Clustering of Unhealthy Behaviors in the Aerobics Center Longitudinal Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Clustering of unhealthy behaviors has been reported in previous studies; however the link with all-cause mortality and differences between those with and without chronic disease requires further investigation. OBJECTIVES: To observe the clustering effects of unhealthy diet, fitness, smoking, and excessive alcohol consumption in adults with and without chronic disease and to assess all-cause mortality risk according to the clustering of unhealthy behaviors. METHODS: Participants were 13,621 adults (aged 20-84) from the Aerobics Center Longitudinal Study. Four health behaviors were observed (diet, fitness, smoking, and drinking). Baseline characteristics of the study population and bivariate relations between pairs of the health behaviors were evaluated separately for those with and without chronic disease using cross-tabulation and a chi-square test. The odds of partaking in unhealthy behaviors were also calculated. Latent class analysis (LCA) was used to assess clustering. Cox regression was used to assess the relationship between the behaviors and mortality. RESULTS: The four health behaviors were related to each other. LCA results suggested that two classes existed. Participants in class 1 had a higher probability of partaking in each of the four unhealthy behaviors than participants in class 2. No differences in health behavior clustering were found between participants with and without chronic disease. Mortality risk increased relative to the number of unhealthy behaviors participants engaged in. CONCLUSION: Unhealthy behaviors cluster together irrespective of chronic disease status. Such findings suggest that multi-behavioral intervention strategies can be similar in those with and without chronic disease.
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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 | 0.000 | 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 it