The Association Between Family Health and Frailty With the Mediation Role of Health Literacy and Health Behavior Among Older Adults in China: Nationwide Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Family health develops from the intersection of the health of each family member and their interactions and capacities as well as the family's internal and external resources. Frailty is the most prominent and typical clinical manifestation during population aging. Family health may be effective in addressing frailty, and this association may be mediated by health literacy and health behaviors. Until now, it is unclear whether and how family health affects frailty in older adults. OBJECTIVE: This study aimed to examine the associations between family health and frailty and the mediation roles of health literacy and health behaviors. METHODS: A total of 3758 participants aged ≥60 years were recruited from a national survey conducted in 2022 in China for this cross-sectional study. Family health was measured using the Short Form of the Family Health Scale. Frailty was measured using the Fatigue, Resistance, Ambulation, Illnesses, and Loss of weight (FRAIL) scale. Potential mediators included health literacy and health behaviors (not smoking, not having alcohol intake, physical exercise for ≥150 minutes per week, longer sleep duration, and having breakfast every day). Ordered logistic regression was applied to explore the association between family health and frailty status. Mediation analysis based on Sobel tests was used to analyze the indirect effects mediated by health literacy and behaviors, and the Karlson-Holm-Breen method was used to composite the indirect effects. RESULTS: Ordered logistic regression showed that family health is negatively associated with frailty (odds ratio 0.94, 95% CI 0.93-0.96) with covariates and potential mediators controlled. This association was mediated by health literacy (8.04%), not smoking (1.96%), longer sleep duration (5.74%), and having breakfast every day (10.98%) through the Karlson-Holm-Breen composition. CONCLUSIONS: Family health can be an important intervention target that appears to be negatively linked to frailty in Chinese older adults. Improving family health can be effective in promoting healthier lifestyles; improving health literacy; and delaying, managing, and reversing frailty.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".