Preoperative Decline and Postoperative Recovery of Wearable-Derived Physical Activity Over a Four-Year Perioperative Period in Total Knee and Hip Arthroplasty: Evidence from the All of Us Research Program

๐Ÿ“… 2026-03-05
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This study addresses the lack of systematic characterization of longitudinal physical activity trajectories in patients undergoing total knee (TKA) and total hip arthroplasty (THA). Leveraging the โ€œAll of Usโ€ program, we integrated electronic health records with Fitbit step-count data and applied piecewise linear mixed-effects models to analyze activity changes over a four-year window. Kaplanโ€“Meier and Cox regression models were employed to assess recovery timelines and associated factors. Using long-term wearable data, we reveal a preoperative progressive functional decline and a three-phase postoperative recovery pattern. The median times to recover to recent and distant preoperative baseline activity levels were 13 and 22 weeks, respectively. Higher preoperative activity levels were strongly associated with greater likelihood of returning to habitual activity, underscoring the critical role of preoperative functional reserve.

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๐Ÿ“ Abstract
Total knee arthroplasty (TKA) and total hip arthroplasty (THA) improve symptoms in end-stage osteoarthritis, yet long-term objective characterization of perioperative physical activity trajectories remains limited. We conducted a longitudinal observational study within the All of Us Research Program dataset, linking electronic health records with continuous Fitbit-derived step count data over a four-year perioperative window (two years before and two years after arthroplasty). Piecewise linear mixed-effects models characterized preoperative declines and postoperative recovery trajectories, and time-to-recovery was evaluated using Kaplan-Meier curves and Cox proportional hazards models under remote and immediate preoperative physical activity baseline definitions. Among 238 participants (147 TKA; 91 THA), both procedures exhibited progressive preoperative decline with distinct procedure-specific patterns and staged postoperative recovery: rapid improvement during weeks 1-6, decelerating gains through weeks 7-19/20, and subsequent stabilization through week 104. Recovery to remote and immediate baselines differed in timing (median 22 vs 13 weeks) and associated predictors. Higher immediate preoperative activity was associated with greater likelihood of recovery to habitual activity levels, underscoring the relevance of preoperative functional reserve and surgical timing. These findings demonstrate the potential of long-term wearable monitoring to refine assessment of functional outcomes, guide recovery expectations, and support perioperative management.
Problem

Research questions and friction points this paper is trying to address.

total knee arthroplasty
total hip arthroplasty
physical activity
perioperative period
wearable monitoring
Innovation

Methods, ideas, or system contributions that make the work stand out.

wearable monitoring
perioperative physical activity trajectories
piecewise linear mixed-effects models
functional recovery
preoperative functional reserve
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Yuezhou Zhang
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
A
Amos Folarin
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Institute of Health Informatics, University College London, London, United Kingdom; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom; Health Data Research UK, University College London, L
C
Callum Stewart
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
Hyunju Kim
Hyunju Kim
Information Science, Cornell University
HCI
R
Rongrong Zhong
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
S
Shaoxiong Sun
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
R
Richard JB Dobson
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Institute of Health Informatics, University College London, London, United Kingdom; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom; Health Data Research UK, University College London, L