Exploring correlates of physical activity using the multi-process action control framework: is there a moderating role for mental health?
Bibliographic record
Abstract
It is well established that individuals with poor mental health are less physically active than individuals with good mental health, in part due to symptoms like fatigue and cognitive difficulties. Despite the role of theory in intervention development, limited work has investigated the application of theoretical models of physical activity (PA) to individuals with poor mental health. This study tests a hypothesised model based on the Multi-Process Action Control (M-PAC) framework in individuals with perceived good vs. poor mental health. A secondary data analysis was performed on a cross-sectional sample of 13,881 Canadian adults. Participants completed a survey with items examining mental health, reflective processes (e.g., attitudes), intention, regulatory processes, and moderate-to-vigorous PA (MVPA). Three quarters (74.8%) of participants self-rated their mental health as good, and one quarter (25.2%) rated it as poor. A moderation model was performed using multigroup path analysis. There were no between-group differences for most direct pathways. The model was partially moderated by mental health. The effects of affective attitudes on intentions (B = 0.28, p < .001) and intentions on regulations (B = 0.36, p < .001) were significantly stronger among those with poor mental health. The strongest total effect on MVPA for the poor mental health group was perceived capability (β = .17, p < .001). The M-PAC framework may be helpful for predicting PA levels among adults with poor perceived mental health. Future research should prospectively test the full M-PAC model to better inform PA intervention research among adults with poor mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".