Explore the vast range of titles available.

Abstract Study Objectives Sleep is an emergent, multi-dimensional risk factor for diabetes. Sleep duration, timing, quality, and insomnia have been associated with diabetes risk and glycemic biomarkers, but the role of sleep regularity in the development of metabolic disorders is less clear. Methods We analyzed data from 2107 adults, aged 19–64 years, from the Sueño ancillary study of the Hispanic Community Health Study/Study of Latinos, followed over a mean of 5.7 years. Multivariable-adjusted complex survey regression methods were used to model cross-sectional and prospective associations between the sleep regularity index (SRI) in quartiles (Q1-least regular, Q4-most regular) and diabetes (either laboratory-confirmed or self-reported antidiabetic medication use), baseline levels of insulin resistance (HOMA-IR), beta-cell function (HOMA-β), hemoglobin A1c (HbA1c), and their changes over time. Results Cross-sectionally, lower SRI was associated with higher odds of diabetes (odds ratio [OR]Q1 vs. Q4 = 1.64, 95% CI: 0.98–2.74, ORQ2 vs. Q4 = 1.12, 95% CI: 0.70–1.81, ORQ3 vs. Q4 = 1.00, 95% CI: 0.62–1.62, ptrend = 0.023). The SRI effect was more pronounced in older (aged ≥ 45 years) adults (ORQ1 vs. Q4 = 1.88, 95% CI: 1.14–3.12, pinteraction = 0.060) compared to younger ones. No statistically significant associations were found between SRI and diabetes incidence, as well as baseline HOMA-IR, HOMA-β, and HbA1c values, or their changes over time among adults not taking antidiabetic medication. Conclusions Our results suggest that sleep regularity represents another sleep dimension relevant for diabetes risk. Further research is needed to elucidate the relative contribution of sleep regularity to metabolic dysregulation and pathophysiology.

Abstract Study Objectives Light is the main time cue for the human circadian system. Sleep and light are intrinsically linked; light exposure patterns can influence sleep patterns and sleep can influence light exposure patterns. However, metrics for quantifying light regularity are lacking, and the relationship between sleep and light regularity is underexplored. We developed new metrics for light regularity and demonstrated their utility in adolescents, across school term and vacation. Methods Daily sleep/wake and light patterns were measured using wrist actigraphy in 75 adolescents (54% male, 17.17 ± 0.83 years) over 2 weeks of school term and a subsequent 2-week vacation. The Sleep Regularity Index (SRI) and social jetlag were computed for each 2-week block. Light regularity was assessed using (1) variation in mean daily light timing (MLiT); (2) variation in daily photoperiod; and (3) the Light Regularity Index (LRI). Associations between SRI and each light regularity metric were examined, and within-individual changes in metrics were examined between school and vacation. Results Higher SRI was significantly associated with more regular LRI scores during both school and vacation. There were no significant associations of SRI with variation in MLiT or daily photoperiod. Compared to school term, all three light regularity metrics were less variable during the vacation. Conclusions Light regularity is a multidimensional construct, which until now has not been formally defined. Irregular sleep patterns are associated with lower LRI, indicating that irregular sleepers also have irregular light inputs to the circadian system, which likely contributes to circadian disruption. Graphical abstract Graphical Abstract

Abstract Study Objectives Little is known about the interrelationships between sleep regularity, obstructive sleep apnea (OSA) and important health markers. This study examined whether irregular sleep is associated with OSA and hypertension, and if this modifies the known association between OSA and hypertension. Methods Six hundred and two adults (age mean(SD) = 56.96(5.51) years, female = 60%) from the Raine Study who were not evening or night shift workers were assessed for OSA (in-laboratory polysomnography; apnea–hypopnea index ≥ 15 events/hour), hypertension (doctor diagnosed, or systolic blood pressure ≥140 mmHg and/or diastolic ≥90 mmHg) and sleep (wrist actigraphy for ≥5 days). A sleep regularity index (SRI) was determined from actigraphy. Participants were categorized by tertiles as severely irregular, mildly irregular, or regular sleepers. Logistic regression models examined the interrelationships between SRI, OSA and hypertension. Covariates included age, sex, body mass index, actigraphy sleep duration, insomnia, depression, activity, alcohol, smoking, and antihypertensive medication. Results Compared to regular sleepers, participants with mildly irregular (OR 1.97, 95% confidence intervals [CI] 1.20 to 3.27) and severely irregular (OR 2.06, 95% CI: 1.25 to 3.42) sleep had greater odds of OSA. Compared to those with no OSA and regular sleep, OSA and severely irregular sleep combined had the highest odds of hypertension (OR 2.34 95% CI: 1.07 to 5.12; p for interaction = 0.02) while those with OSA and regular/mildly irregular sleep were not at increased risk (p for interaction = 0.20). Conclusions Sleep irregularity may be an important modifiable target for hypertension among those with OSA. Graphical Abstract Graphical Abstract

Emerging evidence supports a multidimensional perspective of sleep in the context of health. The sleep health model, and composite sleep health score, are increasingly used in research. However, specific cutoff values that differentiate \"good\" from \"poor\" sleep, have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for sleep health dimensions and examined the relationship between sleep health and cardiometabolic morbidity. Participants from two independent Biomarker Studies in the MIDUS II (N = 432, 39.8% male, age = 56.92 ± 11.45) and MIDUS Refresher (N = 268, 43.7% male, age = 51.68 ± 12.70) cohorts completed a 1-week study where sleep was assessed with daily diaries and wrist actigraphy. Self-reported physician diagnoses, medication use, and blood values were used to calculate total cardiometabolic morbidity. Receiver operating characteristic (ROC) curves were generated in the MIDUS II cohort for each sleep health dimension to determine cutoff values. Using derived cutoff values, logistic regression was used to examine the relationship between sleep health scores and cardiometabolic morbidity in the MIDUS Refresher cohort, controlling for traditional risk factors. Empirically derived sleep health cutoff values aligned reasonably well to cutoff values previously published in the sleep health literature and remained robust across physical and mental health outcomes. Better sleep health was significantly associated with a lower odds of cardiometabolic morbidity (OR [95% CI] = 0.901 [0.814-0.997], p = .044). These results contribute to the ongoing development of the sleep health model and add to the emerging research supporting a multidimensional perspective of sleep and health.

Abstract Study Objectives Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric—the Composite Phase Deviation (CPD)—to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. Methods Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy–Alert, Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. Results CPD for sleep was a significant predictor of average well-being (e.g. Stressed–Calm: b = −6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed–Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. Conclusions Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. Clinical Trial Registration NCT02846077.

Abstract Study Objectives The objectives of this study were to examine the relationships between sleep regularity and nonsuicidal self-injury (NSSI), including lifetime NSSI history and daily NSSI urges. Methods Undergraduate students (N = 119; 18–26 years), approximately half of whom endorsed a lifetime history of repetitive NSSI, completed a 10-day actigraphy and ecological momentary assessment (EMA) protocol. A Sleep Regularity Index was calculated for all participants using scored epoch by epoch data to capture rapid changes in sleep schedules. Participants responded to EMA prompts assessing NSSI urge severity and negative affect three times daily over the 10-day assessment period. Results Results indicate that individuals with a repetitive NSSI history were more likely to experience sleep irregularity than those without a history of NSSI. Findings also suggest that sleep irregularity was associated with more intense urges to engage in NSSI on a daily basis, even after accounting for average daily sleep duration, sleep timing, negative affect, and NSSI history. Neither sleep duration nor sleep timing was associated with NSSI history nor daily NSSI urge intensity. Conclusions Findings suggest that sleep irregularity is linked with NSSI, including NSSI history and intensity of urges to engage in NSSI. The present study not only supports the growing evidence linking sleep disturbance with the risk for self-injury but also demonstrates this relationship using actigraphy and real-time assessments of NSSI urge severity. Findings highlight the importance of delineating the nuances in sleep irregularity that are proximally associated with NSSI risk and identifying targets for intervention.

Abstract Study Objectives There is mixed evidence regarding associations of sleep duration with academic functioning in adolescents and a lack of research on other sleep dimensions, particularly using objective sleep measures. We examined associations of multiple actigraphic sleep dimensions with academic functioning among adolescents. Methods Data were from the sleep sub-study of the age 15 wave of the Future of Families and Child Wellbeing Study (n = 774–782; 52% female), a national, diverse sample of teens. Adolescents wore wrist-actigraphs for ~1 week and completed a survey reporting academic performance and school-related behavioral problems. Regression models assessed whether average sleep duration, timing, maintenance efficiency, and SD-variability were associated with self-reported academic functioning in cross-sectional analyses adjusted for demographic characteristics, depressive symptoms, and anxious symptoms. Results Later sleep timing (hours) and greater sleep variability (SD-hours) were associated with poorer academic outcomes, including sleep onset variability with higher odds of receiving a D or lower (OR = 1.29), sleep onset (β = −.07), sleep offset (β = −.08), and sleep duration variability (β = −.08) with fewer A grades, sleep offset with lower GPA (β = −.07), sleep offset (OR = 1.11), sleep duration variability (OR = 1.31), and sleep onset variability (OR = 1.42) with higher odds of being suspended or expelled in the past 2 years, and sleep duration variability with greater trouble at school (β = .13). Sleep duration, sleep maintenance efficiency, and sleep regularity index were not associated with academic functioning. Conclusions Later sleep timing and greater sleep variability are risk factors for certain academic problems among adolescents. Promoting sufficient, regular sleep timing across the week may improve adolescent academic functioning. Graphical Abstract Graphical Abstract

Depression and anxiety are prevalent mental health disorders. While sleep duration has been extensively studied, sleep regularity may play a critical role. We aimed to examine associations between objectively measured sleep regularity and incident depression and anxiety and to investigate whether meeting recommended sleep duration modifies these associations. In 79,666 UK Biobank participants without baseline depression or anxiety, wrist accelerometers worn for 7 days yielded a sleep regularity index (SRI) and average sleep duration. SRI was categorized as irregular (≤51), moderately irregular (52-70), or regular (≥71). Sleep duration was classified by age-specific recommendations (7-9 hours for ages 18-64 years; 7-8 hours for over 65 years). Cox regression models assessed associations between sleep parameters and mental health outcomes. During a median follow-up of 7.5 years, 1,646 participants developed depression, and 2,097 developed anxiety. Compared to irregular sleepers, regular sleepers had a 38% lower depression risk (hazard ratio [HR], 0.62; 95% confidence interval [CI], 0.52-0.73) and a 33% lower anxiety risk (HR, 0.67; 95%CI, 0.58-0.77). Participants with both irregular sleep and nonrecommended duration exhibited the highest risks (depression HR, 1.91; 95%CI, 1.55-2.35; anxiety HR, 1.61; 95%CI, 1.35-1.93). Notably, irregular sleepers who met duration guidelines still faced elevated risks (depression HR, 1.48; 95%CI, 1.18-1.86; anxiety HR, 1.35; 95%CI, 1.11-1.64). Greater sleep regularity is independently associated with lower depression and anxiety risk regardless of sleep duration, suggesting that sleep-wake consistency should be considered in mental health promotion strategies alongside traditional sleep duration recommendations.

Purpose of ReviewNight-to-night variability in sleep patterns leads to circadian disruption and, consequently, could increase cardiometabolic risk. The purpose of this review is to summarize findings from studies published between 2015 and 2020 examining various measures of night-to-night variability in sleep in relation to metabolic syndrome (MetS), type 2 diabetes (T2D), and their risk factors. We illustrate a potential causal pathway between irregular sleep patterns and T2D, highlighting knowledge gaps along the way.Recent FindingsAcross different measures of sleep variability, irregular sleep patterns were associated with poorer cardiometabolic outcomes. Higher standard deviations (SD) across nights of sleep duration and onset or midpoint of sleep were associated with increased odds of having MetS and clusters of metabolic abnormalities as well as greater adiposity and poorer glycemic control. Conversely, greater regularity of rest-activity patterns related to lower risk for T2D. Social jetlag was associated with glycemic dysregulation, adiposity, T2D, and MetS. These associations are often observed in both metabolically healthy and unhealthy individuals; both higher SD of sleep duration and social jetlag relate to poorer glucose regulation in individuals with diabetes.SummaryThere is consistent evidence of associations of sleep variability with increased risk for adiposity, glucose dysregulation, T2D, and MetS. Although experimental evidence is needed to determine causation, there is support to recommend stabilizing sleep patterns for cardiometabolic risk prevention.

Abstract Study Objectives Vascular dysfunction is a hypothesized mechanism linking poor sleep habits to an increased incidence of cardiovascular diseases (CVDs). However, the vascular profile associated with free-living sleep duration and sleep regularity has not been well elucidated, particularly in young adults. Thus, this study aimed to evaluate the associations between mean sleep duration, regularity in sleep duration, and peripheral vascular function in young adult college students. Methods Fifty-one healthy undergraduate students (20 ± 1 years) completed 14 days of 24-hour wrist actigraphy and subsequent vascular assessments. Macrovascular function was measured using brachial artery flow-mediated dilation (FMD) while microvascular function was measured via passive leg movement (PLM). Results Mean sleep duration was unrelated to FMD and PLM. Conversely, more irregular sleep duration (14-day sleep duration standard deviation [SD]) was unfavorably associated with all three measures of PLM-induced hyperemia (peak leg blood flow [LBF], p = 0.01; change in LBF from baseline to peak, p < 0.01; LBF area under the curve, p < 0.01), and remained significant in regression models which adjusted for sex, body mass index, blood pressure, physical activity, alcohol and caffeine consumption, and sleep duration (all p < 0.05). When using a median split to dichotomize “low” and “high” sleep duration SD groups, those demonstrating high variability in sleep duration exhibited ~45% lower PLM responses compared with those demonstrating low variability. Conclusions Irregular sleep duration is associated with poorer microvascular function as early as young adulthood. These findings support the growing body of evidence that irregular sleep patterns may be an independent and modifiable risk factor for CVD.