A longitudinal analysis of how daily life physical activity versus gym-related if-then plans predict weight loss
Bibliographic record
Abstract
If-then plans are concrete action plans that specify, in an if-then format, where, when, and how one will act in order to achieve a specific goal-directed behaviour (Gollwitzer, 1993). This study examined whether making if-then plans to engage in daily life physical activities (DLPA) results in greater weight loss compared to gym-related physical activities (GRPA). We hypothesized that DLPA will result in greater weight loss than GRPA because DLPA are more feasible and achievable than GRPA. DLPA should thus be more likely to be carried out and contribute to weight loss. ata from a sample of overweight/obese participants (BMI range of 28 to 45 kg/m2) who partook in the 12-month McGill CHIP Healthy Weight Program were collected. The program was designed to teach participants to change various habits through lifestyle changes (e.g., taking the stairs, cycling to work, taking fitness classes). The number and specificity of DLPA and GRPA if-then plans, as well as participants' weight were assessed at every session, for a total of 22 sessions. Multi-level analyses examined the effect of the number and specificity of DLPA plans relative to those of GRPA plans in predicting changes in weight during the program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".