@Article{info:doi/10.2196/71393,
author="Maekawa, Hiroki
and Kume, Yu",
title="Predicting Social Frailty in Older Adults Using Fitbit-Derived Circadian and Heart Rate Biomarkers: Cross-Sectional Study",
journal="JMIR Form Res",
year="2025",
month="Jul",
day="24",
volume="9",
pages="e71393",
keywords="social frailty; digital biomarkers; Fitbit; rest-activity rhythm; heart rate metrics; nonparametric indexes; extended cosinor model; community-dwelling older adults",
abstract="Background: Social frailty poses a potential risk even for relatively healthy older adults, necessitating development of early detection and prevention strategies. Recently, consumer-grade wearable devices have attracted attention due to their ability to continuously collect physiological and activity-related data. These data can potentially be used to calculate digital biomarkers for screening social frailty in older adults. Objective: The objective of this study was to explore digital biomarkers associated with social frailty using sensor data recorded via Fitbit devices and evaluate their relationship with health outcomes in older adults. Methods: This cross-sectional study was conducted in 102 community-dwelling older adults. Participants attending frailty prevention programs wore devices from the Fitbit Inspire series on their nondominant wrist for at least 7 consecutive days, during which step count and heart rate data were collected. Standardized questionnaires were used to assess physical functions, cognitive functions, and social frailty, and based on the scores, the participants were categorized into 3 groups: robust, social prefrailty, and social frailty. The sensor data were analyzed to calculate nonparametric and extended cosinor rhythm metrics, along with heart rate--related metrics. Results: The final sample included 86 participants who were categorized as robust (n=28, 33{\%}), social prefrailty (n=39, 45{\%}), and social frailty (n=19, 22{\%}). The mean age of the participants was 77.14 (SD 5.70) years, and 91{\%} (78/86) were women. Multinomial logistic regression analysis revealed that a step-based rhythm metric (intradaily coefficient of variation) was significantly associated with social frailty (odds ratio 1.05, 95{\%} CI 1.01-1.11; P=.01). The heart rate metrics, including the delta resting heart rate and time of transition from rest to activity, showed significant associations with both social prefrailty (odds ratio 0.82, 95{\%} CI 0.68-0.99; P=.04) and social frailty (odds ratio 0.69, 95{\%} CI 0.50-0.95; P=.01). Specifically, delta resting heart rate, defined as the difference between the overall average heart rate and resting heart rate, exhibited significant negative associations with social prefrailty (odds ratio 0.82, 95{\%} CI 0.68-0.97; P=.02) and social frailty (odds ratio 0.74, 95{\%} CI 0.58-0.94; P=.02). Furthermore, analysis using a linear regression model revealed a significant association between the intradaily coefficient of variation and the word list memory score, a measure of cognitive decline ($\beta$=−0.04; P=.02). Conclusions: This study identified associations between novel rhythm and heart rate metrics calculated from the step count and heart rate recorded by Fitbit devices and social frailty. These findings suggest that consumer-grade wearable devices, which are low cost and accessible, hold promise as tools for evaluating social frailty and its risk factors through enabling the calculation of digital biomarkers. Future research should include larger sample sizes and focus on the clinical applications of these findings. ",
issn="2561-326X",
doi="10.2196/71393",
url="https://formative.jmir.org/2025/1/e71393",
url="https://doi.org/10.2196/71393"
}