Abstract
Background
Physical 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.
Methods
We 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.
Results
Here, 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.
Conclusions
Higher 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.
Plain language summary
This study looked at how physical activity affects the risk of developing long-term health problems. Researchers used data from people who wore Fitbit devices to track their daily activity, including step count, exercise intensity, and how long they were active or sitting. These data were linked with each person’s medical records to understand how activity levels related to chronic diseases such as obesity, diabetes, and depression. The study found that people who were more active—taking more steps, climbing more elevation, and spending more time in intense activity—had a lower chance of developing these diseases. The results suggest that being more active and sitting less can help people stay healthier and may support future public health advice and personal health planning.
Similar content being viewed by others
Data availability
This study used data from the All of Us Research Program’s Control Tier Dataset v8, available to authorized users on the Researcher Workbench, which can be accessed via https://workbench.researchallofus.org/. The source data for the main figures are provided as Supplementary Data files. Specifically, the source data for Figs. 1, 2 are provided in Supplementary Data 1. Source data for Supplementary Figs. 3–10 are identical to the Cox proportional hazards model results reported in Table 2 and are therefore not provided separately.
Code availability
Code used for this study is available to approved researchers upon request. Researchers can access the code on the All of Us Research Workbench platform by contacting our study team.
References
Noncommunicable diseases https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases.
Flegal, K. M., Kit, B. K., Orpana, H. & Graubard, B. I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 309, 71–82 (2013).
Malhi, G. S. & Mann, J. J. Depression. Lancet 392, 2299–2312 (2018).
Warburton, D. E. R., Nicol, C. W. & Bredin, S. S. D. Health benefits of physical activity: the evidence. CMAJ 174, 801–809 (2006).
Lee, I.-M. et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380, 219–229 (2012).
Haskell, W. L., Blair, S. N. & Hill, J. O. Physical activity: health outcomes and importance for public health policy. Prev. Med. 49, 280–282 (2009).
Zhao, A., Cui, E., Leroux, A., Lindquist, M. A. & Crainiceanu, C. M. Evaluating the prediction performance of objective physical activity measures for incident Parkinson’s disease in the UK Biobank. J. Neurol. 270, 5913–5923 (2023).
Meng, Q. et al. Quantifying the association between objectively measured physical activity and multiple sclerosis in the UK Biobank. Med. Sci. Sports Exerc. 55, 2194–2202 (2023).
Prince, S. A. et al. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int. J. Behav. Nutr. Phys. Act. 5, 56 (2008).
Schrack, J. A. et al. Assessing the “physical cliff”: detailed quantification of age-related differences in daily patterns of physical activity. J. Gerontol. A Biol. Sci. Med. Sci. 69, 973–979 (2014).
Adam Noah, J., Spierer, D. K., Gu, J. & Bronner, S. Comparison of steps and energy expenditure assessment in adults of Fitbit tracker and ultra to the Actical and indirect calorimetry. J. Med. Eng. Technol. 37, 456–462 (2013).
Evenson, K. R., Goto, M. M. & Furberg, R. D. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int. J. Behav. Nutr. Phys. Act. 12, 159 (2015).
All of Us Research Program Investigators et al. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).
Mayo, K. R. et al. The All of Us data and research center: creating a secure, scalable, and sustainable ecosystem for biomedical research. Annu. Rev. Biomed. Data Sci. 6, 443–464 (2023).
Curated Data Repository (CDR) version 8 Release Notes. User Support https://support.researchallofus.org/hc/en-us/articles/30294451486356-Curated-Data-Repository-CDR-version-8-Release-Notes.
Master, H. et al. How Fitbit data are being made available to registered researchers in All of Us Research Program. Pac. Symp. Biocomput. 28, 19–30 (2023).
Resources for Using Fitbit Data. All of Us https://support.researchallofus.org/hc/en-us/articles/20281023493908-Resources-for-Using-Fitbit-Data.
Troiano, R. P. et al. Physical activity in the United States measured by accelerometer. Med. Sci. Sports Exerc. 40, 181–188 (2008).
Zheng, N. S. et al. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program. Nat. Med. 30, 2648–2656 (2024).
Leroux, A. et al. NHANES 2011-2014: objective physical activity is the strongest predictor of all-cause mortality. Med. Sci. Sports Exerc. 56, 1926–1934 (2024).
Leroux, A. et al. Quantifying the predictive performance of objectively measured physical activity on mortality in the UK Biobank. J. Gerontol. A Biol. Sci. Med. Sci. 76, 1486–1494 (2021).
Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).
Wei, W.-Q. et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS ONE 12, e0175508 (2017).
Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inform. 7, e14325 (2019).
Perry, A. S. et al. Association of longitudinal activity measures and diabetes risk: an analysis from the National Institutes of Health All of Us Research Program. J. Clin. Endocrinol. Metab. 108, 1101–1109 (2023).
Althoff, T. et al. Large-scale physical activity data reveal worldwide activity inequality. Nature 547, 336–339 (2017).
Schuch, F. et al. Physical activity and sedentary behavior in people with major depressive disorder: a systematic review and meta-analysis. J. Affect. Disord. 210, 139–150 (2017).
Vancampfort, D. et al. Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. World Psychiatry 16, 308–315 (2017).
Mendelson, M. et al. Obstructive sleep apnea syndrome, objectively measured physical activity and exercise training interventions: a systematic review and meta-analysis. Front. Neurol. 9, 73 (2018).
Hall, K. A., Singh, M., Mukherjee, S. & Palmer, L. J. Physical activity is associated with reduced prevalence of self-reported obstructive sleep apnea in a large, general population cohort study. J. Clin. Sleep Med. 16, 1179–1187 (2020).
Paffenbarger, R. S. Jr, Jung, D. L., Leung, R. W. & Hyde, R. T. Physical activity and hypertension: an epidemiological view. Ann. Med. 23, 319–327 (1991).
Diaz, K. M. & Shimbo, D. Physical activity and the prevention of hypertension. Curr. Hypertens. Rep. 15, 659–668 (2013).
Team, T. P. D. Pandas-Dev/Pandas: Pandas. Preprint at https://doi.org/10.5281/zenodo.3509134 (2020).
Harris et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference (SciPy, 2010).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Davidson-Pilon, C. Lifelines: survival analysis in Python. J. Open Source Softw. 4, 1317 (2019).
Master, H. et al. Association of step counts over time with the risk of chronic disease in the All of Us Research Program. Nat. Med. 28, 2301–2308 (2022).
Schuch, F. B. et al. Physical activity and incident depression: a meta-analysis of prospective cohort studies. Am. J. Psychiatry 175, 631–648 (2018).
Arem, H. et al. Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA Intern. Med. 175, 959–967 (2015).
Del Pozo Cruz, B., Ahmadi, M. N., Lee, I.-M. & Stamatakis, E. Prospective associations of daily step counts and intensity with cancer and cardiovascular disease incidence and mortality and all-cause mortality. JAMA Intern. Med. 182, 1139–1148 (2022).
Reiner, M., Niermann, C., Jekauc, D. & Woll, A. Long-term health benefits of physical activity-a systematic review of longitudinal studies. BMC Public Health 13, 813 (2013).
Lee, I.-M. et al. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern. Med. 179, 1105–1112 (2019).
Ekelund, U. et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ 366, l4570 (2019).
Piercy, K. L. et al. The Physical Activity Guidelines for Americans. JAMA 320, 2020–2028 (2018).
Bennell, K., Matheson, G., Meeuwisse, W. & Brukner, P. Risk factors for stress fractures. Sports Med. 28, 91–122 (1999).
Katzmarzyk, P. T., Church, T. S., Craig, C. L. & Bouchard, C. Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med. Sci. Sports Exerc. 41, 998–1005 (2009).
Wilmot, E. G. et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 55, 2895–2905 (2012).
Biswas, A. et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann. Intern. Med. 162, 123–132 (2015).
Chandrasekaran, R., Katthula, V. & Moustakas, E. Patterns of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey. J. Med. Internet Res. 22, e22443 (2020).
Feehan, L. M. et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR MHealth UHealth 6, e10527 (2018).
Chevance, G. et al. Accuracy and precision of energy expenditure, heart rate, and steps measured by combined-sensing Fitbits against reference measures: systematic review and meta-analysis. JMIR MHealth UHealth 10, e35626 (2022).
Su, C. et al. Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit. Med. 7, 184 (2024).
Yang, H. S. et al. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clin. Chem. 66, 1396–1404 (2020).
Yang, H. S. et al. Machine learning highlights downtrending of COVID-19 patients with a distinct laboratory profile. Health Data Sci. 2021, 7574903 (2021).
Brendel, M., Su, C., Hou, Y., Henchcliffe, C. & Wang, F. Comprehensive subtyping of Parkinson’s disease patients with similarity fusion: a case study with BioFIND data. NPJ Parkinsons Dis. 7, 83 (2021).
Zang, C. et al. Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts. Commun. Med. 4, 130 (2024).
Zang, C. et al. Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records. Transl. Psychiatry 14, 316 (2024).
Acknowledgements
This work was supported by the National Institutes of Health’s National Institute on Minority Health and Health Disparities, grant number 1R21MD019134-01. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health. We gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health’s All of Us Research Program for making available the participant data examined in this study.
Author information
Authors and Affiliations
Contributions
Y.H. and R.Z. conceived the study. Y.H. served as the principal author, extracting data, designing and conducting the entire experiment and writing the manuscript. E.C. assisted in conceiving the study design and reviewed the manuscript. S.I., K.L., L.S.C., and M.H. provided clinical expertise and reviewed the manuscript. All authors contributed to the production of the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Communications Medicine thanks Aiden Doherty and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Hou, Y., Cui, E., Lim, K. et al. Association of chronic disease risk and physical activity measured by wearable devices in the All of Us program. Commun Med (2026). https://doi.org/10.1038/s43856-025-01372-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s43856-025-01372-x
