Exercise Physiology, School of Exercise and Nutritional Sciences, San Diego State University, ENS Building 351, 5500, Campanile Drive, 92182-7251 San Diego, CA, USA.
School of Exercise and Nutritional Sciences, San Diego State University, ENS Building 351, 5500, Campanile Drive, 92182-7251 San Diego, CA, USA.
Ann Phys Rehabil Med. 2021 Jan;64(1):101382. doi: 10.1016/j.rehab.2020.03.007. Epub 2020 May 4.
Individuals with disabilities have high prevalence of sedentary lifestyle, obesity, and cardiometabolic disease. Physical activity monitors (i.e., step counters) are ill-suited for tracking wheelchair pushes. The study purpose was to investigate the validity of a consumer-level fitness tracker (Apple Watch) designed for wheelchair users.
Validation study. A total of 15 wheelchair users with disabilities and 15 able-bodied individuals completed 3-min bouts of wheelchair propulsion on a treadmill and arm ergometry at pre-determined cadences as well as overground obstacle and Figure 8 courses. Tracker stroke counts were compared against direct observation.
We found no interaction of tracker counts and ability status across all tasks (P≥0.550), so results are presented for the combined sample. For treadmill tasks, Bland-Altman analysis (bias±limits of agreement) showed good agreement for only higher-rate fixed-frequency tasks (-15±48, -1±14, 0±5, and 0±27 for low, moderate, high, and variable cadence, respectively). Mean absolute percentage error (MAPE) was 22%, 3%, 1%, and 6%, respectively. Intraclass correlation coefficients (ICCs) (95% confidence intervals) were -0.18 (-0.51-0.20), 0.47 (0.13-0.71), 0.98 (0.96-0.99), and 0.22 (-0.16-0.54). We found significant overestimation by the tracker at low frequency (P<0.01). Arm ergometry showed good agreement across all cadences (0±5, -1±3, 0±8, 6±6). MAPE was 1%, 1%, 1%, and 4%. ICCs were 0.88 (0.77-0.94), 0.95 (0.89-0.97), 0.88 (0.76-0.94), and 0.97 (0.87-0.97). We found minimal (2rpm) but significant differences at variable cadence (P<0.01). Overground tasks showed poor agreement for casual-pace and fast-pace obstacle course and Figure 8 task (-5±18, 0±23, and -18±32, respectively). MAPE was 15%, 18%, 21% and ICCs were 0.90 (0.79-0.95), 0.79 (0.59-0.90), and 0.82 (0.64-0.91). Significant differences were found for propulsion at casual pace (P<0.01) and the Figure 8 task (P<0.01).
Apple Watch is suitable for tracking high-frequency standardized (i.e., treadmill) pushing and arm ergometry but not low-frequency pushing or overground tasks.
残疾个体中存在较高比例的久坐生活方式、肥胖和心血管代谢疾病。身体活动监测器（例如计步器）不适合跟踪轮椅推动。本研究旨在调查专为轮椅使用者设计的消费级健身追踪器（Apple Watch）的有效性。
验证研究。共有 15 名残疾轮椅使用者和 15 名身体健全者在跑步机和手臂测力计上以预定的步频进行 3 分钟的轮椅推进，以及在地面障碍物和 8 字形课程上进行。将追踪器的划数与直接观察进行比较。
我们没有发现所有任务中追踪器计数和能力状态之间的交互作用（P≥0.550），因此结果适用于合并样本。对于跑步机任务，Bland-Altman 分析（偏差±协议范围）仅显示高频率固定频率任务的良好一致性（低、中、高和可变步频时分别为-15±48、-1±14、0±5 和 0±27）。平均绝对百分比误差（MAPE）分别为 22%、3%、1%和 6%。组内相关系数（ICC）（95%置信区间）分别为-0.18（-0.51-0.20）、0.47（0.13-0.71）、0.98（0.96-0.99）和 0.22（-0.16-0.54）。我们发现追踪器在低频时存在显著的高估（P<0.01）。手臂测力计在所有步频下均显示出良好的一致性（0±5、-1±3、0±8、6±6）。MAPE 分别为 1%、1%、1%和 4%。ICC 分别为 0.88（0.77-0.94）、0.95（0.89-0.97）、0.88（0.76-0.94）和 0.97（0.87-0.97）。我们发现变量步频时存在最小（2rpm）但显著的差异（P<0.01）。地面任务在休闲和快速障碍物课程和 8 字形任务中显示出较差的一致性（分别为-5±18、0±23 和-18±32）。MAPE 分别为 15%、18%、21%，ICC 分别为 0.90（0.79-0.95）、0.79（0.59-0.90）和 0.82（0.64-0.91）。在休闲步速推进（P<0.01）和 8 字形任务（P<0.01）中发现了显著差异。
Apple Watch 适用于跟踪高频标准化（即跑步机）推动和手臂测力计运动，但不适用于低频推动或地面任务。
