Academic Health Center, School of Nursing, University of Minnesota, Minneapolis (Dr Mays); Syneos Health, Milliken, Colorado (Mr Wesselman); CPC Clinical Research, Aurora, Colorado (Ms White, Ms Greenwalt, and Dr Hiatt); Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Heart and Vascular Center, Lebanon, New Hampshire (Dr Creager); BetaGlue Technologies SpA, Milan, Italy (Dr Amato); and Department of Medicine, Division of Cardiology, School of Medicine, University of Colorado, Aurora (Dr Hiatt).
J Cardiopulm Rehabil Prev. 2021 May 1;41(3):176-181. doi: 10.1097/HCR.0000000000000553.
Monitoring home exercise using accelerometry in patients with peripheral artery disease (PAD) may provide a tool to improve adherence and titration of the exercise prescription. However, methods for unbiased analysis of accelerometer data are lacking. The aim of the current post hoc analysis was to develop an automated method to analyze accelerometry output collected during home-based exercise.
Data were obtained from 54 patients with PAD enrolled in a clinical trial that included a home-based exercise intervention using diaries and an accelerometer. Peak walking time was assessed on a graded treadmill at baseline and 6 mo. In 35 randomly selected patient data sets, visual inspection of accelerometer output confirmed exercise sessions throughout the 6 mo. An algorithm was developed to detect exercise sessions and then compared with visual inspection of sessions to mitigate the heterogeneity in session intensity across the population. Identified exercise sessions were characterized on the basis of total step count and activity duration. The methodology was then applied to data sets for all 54 patients.
The ability of the algorithm to detect exercise sessions compared with visual inspection of the accelerometer output resulted in a sensitivity of 85% and specificity of 90%. Algorithm-detected exercise sessions (total) and intensity (steps/wk) were correlated with change in peak walking time (r = 0.28; r = 0.43).
An algorithm to assess data from an accelerometer successfully detected home-based exercise sessions. Algorithm-identified exercise sessions were correlated with improvements in performance after 6 mo of training in patients with PAD, supporting the effectiveness of monitored home-based exercise.
在患有外周动脉疾病（PAD）的患者中使用加速度计监测家庭运动，可能为提高运动处方的依从性和调整提供工具。然而，目前缺乏对加速度计数据进行无偏分析的方法。本事后分析的目的是开发一种自动分析家庭运动期间收集的加速度计数据的方法。
数据来自参加了一项包括基于家庭的运动干预的临床试验的 54 名 PAD 患者，该试验使用日记和加速度计。在基线和 6 个月时进行了分级跑步机上的最大行走时间评估。在 35 个随机选择的患者数据集，对加速度计输出的视觉检查确认了整个 6 个月的运动课程。开发了一种算法来检测运动课程，然后与课程的视觉检查进行比较，以减轻整个人群中课程强度的异质性。根据总步数和活动持续时间来描述确定的运动课程。然后将该方法应用于所有 54 名患者的数据集中。
与加速度计输出的视觉检查相比，算法检测运动课程的能力导致灵敏度为 85%，特异性为 90%。算法检测到的运动课程（总）和强度（每周步数）与最大行走时间的变化相关（r=0.28；r=0.43）。
评估加速度计数据的算法成功检测了家庭运动课程。经过 6 个月的训练后，算法确定的运动课程与 PAD 患者的表现改善相关，支持了监测家庭运动的有效性。
