Self-Regulatory Self-Efficacy, Action Control, and Planning: There's an App for That!
Bibliographic record
Abstract
INTRODUCTION: Advances in technology have resulted in smartphone-based applications (apps) that could possibly serve to support physical activity (PA). This study compares health action process approach social cognitions between exercisers who do (n = 29) and do not (n = 76) use apps. MATERIALS AND METHODS: Exercisers completed an online questionnaire assessing usage of apps, the health action process approach (HAPA) social cognitions, and PA. RESULTS: A multivariate effect was found between app users and nonusers for HAPA social cognitions. Follow-up univariate analyses were calculated and determined that app users had significantly greater goal-setting efficacy, scheduling efficacy, recovery efficacy, action control, and planning compared with nonusers. CONCLUSIONS: App use may be related to greater self-regulatory cognitions and skills. Future research should further explore app use in relation to social cognitions and long-term PA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".