Can an active lifestyle offset the relationship that poor lifestyle behaviours have on frailty?
Bibliographic record
Abstract
OBJECTIVE: To examine the association of lifestyle behaviours (LSB) with physical activity (PA) and frailty; also, to examine if associations differ by sex and age. METHODS: 24,828 individuals [49.6 ± 17.6 years (range: 20-85), 51.6 % female] from the National Health and Nutrition Examination Survey (cycles 2009-2018) were included. Individuals were divided into Active (≥150 min/week of moderate-to-vigorous physical activity (MVPA)) and Inactive (<150 min/week MVPA) based on self-reported PA. Frailty was measured by a 46-item Frailty Index (FI). LSB consisted of stationary time, sleep, diet quality, and alcohol and smoking habits. LSB was summed into a score [0-5]. Linear regression models were used with each LSB in isolation and the summed LSB with frailty. RESULTS: There were 7,495 (30.1 %) Active and 17,333 (69.8 %) Inactive individuals. The FI was lower in the Active participants (Active: 0.10 ± 0.08; Inactive: 0.15 ± 0.12; p < 0.01). A worse LSB score was associated with an increased FI in all behaviours but females who binge drink and smoke (p-all>0.14). For inactive individuals, all LSBs were associated with an increased FI except those who binge drink and male smokers (p = 0.08). There was a significant association between increased summed LSB and an increased FI (β range: Active, 0.024-0.037; Inactive, 0.028, 0.046. p-all<0.01); the Active group had a lower FI at every age group than the Inactive group (p < 0.001). CONCLUSION: PA was associated with a lower FI even among those with a poor LSB score. This association is dependent on age, with older individuals reporting a stronger association.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".