Can Persuasive Messages Encourage Individuals to Create Action Plans for Physical Activity?
Bibliographic record
Abstract
Given the positive influence of action planning on physical activity, persuasive messages could be designed to promote action planning. The purpose of this paper was to test action planning messages in two studies. Participants were allocated to one of two message groups, reading either a physical activity only or physical activity plus action planning message (Study 1) and either a gain-framed or loss-framed action planning message (Study 2). The percent of individuals who created an action plan and the quality of the plans were evaluated. In Study 1, individuals in the physical activity plus action planning group created as many action plans as the physical activity only group, but their plans were higher quality. In Study 2, Week 2 differences between the gain- and loss-framed message groups were found for action planning. To our knowledge, these studies were the first to investigate message-induced action planning as a behavior. More research is needed to optimize these messages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".