Prediction of Physical Activity Level Using Processes of Change From the Transtheoretical Model: Experiential, Behavioral, or an Interaction Effect?
Bibliographic record
Abstract
PURPOSE: The purpose of the present study was to compare prediction of physical activity (PA) by experiential or behavioral processes of change (POCs) or an interaction between both types of processes. DESIGN: A cross-sectional study. SETTING: This study was conducted using an online questionnaire. PARTICIPANTS: were included. MEASURES: Participants completed the Processes of Change, Stages of Change questionnaires, and the International Physical Activity Questionnaire to evaluate self-reported PA level (total, vigorous, and moderate PA). ANALYSIS: Hierarchical multiple regression models were used to test the prediction of PA level. RESULTS: For both total PA (β = .261; P < .001) and vigorous PA (β = .297; P < .001), only behavioral POCs were a significant predictor. Regarding moderate PA, only the interaction between experiential and behavioral POCs was a significant predictor (β = .123; P = .017). CONCLUSION: Our results provide confirmation that behavioral processes are most prominent in PA behavior. Nevertheless, it is of interest to note that the interaction between experiential and behavioral POCs was the only element predicting moderate PA level. Experiential processes were not associated with PA level.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".