Quantifying At-Home Physiotherapy Participation: SPARS vs Self-Reported Diaries

IF 2 Q2 REHABILITATION
Matthew Rezkalla BSc , Philip Boyer PhD , David Burns MD, PhD , Cristian Renteria PT, MPIA , Cari Whyne PhD
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引用次数: 0

Abstract

The completion of at-home physiotherapy exercise is key to many rehabilitation protocols. This study compares at-home upper extremity physiotherapy participation as measured based on data captured with a smart watch to that recorded in self-report diaries. Daily at-home exercise participation (sessions) was recorded for 53 patients with rotator cuff pathology during their first 2 weeks of a 12-week physiotherapy rehabilitation program. Exercise participation was measured using a physical therapy monitoring system that uses smart watch (accelerometer/gyroscope) data analyzed via a convolutional neural network trained on labeled patient-specific in-clinic data and compared to patient reported diaries. A high level of agreement between diary exercise participation and the measurements derived from the smart watch data (ICC=0.72, n=53) was found, with an AUROC=0.99 for binary identification of exercise periods on labeled clinic data. However, overall patient diaries reported more exercise performed (0.96 additional days on average) than measured by the ML algorithm. ML and accelerometer/gyroscope data collected by embedded sensors in a smartwatch represents an accurate and objective alternative to self-reported diaries for monitoring patient at-home participation. Lower levels recorded by the ML algorithm may indicate some limitations in the technology to fully capture participation or potential over-reporting of participation within diaries. As self-reported diary completion decreases over time, physical therapy monitoring technology may represent an acceptable method for longer term assessment of exercise participation.
量化家庭物理治疗参与:SPARS与自我报告日记
完成家庭物理治疗运动是许多康复方案的关键。这项研究比较了家庭上肢物理治疗的参与情况,这是基于智能手表捕获的数据和记录在自我报告日记中的数据。在为期12周的物理治疗康复计划的前2周,记录了53名患有肩袖病变的患者每天在家锻炼的情况。通过物理治疗监测系统测量运动参与情况,该系统使用智能手表(加速度计/陀螺仪)数据,通过卷积神经网络对标记的患者特异性临床数据进行训练,并与患者报告的日记进行比较。日记运动参与与智能手表数据得出的测量结果高度一致(ICC=0.72, n=53),标记临床数据的运动周期二元识别AUROC=0.99。然而,与ML算法相比,总体患者日记报告了更多的锻炼(平均额外0.96天)。智能手表中的嵌入式传感器收集的ML和加速度计/陀螺仪数据代表了一种准确和客观的替代自我报告的日记,用于监测患者在家的参与情况。ML算法记录的较低水平可能表明该技术在完全捕获参与或潜在的日记中过度报告参与方面存在一些局限性。由于自我报告的日记完成程度随着时间的推移而下降,物理治疗监测技术可能是一种可接受的长期评估运动参与的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
0.00%
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审稿时长
8 weeks
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