Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Power Analysis for Human Melatonin Suppression Experiments.

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study.

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 ±â€…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 ±â€…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.

Predicting neurobehavioral performance of resident physicians in a Randomized Order Safety Trial Evaluating Resident-Physician Schedules (ROSTERS).

OBJECTIVES: Mathematical models of human neurobehavioral performance that include the effects of acute and chronic sleep restriction can be key tools in assessment and comparison of work schedules, allowing quantitative predictions of performance when empirical assessment is impractical. METHODS: Using such a model, we tested the hypothesis that resident physicians working an extended duration work roster, including 24-28 hours of continuous duty and up to 88 hours per week averaged over 4weeks, would have worse predicted performance than resident physicians working a rapidly cycling work roster intervention designed to reduce the duration of extended shifts. The performance metric used was attentional failures (ie, Psychomotor Vigilance Task lapses). Model input was 169 actual work and sleep schedules. Outcomes were predicted hours per week during work hours spent at moderate (equivalent to 16-20 hours of continuous wakefulness) or high (equivalent to ≥20 hours of continuous wakefulness) performance impairment. RESULTS: The model predicted that resident physicians working an extended duration work roster would spend significantly more time at moderate impairment (p = .02, effect size=0.2) than those working a rapidly cycling work roster; this difference was most pronounced during the circadian night (p < .001). On both schedules, performance was predicted to decline from weeks 1 + 2 to weeks 3 + 4 (p < .001), but the rate of decline was significantly greater on extended duration work roster (p < .01). Predicted performance impairment was inversely related to prior sleep duration (p < .001). CONCLUSIONS: These findings demonstrate the utility of a mathematical model to evaluate the predicted performance profile of schedules for resident physicians and others who experience chronic sleep restriction and circadian misalignment.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

The organization of sleep-wake patterns around daily schedules in college students.

Time is a zero-sum game, and consequently, sleep is often sacrificed for waking activities. For college students, daily activities, comprised of scheduled classes, work, study, social and other extracurricular events, are major contributors to insufficient and poor-quality sleep. We investigated the impact of daily schedules on sleep-wake timing in 223 undergraduate students (age: 18-27 years, 37% females) from a United States (U.S.) university, monitored for approximately 30 days. Sleep-wake timing and daily recorded activities (attendance at academic, studying, exercise-based and/or extracurricular activities) were captured by a twice-daily internet-based diary. Wrist-worn actigraphy was conducted to confirm sleep-wake timing. Linear mixed models were used to quantify associations between daily schedule and sleep-wake timing at between-person and within-person levels. Later schedule start time predicted later sleep onset (between and within: p<.001), longer sleep duration on the previous night (within: p<.001), and later wake time (between and within: p<.001). Later schedule end time predicted later sleep onset (between: p<.05, within: p<.001) and shorter sleep duration that night (within: p<.001). For every 1 hour that recorded activities extended beyond 10pm, sleep onset was delayed by 15 minutes at the within-person level and 45 minutes at the between-person level, and sleep duration was shortened by 5 and 23 minutes, respectively. Increased daily documented total activity time predicted earlier wake (between and within: p<.001), later sleep onset that night (within: p<.05), and shorter sleep duration (within: p<.001). These results indicate that daily schedules are an important factor in shaping sleep timing and duration in college students.

Night work, season and alertness as occupational safety hazards in the Arctic: protocol for the Noralert observational crossover study among Norwegian process operators.

INTRODUCTION: The objective of this study is to determine the effects of night work, Arctic seasonal factors and cold working environments on human functions relevant to safety. The study aims to quantify the contribution of (1) several consecutive night shifts, (2) seasonal variation on sleepiness, alertness and circadian rhythm and (3) whether a computational model of sleep, circadian rhythms and cognitive performance can accurately predict the observed sleepiness and alertness. METHODS AND ANALYSIS: In an observational crossover study of outdoor and indoor workers (n=120) on a three-shift schedule from an industrial plant in Norway (70 °N), measurements will be conducted during the summer and winter. Sleep duration and quality will be measured daily by smartphone questionnaire, aided by actigraphy and heart rate measurements. Sleepiness and alertness will be assessed at regular intervals by the Karolinska Sleepiness Scale and the psychomotor vigilance test, respectively. Saliva samples will assess melatonin levels, and a blood sample will measure circadian time. Thermal exposures and responses will be measured by sensors and by thermography. ETHICS AND DISSEMINATION: All participants will give written informed consent to participate in the study, which will be conducted in accordance with the Declaration of Helsinki. The Norwegian Regional Committee for Medical Research Ethics South-East D waivered the need for ethics approval (reference 495816). Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers.

Pilot feasibility testing of biomathematical model recommendations for personalising sleep timing in shift workers.

Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation is undertested. This study tested the feasibility of implementing two biomathematical models-the Phillips-Robinson Model and the Model for Arousal Dynamics-in 28 shift-working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips-Robinson Model. For the Phillips-Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre-study, significant improvements were observed post-study for sleep disturbance (Phillips-Robinson Model), and insomnia severity and sleep-related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof-of-concept for using biomathematical models to recommend sleep in operational contexts.

Mobile app for personalized sleep-wake management for shift workers: A user testing trial.

Objective: Development of personalized sleep-wake management tools is critical to improving sleep and functional outcomes for shift workers. The objective of the current study was to test the performance, engagement and usability of a mobile app (SleepSync) for personalized sleep-wake management in shift workers that aid behavioural change and provide practical advice by providing personalized sleep scheduling recommendations and education. Methods: Shift workers (n = 27; 20 healthcare and 7 from other industries) trialled the mobile app for two weeks to determine performance, engagement and usability. Primary outcomes were self-reported total sleep time, ability to fall asleep, sleep quality and perception of overall recovery on days off. Secondary performance outcomes included sleep disturbances (insomnia and sleep hygiene symptoms, and sleep-related impairments) and mood (anxiety, stress and depression) pre- and post-app use. Satisfaction with schedule management, integration into daily routine and influence on behaviour were used to determine engagement, while the usability was assessed for functionality and ease of use of features. Results: Total sleep time (P = .04), ability to fall asleep (P < .001), quality of sleep (P = .001), insomnia (P = .02), sleep hygiene (P = .01), sleep-related impairments (P = .001), anxiety (P = .001), and stress (P = .006) were all improved, with non-significant improvements in recovery on days off (P = .19) and depression (P = .07). All measures of engagement and usability were scored positively by the majority of users. Conclusions: This pilot trial provides preliminary evidence of the positive impact of the SleepSync app in improving sleep and mood outcomes in shift workers, and warrants confirmation in a larger controlled trial.

Measuring light regularity: sleep regularity is associated with regularity of light exposure in adolescents.

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.

Genome-wide gene by environment study of time spent in daylight and chronotype identifies emerging genetic architecture underlying light sensitivity.

STUDY OBJECTIVES: Light is the primary stimulus for synchronizing the circadian clock in humans. There are very large interindividual differences in the sensitivity of the circadian clock to light. Little is currently known about the genetic basis for these interindividual differences. METHODS: We performed a genome-wide gene-by-environment interaction study (GWIS) in 280 897 individuals from the UK Biobank cohort to identify genetic variants that moderate the effect of daytime light exposure on chronotype (individual time of day preference), acting as "light sensitivity" variants for the impact of daylight on the circadian system. RESULTS: We identified a genome-wide significant SNP mapped to the ARL14EP gene (rs3847634; p < 5 × 10-8), where additional minor alleles were found to enhance the morningness effect of daytime light exposure (ßGxE = -.03, SE = 0.005) and were associated with increased gene ARL14EP expression in brain and retinal tissues. Gene-property analysis showed light sensitivity loci were enriched for genes in the G protein-coupled glutamate receptor signaling pathway and genes expressed in Per2+ hypothalamic neurons. Linkage disequilibrium score regression identified Bonferroni significant genetic correlations of greater light sensitivity GWIS with later chronotype and shorter sleep duration. Greater light sensitivity was nominally genetically correlated with insomnia symptoms and risk for post-traumatic stress disorder (PTSD). CONCLUSIONS: This study is the first to assess light as an important exposure in the genomics of chronotype and is a critical first step in uncovering the genetic architecture of human circadian light sensitivity and its links to sleep and mental health.

Later energy intake relative to mathematically modeled circadian time is associated with higher percentage body fat.

OBJECTIVE: Later circadian timing of energy intake is associated with higher body fat percentage. Current methods for obtaining accurate circadian timing are labor- and cost-intensive, limiting practical application of this relationship. This study investigated whether the timing of energy intake relative to a mathematically modeled circadian time, derived from easily collected ambulatory data, would differ between participants with a lean or overweight/obesity body fat percentage. METHODS: Participants (N = 87) wore a light- and activity-measuring device (actigraph) throughout a cross-sectional 30-day study. For 7 consecutive days within these 30 days, participants used a time-stamped-picture phone application to record energy intake. Body fat percentage was recorded. Circadian time was defined using melatonin onset from in-laboratory collected repeat saliva sampling or using light and activity or activity data alone entered into a mathematical model. RESULTS: Participants with overweight/obesity body fat percentages ate 50% of their daily calories significantly closer to model-predicted melatonin onset from light and activity data (0.61 hours closer) or activity data alone (0.86 hours closer; both log-rank p < 0.05). CONCLUSIONS: Use of mathematically modeled circadian timing resulted in similar relationships between the timing of energy intake and body composition as that observed using in-laboratory collected metrics. These findings may facilitate use of circadian timing in time-based interventions.

The Sleep, Cancer and Rest (SleepCaRe) Trial: Rationale and design of a randomized, controlled trial of cognitive behavioral and bright light therapy for insomnia and fatigue in women with breast cancer receiving chemotherapy.

BACKGROUND: Insomnia and fatigue symptoms are common in breast cancer. Active cancer treatment, such as chemotherapy, appears to be particularly disruptive to sleep. Yet, sleep complaints often go unrecognised and under treated within routine cancer care. The abbreviated delivery of cognitive behavioral therapy for Insomnia (CBTI) and bright light therapy (BLT) may offer accessible and cost-effective sleep treatments in women receiving chemotherapy for breast cancer. METHODS: The Sleep, Cancer and Rest (SleepCaRe) Trial is a 6-month multicentre, randomized, controlled, 2 × 2 factorial, superiority, parallel group trial. Women receiving cytotoxic chemotherapy for breast cancer at tertiary Australian hospitals will be randomly assigned 1:1:1:1 to one of four, non-pharmacological sleep interventions: (a) Sleep Hygiene and Education (SHE); (b) CBTI; (c) BLT; (d) CBT-I + BLT combined and simultaneously delivered. Each sleep intervention is delivered over 6 weeks, and will comprise an introductory session, a mid-point phone call, and regular emails. The primary (insomnia, fatigue) and secondary (health-related quality of life, rest activity rhythms, sleep-related impairment) outcomes will be assessed via online questionnaires at five time-points: baseline (t0, prior to intervention), mid-point intervention (t2, Week 4), post-intervention (t3, Week 7), 3-months (t4, Week 18), and 6-months follow-up (t5, Week 30). CONCLUSIONS: This study will report novel data concerning the comparative and combined efficacy of CBT-I and BLT during chemotherapy. Findings will contribute to the development of evidence-based early sleep and fatigue intervention during chemotherapy for breast cancer. Clinical trial information Registered with the Australian New Zealand Clinical Trials Registry (http://anzctr.org.au/), Registration Number: ACTRN12620001133921.

The CLASS Study (Circadian Light in Adolescence, Sleep and School): protocol for a prospective, longitudinal cohort to assess sleep, light, circadian timing and academic performance in adolescence.

BACKGROUND: During adolescence, sleep and circadian timing shift later, contributing to restricted sleep duration and irregular sleep-wake patterns. The association of these developmental changes in sleep and circadian timing with cognitive functioning, and consequently academic outcomes, has not been examined prospectively. The role of ambient light exposure in these developmental changes is also not well understood. Here, we describe the protocol for the Circadian Light in Adolescence, Sleep and School (CLASS) Study that will use a longitudinal design to examine the associations of sleep-wake timing, circadian timing and light exposure with academic performance and sleepiness during a critical stage of development. We also describe protocol adaptations to enable remote data collection when required during the COVID-19 pandemic. METHODS: Approximately 220 healthy adolescents aged 12-13 years (school Year 7) will be recruited from the general community in Melbourne, Australia. Participants will be monitored at five 6 monthly time points over 2 years. Sleep and light exposure will be assessed for 2 weeks during the school term, every 6 months, along with self-report questionnaires of daytime sleepiness. Circadian phase will be measured via dim light melatonin onset once each year. Academic performance will be measured via national standardised testing (National Assessment Program-Literacy and Numeracy) and the Wechsler Individual Achievement Test-Australian and New Zealand Standardised Third Edition in school Years 7 and 9. Secondary outcomes, including symptoms of depression, anxiety and sleep disorders, will be measured via questionnaires. DISCUSSION: The CLASS Study will enable a comprehensive longitudinal assessment of changes in sleep-wake timing, circadian phase, light exposure and academic performance across a key developmental stage in adolescence. Findings may inform policies and intervention strategies for secondary school-aged adolescents. ETHICS AND DISSEMINATION: Ethical approval was obtained by the Monash University Human Research Ethics Committee and the Victorian Department of Education. Dissemination plans include scientific publications, scientific conferences, via stakeholders including schools and media. STUDY DATES: Recruitment occurred between October 2019 and September 2021, data collection from 2019 to 2023.

Disrupting circadian rhythms promotes cancer-induced inflammation in mice.

Disruption of circadian rhythms occurs in rotating shift-work, jetlag, and in individuals with irregular sleep schedules. Circadian disruption is known to alter inflammatory responses and impair immune function. However, there is limited understanding of how circadian disruption modulates cancer-induced inflammation. Inflammation is a hallmark of cancer and is linked to worse prognosis and impaired brain function in cancer patients. Here, we investigated the effect of circadian disruption on cancer-induced inflammation in an orthotopic breast cancer model. Using a validated chronic jetlag protocol that advances the light-cycle by 8 â€‹h every 2 days to disrupt circadian rhythms, we found that circadian disruption alters cancer-induced inflammation in a tissue-specific manner, increasing inflammation in the body and brain while decreasing inflammation within the tumor tissue. Circadian disruption did not affect inflammation in mice without tumors, suggesting that the impact of circadian disruption may be particularly detrimental in the context of underlying inflammatory conditions, such as cancer. Importantly, circadian disruption did not affect tumor burden, suggesting that increased inflammation was not a result of increased cancer progression. Overall, these findings identify the importance of healthy circadian rhythms for limiting cancer-induced inflammation.

Unanticipated daytime melatonin secretion on a simulated night shift schedule generates a distinctive 24-h melatonin rhythm with antiphasic daytime and nighttime peaks.

The daily rhythm of plasma melatonin concentrations is typically unimodal, with one broad peak during the circadian night and near-undetectable levels during the circadian day. Light at night acutely suppresses melatonin secretion and phase shifts its endogenous circadian rhythm. In contrast, exposure to darkness during the circadian day has not generally been reported to increase circulating melatonin concentrations acutely. Here, in a highly-controlled simulated night shift protocol with 12-h inverted behavioral/environmental cycles, we unexpectedly found that circulating melatonin levels were significantly increased during daytime sleep (p < .0001). This resulted in a secondary melatonin peak during the circadian day in addition to the primary peak during the circadian night, when sleep occurred during the circadian day following an overnight shift. This distinctive diurnal melatonin rhythm with antiphasic peaks could not be readily anticipated from the behavioral/environmental factors in the protocol (e.g., light exposure, posture, diet, activity) or from current mathematical model simulations of circadian pacemaker output. The observation, therefore, challenges our current understanding of underlying physiological mechanisms that regulate melatonin secretion. Interestingly, the increase in melatonin concentration observed during daytime sleep was positively correlated with the change in timing of melatonin nighttime peak (p = .002), but not with the degree of light-induced melatonin suppression during nighttime wakefulness (p = .92). Both the increase in daytime melatonin concentrations and the change in the timing of the nighttime peak became larger after repeated exposure to simulated night shifts (p = .002 and p = .006, respectively). Furthermore, we found that melatonin secretion during daytime sleep was positively associated with an increase in 24-h glucose and insulin levels during the night shift protocol (p = .014 and p = .027, respectively). Future studies are needed to elucidate the key factor(s) driving the unexpected daytime melatonin secretion and the melatonin rhythm with antiphasic peaks during shifted sleep/wake schedules, the underlying mechanisms of their relationship with glucose metabolism, and the relevance for diabetes risk among shift workers.

Wearable light spectral sensor optimized for measuring daily α-opic light exposure.

Light has many non-visual effects on human physiology, including alterations in sleep, mood, and alertness. These effects are mainly mediated by photoreceptors containing the photopigment melanopsin, which has a peak sensitivity to short wavelength ('blue') light. Commercially available light sensors are commonly wrist-worn and report photopic illuminance and are calibrated to perceive visual brightness and hence cannot be used to investigate the non-visual impacts of light. In this paper, we report the development of a wearable spectrophotometer designed to be worn as a pendant or affixed to clothing to capture spectral power density data close to eye level in the visible wavelength range 380-780 nm. From this, the relative impact of a given light stimulus can be determined for each photoreceptive input in the human eye by calculating effective illuminances. This device showed high accuracy for all effective illuminances while measuring a range of commonly encountered light sources by calibrating for directional response, dark noise, sensor saturation, non-linearity, stray-light and spectral response. Features of the device include IoT-integration, onboard data storage and processing, Bluetooth Low Energy (BLE) enabled data transfer, and cloud storage in one cohesive unit.