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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., Competing Interests: M.K. has previously received payment for internships carried out at Novartis, and received payment from Evidation Health for the internship during which this work was carried out. N.M. is currently an employee of and holds stock options in Evidation Health. J.G. was previously an employee of Novartis. L.F. is a cofounder and employee of Evidation Health. E.R. is currently an employee of and holds stock options in Evidation Health. I.C. was previously an employee of Novartis and is currently an employee of and holds stock options in Evidation Health. He has received payment for lecturing on Digital Health at ETH Zurich and FHNW Muttenz. He is an Editorial Board Member at Karger Digital Biomarkers and a founding member of the Digital Medicine Society., (Copyright © 2020 by S. Karger AG, Basel.)

We conducted a survey about recent surgical procedures on a large connected population and requested each individual's permission to access data from commercial wearable devices they may have been wearing around the time of the procedure. For subcohorts of 66-118 patients who reported having a weight loss procedure and who had dense Fitbit data around their procedure date, we examined several daily measures of behavior and physiology in the 12 weeks leading up to and the 12 weeks following their procedures. We found that the weeks following weight loss operations were associated with fewer daily total steps, smaller proportions of the day spent walking, lower resting and 95th percentile heart rates, more total sleep time, and greater sleep efficiency. We demonstrate that consumer-grade activity trackers can capture behavioral and physiological changes resulting from weight loss surgery and these devices have the potential to be used to develop measures of patients' postoperative recovery that are convenient, sensitive, scalable, individualized, and continuous., Competing Interests: E.R., N.M., and B.B. are employees of Evidation Health; L.F. is a cofounder and employee of Evidation Health., (Copyright © 2020 by S. Karger AG, Basel.)