Automaticity mediates the association between action planning and physical activity, especially when autonomous motivation is high
Bibliographic record
Abstract
Objectives Action planning promotes physical activity (PA). However, mechanisms underlying this association are poorly understood, as are the variables that moderate this link remain unexplored. To fill these gaps, we investigated whether automaticity mediated the association between action planning and PA, and whether autonomous motivation moderated this mediation.Methods and Measures PA was measured by accelerometry over seven days among a sample of 124 adults. Action planning, automaticity, and autonomous motivation were assessed by questionnaires.Results Structural equation models revealed that automaticity mediated the association between action planning and PA (total effect, β = .29, p < .001) – action planning was associated with automaticity (a path, β = .47, p < .001), which in turn related to PA (b path, β = .33, p = .003). Autonomous motivation moderated the a path (β = .16, p = .035) – action planning was more strongly associated with automaticity when autonomous motivation was high (+1 standard-deviation [SD]) (unstandardized b = 0.77, p < .001) versus low (-1 SD) (b = 0.35, p = .023).Conclusion These findings not only support that action planning favors an automatic behavioral regulation, but also highlight that a high autonomous motivation toward PA may reinforce this mechanism.
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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 | 0.001 | 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".