Habit in the Physical Activity Domain: Integration With Intention Temporal Stability and Action Control
Bibliographic record
Abstract
The purpose of this study was to explore the role of habit in predicting physical activity with the theory of planned behavior (TPB). The study extended previous research by (a) including a measure of temporal intention stability in the regression equation, and (b) unpacking the intention x behavior x habit relationship. Participants were 153 undergraduate students who completed a habit measure and measures of the TPB at Time 1 followed by measures of intention and behavior 2 weeks later. Results using regression analysis demonstrated that habit explained 7% additional variance after accounting for the TPB and temporal stability of intention and its interaction with intention. Follow-up analyses showed considerable asymmetry in the three-way relationship between intention, behavior, and habit, where high habit participants were composed primarily of intenders (i.e., intended to be active >3 times/week at 30 min) who engaged in regular physical activity (70%, n = 28) and low habit participants were inactive nonintenders (i.e., did not intend to be active >3 times/week at 30 min and were subsequently not active; 69%, n = 25). The results support the notion that some properties of physical activity may have an automatic component and that habits may be important to physical activity action control.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".