Action planning fosters adoption of regular physical activity behavior among low-control individuals with high intention
Bibliographic record
Abstract
This study aims to document how action planning (AcP) and coping planning (CP) (Gollwitzer, 1999) combines with the intention and perceived behavioral control (PBC) variables (Ajzen, 1991) to predict physical activity (PA) behavior.It was hypothesized that: 1) intention and the use of planning would each have a main effect on PA behavior, 2) AcP, with or without CP, would be useful to individuals with low PBC if their intention was high.In a quasi-experimental prospective design, 122 individuals were asked to engage regularly in PA for the 5 following weeks with the help of AcP alone, AcP and CP, or no planning at all.TPB variables and past month PA behavior were measured at T1 and frequency of PA was measured at T2. Results of an ANCOVA 2 (low vs high intention) X 3 (no planning, AcP, AcP + CP) X 3 (low, average or high PBC), which controlled for the infl uence of past behavior, revealed a main effect of intention and a signifi cant interaction between intention, planning and PBC.Simple effects analysis demonstrated that AcP alone improved PA frequency among low PBC individuals with high intention.Limits of the design as well as conceptual implications are discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".