The Relationship between Perceived Health Message Motivation and Social Cognitive Beliefs in Persuasive Health Communication
Bibliographic record
Abstract
People respond to different types of health messages in persuasive health communication aimed at motivating behavior change. Hence, in human factors design, there is a need to tailor health applications to different user groups rather than change the human characteristics and conditions. However, in the domain of fitness app design, there is limited research on the relationship between users’ perceived motivation of health messages and their social–cognitive beliefs about exercise, and how this relationship is moderated by gender. Knowledge of the gender difference will help in tailoring fitness apps to the two main gender types. Hence, I conducted an empirical study to investigate the types of health messages that are most likely to motivate users and how these messages are related to outcome expectation, self-efficacy, and self-regulation beliefs in the context of exercise modeling. The results of the data analysis show that users are more motivated by illness- and death-related messages compared with obesity-, social stigma-, and financial cost-related messages. Moreover, illness- and death-related messages have a significant relationship with users’ social–cognitive beliefs about bodyweight exercise. These findings indicate that, in the fitness domain, illness- and death-related messages may be employed as a persuasive technique to motivate regular exercise.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".