Evidence map›Paper›PMID 41535454›Full record

ArticleCommunications medicine2026

Association of chronic disease risk and physical activity measured by wearable devices in the All of Us program.

Yu Hou, Erjia Cui, Kelvin Lim, Lisa S Chow, Michael Howell, Sayeed Ikramuddin, Rui Zhang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yu HouDivision of Computational Health Sciences, University of Minnesota, Minneapolis, MN, USA.ORCID http://orcid.org/0009-0009-7184-6592
Erjia CuiDivision of Biostatistics & Health Data Science, University of Minnesota, Minneapolis, MN, USA.
Kelvin LimDepartment of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0002-2390-7268
Lisa S ChowDivision of Diabetes, Endocrinology and Metabolism, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0002-6210-2307
Michael HowellDepartment of Neurology, University of Minnesota, Minneapolis, MN, USA.
Sayeed IkramuddinDepartment of Surgery, University of Minnesota, Minneapolis, MN, USA.
Rui ZhangDivision of Computational Health Sciences, University of Minnesota, Minneapolis, MN, USA. ruizhang@umn.edu.ORCID http://orcid.org/0000-0001-8258-3585

Funding

U.S. Department of Health & Human Services | NIH | National Institute on Minority Health and Health Disparities (NIMHD) 1R21MD019134
6 · The paper itself

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

PMID41535454
PMCPMC13624255

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.