Altered coordination between sleep timing and cortisol profiles in night working female hospital employees.

BACKGROUND: Cortisol typically peaks in the morning after waking up and declines throughout the day, reaching its lowest levels during nighttime sleep. Shift work can cause misalignment between cortisol levels and sleep-wake timing. We analyzed this misalignment in female shift workers focusing on the timing and extent of these changes. METHODS: We conducted a cross-sectional study involving 68 shift workers (aged 37 ± 10 years) and 21 non-shift workers (aged 45 ± 10 years) from a hospital. Shift workers were monitored through two day shifts and three night shifts, whereas non-shift workers were monitored during two day shifts. Each participant collected six to eight saliva samples (depending on their shift type) and provided sleep timing information, which was recorded via polysomnography and sleep diaries. Generalized additive mixed models were used to estimate shift-specific differences in cortisol smooth curves. Summary measures calculated for the cortisol smooth curves included cortisol awakening response, peak-to-bed slope, and total output. RESULTS: Between shift workers and non-shift workers, we observed similar diurnal cortisol profiles with a steep negative diurnal slope during day shifts. In shift workers on night shifts, a flattened U-shaped cortisol profile after the post-awakening maximum was observed, with a peak-to-bed slope close to zero. When comparing night to day shifts in the group of shift workers, mean cortisol levels were lower between 42 and 56 minutes and 1.8-11.9 hours after waking up, and higher between 14.9 and 22 hours after waking up. CONCLUSION: Our findings indicate altered cortisol profiles in female hospital employees on night shifts. Specifically, cortisol levels were lower at night when higher levels would typically be necessary for work activities, and higher at bedtime after a night shift, when levels should normally be low.

Effect of timed dosing of usual antihypertensives according to patient chronotype on cardiovascular outcomes: the Chronotype sub-study cohort of the Treatment in Morning versus Evening (TIME) study.

Background: Timing drug administration to endogenous circadian rhythms may enhance treatment efficacy. In the Chronotype sub-study of the Treatment in Morning versus Evening (TIME) clinical trial we examined whether timing of usual antihypertensive medications according to patient chronotype (a behavioural marker of personal circadian rhythm) may influence clinical cardiovascular outcomes. Methods: This was a cohort sub-study of TIME, a prospective, randomised, open-label, blinded-endpoint, UK clinical trial of morning versus evening dosing of usual antihypertensive medications and cardiovascular outcomes. On August 3rd, 2020, all active TIME participants were invited to complete a validated chronotype questionnaire. Chronotype was quantitatively assessed as the mid sleep time on free days corrected for sleep debt on workdays (MSFsc). We analysed associations between chronotype and antihypertensive dosing time and explored their combined effect on cardiovascular outcomes (a composite endpoint of hospitalisation for non-fatal myocardial infarction (MI) or non-fatal stroke, and single components) using proportional hazard time-to-event models adjusted for baseline covariates. These were used to specifically test for interactions between dosing time and chronotype. Findings: Between August 3, 2020, and March 31, 2021, 5358 TIME participants completed the online questionnaire. 2778 were previously randomised to morning dosing and 2580 to evening dosing of their usual antihypertensives. Chronotype was symmetrically distributed around a median MSFsc of 3:07 am. The composite endpoint increased for later MSFsc (later chronotype) dosed in the morning but not in those dosed in the evening (hazard ratios 1.46 [95% CI 1.14-1.86] and 0.96 [95% CI 0.70-1.30] per hour of MSFsc, respectively; interaction p = 0.036). Later chronotype was associated with increased risk of hospitalisation for non-fatal MI in the morning dosing group, and reduced risk in the evening dosing group (hazard ratios 1.62 [95% CI 1.18-2.22] and 0.66 [95% CI 0.44-1.00] per hour of MSFsc, respectively; interaction p < 0.001). No interaction between chronotype and antihypertensive dosing time was observed for stroke events. Interpretation: Alignment of dosing time of usual antihypertensives with personal chronotype could lower the incidence of non-fatal MI compared to a 'misaligned' dosing time regimen. Future studies are warranted to establish whether synchronizing administration time of antihypertensive therapy with individual chronotype reduces risk of MI. Funding: The TIME study was funded by the British Heart Foundation (CS/14/1/30659) with support from the British and Irish Hypertension Society.

An inventory of human light exposure behaviour.

Light exposure is an essential driver of health and well-being, and individual behaviours during rest and activity modulate physiologically relevant aspects of light exposure. Further understanding the behaviours that influence individual photic exposure patterns may provide insight into the volitional contributions to the physiological effects of light and guide behavioural points of intervention. Here, we present a novel, self-reported and psychometrically validated inventory to capture light exposure-related behaviour, the Light Exposure Behaviour Assessment (LEBA). An expert panel prepared the initial 48-item pool spanning different light exposure-related behaviours. Responses, consisting of rating the frequency of engaging in the per-item behaviour on a five-point Likert-type scale, were collected in an online survey yielding responses from a geographically unconstrained sample (690 completed responses, 74 countries, 28 time zones). The exploratory factor analysis (EFA) on an initial subsample (n = 428) rendered a five-factor solution with 25 items (wearing blue light filters, spending time outdoors, using a phone and smartwatch in bed, using light before bedtime, using light in the morning and during daytime). In a confirmatory factor analysis (CFA) performed on an independent subset of participants (n = 262), we removed two additional items to attain the best fit for the five-factor solution (CFI = 0.95, TLI = 0.95, RMSEA = 0.06). The internal consistency reliability coefficient for the total instrument yielded McDonald's Omega = 0.68. Measurement model invariance analysis between native and non-native English speakers showed our model attained the highest level of invariance (residual invariance CFI = 0.95, TLI = 0.95, RMSEA = 0.05). Lastly, a short form of the LEBA (n = 18 items) was developed using Item Response Theory on the complete sample (n = 690). The psychometric properties of the LEBA indicate the usability for measuring light exposure-related behaviours. The instrument may offer a scalable solution to characterise behaviours that influence individual photic exposure patterns in remote samples. The LEBA inventory is available under the open-access CC-BY license. Instrument webpage: https://leba-instrument.org/ GitHub repository containing this manuscript: https://github.com/leba-instrument/leba-manuscript .

Correlations in sleeping patterns and circadian preference between spouses.

Spouses may affect each other's sleeping behaviour. In 47,420 spouse-pairs from the UK Biobank, we found a weak positive phenotypic correlation between spouses for self-reported sleep duration (r = 0.11; 95% CI = 0.10, 0.12) and a weak inverse correlation for chronotype (diurnal preference) (r = -0.11; -0.12, -0.10), which replicated in up to 127,035 23andMe spouse-pairs. Using accelerometer data on 3454 UK Biobank spouse-pairs, the correlation for derived sleep duration was similar to self-report (r = 0.12; 0.09, 0.15). Timing of diurnal activity was positively correlated (r = 0.24; 0.21, 0.27) in contrast to the inverse correlation for chronotype. In Mendelian randomization analysis, positive effects of sleep duration (mean difference=0.13; 0.04, 0.23 SD per SD) and diurnal activity (0.49; 0.03, 0.94) were observed, as were inverse effects of chronotype (-0.15; -0.26, -0.04) and snoring (-0.15; -0.27, -0.04). Findings support the notion that an individual's sleep may impact that of their partner, promoting opportunities for sleep interventions at the family-level.

Sleep Health at the Genomic Level: Six Distinct Factors and Their Relationships With Psychopathology.

Background: Poor sleep is associated with many negative health outcomes, including multiple dimensions of psychopathology. In the past decade, sleep researchers have advocated for focusing on the concept of sleep health as a modifiable health behavior to mitigate or prevent these outcomes. Sleep health dimensions often include sleep efficiency, duration, satisfaction, regularity, timing, and daytime alertness. However, there is no consensus on how to best operationalize sleep health at the phenotypic and genetic levels. In some studies, specific sleep health domains were examined individually, while in others, sleep health domains were examined together (e.g., with an aggregate sleep health score). Methods: Here, we compared alternative sleep health factor models using genomic structural equation modeling on summary statistics from previously published genome-wide association studies of self-reported and actigraphic sleep measures with effective sample sizes up to 452,633. Results: Our best-fitting sleep health model had 6 correlated genetic factors pertaining to 6 sleep health domains: circadian preference, efficiency, alertness, duration, noninsomnia, and regularity. All sleep health factors were significantly correlated (|rgs| = 0.11-0.51), except for the circadian preference factor with duration and noninsomnia. Better sleep health was generally significantly associated with lower genetic liability for psychopathology (|rgs| = 0.05-0.48), yet the 6 sleep health factors showed divergent patterns of associations with different psychopathology factors, especially when controlling for covariance among the sleep health factors. Conclusions: These results provide evidence for genetic separability of sleep health constructs and their differentiation with respect to associations with mental health.

Are sleep disorders associated with the risk of gastrointestinal cancer?-A case-control study.

PURPOSE: Sleep disorders are among the most common health problems worldwide and are linked to a variety of physical and mental health problems. Recently, there has been increasing evidence of an association between sleep disorders and cancer risk. We aimed to investigate this association specifically for cancers of the gastrointestinal (GI) tract. METHODS: Using the DA database (IQVIA), adult patients diagnosed with GI cancer between January 2010 and December 2022 were retrospectively compared to a 1:1 propensity score-matched cohort of patients without cancer. The outcome of the study was the association between sleep disorders and subsequent diagnosis of GI cancer. To determine whether sleep disorders were more common in patients with GI cancer than in patients without GI cancer, logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CI). RESULTS: After matching, 37,161 cases with GI cancer and 37,161 controls without cancer were available for analysis. No association with cancer was found for sleep disorders in the overall history before the index date (OR 1.04; 95% CI 0.96-1.12), but considering sleep disorders documented within 1 year before the index date showed a positive association with GI cancer overall (OR 1.20; 95% CI 1.08-1.34). Stratified analyses by cancer site revealed higher odds of sleep disorders prior to diagnosis of gastric, pancreatic, and colorectal cancer. CONCLUSION: Our findings suggest that sleep disorders might be indicative of short-term health outcomes, including GI cancer, suggesting a role for sleep disorder screening in the context of cancer prevention efforts.

Evening types as determined by subjective and objective measures are more emotional eaters.

OBJECTIVE: This study aimed to determine the association between being an evening type (ET; defined subjectively by the Morning-Evening Questionnaire or objectively by the dim-light melatonin onset [DLMO] timing) and reporting emotional eating (EE) behaviors. METHODS: Cross-sectional analyses were conducted in 3964 participants (four international cohorts: ONTIME and ONTIME-MT [both Spain], SHIFT [the US], and DICACEM [Mexico]), in which chronotype (Morning-Evening Questionnaire), EE behaviors (Emotional Eating Questionnaire), and dietary habits (dietary records or food-frequency questionnaire) were assessed. Among 162 participants (ONTIME-MT subsample), additional measures of DLMO (physiological gold standard of circadian phase) were available. RESULTS: In three populations, ETs presented with a higher EE score than morning types (p < 0.02); and they made up a higher proportion of emotional eaters (p < 0.01). ETs presented with higher scores on disinhibition/overeating as well as food craving factors and experienced these behaviors more frequently than morning types (p < 0.05). Furthermore, a meta-analysis showed that being an ET was associated with a higher EE score by 1.52 points of a total of 30 points (95% CI: 0.89-2.14). The timing of DLMO in the early, intermediate, and late objective chronotypes occurred at 21:02 h, 22:12 h, and 23:37 h, with late types showing a higher EE score (p = 0.043). CONCLUSIONS: Eveningness associated with EE in populations with different cultural, environmental, and genetic backgrounds. Individuals with late DLMO also showed more EE.

Sleep health dimensions and shift work as longitudinal predictors of cognitive performance in the UK Biobank cohort.

STUDY OBJECTIVES: The long-term effects of sleep health and shift work on cognitive performance are unclear. In addition, research has been limited by small sample sizes and short follow-up periods. We conducted one of the largest examinations of the longitudinal influence of sleep health dimensions and shift work on cognitive performance in people of middle and old age using data from the UK Biobank. The hypothesis was that poor sleep health and shift work would predict lower cognitive performance. METHODS: Self-reported sleep duration, daytime sleepiness, insomnia symptoms, chronotype, and shift work status were assessed as predictors at baseline. Cognitive performance was operationalized by a touchscreen test battery at follow-up between 7.4 ±â€…2.2 and 9.0 ±â€…0.9 years after baseline assessment, depending on the specific task. Models were performed for each cognitive domain including relevant confounders (e.g. depression). The alpha level was set at p < 0.01 for all analyzes. RESULTS: The study sample comprised 9394 participants for the reasoning task, 30 072 for the reaction time task, 30 236 for the visual memory task, 2019 for the numeric memory task, and 9476 for the prospective memory task. Shift work without night shifts (ß = -2.0 × 10-1 ± 6.5 × 10-2, p = 0.002) and with night shifts (ß = -1.9 × 10-1 ± 7.2 × 10-2, p = 0.010) predicted a significantly reduced performance in the reasoning task. Short sleep duration (ß = -2.4 × 10-1 ± 7.9 × 10-2, p = 0.003) and shift work without night shifts (ß = -3.9 × 10-1 ± 1.2 × 10-1, p = 0.002) predicted a significantly lower performance in the task probing prospective memory. CONCLUSIONS: Our results suggest that, after controlling for confounding variables, shift work, and short sleep duration are important predictors for cognitive performance in people of middle and old age. Further work is required to examine causal mechanisms of the observed associations.

COVID-19 stay-at-home restrictions increase the alignment in sleep and light exposure between school days and weekends in university students.

Younger adults have a biological disposition to sleep and wake at later times that conflict with early morning obligations like work and school; this conflict leads to inadequate sleep duration and a difference in sleep timing between school days and weekends. The COVID-19 pandemic forced universities and workplaces to shut down in person attendance and implement remote learning and meetings that decreased/removed commute times and gave students more flexibility with their sleep timing. To determine the impact of remote learning on the daily sleep-wake cycle we conducted a natural experiment using wrist actimetry monitors to compare activity patterns and light exposure in three cohorts of students: pre-shutdown in-person learning (2019), during-shutdown remote learning (2020), and post-shutdown in-person learning (2021). Our results show that during-shutdown the difference between school day and weekend sleep onset, duration, and midsleep timing was diminished. For instance, midsleep during school days pre-shutdown occurred 50 min later on weekends (5:14 ±â€…12 min) than school days (4:24 ±â€…14 min) but it did not differ under COVID restrictions. Additionally, we found that while the interindividual variance in sleep parameters increased under COVID restrictions the intraindividual variance did not change, indicating that the schedule flexibility did not cause more irregular sleep patterns. In line with our sleep timing results, school day vs. weekend differences in the timing of light exposure present pre- and post-shutdown were absent under COVID restrictions. Our results provide further evidence that increased freedom in class scheduling allows university students to better and consistently align sleep behavior between school days and weekends.

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.

Daytime light exposure is a strong predictor of seasonal variation in sleep and circadian timing of university students.

In the absence of electric light, sleep for humans typically starts soon after dusk and at higher latitudes daily sleep timing changes seasonally as photoperiod changes. However, access to electric light shields humans from natural photoperiod changes, and whether seasonal changes in sleep occur despite this isolation from the natural light-dark cycle remains a matter of controversy. We measured sleep timing in over 500 university students living in the city of Seattle, WA (47.6°N) throughout the four seasons; we show that even when students are following a school schedule, sleep timing is delayed during the fall and winter. For instance, during the winter school days, students fell asleep 35 min later and woke up 27 min later (under daylight-savings time) than students during the summer school days, a change that is an hour larger relative to solar midnight. Furthermore, chronotype defined by mid-sleep on free days corrected for oversleep (MSFc), an indirect estimate of circadian phase, was more than 30 min later in the winter compared with the summer. Analysis of the effect of light exposure showed that the number of hours of light exposure to at least 50 lux during the daytime was a stronger predictor of MSFc than the exposure time to this illuminance after dusk. Specifically, MSFc was advanced by 30 min for each additional hour of light exposure during daytime and delayed by only 15 min for each additional hour of postdusk exposure to light. Additionally, the time of the day of exposure to high light intensities was more predictive of MSFc when daytime exposure was considered than when exposure for the full 24-h day was considered. Our results show that although sleep time is highly synchronized to social time, a delayed timing of sleep is evident during the winter months. They also suggest that daily exposure to daylight is key to prevent this delayed phase of the circadian clock and thus circadian disruption that is typically exacerbated in high-latitude winters.

Sleep duration, plasma metabolites, and obesity and diabetes: a metabolome-wide association study in US women.

Short and long sleep duration are associated with adverse metabolic outcomes, such as obesity and diabetes. We evaluated cross-sectional differences in metabolite levels between women with self-reported habitual short (<7 h), medium (7-8 h), and long (≥9 h) sleep duration to delineate potential underlying biological mechanisms. In total, 210 metabolites were measured via liquid chromatography-mass spectrometry in 9207 women from the Nurses' Health Study (NHS; N = 5027), the NHSII (N = 2368), and the Women's Health Initiative (WHI; N = 2287). Twenty metabolites were consistently (i.e. praw < .05 in ≥2 cohorts) and/or strongly (pFDR < .05 in at least one cohort) associated with short sleep duration after multi-variable adjustment. Specifically, levels of two lysophosphatidylethanolamines, four lysophosphatidylcholines, hydroxyproline and phenylacetylglutamine were higher compared to medium sleep duration, while levels of one diacylglycerol and eleven triacylglycerols (TAGs; all with ≥3 double bonds) were lower. Moreover, enrichment analysis assessing associations of metabolites with short sleep based on biological categories demonstrated significantly increased acylcarnitine levels for short sleep. A metabolite score for short sleep duration based on 12 LASSO-regression selected metabolites was not significantly associated with prevalent and incident obesity and diabetes. Associations of single metabolites with long sleep duration were less robust. However, enrichment analysis demonstrated significant enrichment scores for four lipid classes, all of which (most markedly TAGs) were of opposite sign than the scores for short sleep. Habitual short sleep exhibits a signature on the human plasma metabolome which is different from medium and long sleep. However, we could not detect a direct link of this signature with obesity and diabetes risk.

Brighter Time: A Smartphone App Recording Cognitive Task Performance and Illuminance in Everyday Life.

Light is an influential regulator of behavioural and physiological state in mammals. Features of cognitive performance such as memory, vigilance and alertness can be altered by bright light exposure under laboratory and field conditions. However, the importance of light as a regulator of performance in everyday life is hard to assess and has so far remained largely unclear. We set out to address this uncertainty by developing a tool to capture measures of cognitive performance and light exposure, at scale, and during everyday life. To this end, we generated an app (Brighter Time) which incorporated a psychomotor vigilance (PVT), an N-back and a visual search task with questionnaire-based assessments of demographic characteristics, general health, chronotype and sleep. The app also measured illuminance during task completion using the smartphone's intrinsic light meter. We undertook a pilot feasibility study of Brighter Time based on 91-week-long acquisition phases within a convenience sample (recruited by local advertisements and word of mouth) running Brighter Time on their own smartphones over two study phases in winter and summer. Study compliance was suitable (median = 20/21 requested task completions per subject). Statistically significant associations were observed between subjective sleepiness and performance in all tasks. Significant daily variations in PVT and visual search performance were also observed. Higher illuminance was associated with reduced reaction time and lower inverse efficiency score in the visual search. Brighter Time thus represents a viable option for large-scale collection of cognitive task data in everyday life, and is able to reveal associations between task performance and sleepiness, time of day and current illuminance. Brighter Time's utility could be extended to exploring associations with longer-term patterns of light exposure and/or other light metrics by integrating with wearable light meters.

Impairments in glycemic control during Eastbound transatlantic travel in healthy adults.

Study Objectives: Repeated bouts of circadian misalignment impair glucose tolerance. However, whether circadian misalignment associated with travel and jet lag impair glucose homeostasis in a free-living population is not known. The goal of the present study was to examine glycemic control during one week of Eastbound transatlantic travel in healthy men and women. Methods: Seven healthy participants (5 women; age: 35.6 ± 2.5 years, BMI: 23.9 ± 2.4 m/kg2) traveled from Colorado, USA (GMT-7) to Europe (GMT and GMT+1) and wore a continuous glucose monitor (Freestyle Libre Pro) for 8-14 days before, during, and after travel. Indices of glycemic control were summarized over 24-hour periods and by day and night. Results: Mean glucose, peak glucose, and time spent in hyperglycemia increased linearly throughout the travel period relative to baseline levels. Mean glucose concentrations rose 1.03 mg/dL (95% CI: 0.34, 1.74) and duration of hyperglycemia increased by 17 min (95% CI: 5.5, 28.6) each 24-hour period. Increases in 24-hour glucose were primarily driven by increases in daytime parameters with rising mean glucose (0.72 mg/dL per day, [95% CI: -0.1, 1.5]) and duration of hyperglycemia (13.2 min per day [95% CI: 4.3, 22.1]). Mean glucose, but not peak glucose or time spent in hyperglycemia, increased each night (0.7 mg/dL per night [95% CI: 0.2, 1.2]). Conclusions: Eastbound transatlantic travel induced a progressive worsening of glucose metrics during 24-hour, day, and night periods. Future research on managing glycemic control during jet lag in people with metabolic disorders is warranted. Clinical Trial Registration: None.

Development of the circadian system in early life: maternal and environmental factors.

In humans, an adaptable internal biological system generates circadian rhythms that maintain synchronicity of behavior and physiology with the changing demands of the 24-h environment. Development of the circadian system begins in utero and continues throughout the first few years of life. Maturation of the clock can be measured through sleep/wake patterns and hormone secretion. Circadian rhythms, by definition, can persist in the absence of environmental input; however, their ability to adjust to external time cues is vital for adaptation and entrainment to the environment. The significance of these external factors that influence the emergence of a stable circadian clock in the first years of life remain poorly understood. Infants raised in our post-modern world face adverse external circadian signals, such as artificial light and mistimed hormonal cues via breast milk, which may increase interference with the physiological mechanisms that promote circadian synchronization. This review describes the very early developmental stages of the clock and common circadian misalignment scenarios that make the developing circadian system more susceptible to conflicting time cues and temporal disorder between the maternal, fetal, infant, and peripheral clocks.

Challenged by extremely irregular school schedules, Uruguayan adolescents only set their waking time.

INTRODUCTION: Early school start times adversely influence adolescents' sleep duration. Late school start times and remote learning are associated with longer sleep durations. We leveraged a highly variable school schedule adopted after the first SARS-CoV-2 wave in Uruguay to study the influence of a hybrid mode of instruction (in-person/remote) with irregular school start times on adolescents' sleep. METHODS: Objective sleep on school nights (120 observations) was determined using accelerometry recordings of 15 high-school students (3 males 15-17 years old). We used mixed-effects regressions to explore the influence of the mode of instruction (in-person/remote) and school start time on adolescents' sleep patterns (onset, end, and duration). RESULTS: An irregular individual regime of 2-6 school days per week, either in-person or remote, and with irregular start times (range: 07:30-12:00) were observed during the studied period. Remote learning delayed sleep end by 48 ± 9 min and increased sleep duration by 36 ± 12 min. In addition, 1 h delay in school start time delayed sleep end in 36 ± 4 min and increased sleep duration in 34 ± 5 min. CONCLUSIONS: A strong linear association of the mode of instruction and the school start time with participants' sleep was observed: Only waking time was associated with both school start time and mode of instruction, and sleep duration increased when students were taught remotely and when school started later. These results add evidence to the effectiveness of delaying school start time to improve adolescents' sleep. However, these efforts may not overcome the influence of a very late circadian orientation, as observed in Uruguayan adolescents.

Night work, chronotype and cortisol at awakening in female hospital employees.

To examine the effect of night shift on salivary cortisol at awakening (C1), 30 min later (C2), and on the cortisol awakening response (CAR, the difference between C2 and C1). We compared shift and non-shift workers with a focus on the impact of worker chronotype. Our study included 66 shift-working females (mean age = 37.3 years, SD = 10.2) and 21 non-shift working females (mean age = 47.0 years, SD = 8.9). The shift workers collected their saliva samples at C1 and C2 on each two consecutive day shifts and night shifts. Non-shift workers collected their samples on two consecutive day shifts. We applied linear mixed-effects models (LMM) to determine the effect of night shift on CAR and log-transformed C1 and C2 levels. LMMs were stratified by chronotype group. Compared to non-shift workers, shift workers before day shifts (i.e. after night sleep) showed lower cortisol at C1 (exp [Formula: see text]=0.58, 95% CI 0.42, 0.81) but not at C2. In shift workers, the CARs after night shifts (i.e. after day sleep) were lower compared to CARs before day shifts ([Formula: see text]= - 11.07, 95% CI - 15.64, - 6.50). This effect was most pronounced in early chronotypes (early: [Formula: see text]= - 16.61, 95% CI - 27.87, - 5.35; intermediate: [Formula: see text]= - 11.82, 95% CI - 18.35, - 5.29; late: [Formula: see text]= - 6.27, 95% CI - 14.28, 1.74). Chronotype did not modify the association between night shift and CAR. In our population of shift workers, there was a mismatch between time of waking up and their natural cortisol peak at waking up (CAR) both during day and night shift duties.

Recommendations for daytime, evening, and nighttime indoor light exposure to best support physiology, sleep, and wakefulness in healthy adults.

Ocular light exposure has important influences on human health and well-being through modulation of circadian rhythms and sleep, as well as neuroendocrine and cognitive functions. Prevailing patterns of light exposure do not optimally engage these actions for many individuals, but advances in our understanding of the underpinning mechanisms and emerging lighting technologies now present opportunities to adjust lighting to promote optimal physical and mental health and performance. A newly developed, international standard provides a SI-compliant way of quantifying the influence of light on the intrinsically photosensitive, melanopsin-expressing, retinal neurons that mediate these effects. The present report provides recommendations for lighting, based on an expert scientific consensus and expressed in an easily measured quantity (melanopic equivalent daylight illuminance (melaponic EDI)) defined within this standard. The recommendations are supported by detailed analysis of the sensitivity of human circadian, neuroendocrine, and alerting responses to ocular light and provide a straightforward framework to inform lighting design and practice.

How Accurately Can We Recall the Timing of Food Intake? A Comparison of Food Times from Recall-Based Survey Questions and Daily Food Records.

BACKGROUND: There currently are no standard, low-cost, and validated methods to assess the timing of food intake. OBJECTIVES: The aim of this study was to validate simple, recall-based questions that can characterize food timing in free-living populations. METHODS: The concordance between recall-based survey questions and food times estimated from multiple daily food records was assessed in 249 generally healthy, free-living adults from the Shift Work, Heredity, Insulin, and Food Timing (SHIFT) Study. At baseline, participants were asked: "At what time do you first start and stop eating on weekdays/workdays and weekends/non-workdays?" and "At what time do you have your main meal on weekdays/workdays and weekends/non-workdays?" Participants were then asked to complete ≤14 d of food records noting the start time of each eating occasion. The timing of the first, last, and main (largest percentage calories) eating occasions were determined from food records. Wilcoxon matched pairs signed rank and Kendall's coefficient of concordance were used to compare differences and determine agreements between the methods for these food timing parameters, as well as for the midpoint between first and last eating occasion. RESULTS: Eating occasions on work and free days showed significant agreements between the 2 methods, except for the main eating occasion on free days. Significant agreements were generally modest and ranged from 0.16 (workdays main eating occasion) to 0.45 (workdays first eating occasion). Generally, times based on recall were later than those estimated from food records, and the differences in estimated times were smaller on workdays compared with free days, and smaller for the first compared with the last eating occasion. Main eating occasions from food records often varied between lunch and dinner times, contributing to low concordance with recalled times. CONCLUSIONS: Modest agreements were found between food times derived from simple, recall-based survey questions and food times estimated from multiple-day food records. Single administration of these questions can effectively characterize the overall timing of eating occasions within a population for chrononutrition research purposes.

The effects of the COVID-19 pandemic on weight loss in participants in a behavioral weight-loss intervention.

OBJECTIVE: This study aimed to assess the effects of the COVID-19 pandemic on weight loss, physical activity, and sleep in adults with overweight or obesity participating in a 39-week weight-loss intervention. METHODS: Participants (n = 81, 85% female, mean [SD] age 38.0 [7.8] years, BMI 34.1 [5.7] kg/m2 ) were enrolled in 3 separate cohorts. Cohorts 1 and 2 were studied prior to the pandemic (pre-COVID cohorts). Cohort 3 (COVID cohort) transitioned to a virtual intervention at week 6, when "stay-at-home" orders were implemented in Colorado. Weight was assessed at baseline, week 12, and week 39 with clinic scales before the pandemic and home scales during the pandemic. Diet was assessed with Likert scales at weeks 4, 8, and 12. Physical activity and sleep were assessed at baseline and week 12 with actigraphy. RESULTS: Participants in the COVID cohort reported greater dietary adherence (p = 0.004) and lost more weight than those in the pre-COVID cohorts at week 12 (-7.7 [3.3] kg vs. -3.7 [3.0] kg, p < 0.001) and week 39 (-8.5 [4.4] kg vs. -2.8 [4.6] kg, p < 0.001). Energy intake did not differ between cohorts (p = 0.51). The COVID cohort increased both sedentary time while awake and time in bed at night. CONCLUSIONS: Although the pandemic caused disruptions for the COVID cohort, participants still achieved weight loss with continued behavioral support.