The Role of Intention, Behavioral Regulation, and Physical Activity Behavior in the Prediction of Physical Activity Identity across Time
Bibliographic record
Abstract
Physical activity identity represents an important determinant of sustained physical activity behavior. The purpose of this investigation was to examine whether intention, behavioral regulation, and moderate-to-vigorous physical activity (MVPA) behavior explain significant variation in physical activity identity across time. Using a repeated measures observational design, lower-active adults new or returning to physical activity participation (N = 66) completed measures of study variables every three weeks over the course of a nine-week period (four assessments total). Based on the results of mixed-effects regression modelling, there was a small, non-significant increase in physical activity identity across time (b = 0.07, p = 0.13). Intention, MVPA, and behavioral regulation mostly had significant (ps < 0.05) bivariate correlations with physical activity identity at the same time point of assessment. Behavioral regulation explained significant variation in physical activity identity across time (b = 0.26, p < 0.0001), but intention and MVPA were non-significant (ps > 0.05) after including a random intercept and controlling for behavioral regulation. Identity was resistant to change among new physical activity initiates in this study and longer time frames of assessment are needed (e.g., six months). Behavioral regulation should be examined as a determinant of physical activity identity in future investigations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".