Does optimizing Choose to Move – a health-promoting program for older adults – enhance scalability, program implementation and effectiveness?
Bibliographic record
Abstract
Abstract Background Investment in scale-up and sustainment of effective health-promoting programs is often hampered by competing demands on scarce health dollars. Thus, optimizing programs to reduce resource use (e.g., delivery costs) while maintaining effectiveness is necessary to promote health at scale. Using a phased approach (2015–2024), we adapted and scaled-up an evidence-based, health-promoting program for older adults (Choose to Move; CTM). For CTM Phase 4 we undertook a systematic, data-driven adaptation process to reduce resource use. In this paper we: 1) describe the CTM Phase 4 program (‘CTM Phase 4’) and assess its 2) implementation and 3) effectiveness. Methods For CTM Phase 4 (30-min one-on-one consultation and 8, 60-min group meetings with an activity coach), we reduced activity coach hours by 40% compared to Phase 3. To evaluate effectiveness of CTM Phase 4 we conducted a type 2 hybrid effectiveness-implementation study involving 137 programs (1126 older adults; 59–74 years, 75 + years) delivered by 29 activity coaches. We assessed implementation indicators (e.g., dose, fidelity, adaptation, participant responsiveness, self-efficacy) via survey in activity coaches and older adults. We assessed older adults’ physical activity (PA), mobility, social isolation, and loneliness before and after (0, 3 months) the program. Results Implementation indicators demonstrated that CTM Phase 4 was delivered successfully. Post-intervention, PA (+ 1.5 days/week; 95% CI 1.3, 1.6), mobility limitations (-6.4%), and scores for mobility (+ 0.7; 95% CI: 0.4, 1.3), social isolation (+ 0.69; 95% CI: 0.50, 0.89), and loneliness (-0.24; 95% CI: -0.34, -0.13) were improved in those < 75 years. Among those ≥ 75 years, PA (+ 1.0 days/week; 95% CI, 0.7, 1.2), mobility score (+ 1.1; 95% CI: 0.4, 1.8), and social isolation score (+ 0.31; 95% CI: 0.002, 0.61) were improved post-intervention. Overall, participant-level benefits were comparable to those observed in Phase 3. Conclusions CTM was co-designed as a flexible program, adapted over time based on user group needs and preferences. This flexibility enabled us to reduce activity coach delivery hours without compromising implementation or benefits to older adults’ health. Optimizing effective health-promoting programs to enhance their scalability and sustainability provides an important pathway to improved population health. Trial Registration ClinicalTrials.gov, NCT05678985. Registered 10 January 2023 – Retrospectively registered, https://clinicaltrials.gov/study/NCT05678985 .
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".