ArticleCommunications medicine2026
Association of chronic disease risk and physical activity measured by wearable devices in the All of Us program.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- Digital health solutions for chronic disease physical activity management: wearable devices, artificial intelligence, and public health implementation.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPhysical activity plays an important role in preventing chronic diseases, but most studies rely on self-reported or short-term data that fail to capture habitual behavior. This study utilizes Fitbit data to investigate the relationship between physical activity and various chronic diseases.
methodsWe analyzed data from 22,019 participants in the All of Us Research Program who shared at least six months of Fitbit activity data linked with electronic health records. Various physical activity patterns were evaluated using Cox proportional hazards and logistic regression models, adjusting for age, sex, and body mass index (BMI). To test robustness, sensitivity analyses were conducted using obesity defined by BMI, applying a two-year exclusion window for outcome diagnoses to mitigate potential reverse causation, and incorporating lifestyle covariates (smoking and alcohol use) under a simplified directed acyclic graph (DAG) framework to address residual confounding.
resultsHere, we show that higher physical activity levels are associated with lower risks of multiple chronic conditions. Higher daily step counts were negatively associated with obesity and type 2 diabetes, while greater elevation gains and longer vigorous activity are associated with lower risks of conditions such as morbid obesity, obstructive sleep apnea, and major depressive disorder. All sensitivity analyses yield consistent results, supporting the robustness of findings against reverse causation and lifestyle confounding.
conclusionsHigher physical activity and lower sedentary time may help prevent diverse chronic diseases. These findings demonstrate the potential of large-scale wearable data to inform personalized prevention and population health strategies.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.