Introduction: A major challenge in the monitoring of rehabilitation is the lack of long-term individual baseline data which would enable accurate and objective assessment of functional recovery. Consumer-grade wearable devices enable the tracking of individual everyday functioning prior to illness or other medical events which necessitate the monitoring of recovery trajectories. Methods: For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device data of steps, heart rate, and sleep from 26 weeks before to 26 weeks after the self-reported surgery date. We identified subgroups of individuals who self-reported surgeries for bone fracture repair (n = 355), tendon or ligament repair/reconstruction (n = 773), and knee or hip joint replacement (n = 196). We used linear mixed models to estimate the average effect of time relative to surgery on daily activity measurements while adjusting for gender, age, and the participant-specific activity baseline. We used a sub-cohort of 127 individuals with dense wearable data who underwent tendon/ligament surgery and employed XGBoost to predict the self-reported recovery time. Results: The 1,324 study individuals were all US residents, predominantly female (84%), white or Caucasian (85%), and young to middle-aged (mean age 36.2 years). We showed that 12 weeks pre- and 26 weeks post-surgery trajectories of daily behavioral measurements (steps sum, heart rate, sleep efficiency score) can capture activity changes relative to an individual’s baseline. We demonstrated that the trajectories differ across surgery types, recapitulate the documented effect of age on functional recovery, and highlight differences in relative activity change across self-reported recovery time groups. Finally, using a sub-cohort of 127 individuals, we showed that long-term recovery can be accurately predicted, on an individual level, only 1 month after surgery (AUROC 0.734, AUPRC 0.8). Furthermore, we showed that predictions are most accurate when long-term, individual baseline data are available. Discussion: Leveraging long-term, passively collected wearable data promises to enable relative assessment of individual recovery and is a first step towards data-driven intervention for individuals.

中文翻译：

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使用消费者可穿戴设备预测下肢手术的主观恢复

简介：康复监测的一个主要挑战是缺乏能够准确和客观地评估功能恢复的长期个人基线数据。消费级可穿戴设备能够在疾病或其他需要监测恢复轨迹的医疗事件之前跟踪个人日常功能。方法：对于 1,324 名接受下肢手术的个体，我们收集了他们在自我报告的手术日期前 26 周至术后 26 周的步数、心率和睡眠的 Fitbit 设备数据。我们确定了自我报告的骨折修复手术（n = 355）、肌腱或韧带修复/重建（n = 773）以及膝关节或髋关节置换术（n = 196）的个体亚组。我们使用线性混合模型来估计与手术相关的时间对日常活动测量的平均影响，同时调整性别、年龄和参与者特定的活动基线。我们使用了 127 名具有密集可穿戴数据的人的子队列，这些人接受了肌腱/韧带手术，并使用 XGBoost 来预测自我报告的恢复时间。结果：1,324 名研究人员均为美国居民，主要是女性 (84%)、白人或高加索人 (85%)，以及年轻至中年人（平均年龄 36.2 岁）。我们展示了手术前 12 周和手术后 26 周的日常行为测量轨迹（步数总和、心率、睡眠效率评分）可以捕捉相对于个体基线的活动变化。我们证明了不同手术类型的轨迹不同，概括记录的年龄对功能恢复的影响，并强调自我报告的恢复时间组之间相对活动变化的差异。最后，使用 127 人的亚组，我们表明，在个体水平上，手术后仅 1 个月就可以准确预测长期恢复（AUROC 0.734，AUPRC 0.8）。此外，我们表明，当长期的个人基线数据可用时，预测是最准确的。讨论：利用长期、被动收集的可穿戴数据有望实现对个人康复的相对评估，并且是迈向个人数据驱动干预的第一步。我们表明，在个体水平上，手术后仅 1 个月就可以准确预测长期恢复（AUROC 0.734，AUPRC 0.8）。此外，我们表明，当长期的个人基线数据可用时，预测是最准确的。讨论：利用长期、被动收集的可穿戴数据有望实现对个人康复的相对评估，并且是迈向个人数据驱动干预的第一步。我们表明，在个体水平上，手术后仅 1 个月就可以准确预测长期恢复（AUROC 0.734，AUPRC 0.8）。此外，我们表明，当长期的个人基线数据可用时，预测是最准确的。讨论：利用长期、被动收集的可穿戴数据有望实现对个人康复的相对评估，并且是迈向个人数据驱动干预的第一步。