Holly Wilson-Jene, Rachel Cowan, Rachel Post, Jon Pearlman
{"title":"Drum rolling resistance consistently predicts SmartWheel rolling resistance and resultant force for manual wheelchair wheels and casters.","authors":"Holly Wilson-Jene, Rachel Cowan, Rachel Post, Jon Pearlman","doi":"10.1177/20556683251374919","DOIUrl":null,"url":null,"abstract":"<p><p><b>Introduction:</b> Minimizing repetitive strain (RS) is a key recommendation from clinical practice guidelines for preservation of upper limb. Propulsion force, which is required to overcome wheel rolling resistance (RR), is a major source of RS. A drum-based RR test method has been developed but has not been directly validated against propulsion forces. A previous validation study compared Drum versus treadmill RR, with excellent consistency (intraclass correlation coefficient (ICC) = 0.94) and accuracy. <b>Methods:</b> Drum RR system estimates (N = 192) were compared to SmartWheel RR measurements and resultant force (Fres) for multiple wheelchair setups and surfaces. We hypothesized that Drum RR will consistently predict SmartWheel RR and Fres based on ICC and accuracy based on Bland Altman limits of agreement (LOA) and coefficient of determination (R<sup>2</sup>). <b>Results:</b> RR ICC = 0.966, 95%CI [0.955-0.975], mean difference between methods (4.2 N), and LOA [+/-5.5 N], which varied by surface. Drum RR explained 88% (R<sup>2</sup>) of SmartWheel RR variability. Drum RR prediction of Fres ICC = 0.83, 95%CI [0.77-0.87], explaining 60% of variability. <b>Conclusions:</b> Drum RR consistently predicts SmartWheel RR with excellent reliability and reasonable accuracy, and predicts Fres with good reliability and reasonable accuracy, reinforcing the validity of Drum RR for predicting system-level RR, and use guiding wheel selection to reduce RS.</p>","PeriodicalId":43319,"journal":{"name":"Journal of Rehabilitation and Assistive Technologies Engineering","volume":"12 ","pages":"20556683251374919"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409041/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Rehabilitation and Assistive Technologies Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/20556683251374919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Minimizing repetitive strain (RS) is a key recommendation from clinical practice guidelines for preservation of upper limb. Propulsion force, which is required to overcome wheel rolling resistance (RR), is a major source of RS. A drum-based RR test method has been developed but has not been directly validated against propulsion forces. A previous validation study compared Drum versus treadmill RR, with excellent consistency (intraclass correlation coefficient (ICC) = 0.94) and accuracy. Methods: Drum RR system estimates (N = 192) were compared to SmartWheel RR measurements and resultant force (Fres) for multiple wheelchair setups and surfaces. We hypothesized that Drum RR will consistently predict SmartWheel RR and Fres based on ICC and accuracy based on Bland Altman limits of agreement (LOA) and coefficient of determination (R2). Results: RR ICC = 0.966, 95%CI [0.955-0.975], mean difference between methods (4.2 N), and LOA [+/-5.5 N], which varied by surface. Drum RR explained 88% (R2) of SmartWheel RR variability. Drum RR prediction of Fres ICC = 0.83, 95%CI [0.77-0.87], explaining 60% of variability. Conclusions: Drum RR consistently predicts SmartWheel RR with excellent reliability and reasonable accuracy, and predicts Fres with good reliability and reasonable accuracy, reinforcing the validity of Drum RR for predicting system-level RR, and use guiding wheel selection to reduce RS.