Sedentary Behavior and Physical Activity Are Independent Predictors of Successful Aging in Middle-Aged and Older Adults
Bibliographic record
Abstract
Background. Sedentary behavior is emerging as an important risk factor for poor health. Physical activity has proven to be important in determining overall successful aging (SA) among older adults; however, no data exists on the influence of sedentary behavior on SA. The purpose of this analysis was to determine whether there is an association between sedentary behavior and successful aging, independent of physical activity levels. Methods. 9,478 older (M = 4,245; F = 5,233) and 10,060 middle-aged (M = 4.621; F = 5,439) adults from the Healthy Aging cycle of the Canadian Community Health Survey were analyzed. Multivariate logistic regressions were conducted with SA and its three components as outcomes while physical activity and sedentary behavior were entered as main exposures. Results. Among older adults, compared to those who were sedentary (4 hours or more/day), those who were moderately (2-4 hours/day) and least sedentary (<2 hours/day) were 38% (OR: 1.38; CI: 1.12-1.69) and 43% (OR: 1.43; CI: 1.23-1.67) more likely to age successfully, respectively. Among middle-aged adults, those who were least sedentary were 43% (OR: 1.43; CI: 1.25-1.63) more likely to age successfully. Conclusions. These novel findings suggest that sedentary activities are significantly associated with lower odds of SA among middle-aged and older adults, potentially in a dose-dependent manner.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".