Understanding consistent exercise maintenance: Psychosocial factors related to long-term success
Bibliographic record
Abstract
Regular exercise requires self-regulation for successful pursuit over weeks, months, and years. However, successful maintainers have not been the focus of psychological investigation. Indeed, those maintaining exercise over years have rarely been examined. We used recent theorizing about maintenance of health behaviours (Kwasnicka et al., 2013) to identify psychosocial factors characteristic of successful long-term patterns of exercise maintenance. Participants (N = 358) completed an online survey assessing outcome expectations, satisfaction, task self-efficacy, and self-regulatory efficacy (SRE) to overcome barriers and recover from lapses. Maintainers included individuals who consistently followed their pattern of weekly exercise for more than 6 months for at least 2 days per week lasting 30 minutes or more. Three groups were identified based on frequency of exercise bouts: low, 2-3 days; medium, 4-5 days; and high, 6-7 days. The sample average was 7 ± 3.92 years of maintenance of their weekly pattern. MANOVA revealed that high frequency maintainers reported significantly higher ratings of proximal outcome expectations, satisfaction, SRE barriers, and SRE recovery (ps
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".