Health Locus of Control Is Associated With Physical Activity and Other Health Behaviors in Cardiac Patients
Bibliographic record
Abstract
PURPOSE: Physical inactivity, smoking, and excessive alcohol use are well-recognized modifiable risk factors for cardiovascular disease (CVD), yet uptake of strategies to mitigate these poor health behaviors varies widely among patients with cardiovascular disease. Part of this variation may be explained by health locus of control (HLOC), defined as the extent to which individuals believe their health is a consequence of their own actions, chance, or the influence of others (eg, physicians). METHODS: A total of 599 cardiac outpatients (30% female, 61.4 ± 9.4 y of age) completed the Multidimensional Health Locus of Control questionnaire and a structured health behavior questionnaire assessing physical activity, smoking, and alcohol use, at baseline and a 4-y follow-up. Relationships between health behaviors and HLOC were assessed cross-sectionally and longitudinally using general linear models and logistic regression models adjusting for medical and sociodemographic factors. RESULTS: Higher Internal HLOC was found to be associated with higher levels of leisure time physical activity (LTPA) (β = .21, P = .0008), while lower Internal HLOC was associated with decreasing levels of alcohol consumption over time (β = .26, P = .03). Increasing Chance HLOC was related to lower levels of leisure time physical activity (β = -.15, P = .047) and increased likelihood of being a smoker (β = .10, P = .01), and increasing physician HLOC was associated with decreased likelihood of being a smoker (β = -.17, P = .01). CONCLUSIONS: Associations between HLOC and multiple health behaviors were observed in a large sample of cardiac outpatients. Results suggest that assessing and targeting HLOC beliefs of cardiac patients may be clinically relevant for behavior change in settings, such as in rehabilitation programs where behavior change is a goal.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".