Comparison of healthy lifestyle behaviors among individuals with and without cardiovascular diseases from urban and rural areas in China: A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: The study aimed to explore the gap of prevalence of healthy lifestyle behaviors including smoking cessation, quitting drinking, physical activity and healthy eating between Chinese adults with and without cardiovascular diseases (CVDs). METHODS: This study is a cross-sectional component of Prospective Urban Rural Epidemiology (PURE)-China study, which recruited ~46,000 participants from 70 rural and 45 urban communities between 2005 and 2009. Participants were divided into disease (with CVDs) and control (without any diseases) groups. The adjusted rates were estimated for different strata by the generalized, linear mixed-effects model, including community as a random effect with additional adjustment for age, sex, education and income. RESULTS: Among 40,490 participants, <10% had all four healthy lifestyle behaviors (disease group versus control group: urban areas: 7.8% versus 8.1%; rural areas: 3.4% versus 3.2%). The rates of smoking cessation and quitting drinking were significantly higher in disease group for both urban and rural residents (P<0.001). In urban areas, higher rates were observed in all other three healthy lifestyle behaviors except physical activity in low-income regions (P<0.05). Similarly, the higher trends were observed for stopping smoking and drinking while opposite trends for healthy eating among rural residents from low-income regions (P<0.05). CONCLUSIONS: Our study showed that the prevalence of adopting all four behaviors was low among Chinese adults. Individuals with CVDs were more likely to follow healthy lifestyle behaviors, but it still indicated a large gap between the actual and ideal adoption of healthy lifestyle behaviors, which called for the promotion of population-wide strategies to modify lifestyle behaviors in addition to individual health-care intervention strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".