Matched or nonmatched interventions based on the transtheoretical model to promote physical activity. A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Purpose The aim of this study was to examine whether the efficacy of transtheoretical model (TTM)-based interventions on physical activity (PA) varied according to the following criteria: (1) interventions targeted the actual stages of change (SOCs) or did not; (2) participants were selected according to their SOC or were not; and (3) its theoretical constructs (decisional balance, temptation, self-efficacy, processes of change). Methods Thirty-three randomized controlled trials assessing TTM-based interventions promoting PA in adults were systematically identified. Results The between-group heterogeneity statistic (Qb) did not reveal any differential efficacy either in interventions targeting the actual SOC compared with those that did not (Qb = 1.48, p = 0.22) or in interventions selecting participants according to their SOC compared with those that did not (Qb = 0.01, p = 0.91). TTM-based interventions enhanced PA behavior whether they targeted the actual SOC (Cohen's d = 0.36; 95% confidence interval (CI): 0.22–0.49) or not ( d = 0.23; 95%CI: 0.09–0.38) and whether they selected their participants according to their SOC ( d = 0.33; 95%CI: 0.13–0.53) or not ( d = 0.32; 95%CI: 0.19–0.44). The moderators of the efficacy of TTM-based interventions were the number of theoretical constructs used to tailor the intervention (Qb = 8.82, p = 0.003), the use of self-efficacy (Qb = 6.09, p = 0.01), and the processes of change (Qb = 3.51, p = 0.06). Conclusion TTM-based interventions significantly improved PA behavior, and their efficacy was not moderated by SOC but by the TTM theoretical constructs.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".