Status Quo or Drop-Off: Do Older Adults Maintain Benefits From Choose to Move—A Scaled-Up Physical Activity Program—12 Months After Withdrawing the Intervention?
Bibliographic record
Abstract
BACKGROUND: Choose to Move is one of few scaled-up health-promoting interventions for older adults. The authors evaluated whether Choose to Move participants maintained their intervention-related gains in physical activity (PA), mobility, and social connectedness 12 months after the intervention ended. METHODS: The authors assessed PA, mobility, loneliness, social isolation, and muscle strength via questionnaire and objective measures in 235 older adults at 0 months (baseline), 6 months (end of intervention), and 18 months (12-months postintervention). The authors fitted linear mixed models to examine the change in each outcome from 6 to 18 months (primary objective) and 0 to 18 months (secondary objective) and reported by age group (60-74 and ≥75 y). RESULTS: In younger participants, PA decreased between 6 and 18 months, but remained significantly higher than at baseline. Intervention-related benefits in loneliness, social isolation, mobility, and muscle strength were maintained between 6 and 18 months in the younger participants. Older participants maintained their intervention benefits in loneliness, mobility, and muscle strength. When compared with baseline values, PA levels in older participants were unchanged, whereas social isolation increased. CONCLUSIONS: Older adults maintained some, but not all, health benefits of Choose to Move 12 months after the intervention ended. Long-term commitments are needed to deliver effective health-promoting interventions for older adults if benefits are to be maintained.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".