The integrated common-sense model of illness self-regulation: predicting healthy eating, exercise behaviors, and health among individuals at risk of metabolic syndrome
Bibliographic record
Abstract
BACKGROUND: Little is known about the potential mechanisms of healthy eating and exercise change, and design interventions which aim to promote healthy eating and exercise change among individuals at risk of metabolic syndrome. This study aimed to identify key determinants of healthy eating, exercise behaviors, and health among individuals at risk of metabolic syndrome using the integrated common-sense model of illness self-regulation. METHOD: A cross-sectional study with a multi-wave data collection strategy. A total of 275 participants at risk of metabolic syndrome based on the clinical prediction model were included in the final analysis. Path analysis was employed to explore the pattern of relationships between key variables using AMOS. RESULTS: The mediation analysis suggested that personal and treatment control, and coherence can positively affect self-reported health via intentions and health behaviors (exercise and healthy eating). Additionally, relationships between self-efficacy (exercise and healthy eating) and health outcomes can be mediated by health behaviors, and both intentions and health behaviors. CONCLUSIONS: This current research used the integrated common-sense model of illness self-regulation to predict healthy eating, exercise behaviors, and self-reported health among individuals at risk of metabolic syndrome. The results suggested that self-efficacy, intention, consequences, personal control, treatment control, and coherence were the key determinants of behavior and health, which can help design interventions to encourage healthy eating and exercise changes among individuals with a high risk of MetS.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".