Evidence map›Paper›PMID 41728275›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Patterns and Clinical Outcomes of Physical Activity and Sedentary Behavior Across 20 Million Days of Wearable Monitoring in U.S. Adults.

Arash A Nargesi, Valentina D'Souza, Tal Shnitzer, Andri Kadaifciu, Aurora Cremer, Sean Joseph Jurgens, Nakia Mack, Ryan Lupi, Romuladus Azuine, Geoffrey S Ginsburg and 5 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Arash A NargesiData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Valentina D'SouzaData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Tal ShnitzerData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Andri KadaifciuData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Aurora CremerData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Sean Joseph JurgensCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Nakia MackAll of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
Ryan LupiAll of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
Romuladus AzuineAll of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
Geoffrey S GinsburgAll of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
Chris LuntAll of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
Christopher D AndersonMcCance Center for Brain Health, Massachusetts General Hospital, Boston, USA.
Sam FriedmanData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Patrick T EllinorCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Mahnaz MaddahData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

Funding

All of Us Research Program Data and Research CenterOT2OD035404 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Melissa Basford, DAVID GLAZER · 2023 to 2026
$210.4M
IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVALR01HL092577 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2009 to 2025
$20.6M
TRAINING PROGRAM IN CARDIOVASCULAR RESEARCHT32HL007604 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MARK W FEINBERG · 1985 to 2026
$19.0M
PRISM: Ethically-guided multimodal AI models for predicting disease pathogenesis in individuals with pathogenic variantsUG3HG014379 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI KARCZEWSKI, KONRAD, TATONETTI, NICHOLAS P · 2025 to 2025
$3.2M
Development of Real-World Motor Outcome Measures in Ataxia-TelangiectasiaR01NS134597 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Anoopum Satyawan Gupta · 2024 to 2026
$1.9M
Mentoring in Arrhythmia ResearchK24HL105780 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS · 2011 to 2020
$1.2M
NHGRI NIH HHS UG3 HG014379NHLBI NIH HHS K24 HL105780NHLBI NIH HHS R01 HL092577NHLBI NIH HHS T32 HL007604NIH HHS OT2 OD035404NINDS NIH HHS R01 NS134597
6 · The paper itself

Abstract

Background: Physical activity is a key modifiable determinant of health, yet current guidelines primarily emphasize moderate-to-vigorous physical activity and provide limited guidance on other actionable dimensions such as daily step count and sedentary time. The growing availability of wearables enables high-resolution measurement of these metrics and assessment of their associations with clinical outcomes. Methods: Minute-level wearable data from adult participants of Results: Among 50,300 participants (68% female, 71% White, median age 55 years), 19,845,612 person-days of wearable monitoring over a median follow-up of 13 months were included in this analysis. Step counts peaked at midday with weekly highs on Saturdays and seasonal highs in May, while sedentary time was highest in midafternoon and winter. Higher step counts and lower sedentary time were observed among males, individuals with higher income and education, and residents of the Northeast, upper Midwest, and West Coast. In PheWAS (n=31,446), higher step count and lower sedentary time were associated with lower risk of multiple diseases, including obesity, cardiometabolic risk factors, and mood and anxiety disorders. Dose response analyses demonstrated heterogeneous relationships across disease groups, with cardiovascular benefits plateauing beyond 9,000-10,000 steps/day and below 600-640 minutes/day of sedentary time. Conclusions: In this nationwide cohort of U.S. adults, wearable-derived physical activity and sedentary behavior showed distinct temporal and geographic patterns with significant disease-specific associations with clinical outcomes. These findings support the use of longitudinal high-resolution wearable data for advancing personalized guidelines of physical activity and sedentary behavior.

Indexed as

digital healthPhysical activitysedentary behaviorstep countwearables

Identifiers

PMID41728275
PMCPMC12919138

What OpenQuestion holds

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Registered trials

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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.