Are costs optimized as scale-up of Choose to Move–an effective health-promoting intervention for older adults–proceeds?
Bibliographic record
Abstract
BACKGROUND: Few studies have examined costs of implementing evidence-based interventions (EBIs) as scale-up proceeds. Across four phases, we co-adapted and scaled up an effective EBI designed to promote older adults’ health (Choose to Move; CTM). Following formative evaluation (2015), Phases 1–2 (2016-17) comprised the CTM pilot and early scale-up. For Phase 3 (2018-20), we adapted CTM to establish “best fit” and support broad scale-up. In response to COVID-19 (2020), we adapted CTM for virtual delivery. For Phase 4 (2020-22), we adapted CTM to reduce resource use. We aimed to (1) identify, measure, and value costs of implementing CTM across four phases (7 years) of scale-up; and (2) analyze change in implementation costs alongside changes in intervention effect sizes to assess cost-consequence trends from Phases 1–2 through Phase 4. METHODS: We conducted a trial-based cost and cost-consequence analysis of CTM Phases 1–2 through Phase 4 from a program provider perspective. Program costs were identified, measured, and valued using micro-costing techniques; variation in program cost was explored using scenario analyses. We compared Phase 4 intervention effects against those of Phases 1–2 and Phase 3 to examine how changes in implementation costs corresponded with changes in effect size. RESULTS: For Phases 1–2, total cost ($CDN, 2024) of CTM implementation was $863,559 for 55 programs (534 participants; $1,617/participant). Phase 3 costs were $1,564,446 for 165 programs (1668 participants; $938/participant). Phase 4 costs were $760,983 for 135 programs (1278 participants; $595/participant), a reduction of 63% and 37% compared with Phases 1–2 and Phase 3, respectively. Compared with Phases 1–2, Phase 4 had a greater positive effect on social isolation but effect sizes for physical activity, mobility and loneliness were reduced. Phase 4 had a greater positive effect on physical activity and mobility in all participants, and loneliness among those < 75 years, compared with Phase 3. CONCLUSIONS: Costs associated with broad scale-up of EBIs are rarely investigated. We sought innovative ways to maximize impact of a health-promoting EBI, while minimizing costs. Our analysis highlights how strategic adaptations can enhance cost efficiency while improving intervention outcomes; this represents an emergent application of economic analysis within scale-up science. TRIAL REGISTRATION: Retrospectively registered at ClinicalTrials.gov, NCT05678985 (CTM Phase 4) and NCT05497648 (CTM Phase 3).
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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 | 0.034 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".