Maintaining physical activity in older adults: The importance of health-specific control strategies
Bibliographic record
Abstract
The Lines-of-Defense model postulates that older adults should engage in important health goals and behaviors for as long as possible and adjust them downwardly only when they become impossible to pursue. This process is thought to be supported by goal engagement and self-protective control strategies. We tested this model in a 4-year longitudinal study of 236 older adults by predicting the maintenance of physical activity using accelerometers. We hypothesized that older adults would exert shifts from more strenuous (e.g., vigorous and moderate intensity) to less strenuous (e.g., light intensity) physical activity over time. In addition, we expected that these processes would be supported by the use of health-specific control strategies. Multilevel modeling revealed that older adults experienced declines in moderate and vigorous physical activity but increases in light physical activity. Health engagement predicted an accelerated increase in light physical activity, and exerted substantial, but longitudinally decreasing, benefits for moderate physical activity. Health-related self-protection, by contrast, predicted the maintenance of vigorous physical activity over time. These results support the Lines-of-Defense model by demonstrating that control strategies can predict the maintenance of older adults' physical activity levels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".