11 million days of longitudinal Fitbit data reveal novel future health insights
Abstract
Wearable devices offer a scalable, objective measurement of physical activity, but most studies reduce these data to a single metric over 7-days. Here, we compare wearable-derived phenotyping approaches to understand their impact on activity-disease associations, analyzing 11 million days of Fitbit data from 29,303 All of Us Research Program participants. We derive 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), then run phenome-wide analyses across > 700 incident and >1,400 prevalent disease outcomes. Though all phenotypes are highly correlated, longer measurement windows yield stronger, more stable associations: 1-year step count was associated with 389 prevalent and 61 incident outcomes (versus 271 and 33 for 1-day step count) after Bonferroni-correction, including novel links to chronic pain syndrome and SARS-CoV-2. Differences between prevalent and incident associations suggest that activity metrics may act as both early markers of disease and risk factors. These findings highlight how large-scale, longitudinal wearable data can advance understanding of health and disease.
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BibTeX
@article{fulda2026million,
title = {{11 million days of longitudinal Fitbit data reveal novel future health insights}},
author = {Evelynne S. Fulda and Bennett J. Waxse and Slavina B. Goleva and Tam C. Tran and Henry J. Taylor and Caitlin P. Bailey and Dana L. Wolff-Hughes and Huan Mo and Chenjie Zeng and Jacob M. Keaton and Tracey M. Ferrara and Anya Topiwala and Aiden Doherty and Joshua C. Denny},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78529-2},
url = {https://www.nature.com/articles/s41467-026-78529-2},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - 11 million days of longitudinal Fitbit data reveal novel future health insights
AU - Evelynne S. Fulda
AU - Bennett J. Waxse
AU - Slavina B. Goleva
AU - Tam C. Tran
AU - Henry J. Taylor
AU - Caitlin P. Bailey
AU - Dana L. Wolff-Hughes
AU - Huan Mo
AU - Chenjie Zeng
AU - Jacob M. Keaton
AU - Tracey M. Ferrara
AU - Anya Topiwala
AU - Aiden Doherty
AU - Joshua C. Denny
PY - 2026
DA - 2026/10/14
JO - Nature Communications
DO - 10.1038/s41467-026-78529-2
UR - https://www.nature.com/articles/s41467-026-78529-2
AB - Wearable devices offer a scalable, objective measurement of physical activity, but most studies reduce these data to a single metric over 7-days. Here, we compare wearable-derived phenotyping approaches to understand their impact on activity-disease associations, analyzing 11 million days of Fitbit data from 29,303 All of Us Research Program participants. We derive 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), then run phenome-wide analyses across > 700 incident and >1,400 prevalent disease outcomes. Though all phenotypes are highly correlated, longer measurement windows yield stronger, more stable associations: 1-year step count was associated with 389 prevalent and 61 incident outcomes (versus 271 and 33 for 1-day step count) after Bonferroni-correction, including novel links to chronic pain syndrome and SARS-CoV-2. Differences between prevalent and incident associations suggest that activity metrics may act as both early markers of disease and risk factors. These findings highlight how large-scale, longitudinal wearable data can advance understanding of health and disease.
SN - 2041-1723
ER -