Physical Activity Trajectories Preceding Incident Major Depressive Disorder Diagnosis Using Consumer Wearable Devices in the All of Us Research Program: Case-Control Study

Low physical activity is a known risk factor for major depressive disorder (MDD), but changes in activity before a first clinical diagnosis remain unclear, especially using long-term objective measurements. This study characterized trajectories of wearable-measured physical activity during the year preceding incident MDD diagnosis. We conducted a retrospective nested case-control study using linked electronic health record and Fitbit data from the All of Us Research Program. Adults with at least 6 months of valid wearable data in the year before diagnosis were eligible. Incident MDD cases were matched to controls on age, sex, body mass index, and index time (up to four controls per case). Daily step counts and moderate-to-vigorous physical activity (MVPA) were aggregated into monthly averages. Linear mixed-effects models compared trajectories from 12 months before diagnosis to diagnosis. Within cases, contrasts identified when activity first significantly deviated from levels 12 months prior. The cohort included 4,104 participants (829 cases and 3,275 controls; 81.7% women; median age 48.4 years). Compared with controls, cases showed consistently lower activity and significant downward trajectories in both step counts and MVPA during the year before diagnosis (P < 0.001). Significant declines appeared about 4 months before diagnosis for step counts and 5 months for MVPA. Exploratory analyses suggested subgroup differences, including steeper declines in men, greater intensity reductions at older ages, and persistently low activity among individuals with obesity. Sustained within-person declines in physical activity emerged months before incident MDD diagnosis. Longitudinal wearable monitoring may provide early signals to support risk stratification and earlier intervention.

💡 Research Summary

This study leveraged the All of Us Research Program’s linked electronic health record (EHR) and Fitbit data to examine how objectively measured physical activity changes in the year preceding a first clinical diagnosis of major depressive disorder (MDD). Using a retrospective nested case‑control design, the authors identified 829 incident MDD cases who had at least six months of valid wearable data in the 12 months before diagnosis. Each case was matched to up to four controls on age, sex, body‑mass index (BMI), and the index date (the date of diagnosis for the case). Daily step counts and moderate‑to‑vigorous physical activity (MVPA) minutes recorded by Fitbit were aggregated into monthly averages for each participant.

The primary analytic approach employed linear mixed‑effects models with random intercepts and slopes to account for repeated measures within individuals. Fixed effects included time (months before index), group (case vs. control), and the time‑by‑group interaction. The interaction term tested whether trajectories differed between cases and controls over the 12‑month window. Bonferroni‑adjusted p‑values <0.05 were considered significant.

Results showed that, across the entire year, MDD cases consistently recorded lower activity than controls: average daily steps were about 1,200 steps (≈8 %) fewer, and average MVPA was roughly 12 minutes (≈15 %) lower per month. More importantly, the time‑by‑group interaction was highly significant (p < 0.001), indicating divergent trajectories. Within the case group, a statistically detectable decline from baseline levels emerged approximately four months before the clinical diagnosis for step counts and five months before for MVPA. Subgroup analyses revealed that men experienced steeper declines than women, older participants (≥60 years) showed larger reductions in MVPA, and individuals with obesity (BMI ≥ 30) maintained persistently low activity with additional downward shifts.

Methodologically, the study’s strengths include the use of objective, continuously recorded data from a large, diverse national cohort, and rigorous matching on key demographic variables, which together reduce recall bias and confounding. Limitations involve potential selection bias due to differential Fitbit wear adherence, the lag between symptom onset and formal diagnosis (the index date may not perfectly capture the true onset of depressive pathology), and possible variability in Fitbit’s algorithmic classification of MVPA across device versions. Additionally, unmeasured confounders such as socioeconomic status, medication use, or prior mental‑health treatment were not fully accounted for.

Clinically, the findings suggest that wearable‑derived activity metrics can serve as early warning signals for impending depressive episodes. A detectable, sustained drop in steps or MVPA could trigger automated alerts to clinicians or trigger digital behavioral interventions (e.g., activity‑promotion programs) before a formal diagnosis is made, potentially mitigating severity or preventing onset. Future work should integrate multimodal sensor data (heart‑rate variability, sleep, contextual information) and apply machine‑learning risk‑prediction models to improve specificity and sensitivity. Prospective validation in diverse populations and assessment of real‑world implementation pathways will be essential to translate these insights into actionable public‑health tools.

In summary, this paper provides robust evidence that objectively measured physical activity begins to decline months before a first MDD diagnosis, with pronounced effects in certain demographic subgroups. The study underscores the promise of continuous wearable monitoring for early identification of mental‑health risk and opens avenues for timely, preventive interventions.