{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T06:42:07Z","timestamp":1770100927644,"version":"3.49.0"},"posted":{"date-parts":[[2026,1,30]]},"group-title":"Epidemiology","reference-count":30,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,1,30]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Insufficient physical activity (PA) is associated with higher risk of morbidity and premature mortality. Wearable devices offer a scalable, objective measurement of physical activity, but most studies reduce these data to a single activity metric measured over a fixed 7-day period. We compared different wearable-derived phenotyping approaches to understand their impact on activity-disease associations.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    We analyzed 11 million days of Fitbit data from 29,351 participants in the\n                    <jats:italic>All of Us<\/jats:italic>\n                    Research Program, deriving four daily activity metrics (step count, peak 1-min cadence, peak 30-min cadence, and heart rate per step) across five time-windows (1-day, 1-week, 1-month, 6-months, 1-year). We performed phenome-wide analyses on &gt;700 incident and &gt;1,300 prevalent disease outcomes identified from linked electronic health records.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>Among participants with EHR and Fitbit data (mean age 57.3 years, 69% female, 47% with &gt;1 year of Fitbit data), all 20 phenotypes were highly correlated (median Pearson r = 0.71). Longer measurement windows yielded stronger and more stable associations, with 1-year step count associated with 373 prevalent and 37 incident outcomes (versus 231 and 17 for 1-day step count) after Bonferroni-correction, including novel associations with chronic pain syndrome, SARS-CoV-2, and autoimmune disease. Differences between prevalent and incident associations suggest that activity metrics can act as both early markers of disease or risk factors.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation<\/jats:title>\n                  <jats:p>These findings highlight how large-scale, longitudinal wearable data can advance understanding of health and disease and inform scalable approaches for clinical risk stratification.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Funding<\/jats:title>\n                  <jats:p>National Institutes of Health Intramural Research Program, Wellcome Trust<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>RESEARCH IN CONTEXT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Evidence before this study<\/jats:title>\n                    <jats:p>Low levels of physical activity relate to numerous health outcomes. However, prior studies are limited by a focus on disease prevalence and by a lack of examination across a broad range of health outcomes. Further, the strength of these associations, depends on how physical activity is measured. Prior work shows that wearable devices capture activity more reliably than self-report surveys and typically yield stronger associations with disease risk. Most wearable-based studies rely on short monitoring windows: often seven days or fewer. To our knowledge, no study has systematically evaluated how the duration of wearable-based phenotyping influences estimates of disease risk.<\/jats:p>\n                    <jats:p>To explore this, we searched PubMed using the terms \u201cwearable phenotyping\u201d AND \u201cdisease risk\u201d, resulting in 48 articles published between 2016 and 2025. Although some studies compared different wearable-derived phenotypes (e.g., step count vs. sleep duration) or explored how the number of observed days affects data quality, none directly evaluated how the length of the phenotyping period shapes associations with disease risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Added value of this study<\/jats:title>\n                    <jats:p>Using nearly 11 million person-days of Fitbit data from \u223c30,000 participants, this study evaluates how four wearable-derived activity metrics, summarized across five time windows, influence estimates of activity-disease associations. We identified over 300 previously unreported associations for any of our four metrics and various health outcomes. Longer phenotyping windows consistently yielded stronger associations than shorter ones, although all windows remained informative. These findings highlight the importance of extended wearable monitoring for robust risk characterization. We further compared incident cases with both prevalent and incident outcomes, illustrating the roles of physical activity as a potentially modifiable risk factor, and an early marker of disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Implications of all the available evidence<\/jats:title>\n                    <jats:p>These findings have two important implications. First, longer periods of wearable data collection improve the accuracy of disease risk estimation and should be considered in the design of epidemiologic studies and in the development of clinical guidelines. Although associations between physical activity and disease were directionally consistent across all time windows, effect sizes varied substantially, an observation with important consequences for public health recommendations. Second, this study represents one of the first large-scale demonstrations of long-term wearable monitoring for real-world risk stratification, marking an important advance toward individualized health assessment and intervention.<\/jats:p>\n                  <\/jats:sec>\n                <\/jats:sec>","DOI":"10.64898\/2026.01.29.26344899","type":"posted-content","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T22:51:38Z","timestamp":1769813498000},"source":"Crossref","is-referenced-by-count":0,"title":["11 million days of longitudinal wearable data reveal novel future health insights"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5903-131X","authenticated-orcid":false,"given":"Evelynne S.","family":"Fulda","sequence":"first","affiliation":[{"name":"Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA"},{"name":"Nuffield Department of Population Health, University of 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