Explore the vast range of titles available.

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average; these traditional metrics include intra-individual standard deviation (StDev), interdaily stability (IS), and social jet lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: composite phase deviation (CPD) and sleep regularity index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep–wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect the measurement of sleep regularity: “scrambling” the order of days; daily vs. weekly variation; naps; awakenings; “all-nighters”; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep–wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

Before the invention of electric lighting, humans were primarily exposed to intense (>300 lux) or dim (<30 lux) environmental light—stimuli at extreme ends of the circadian system’s dose–response curve to light. Today, humans spend hours per day exposed to intermediate light intensities (30–300 lux), particularly in the evening. Interindividual differences in sensitivity to evening light in this intensity range could therefore represent a source of vulnerability to circadian disruption by modern lighting. We characterized individual-level dose–response curves to light-induced melatonin suppression using a within-subjects protocol. Fifty-five participants (aged 18–30) were exposed to a dim control (<1 lux) and a range of experimental light levels (10–2,000 lux for 5 h) in the evening. Melatonin suppression was determined for each light level, and the effective dose for 50% suppression (ED50) was computed at individual and group levels. The group-level fitted ED50 was 24.60 lux, indicating that the circadian system is highly sensitive to evening light at typical indoor levels. Light intensities of 10, 30, and 50 lux resulted in later apparent melatonin onsets by 22, 77, and 109 min, respectively. Individual-level ED50 values ranged by over an order of magnitude (6 lux in the most sensitive individual, 350 lux in the least sensitive individual), with a 26% coefficient of variation. These findings demonstrate that the same evening-light environment is registered by the circadian system very differently between individuals. This interindividual variability may be an important factor for determining the circadian clock’s role in human health and disease.

Abstract Abnormally short and long sleep are associated with premature mortality, and achieving optimal sleep duration has been the focus of sleep health guidelines. Emerging research demonstrates that sleep regularity, the day-to-day consistency of sleep–wake timing, can be a stronger predictor for some health outcomes than sleep duration. The role of sleep regularity in mortality, however, has not been investigated in a large cohort with objective data. We therefore aimed to compare how sleep regularity and duration predicted risk for all-cause and cause-specific mortality. We calculated Sleep Regularity Index (SRI) scores from > 10 million hours of accelerometer data in 60 977 UK Biobank participants (62.8 ± 7.8 years, 55.0% female, median[IQR] SRI: 81.0[73.8–86.3]). Mortality was reported up to 7.8 years after accelerometer recording in 1859 participants (4.84 deaths per 1000 person-years, mean (±SD) follow-up of 6.30 ± 0.83 years). Higher sleep regularity was associated with a 20%–48% lower risk of all-cause mortality (p < .001 to p = 0.004), a 16%–39% lower risk of cancer mortality (p < 0.001 to p = 0.017), and a 22%–57% lower risk of cardiometabolic mortality (p < 0.001 to p = 0.048), across the top four SRI quintiles compared to the least regular quintile. Results were adjusted for age, sex, ethnicity, and sociodemographic, lifestyle, and health factors. Sleep regularity was a stronger predictor of all-cause mortality than sleep duration, by comparing equivalent mortality models, and by comparing nested SRI-mortality models with and without sleep duration (p = 0.14–0.20). These findings indicate that sleep regularity is an important predictor of mortality risk and is a stronger predictor than sleep duration. Sleep regularity may be a simple, effective target for improving general health and survival. Graphical Abstract Graphical Abstract

Light improves mood. The amygdala plays a critical role in regulating emotion, including fear-related responses. In rodents the amygdala receives direct light input from the retina, and light may play a role in fear-related learning. A direct effect of light on the amygdala represents a plausible mechanism of action for light’s mood-elevating effects in humans. However, the effect of light on activity in the amygdala in humans is not well understood. We examined the effect of passive dim-to-moderate white light exposure on activation of the amygdala in healthy young adults using the BOLD fMRI response (3T Siemens scanner; n = 23). Participants were exposed to alternating 30s blocks of light (10 lux or 100 lux) and dark (<1 lux), with each light intensity being presented separately. Light, compared with dark, suppressed activity in the amygdala. Moderate light exposure resulted in greater suppression of amygdala activity than dim light. Furthermore, functional connectivity between the amygdala and ventro-medial prefrontal cortex was enhanced during light relative to dark. These effects may contribute to light’s mood-elevating effects, via a reduction in negative, fear-related affect and enhanced processing of negative emotion.

Robust circadian rhythms are essential for optimal health. The central circadian clock controls temperature rhythms, which are known to organize the timing of peripheral circadian rhythms in rodents. In humans, however, it is unknown whether temperature rhythms relate to the organization of circadian rhythms throughout the body. We assessed core body temperature amplitude and the rhythmicity of 929 blood plasma metabolites across a 40-h constant routine protocol, controlling for behavioral and environmental factors that mask endogenous temperature rhythms, in 23 healthy individuals (mean [± SD] age = 25.4 ± 5.7 years, 5 women). Valid core body temperature data were available in 17/23 (mean [± SD] age = 25.6 ± 6.3 years, 1 woman). Individuals with higher core body temperature amplitude had a greater number of metabolites exhibiting circadian rhythms (R 2  = 0.37, p = .009). Higher core body temperature amplitude was also associated with less variability in the free-fitted periods of metabolite rhythms within an individual (R 2  = 0.47, p = .002). These findings indicate that a more robust central circadian clock is associated with greater organization of circadian metabolite rhythms in humans. Metabolite rhythms may therefore provide a window into the strength of the central circadian clock.

The association of irregular sleep schedules with circadian timing and academic performance has not been systematically examined. We studied 61 undergraduates for 30 days using sleep diaries, and quantified sleep regularity using a novel metric, the sleep regularity index (SRI). In the most and least regular quintiles, circadian phase and light exposure were assessed using salivary dim-light melatonin onset (DLMO) and wrist-worn photometry, respectively. DLMO occurred later (00:08 ± 1:54 vs. 21:32 ± 1:48; p < 0.003); the daily sleep propensity rhythm peaked later (06:33 ± 0:19 vs. 04:45 ± 0:11; p < 0.005); and light rhythms had lower amplitude (102 ± 19 lux vs. 179 ± 29 lux; p < 0.005) in Irregular compared to Regular sleepers. A mathematical model of the circadian pacemaker and its response to light was used to demonstrate that Irregular vs. Regular group differences in circadian timing were likely primarily due to their different patterns of light exposure. A positive correlation (r = 0.37; p < 0.004) between academic performance and SRI was observed. These findings show that irregular sleep and light exposure patterns in college students are associated with delayed circadian rhythms and lower academic performance. Moreover, the modeling results reveal that light-based interventions may be therapeutically effective in improving sleep regularity in this population.

Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.