Abstract 034: A Healthy Lifestyle Score Including Sleep Duration And Risk Of Cardiovascular Disease
Bibliographic record
Abstract
Objectives: The aim of this study was to evaluate the relationship between a lifestyle score including sleep duration and CVD risk, and to estimate whether adding sleep duration into a traditional lifestyle score improved CVD risk prediction. Methods: A prospective analysis was conducted among 67250 women in the Nurses’ Health Study and 29279 men in the Health Professionals Follow-up Study who were followed from 1986 to 2016. The traditional lifestyle score was defined as not smoking, normal BMI(18.5-24.9 kg/m 2 ), ≥30 min/d of moderate physical activity, higher diet quality (top 40% of AHEI), moderate alcohol intake (women:5-15g/day; men:5-30g/day). Low-risk sleep duration, defined as sleeping ≥6 to <8 hours/day, was included as an additional component. Cox proportion hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of CVD, CHD, and stroke. We used the likelihood ratio test and C-statistics to compare the predictive value of the two scores. Results: A total of 11826 incident CVD cases were documented. In multivariable-adjusted models, each low-risk factor was independently and significantly associated with lower risk of CVD, CHD, and stroke. The multivariable-adjusted HRs (95% CIs) comparing six with zero low-risk factors in the healthy lifestyle score were 0.17(0.12, 0.23) for CVD, 0.15(0.10, 0.22) for CHD, and 0.19(0.12, 0.33) for stroke. Approximately 67% of CVD and CHD cases, and 62% stroke cases were attributable to poor adherence to a healthy lifestyle. P- value for likelihood ratio test comparing nested models including the traditional lifestyle score vs traditional lifestyle score plus sleep duration was <0.001. Adding sleep duration to the traditional score prediction model increased the C-statistics from 0.63 (95% CI: 0.62, 0.63) to 0.64 (95% CI: 0.63, 0.65)( P <0.001). Conclusions: Incorporating sleep duration into traditional lifestyle scores improves prediction of CVD risk and warrants consideration for inclusion in lifestyle recommendations.
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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 | 0.001 | 0.001 |
| 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 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".