The association between patterns of physical activity and sedentary time with frailty in relation to cardiovascular disease
Bibliographic record
Abstract
Abstract Objective The associations of moderate‐vigorous physical activity (MVPA) bouts and patterns of sedentary time (ST) with frailty according to cardiovascular disease (CVD) status are unknown. Methods Accelerometry in adults ≥50 years old from the 2003‐2004 and 2005‐2006 National Health and Nutrition Examination Survey were used. Bouted and sporadic MVPA in ≥10‐minute or <1‐minute bouts were assessed based on meeting a percentage of physical activity guidelines of 150 minutes/wk, respectively. ST patterns included: prolonged ST lasting ≥30 minutes, and the frequency, intensity, and duration of breaks from ST. A 46‐item frailty index defined frailty. Multivariable linear regression was used. Results There were 827 and 1490 CVD‐free and CVD participants, respectively. Meeting a higher percentage of the physical activity guidelines through bouted MVPA was associated with lower frailty in CVD‐only participants (P < 0.05 for CVD interaction). Sporadic MVPA was associated with lower frailty levels in both groups. Prolonged ST was associated with worse frailty in CVD (P > 0.05 for CVD interaction). Frequency of ST breaks was not associated with frailty. Average ST break intensity was protective in both groups. The duration of breaks in ST was associated with lower frailty in CVD participants only (P > 0.05 for CVD interaction). Conclusion Insufficient MVPA and prolonged ST are detrimental despite CVD status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".