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.

Vulnerability to sleep-related affective disturbances? A closer look at dysfunctional beliefs and attitudes about sleep as a moderator of daily sleep-affect associations in young people.

STUDY OBJECTIVES: Sleep and affect are closely related. Whether modifiable cognitive factors moderate this association is unclear. This study examined whether Dysfunctional Beliefs and Attitudes about Sleep moderate the impact of sleep on next-day affect in young people. METHODS: Four hundred and sixty-eight young people (205 adolescents, 54.1% female, M ±â€¯SDage=16.92 ±â€¯0.87; 263 emerging adults, 71.9% female, M±SDage=21.29 ±â€¯1.73) self-reported sleep and affect, and wore an actigraph for 7-28 days, providing >5000 daily observations. Linear mixed-effects models tested whether Dysfunctional Beliefs and Attitudes about Sleep moderated daily associations between self-reported and actigraphic sleep duration, sleep efficiency, and next-day affect on between- and within-person levels. Both valence (positive/negative) and arousal (high/low) dimensions of affect were examined. Covariates included age, sex, race/ethnicity, day of week, and previous-day affect. RESULTS: Dysfunctional Beliefs and Attitudes about Sleep significantly moderated sleep and high arousal positive affect associations on between- but not within-person levels. Individuals with higher Dysfunctional Beliefs and Attitudes about Sleep (+1 SD) and lower average sleep duration (actigraphic: p = .020; self-reported: p = .047) and efficiency (actigraphic: p = .047) had significantly lower levels of high arousal positive affect. After adjusting for multiple comparisons, Dysfunctional Beliefs and Attitudes about Sleep did not moderate relationships between sleep duration and low arousal positive affect (p ≥ .340). CONCLUSIONS: Young people with more unhelpful beliefs about sleep and shorter, or poorer, sleep may experience dampened levels of high arousal positive affect. DBAS may constitute a modifiable factor increasing affective vulnerability on a global but not day-to-day level. Intervention studies are needed to determine if changing Dysfunctional Beliefs and Attitudes about Sleep may reduce sleep-related affect disturbances in young people.

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.

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.

In-person vs home schooling during the COVID-19 pandemic: Differences in sleep, circadian timing, and mood in early adolescence.

During the COVID-19 pandemic, schools around the world rapidly transitioned from in-person to remote learning, providing an opportunity to examine the impact of in-person vs remote learning on sleep, circadian timing, and mood. We assessed sleep-wake timing using wrist actigraphy and sleep diaries over 1-2 weeks during in-person learning (n = 28) and remote learning (n = 58, where n = 27 were repeat assessments) in adolescents (age M ± SD = 12.79 ± 0.42 years). Circadian timing was measured under a single condition in each individual using salivary melatonin (Dim Light Melatonin Onset; DLMO). Online surveys assessed mood (PROMIS Pediatric Anxiety and Depressive Symptoms) and sleepiness (Epworth Sleepiness Scale - Child and Adolescent) in each condition. During remote (vs in-person) learning: (i) on school days, students went to sleep 26 minutes later and woke 49 minutes later, resulting in 22 minutes longer sleep duration (all P < .0001); (ii) DLMO time did not differ significantly between conditions, although participants woke at a later circadian phase (43 minutes, P = .03) during remote learning; and (iii) participants reported significantly lower sleepiness (P = .048) and lower anxiety symptoms (P = .006). Depressive symptoms did not differ between conditions. Changes in mood symptoms were not mediated by sleep. Although remote learning continued to have fixed school start times, removing morning commutes likely enabled adolescents to sleep longer, wake later, and to wake at a later circadian phase. These results indicate that remote learning, or later school start times, may extend sleep and improve some subjective symptoms in adolescents.

Prediction of shiftworker alertness, sleep, and circadian phase using a model of arousal dynamics constrained by shift schedules and light exposure.

STUDY OBJECTIVES: The study aimed to, for the first time, (1) compare sleep, circadian phase, and alertness of intensive care unit (ICU) nurses working rotating shifts with those predicted by a model of arousal dynamics; and (2) investigate how different environmental constraints affect predictions and agreement with data. METHODS: The model was used to simulate individual sleep-wake cycles, urinary 6-sulphatoxymelatonin (aMT6s) profiles, subjective sleepiness on the Karolinska Sleepiness Scale (KSS), and performance on a Psychomotor Vigilance Task (PVT) of 21 ICU nurses working day, evening, and night shifts. Combinations of individual shift schedules, forced wake time before/after work and lighting, were used as inputs to the model. Predictions were compared to empirical data. Simulations with self-reported sleep as an input were performed for comparison. RESULTS: All input constraints produced similar prediction for KSS, with 56%-60% of KSS scores predicted within ±1 on a day and 48%-52% on a night shift. Accurate prediction of an individual's circadian phase required individualized light input. Combinations including light information predicted aMT6s acrophase within ±1 h of the study data for 65% and 35%-47% of nurses on diurnal and nocturnal schedules. Minute-by-minute sleep-wake state overlap between the model and the data was between 81 ± 6% and 87 ± 5% depending on choice of input constraint. CONCLUSIONS: The use of individualized environmental constraints in the model of arousal dynamics allowed for accurate prediction of alertness, circadian phase, and sleep for more than half of the nurses. Individual differences in physiological parameters will need to be accounted for in the future to further improve predictions.

Attitudes Towards Sleep as a Time Commitment are Associated with Sleep Regularity.

Objective: Irregular sleep-wake patterns are associated with poor health outcomes. However, factors that lead individuals to adopt more regular sleep-wake patterns are not well understood. This study aimed to (i) examine the relationship between sleep regularity and attitudes toward sleep in undergraduates; (ii) test an intervention to improve sleep regularity based on personalized feedback; and (iii) investigate whether changes in attitudes toward sleep associate with improved sleep regularity.Methods: Sleep-wake timing of 45 students (19.7 ± 1.8 years) was monitored daily over two weeks using an app-based diary. The least regular sleepers, calculated using the Sleep Regularity Index (SRI ≤ 81.4; N = 22), completed a four-week randomized control intervention (RCI) designed to improve sleep regularity. The Charlotte Attitudes Toward Sleep (CATS) scale was administered at baseline and post-RCI, with subscales measuring attitudes toward sleep as a time commitment (Time), and as a beneficial/enjoyable behavior (Benefits).Results: CATS Time was positively associated with SRI at baseline (r2 = 0.16, p =.006) and during the four-week RCI (r2 = 0.29, p =.01). CATS Benefits was not associated with SRI but was associated with sleep quality. There was no significant improvement in SRI during the intervention. The relationship between change in CATS Time and change in SRI (baseline vs. RCI) differed between intervention and control groups (r2 = 0.27, p =.03).Conclusions: Attitudes toward sleep as a time commitment are associated with sleep regularity and should be considered as a target in future interventions aiming to improve sleep regularity.

Accuracy of the GENEActiv Device for Measuring Light Exposure in Sleep and Circadian Research.

Light is a variable of key interest in circadian rhythms research, commonly measured using wrist-worn sensors. The GENEActiv Original is a cost-effective and practical option for assessing light in ambulatory settings. With increasing research on health and well-being incorporating sleep and circadian factors, the validity of wearable devices for assessing light environments needs to be evaluated. In this study, we tested the accuracy of the GENEActiv Original devices (n = 10) for recording light under a range of ecologically relevant lighting conditions, including LED, fluorescent, infrared, and outdoor lighting. The GENEActiv output had a strong linear relationship with photopic illuminance. However, the devices consistently under-reported photopic illuminance, especially below 100 lux. Accuracy below 100 lux depended on the light source, with lower accuracy and higher variability under fluorescent lighting. The device's accuracy was also tested using light sources of varying spectral composition, which indicated that the device tends to under-report photopic illuminance for green light sources and over-report for red light sources. Furthermore, measures of photopic illuminance were impacted by infrared light exposure. We conclude that the GENEActiv Original is suitable for mapping light patterns within an individual context, and can reasonably differentiate indoor vs. outdoor lighting, though the accuracy is variable at low light conditions. Given the human circadian system's high sensitivity to light levels below 100 lux, if using the GENEActiv Original, we recommend also collecting light source data to better understand the impact on the circadian system, especially where participants spend prolonged periods in dim lighting.

The Role of Light Sensitivity and Intrinsic Circadian Period in Predicting Individual Circadian Timing.

There is large interindividual variability in circadian timing, which is underestimated by mathematical models of the circadian clock. Interindividual differences in timing have traditionally been modeled by changing the intrinsic circadian period, but recent findings reveal an additional potential source of variability: large interindividual differences in light sensitivity. Using an established model of the human circadian clock with real-world light recordings, we investigated whether changes in light sensitivity parameters or intrinsic circadian period could capture variability in circadian timing between and within individuals. Healthy participants (n = 12, aged 18-26 years) underwent continuous light monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim-light melatonin onset (DLMO) was measured each week. Using the recorded light patterns, a sensitivity analysis for predicted DLMO times was performed, varying 3 model parameters within physiological ranges: (1) a parameter determining the steepness of the dose-response curve to light (p), (2) a parameter determining the shape of the phase-response curve to light (K), and (3) the intrinsic circadian period (tau). These parameters were then fitted to obtain optimal predictions of the three DLMO times for each individual. The sensitivity analysis showed that the range of variation in the average predicted DLMO times across participants was 0.65 h for p, 4.28 h for K, and 3.26 h for tau. The default model predicted the DLMO times with a mean absolute error of 1.02 h, whereas fitting all 3 parameters reduced the mean absolute error to 0.28 h. Fitting the parameters independently, we found mean absolute errors of 0.83 h for p, 0.53 h for K, and 0.42 h for tau. Fitting p and K together reduced the mean absolute error to 0.44 h. Light sensitivity parameters captured similar variability in phase compared with intrinsic circadian period, indicating they are viable targets for individualizing circadian phase predictions. Future prospective work is needed that uses measures of light sensitivity to validate this approach.

Application of a Limit-Cycle Oscillator Model for Prediction of Circadian Phase in Rotating Night Shift Workers.

Practical alternatives to gold-standard measures of circadian timing in shift workers are needed. We assessed the feasibility of applying a limit-cycle oscillator model of the human circadian pacemaker to estimate circadian phase in 25 nursing and medical staff in a field setting during a transition from day/evening shifts (diurnal schedule) to 3-5 consecutive night shifts (night schedule). Ambulatory measurements of light and activity recorded with wrist actigraphs were used as inputs into the model. Model estimations were compared to urinary 6-sulphatoxymelatonin (aMT6s) acrophase measured on the diurnal schedule and last consecutive night shift. The model predicted aMT6s acrophase with an absolute mean error of 0.69 h on the diurnal schedule (SD = 0.94 h, 80% within ±1 hour), and 0.95 h on the night schedule (SD = 1.24 h, 68% within ±1 hour). The aMT6s phase shift from diurnal to night schedule was predicted to within ±1 hour in 56% of individuals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-individual variability in circadian phase shift during night shifts was limited. This study provides the basis for further adaptation and validation of models for predicting circadian phase in rotating shift workers.

Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.

A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other sleep schedules, including rotating shift work. Ambulatory wrist blue light irradiance and skin temperature data were collected in 16 healthy individuals on fixed and habitual sleep schedules, and 28 rotating shift workers. Artificial neural network models were trained to predict the circadian rhythm of (i) salivary melatonin on a fixed sleep schedule; (ii) urinary aMT6s on both fixed and habitual sleep schedules, including shift workers on a diurnal schedule; and (iii) urinary aMT6s in rotating shift workers on a night shift schedule. To determine predicted circadian phase, center of gravity of the fitted bimodal skewed baseline cosine curve was used for melatonin, and acrophase of the cosine curve for aMT6s. On a fixed sleep schedule, the model predicted melatonin phase to within ± 1 hour in 67% and ± 1.5 hours in 100% of participants, with mean absolute error of 41 ± 32 minutes. On diurnal schedules, including shift workers, the model predicted aMT6s acrophase to within ± 1 hour in 66% and ± 2 hours in 87% of participants, with mean absolute error of 63 ± 67 minutes. On night shift schedules, the model predicted aMT6s acrophase to within ± 1 hour in 42% and ± 2 hours in 53% of participants, with mean absolute error of 143 ± 155 minutes. Prediction accuracy was similar when using either 1 (wrist) or 11 skin temperature sensor inputs. These findings demonstrate that the model can predict circadian timing to within ± 2 hours for the vast majority of individuals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions.

The Impact of Shift Work on Sleep, Alertness and Performance in Healthcare Workers.

Shift work is associated with impaired alertness and performance due to sleep loss and circadian misalignment. This study examined sleep between shift types (day, evening, night), and alertness and performance during day and night shifts in 52 intensive care workers. Sleep and wake duration between shifts were evaluated using wrist actigraphs and diaries. Subjective sleepiness (Karolinska Sleepiness Scale, KSS) and Psychomotor Vigilance Test (PVT) performance were examined during day shift, and on the first and subsequent night shifts (3rd, 4th or 5th). Circadian phase was assessed using urinary 6-sulphatoxymelatonin rhythms. Sleep was most restricted between consecutive night shifts (5.74 ± 1.30 h), consecutive day shifts (5.83 ± 0.92 h) and between evening and day shifts (5.20 ± 0.90 h). KSS and PVT mean reaction times were higher at the end of the first and subsequent night shift compared to day shift, with KSS highest at the end of the first night. On nights, working during the circadian acrophase of the urinary melatonin rhythm led to poorer outcomes on the KSS and PVT. In rotating shift workers, early day shifts can be associated with similar sleep restriction to night shifts, particularly when scheduled immediately following an evening shift. Alertness and performance remain most impaired during night shifts given the lack of circadian adaptation to night work. Although healthcare workers perceive themselves to be less alert on the first night shift compared to subsequent night shifts, objective performance is equally impaired on subsequent nights.

Sleepiness and driving events in shift workers: the impact of circadian and homeostatic factors.

We aimed to characterize objective and subjective sleepiness and driving events during short work commutes and examine the impact of circadian and homeostatic factors across different shift types in a shift worker population. Thirty-three nurses were monitored for 2 weeks over day (07:00-15:30), evening (13:00-21:30), and night shifts (21:00-07:30). Sleep was measured via daily sleep logs and wrist actigraphy. Driving logs were completed for each work commute, reporting driving events and a predrive Karolinska Sleepiness Scale (KSS). Ocular data from a subset of participants (n = 11) assessed objective sleepiness using infrared oculography during commutes. Circadian phase was assessed at three time points via urinary 6-sulphatoxymelatonin (aMT6s) collected over 24-48 hours. Subjective and objective sleepiness and sleep-related and hazardous driving events significantly increased following night shift compared with preshift. There were significant shift differences with KSS, sleep-related and inattention-related events highest during the postnight shift commute, compared with day and evening shifts. Sleep-related events were highest following the first night shift, while inattention-related events were most frequent after consecutive night shifts. KSS, sleep-related and hazardous events were increased during drives following ≥16 hours of wakefulness. KSS and sleep-related events increased during drives within ±3 hours of aMT6s acrophase. An interaction between homeostatic and circadian processes was observed, with KSS and sleep-related events highest within ±3 hours of acrophase, when wakefulness was ≥16 hours. In naturalistic conditions, subjective and objective sleepiness and driving events are increased following night shifts, even during short (~30 minutes) commutes and exacerbated by an interaction between circadian phase and duration of wakefulness.

Temporal dynamics of circadian phase shifting response to consecutive night shifts in healthcare workers: role of light-dark exposure.

KEY POINTS: Shift work is highly prevalent and is associated with significant adverse health impacts. There is substantial inter-individual variability in the way the circadian clock responds to changing shift cycles. The mechanisms underlying this variability are not well understood. We tested the hypothesis that light-dark exposure is a significant contributor to this variability; when combined with diurnal preference, the relative timing of light exposure accounted for 71% of individual variability in circadian phase response to night shift work. These results will drive development of personalised approaches to manage circadian disruption among shift workers and other vulnerable populations to potentially reduce the increased risk of disease in these populations. ABSTRACT: Night shift workers show highly variable rates of circadian adaptation. This study examined the relationship between light exposure patterns and the magnitude of circadian phase resetting in response to night shift work. In 21 participants (nursing and medical staff in an intensive care unit) circadian phase was measured using 6-sulphatoxymelatonin at baseline (day/evening shifts or days off) and after 3-4 consecutive night shifts. Daily light exposure was examined relative to individual circadian phase to quantify light intensity in the phase delay and phase advance portions of the light phase response curve (PRC). There was substantial inter-individual variability in the direction and magnitude of phase shift after three or four consecutive night shifts (mean phase delay -1:08 ± 1:31 h; range -3:43 h delay to +3:07 h phase advance). The relative difference in the distribution of light relative to the PRC combined with diurnal preference accounted for 71% of the variability in phase shift. Regression analysis incorporating these factors estimated phase shift to within ±60 min in 85% of participants. No participants met criteria for partial adaptation to night work after three or four consecutive night shifts. Our findings provide evidence that the phase resetting that does occur is based on individual light exposure patterns relative to an individual's baseline circadian phase. Thus, a 'one size fits all' approach to promoting adaptation to shift work using light therapy, implemented without knowledge of circadian phase, may not be efficacious for all individuals.