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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".