The Influence of Lifestyle Behaviors on the Incidence of Frailty
Bibliographic record
Abstract
BACKGROUND: Frailty is a clinical state defined as an increase in an individual's vulnerability to developing adverse health-related outcomes. OBJECTIVES: We propose that healthy behaviors could lower the incidence of frailty. The aim is to describe the association between healthy behaviors (physical activity, vaccination, tobacco use, and cancer screening) and the incidence of frailty. DESIGN: This is a secondary longitudinal analysis of the Mexican Health and Aging Study (MHAS) cohort. SETTING: MHAS is a population-based cohort, of community-dwelling Mexican older adults. With five assessments currently available, for purposes of this work, 2012 and 2015 waves were used. PARTICIPANTS: A total of 6,087 individuals 50-year or older were included. MEASUREMENTS: Frailty was defined using a 39-item frailty index. Healthy behaviors were assessed with questions available in MHAS. Individuals without frailty in 2012 were followed-up three years in order to determine their frailty incidence, and its association with healthy behaviors. Multivariate logistic regression models were used to assess the odds of frailty occurring according to the four health-related behaviors mentioned above. RESULTS: At baseline (2012), 55.2% of the subjects were male, the mean age was 62.2 (SD ± 8.5) years old. The overall incidence (2015) of frailty was 37.8%. Older adults physically active had a lower incidence of frailty (48.9% vs. 42.2%, p< 0.0001). Of the activities assessed in the adjusted multivariate models, physical activity was the only variable that was independently associated with a lower risk of frailty (odds ratio: 0.79, 95% confidence interval 0.71-0.88, p< 0.001). CONCLUSIONS: Physically active older adults had a lower 3-year incidence of frailty even after adjusting for confounding variables. Increasing physical activity could therefore represent a strategy for reducing the incidence of frailty. Other so-called healthy behaviors were not associated with incident frailty, however there is still uncertainty on the interpretation of those results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".