Messages for men: The efficacy of EPPM-based messages targeting men's physical activity.
Bibliographic record
Abstract
OBJECTIVE: The majority of men are insufficiently active. Men's tendencies to participate in risky behaviors and their inactivity likely contribute to their increased risk of morbidity and mortality. Physical activity decreases the risk of developing many chronic diseases and may be an optimal behavior to target in men's health interventions. However, educational resources promoting physical activity for men are lacking. To address this gap, we tested the efficacy of messages based upon the Extended Parallel Process Model (EPPM; Witte, 1992) to increase men's physical activity intentions and behaviors. METHOD: Men who were not meeting physical activity guidelines (n = 611) were randomly assigned to read high or low efficacy physical activity messages paired with high or no health risk information. Participants read four brief messages on four consecutive days. Intentions were assessed at baseline and the first follow-up (Day 5). Manipulation check measures were assessed at Day 5. Behavior was assessed at baseline and the second follow-up (Day 14). RESULTS: Overall, the messages had small sized effects. A completer analysis revealed that although men's intentions to be active increased over the course of the study regardless of the messages they received, only men who received risk information significantly increased their physical activity. Men who received low efficacy and risk information were less likely to meet the physical activity guidelines at Day 14 than men who only received low efficacy information. CONCLUSIONS: From these results, we suggest preliminary recommendations for the development of physical activity messages for men and areas for future EPPM-based research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".