Examining serial mediation of past physical activity and self-efficacy in a theory-based physical activity intervention
Bibliographic record
Abstract
Interventions provide a practical context to test core theoretical tenants. Small Steps for Big Changes was a randomized trial designed to modify self-efficacy beliefs to increase free-living physical activity for one year in adults at risk of developing type 2 diabetes. While many studies have examined psychosocial mediators influencing physical activity, few studies have examined serial mediation. The purpose of this study was to examine the serial mediating effects of 6-month physical activity with 12-month self-efficacy (self-regulatory efficacy or task self-efficacy) on participants' 12-month physical activity levels. Hayes' PROCESS macro was used to run a serial mediation analysis to estimate indirect and direct effects. Adults (N=99) who were overweight and had low physical activity levels (mean age=50.9 years, 69.7% female, mean BMI=31.4 kg/cm2) were randomized to receiving either high-intensity interval training (n=47; HIIT) or moderate-intensity continuous training (n=52; MICT). All participants received the same brief behavioural counselling to enhance their self-efficacy. Self-regulatory efficacy and 6-month physical activity levels were found to individually and sequentially mediate the intervention effects (total indirect effect: [29.80, 13.61-49.07]). Self-regulatory efficacy was found to be a causal mechanism in predicting greater physical activity adherence for those in the MICT compared to the HIIT. Task self-efficacy did not mediate intervention effects. Analytic methods used in this study present an innovative method for theory-testing. In line with the social cognitive theory, past physical activity in series with self-regulatory efficacy, was an important mechanism in predicting physical activity one year following a diabetes prevention program.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".