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

Development and Evaluation of Research Methods, Instruments and Tools (1396) New Methods (1846) Cardiology (285) Ubiquitous Health (uHealth) (276) Quantified Self and Wellness (149) Wearable Devices and Sensors (1639) Aging with Chronic Disease (546) Digital Biomarkers and Digital Phenotyping (467)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71393, first published .
Man with earbuds checks fitness tracker on beach

Predicting Social Frailty in Older Adults Using Fitbit-Derived Circadian and Heart Rate Biomarkers: Cross-Sectional Study

Predicting Social Frailty in Older Adults Using Fitbit-Derived Circadian and Heart Rate Biomarkers: Cross-Sectional Study

Authors of this article:

Hiroki Maekawa1, 2 Author Orcid Image ;   Yu Kume3 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Yahya F, Cooper M, Saif W, Kassem M, Nazar H. Development of a Hospital-at-Home Digital Twin for Patients With Frailty: Scoping Review. Journal of Medical Internet Research 2025;27:e81510 View
  2. FUJIO Y, OKAMOTO S, YOSHIZAWA Y, TABATA H, ENOMOTO Y, SAKAI T, SAKAI K, URAGAMI G, TANO M, HAMADA C, TAMURA Y. An Exploratory Study on the Utilization of an AI-based Cognitive Function Assessment Application for the Early Detection of Frailty in Older Adults. Asian Journal of Human Services 2026;30(2):n/a View

Conference Proceedings

  1. Sarmiento-Rojas J, Aya-Parra P, Perdomo O. 2026 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). TinyML Architecture for Indoor Localization and Posture Recognition in Older Adults: Validation in a Clinical Simulation Environment View