Predicting Social Frailty in Older Adults Using Fitbit-Derived Circadian and Heart Rate Biomarkers: Cross-Sectional Study
Bibliographic record
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 (β=-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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".