Association between Shift Work Schedules and Cardiovascular Events in a Multi-Ethnic Cohort.

BACKGROUND: Shift work is known to increase the risk of cardiometabolic diseases and mortality. We investigate the relationship between shift work schedules and cardiometabolic risk factors (smoking, hypertension, and obesity) and their association with cardiometabolic diseases (diabetes and cardiovascular diseases) in a multi-ethnic population from Singapore. METHODS: 2469 participants from the Singapore-based Multi-Ethnic Cohort underwent physical and clinical assessments. Shift work schedules (morning, evening, night, and mixed) were assessed using a validated questionnaire. RESULTS: Among shift workers, night shift workers had a significantly higher prevalence of smoking (54.5%), diabetes (27.3%), and cardiovascular events (14.1%). Compared to non-shift workers, workers in the night (OR = 2.10, 95%CI: 1.26-3.41) and mixed (OR = 1.74, 95%CI: 1.22-2.48) shift groups were more likely to be current smokers. A significant association between shift duration and smoking (OR = 1.02, 95%CI: 1.00-1.03) was also observed, with longer shift duration (in years) leading to an increase in smoking behavior. No significant associations were found between shift work schedules and hypertension, obesity (BMI), diabetes, and cardiovascular disease, as well as other cardiometabolic risk factors and diseases. CONCLUSION: This study found that shift schedules and shift duration were most strongly associated with smoking status after covariate adjustments (age, gender, ethnicity, socioeconomic status, and work arrangement), with night and mixed shift types being strongly associated with current smoker status. As smoking is a modifiable risk factor for cardiometabolic disease, employers of shift workers should increase work-based health interventions to control smoking and promote a healthier workforce.

Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker.

Purpose: To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware. Patients and Methods: 58 healthy, East Asian adults aged 23-69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics. Results: Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen's d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males. Conclusion: These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.