Faculty Bibliography

PURPOSE OF REVIEW/OBJECTIVE:In this current review, we describe the benefits of community-based and "precision and personalized population health" (P3H) approaches to assessing and addressing sleep health problems and sleep-related cardiovascular diseases (CVD) among vulnerable populations such as racial/ethnic minorities, the elderly, and the socioeconomically disadvantaged. RECENT FINDINGS/RESULTS:Very few sleep health programs utilize a community-based or P3H approach, which may account for low estimates of sleep health problems, related CVD outcomes, and inadequate healthcare infrastructure to address sleep-related health outcomes at the community and population level. We describe community-based and P3H approaches and programs as solutions to accurately capture estimates of sleep health and reduce burden of sleep health problems and corollary CVD outcomes at the level of the community and population. Specifically, we describe seven critical steps needed to successfully implement a community-based and P3H approach to address sleep health problems. Community-based and P3H approaches are effective strategies to assessing and addressing sleep health problems and related health conditions.

Patient nonadherence to healthy lifestyle behaviors and medical treatments (like medication adherence) accounts for a significant portion of chronic disease burden. Despite the plethora of behavioral interventions to overcome key modifiable/nonmodifiable barriers and enable facilitators to adherence, short- and long-term adherence to healthy lifestyle behaviors and medical treatments is still poor. To optimize adherence, we aimed to provide a novel mobile health solution steeped in precision and personalized population health and a pantheoretical approach that increases the likelihood of adherence. We have described the stages of a pantheoretical approach utilizing tailoring, clustering/profiling, personalizing, and optimizing interventions/strategies to obtain adherence and highlight the minimal engineering needed to build such a solution.

BACKGROUND:This study examined the relationships between resilience and sleep disturbance in a diverse sample of older women with a history of hypertension and whether this relationship is moderated by individuals' race/ethnicity. METHODS:Sample includes 700 females from a community-based study in Brooklyn, New York with a mean age of 60.7 years (SD=6.52). Of the participants, 28.1% were born in the U.S.; 71% were African-descent, 17.4% were European and 11.6% were Hispanics descents. Data were gathered on demographics and sleep disturbance using the Comprehensive Assessment and Referral Evaluation (CARE) and the Stress Index Scale (SIS). Resilience Factors were assessed with both the Index of Self-Regulation of Emotion (ISE) and religious health beliefs. Chi-Square, Anova, Student t-tests, and multilinear regression analysis were conducted to explore associations between resilience factors and sleep disturbance. Associations between resilience factors and sleep disturbance were examined using stratified multilinear regression analysis in three models by race/ethnicity. Regression models was conducted examining the interaction between resilience factors and stress RESULTS: Resilience factor, ISE emerged as the strongest independent predictor of sleep disturbance [B(SE) = -0.368(0.008); p < .001] for African descents. ISE was not a significant predictor of sleep disturbance among Hispanic participants [B(SE) = -0.218(0.022);p = .052], however interaction effect analysis revealed that stress level moderates significantly the relationship between ISE, and their sleep disturbance [B(SE) = 0.243(0.001);p = .036]. CONCLUSIONS:Results of our study suggest that resilience factors might be a more important protective factor for sleep disturbance among diverse older females.

None/UNASSIGNED:Polysomnography (PSG) remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.

BACKGROUND:Population estimates of sleep duration and quality are inconsistent because they rely primarily on self-reported data. Passive and ubiquitous digital tracking and wearable devices may provide more accurate estimates of sleep duration and quality. OBJECTIVE:This study aimed to identify trends in sleep duration and quality in New York City based on 2 million nights of data from users of a popular mobile sleep app. METHODS:We examined sleep duration and quality using 2,161,067 nights of data captured from 2015 to 2018 by Sleep Cycle, a popular sleep-tracking app. In this analysis, we explored differences in sleep parameters based on demographic factors, including age and sex. We used graphical matrix representations of data (heat maps) and geospatial analyses to compare sleep duration (in hours) and sleep quality (based on time in bed, deep sleep time, sleep consistency, and number of times fully awake), considering potential effects of day of the week and seasonality. RESULTS:Women represented 46.43% (1,003,421/2,161,067) of the sample, and men represented 53.57% (1,157,646/2,161,067) of individuals in the sample. The average age of the sample was 31.0 years (SD 10.6). The mean sleep duration of the total sample was 7.11 hours (SD 1.4). Women slept longer on average (mean 7.27 hours, SD 1.4) than men (mean 7 hours, SD 1.3; P<.001). Trend analysis indicated longer sleep duration and higher sleep quality among older individuals than among younger (P<.001). On average, sleep duration was longer on the weekend nights (mean 7.19 hours, SD 1.5) than on weeknights (mean 7.09 hours, SD 1.3; P<.001). CONCLUSIONS:Our study of data from a commercially available sleep tracker showed that women experienced longer sleep duration and higher sleep quality in nearly every age group than men, and a low proportion of young adults obtained the recommended sleep duration. Future research may compare sleep measures obtained via wearable sleep trackers with validated research-grade measures of sleep.

(a) Background: In patients with sleep apnea, poor adherence to positive airway pressure (PAP) therapy has been associated with mortality. Regional studies have suggested that lower socioeconomic status is associated with worse PAP adherence but population-level data is lacking. (b) Methods: De-identified data from a nationally representative database of PAP devices was geo-linked to sociodemographic information. (c) Results: In 170,641 patients, those in the lowest quartile of median household income had lower PAP adherence (4.1 + 2.6 hrs/night; 39.6% adherent by Medicare criteria) than those in neighborhoods with highest quartile median household income (4.5 + 2.5 hrs/night; 47% adherent by Medicare criteria; p < 0.0001). In multivariate regression, individuals in neighborhoods with the highest income quartile were more adherent to PAP therapy than those in the lowest income quartile after adjusting for various confounders (adjusted Odds Ratio (adjOR) 1.18; 95% confidence interval (CI) 1.14, 1.21; p < 0.0001). Over the past decade, PAP adherence improved over time (adjOR 1.96; 95%CI 1.94, 2.01), but health inequities in PAP adherence remained even after the Affordable Care Act was passed. (d) Conclusion: In a nationally representative population, disparities in PAP adherence persist despite Medicaid expansion. Interventions aimed at promoting health equity in sleep apnea need to be undertaken.

None/UNASSIGNED:Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associated events during a polysomnogram (PSG). This position statement outlines the potential opportunities and limitations of integrating artificial intelligence (AI) into the practice of sleep medicine. Additionally, although the most apparent and immediate application of AI in our field is the assisted scoring of PSG, we propose potential clinical use cases that transcend the sleep laboratory and are expected to deepen our understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase our knowledge of the role of sleep in health at a population level.

The Center for Epidemiological Studies Depression-10 (CES-D-10) scale is known for its good psychometric properties in measuring depressive symptoms, however, some researchers question its applicability across various settings. This study explored the factor structure of the CES-D-10 in low- and middle-income countries (LMICs). This cross-sectional survey consisted of 16,723 university students across 27 LMICs that completed self-report instruments assessing socio-demographic information and depressive symptoms using the CES-D-10. Data analysis included: exploratory factor analysis, item response theory and differential item functioning. Results indicate that a two-factor model (depressive affect and positive affect) had the best fit for this population and accounted for 52% of the total observed variance with an internal consistency, α = .77 for the depressive affect items and α = .57 for the positive affect items. The graded response model (GRM), however, indicated that the depressive affect factor had a good fit, unlike the positive affect factor. The depressive affect factor was found to consistently model depression for females better than males. Relative to their Asian counterparts, African, Caribbean and South American participants of similar depressive affect responded differently on all items of the depressive affect factor. The depressive affect factor seems most ideal for LMICs and shows gender and cross-cultural variability.

Purpose of review/UNASSIGNED:To systematically review the available research studies that characterize the benefits, uncertainty, or weaknesses of commercially-available sleep tracking technology. Recent findings/UNASSIGNED:Sleep is a vital component of health and well-being. Research shows that tracking sleep using commercially available sleep tracking technology (e.g., wearable or smartphone-based) is increasingly popular in the general population. Methods/UNASSIGNED:Systematic literature searches were conducted using PubMed/Medline, Embase (Ovid) the Cochrane Library, PsycINFO (Ovid), CINAHL, and Web of Science Plus (which included results from Biosis Citation Index, INSPEC, and Food, Science & Technology Abstracts) (n=842). Study Inclusion and Exclusion Criteria/UNASSIGNED:Three independent reviewers reviewed eligible articles that administered a commercially-available sleep tracker to participants and reported on sleep parameters as captured by the tracker, including either sleep duration or quality. Eligible articles had to include sleep data from users for >=4 nights.