Associating modifiable lifestyle factors with multimorbidity in community dwelling individuals from mainland China
Bibliographic record
Abstract
AIMS: Lifestyle factors have been well-established as essential targets for fighting individual chronic diseases, but little research has concentrated on multimorbidity from the perspective of multiple lifestyle factors in the Chinese population. Thus, this study aimed to explore the associations of lifestyle factors with the occurrence of multimorbidity. METHODS AND RESULTS: Cross-sectional data retrieved from the China Health and Retirement Longitudinal Study were used for analysis. Multimorbidity was calculated on a simple count of self-reported chronic conditions. Lifestyle factors included sleep duration, physical activity, alcohol intake, smoking status, and body mass index. Logistic regression analysis was used to examine the independent and accumulating effects of lifestyle factors on multimorbidity. Latent class analysis was performed to explore the lifestyle patterns. Six thousand, five hundred, and ninety-one valid subjects were included for analysis. Overall, the community dweller's median number of chronic conditions was 1 (range 1-11) and median number of high-risk lifestyle factors was 2 (range 0-5). All lifestyle factors were associated with the occurrence of multimorbidity but varied between genders. We also identified that participants who accumulated more unhealthy lifestyle factors having a higher likelihood of multimorbidity. 'Physical activity and weight', 'smoke and drink', and 'sleep and weight' dominated high-risk lifestyles were the most common lifestyle patterns. CONCLUSION: This study revealed the associations of unhealthy lifestyle factors and their accumulating effect with multimorbidity in Chinese community dwellers. Three common lifestyle patterns indicated that a holistic approach focused on engaging and changing multiple modifiable lifestyle behaviours within an individual might be more effective in managing multimorbidity.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".