Associations of actigraphy derived rest activity patterns and circadian phase with clinical symptoms and polysomnographic parameters in chronic insomnia disorders

Rest-Activity Rhythms Are Associated With Prevalent Cardiovascular Disease, Hypertension, Obesity, and Central Adiposity in a Nationally Representative Sample of US Adults

BACKGROUND

Rest-activity rhythms (RARs), a measure of circadian rhythmicity in the free-living setting, are related to mortality risk, but evidence is limited on associations with cardiovascular disease (CVD) and its risk factors.

METHODS AND RESULTS

Participants included 4521 adults from the 2013 to 2014 National Health and Nutrition Examination Survey physical activity monitoring examination. Wrist-worn ActiGraph GT3X+ data were used to estimate RARs. Multivariable logistic models evaluated associations of RARs with prevalent CVD, hypertension, obesity, and central adiposity. Participants (mean age, 49 years) in the highest versus lowest tertile of relative amplitude (greater circadian rhythmicity) had 39% to 62% lower odds of prevalent CVD, hypertension, obesity, and central adiposity. A more active wake period was associated with 19% to 72% lower CVD, hypertension, obesity, and central adiposity odds. Higher interdaily stability (regular sleep-wake and rest-activity patterns) was related to 52% and 23% lower CVD and obesity odds, respectively. In contrast, participants in the highest versus lowest tertile of intradaily variability (fragmented RAR and inefficient sleep) had >3-fold and 24% higher CVD and obesity odds, respectively. A later and less restful sleep period was associated with 36% to 2-fold higher CVD, hypertension, obesity, and central adiposity odds. A statistically significant linear trend was observed for all associations (P-trend<0.05).

CONCLUSIONS

A robust, stable, and less fragmented RAR, an active wake period, and an earlier and more restful sleep period are associated with lower prevalent CVD, hypertension, obesity, and central adiposity, with evidence of a dose-response relationship. The magnitude, timing, and regularity of sleep-wake and rest-activity patterns may be important targets for reducing cardiovascular risk.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived From Wearable Physiological Data

Goal: To investigate whether a deep learning model can detect Covid-19 from disruptions in the human body's physiological (heart rate) and rest-activity rhythms (rhythmic dysregulation) caused by the SARS-CoV-2 virus. Methods: We propose CovidRhythm, a novel Gated Recurrent Unit (GRU) Network with Multi-Head Self-Attention (MHSA) that combines sensor and rhythmic features extracted from heart rate and activity (steps) data gathered passively using consumer-grade smart wearable to predict Covid-19. A total of 39 features were extracted (standard deviation, mean, min/max/avg length of sedentary and active bouts) from wearable sensor data. Biobehavioral rhythms were modeled using nine parameters (mesor, amplitude, acrophase, and intra-daily variability). These features were then input to CovidRhythm for predicting Covid-19 in the incubation phase (one day before biological symptoms manifest). Results: A combination of sensor and biobehavioral rhythm features achieved the highest AUC-ROC of 0.79 [Sensitivity = 0.69, Specificity = 0.89, F[Formula: see text] = 0.76], outperforming prior approaches in discriminating Covid-positive patients from healthy controls using 24 hours of historical wearable physiological. Rhythmic features were the most predictive of Covid-19 infection when utilized either alone or in conjunction with sensor features. Sensor features predicted healthy subjects best. Circadian rest-activity rhythms that combine 24 h activity and sleep information were the most disrupted. Conclusions: CovidRhythm demonstrates that biobehavioral rhythms derived from consumer-grade wearable data can facilitate timely Covid-19 detection. To the best of our knowledge, our work is the first to detect Covid-19 using deep learning and biobehavioral rhythms features derived from consumer-grade wearable data.

Association Between Diabetic Retinopathy and Insomnia Risk: A Nationwide Population-Based Study

BACKGROUND

Previous studies have suggested a close link between sleep disturbances and diabetic retinopathy (DR). However, to date, no confirmatory findings have been reported. We aimed to explore the risk of insomnia in DR by considering demographic factors and diabetes mellitus (DM)-related variables.

METHODS

A nationwide population-based cohort of 2,206,619 patients with type 2 diabetes from the Korean National Insurance Service Database was followed up for insomnia incidence. DR, non-proliferative DR (NPDR), and proliferative DR (PDR) were defined according to ICD-10 codes. The interactive effects of sex, age, and DM-related variables were analyzed to evaluate their impact on insomnia risk in DR.

RESULTS

Compared with the non-DR group, insomnia risk was increased in the DR [(adjusted hazard ratio (aHR): 1.125, 95% confidence interval (CI):1.108-1.142), NPDR (aHR:1.117, 95% CI:1.099-1.134), and PDR (aHR:1.205, 95% CI: 1.156-1.256), even after controlling for comorbidities, lifestyle factors, and DM-related variables. The men and youngest age groups (<40 years) were most vulnerable to insomnia risk. Sex, age, DM duration, and chronic kidney disease (CKD) status exerted interactive effects with DR status in increasing the insomnia risk. In the PDR group, sex, age, DM duration, insulin therapy status, and CKD status exerted interactive effects that increased the risk of insomnia.

CONCLUSION

Insomnia risk is significantly higher in patients with DR, and clinical attention is warranted.