A systematic review of the effects of non-conscious regulatory processes in physical activity
Bibliographic record
Abstract
Physical activity theories have almost exclusively focused on conscious regulatory processes such as plans, beliefs, and expected value. The aim of this review was to aggregate the burgeoning evidence showing that physical activity is also partially determined by non-conscious processes (e.g., habits, automatic associations, priming effects). A systematic search was conducted and study characteristics, design, measures, effect size of the principle summary measures, and main conclusions of 52 studies were extracted by two independent coders. The findings support that habitual regulatory processes measured via self-report are directly associated with physical activity beyond conscious processes, and that there is likely interdependency between habit strength and intentions. Response latency measures of automatic associations with physical activity were widely disparate, precluding conclusions about specific effects. A small body of evidence demonstrated a variety of priming effects on physical activity. Overall, it is evident that physical activity is partially regulated by non-conscious processes, but there remain many unanswered questions for this area of research. Future research should refine the conceptualisation and measurement of non-conscious regulatory processes and determine how to harness them to promote physical activity.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".