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.
Major depressive disorder (MDD) is a leading cause of disability worldwide [1] and is associated with substantial adverse outcomes, including premature mortality [2], functional impairment [3], increased medical comorbidity [4], and suicide [5]. Given its substantial individual and societal burden [1], early identification of individuals in the prodromal or initial stages of MDD is critically important, as preventive strategies [6] and early interventions [7] may be most effective during this period. However, symptom onset frequently precedes clinical recognition and formal diagnosis in routine clinical settings [8,9], reflecting prolonged durations of untreated illness in MDD.
Low physical activity (PA) has been identified as a risk factor for MDD in prospective cohort studies and meta-analyses [10][11][12]. Longitudinal evidence further suggests that reductions in PA may precede worsening depressive symptoms [13], supporting the possibility that declines in PA can occur before the clinical diagnosis of MDD. However, much of the existing evidence relies on self-reported PA, which is subject to recall bias [11], or on accelerometerbased assessments conducted over short durations or at infrequent follow-up time points [14].
Consequently, current studies provide limited insight into the timing and pattern of PA changes preceding a clinical MDD diagnosis, including when PA deviates from an individual’s prior activity level and whether these changes differ across population subgroups.
Addressing these questions requires longitudinal, objective measures of PA collected in realworld settings and reliably linked to clinical diagnostic information, which allow behavioral change to be characterized prior to diagnosis as illness unfolds in routine care.
Advances in sensor technology and the widespread adoption of consumer wearable devices have enabled passive, continuous monitoring of real-world PA at scale, with devices such as Fitbit trackers capturing daily step counts and activity intensity-key correlates of functioning and psychomotor activity [15]-over extended periods with minimal user burden [16,17].
Recent mobile health studies have demonstrated negative associations between wearablemeasured PA and depression severity [18][19][20], suggesting that such measures may complement symptom-based assessments by informing risk stratification and prompting earlier clinical evaluation. Nevertheless, prior mobile health studies have been limited by modest sample sizes, relatively short monitoring durations, or the absence of linkage to clinical diagnoses [18], constraining their ability to characterize behavioral changes in relation to clinical diagnosis timing.
Few large-scale datasets integrate long-term wearable data with electronic health records (EHRs) in a manner that enables examination of pre-diagnostic PA trajectories and subgroup heterogeneity. The All of Us Research Program (AoURP) provides such an integrated resource, comprising a large, ongoing national cohort in which participants consent to share EHRs, health surveys, biospecimens, physical measurements, and wearable data [21]. By linking historical and prospective Fitbit data with EHR-based diagnoses, this study enables examination of long-term PA trajectories preceding clinical MDD diagnosis. Here, we aimed to determine whether PA trajectories differ between individuals with incident MDD and matched controls during the 12 months preceding diagnosis. Among individuals who developed MDD, we further examined when PA begins to deviate from earlier levels and whether the pattern of pre-diagnostic changes varies across population subgroups.
We used data from the AoURP, an ongoing national longitudinal cohort funded by the US National Institutes of Health [21], with the long-term goal of enrolling at least 1 million participants. The study design and data collection procedures have been described previously [21,22].
The present analysis used the controlled tier dataset, version 8 (C2024Q3R8), including participants enrolled between May 2017 and October 2023. Participant demographics and baseline data were collected during the digital enrollment. For participants who consented to share EHR and Fitbit data, their historical (pre-enrollment) EHR data and Fitbit data were made available through their participating health care provider organizations and linked Google Fitbit accounts, respectively [21][22][23]. In this data version, 36,614 individuals had linked EHR and Fitbit data available.
This study involved a secondary analysis of deidentified data obtained from the AoURP and therefore did not require additional ethics review. Access to deidentified data was restricted to authorized study investigators who completed required All of Us Responsible Conduct of Research training, and all analyses were conducted within the secure, cloud-based Researcher Workbench environment. In accordance with the AoURP Data and Statistics Dissemination Policy, analytic r
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