161/cir.0000000000000757 OpenUrl CrossRef 38. Bohannon RW , Andrews AW , Smith MB . Rehabilitation goals of patients with hemiplegia . International Journal of Rehabilitation Research . 1988 ; 11 ( 2 ): 181 – 184 . OpenUrl CrossRef Web of Science 39. Lord SE , McPherson K , McNaughton HK , Rochester L , Weatherall M. Community ambulation after stroke: how important and obtainable is it and what measures appear predictive? Arch Phys Med Rehabil . Feb 2004 ; 85 ( 2 ): 234 – 9 . doi: 10.1016/j.apmr.2003.05.002 OpenUrl CrossRef PubMed Web of Science 40. Harris JE , Eng JJ . Goal Priorities Identified through Client-Centred Measurement in Individuals with Chronic Stroke . Physiother Can . Jun 2004 ; 56 ( 3 ): 171 – 176 . doi: 10.2310/6640.2004.00017 OpenUrl CrossRef PubMed 41. ↵ Lang CE , Macdonald JR , Reisman DS , et al. Observation of amounts of movement practice provided during stroke rehabilitation . Arch Phys Med Rehabil . Oct 2009 ; 90 ( 10 ): 1692 – 8 . doi: 10.1016/j.apmr.2009.04.005 OpenUrl CrossRef PubMed Web of Science 42. ↵ Boyne P , Billinger S , MacKay-Lyons M , Barney B , Khoury J , Dunning K. Aerobic Exercise Prescription in Stroke Rehabilitation: A Web-Based Survey of US Physical Therapists . J Neurol Phys Ther . Apr 2017 ; 41 ( 2 ): 119 – 128 . doi: 10.1097/npt.0000000000000177 OpenUrl CrossRef PubMed 43. ↵ Lam JM , Globas C , Cerny J , et al. Predictors of response to treadmill exercise in stroke survivors . Neurorehabil Neural Repair . Jul -Aug 2010 ; 24 ( 6 ): 567 – 74 . doi: 10.1177/1545968310364059 OpenUrl CrossRef PubMed Web of Science 44. Moore JL , Roth EJ , Killian C , Hornby TG . Locomotor training improves daily stepping activity and gait efficiency in individuals poststroke who have reached a “plateau” in recovery . Stroke . Jan 2010 ; 41 ( 1 ): 129 – 35 . doi: 10.1161/strokeaha.109.563247 OpenUrl Abstract / FREE Full Text 45. Ivey FM , Stookey AD , Hafer-Macko CE , Ryan AS , Macko RF . Higher Treadmill Training Intensity to Address Functional Aerobic Impairment after Stroke . J Stroke Cerebrovasc Dis . Nov 2015 ; 24 ( 11 ): 2539 – 46 . doi: 10.1016/j.jstrokecerebrovasdis.2015.07.002 OpenUrl CrossRef PubMed 46. Boyne P , Dunning K , Carl D , Gerson M , Khoury J , Kissela B. High-intensity interval training in stroke rehabilitation . Top Stroke Rehabil . Jul -Aug 2013 ; 20 ( 4 ): 317 – 30 . doi: 10.1310/tsr2004-317 OpenUrl CrossRef PubMed 47. Boyne P , Dunning K , Carl D , Gerson M , Khoury J , Kissela B. Within-session responses to high-intensity interval training in chronic stroke . Med Sci Sports Exerc . Mar 2015 ; 47 ( 3 ): 476 – 84 . doi: 10.1249/mss.0000000000000427 OpenUrl CrossRef PubMed 48. Holleran CL , Rodriguez KS , Echauz A , Leech KA , Hornby TG . Potential Contributions of Training Intensity on Locomotor Performance in Individuals With Chronic Stroke . Journal of Neurologic Physical Therapy . 2015 ; 39 ( 2 ): 95 – 102 . doi: 10.1097/npt.0000000000000077 OpenUrl CrossRef PubMed 49. ↵ Hornby TG , Henderson CE , Plawecki A , et al. Contributions of Stepping Intensity and Variability to Mobility in Individuals Poststroke . Stroke . Sep 2019 ; 50 ( 9 ): 2492 – 2499 . doi: 10.1161/strokeaha.119.026254 OpenUrl CrossRef PubMed 50. ↵ Franceschini M , Boffa A , Pignotti E , Andriolo L , Zaffagnini S , Filardo G. The Minimal Clinically Important Difference Changes Greatly Based on the Different Calculation Methods . Am J Sports Med . Mar 2023 ; 51 ( 4 ): 1067 – 1073 . doi: 10.1177/03635465231152484 OpenUrl CrossRef PubMed 51. ↵ Bloom DA , Kaplan DJ , Mojica E , et al. The Minimal Clinically Important Difference: A Review of Clinical Significance . Am J Sports Med . Feb 2023 ; 51 ( 2 ): 520 – 524 . doi: 10.1177/03635465211053869 OpenUrl CrossRef PubMed 52. ↵ Fedorov V , Mannino F , Zhang R. Consequences of dichotomization . Pharmaceutical Statistics . 2009 ; 8 ( 1 ): 50 – 61 . doi: 10.1002/pst.331 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted April 16, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Using Exercise Intensity to Predict a Minimal Clinically Important Difference in the Six-Minute Walk Test in People with Chronic Stroke Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Using Exercise Intensity to Predict a Minimal Clinically Important Difference in the Six-Minute Walk Test in People with Chronic Stroke Kiersten M. McCartney , Pierce Boyne , Ryan T. Pohlig , Susanne M. Morton , Darcy S. Reisman medRxiv 2025.04.15.25325882; doi: https://doi.org/10.1101/2025.04.15.25325882 Share This Article: Copy Citation Tools Using Exercise Intensity to Predict a Minimal Clinically Important Difference in the Six-Minute Walk Test in People with Chronic Stroke Kiersten M. McCartney , Pierce Boyne , Ryan T. Pohlig , Susanne M. Morton , Darcy S. Reisman medRxiv 2025.04.15.25325882; doi: https://doi.org/10.1101/2025.04.15.25325882 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Rehabilitation Medicine and Physical Therapy Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15227) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6597) Geriatric Medicine (668) Health Economics (997) Health Informatics (4534) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0034c1dcfc18650',t:'MTc3OTUzMTMwNA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();