Accessibility settings

This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (5109) Public (e)Health, Digital Epidemiology and Public Health Informatics (2255) mHealth for Data Collection and Research (1700) mHealth for Wellness, Behavior Change and Prevention (4237) Wearable Devices and Sensors (1553) Instruments and Questionnaires for Physical Activity and Lifestyle (152) Fitness Trackers and Smart Pedometers/Accelerometers (789) Innovations and Technology for Physical Activity Education (1059)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65095, first published .
Diverse group of people enjoying outdoor fitness activities in a park

Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study

Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study

Authors of this article:

Rujul Singh1 Author Orcid Image ;   Macy K Tetrick1 Author Orcid Image ;   James L Fisher2 Author Orcid Image ;   Peter Washington3 Author Orcid Image ;   Jane Yu4 Author Orcid Image ;   Electra D Paskett1, 5 Author Orcid Image ;   Frank J Penedo6, 7 Author Orcid Image ;   Steven K Clinton8 Author Orcid Image ;   Roberto M Benzo1 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

Journals

  1. Stavarache I, Cernomaz A, Țarcă V. Testing the reliability of commercially available smart devices to    remotely assess the intensity of physical exertion during respiratory rehabilitation. Balneo and PRM Research Journal 2025;16(Vol 16 No. 3):832 View
  2. BAILEY C, DODD K, MCCLAIN J, SEO I, WHEELER W, WOLFF-HUGHES D. Fitbit Physical Activity and Sleep Data in the All of Us Research Program: Data Exploration and Processing Considerations for Research. Medicine & Science in Sports & Exercise 2025;57(12):2946 View
  3. Xi X, Mazalan N, Nazarudin M. A Competitive Intelligence Framework for Predicting Physical Fitness Outcomes Using Data Analytics. Journal of Sustainable Competitive Intelligence 2026;16:e0636 View
  4. Karasawa S, Nemoto Y, Hayashi H, Kondo N, Kawachi I, Kamada M. Long-term changes in daily step count goals following the introduction of a gamified smartphone application in Japan. Journal of Epidemiology 2026 View
  5. Montoye A, Arumugam A, McNarry M, Mackintosh K, Conger S. Advancing Equity in Device-Based Physical Behavior Research: Balancing Representation, Context, and Feasibility—A Response to Baumgart et al.. Journal for the Measurement of Physical Behaviour 2026;9(1) View
  6. Baumgart J, Coyle-Asbil H, Kebede M, Otterbein M, Mancini M, Beauchamp M, Pfeiffer K, Milton K, Martin Ginis K, Helbostad J, Hausdorff J, Vereijken B, Lincoln A, Atkin A, Mendoza A, Ann Vallis L. Research for Equitable Health Outcomes: A Call for Diverse Participant Samples in Wearable Device Harmonization and Standardization Approaches. Journal for the Measurement of Physical Behaviour 2026;9(1) View
  7. Patel M, Gallis J, Zeitlin A, Crosthwaite P, Lim A, Collins K, Oppezzo M, Rosas L. Optimizing Goal Difficulty in a Digital Weight Loss Intervention: The Ignite Pilot Randomized Trial. Obesity Science & Practice 2026;12(4) View
  8. Zacharis T. Physical Activity and Healthy Ageing & Longevity Data: Interoperability, Reuse, and Innovation in a Federated European Context. Open Research Europe 2026;6:263 View