Abstract Introduction Shift workers exhibit substantial individual variability in circadian response to work rosters . Light exposure has been shown to explain a considerable proportion of this variability during night shifts. This study aims to explore the predictive role of light exposure for circadian timing across diverse roster patterns. Methods Thirty-eight shift workers (37.16±9.06 years, 18 females) were monitored during a usual 6-day roster. Roster patterns included consecutive night shifts, consecutive early morning shifts, early morning-to-night rotations and night-to-early morning rotations. Sleep-wake timing was assessed using daily sleep diaries and wrist actigraphy. Circadian phase was assessed on the first and last day of the roster via 6-sulphatoxymelatonin (aMT6s) acrophase (peak) in urine. Light exposure was continuously measured via a lapel-worn light sensor. The difference in light intensity between the main phase advance (3-7h after acrophase) and phase delay zones (3-7h before acrophase) of the light circadian phase response curve was calculated across 6 days. Phase advance/delay zones were adjusted daily to account for individual phase shifts. Results Substantial individual variability was observed in the magnitude and direction of phase shifts across roster patterns (mean magnitude of shift = 1.76 ± 1.45 h; range = -6.57 to +3.21 h). Following consecutive night shifts, aMT6s phase shifts varied from -5.13 h to +1.17 h (mean = -1.81 ± 1.74 h, 10 delay, 1 advance). Following consecutive morning shifts, aMT6s phase shifts varied from -5.41 h to +3.21 h (mean = +0.70 ± 1.82 h, 7 advance, 2 delay). The difference in light exposure between the advance and delay zones across 6 days significantly predicted the degree of phase shift in either direction (β = 0.001, p = .01, R² = .16). Including diurnal preference improved the model to explain 31% of the variance (adjusted R² = .31). Conclusion Findings highlight that individual variability in circadian phase responses to various roster patterns was partly explained by differences in light exposure during the critical periods of the light phase response curve. This research supports the prediction of circadian timing to inform personalised interventions for shift workers. Support (if any) National Health and Medical Research Council (NHMRC) (APP 2001234).

Abstract A potential contributor to insufficient sleep among college students is their daily schedule, with sleep sacrificed for other waking activities. We investigated how daily schedules predict day-to-day sleep-wake timing in college students. 223 undergraduate college students (M±SD = 19.2±1.4 years, 37% females) attending a Massachusetts university in the US between 2013–2016 were monitored for approximately 30 days during semester. Sleep-wake timing was measured using daily online sleep diaries and wrist-actigraphy. Daily schedules were measured using daily online diaries that included self-reported timing and duration of academic, exercise-based, and extracurricular activities, and duration of self-study. Linear mixed models were used to examine the association between sleep-wake patterns and daily schedules at both the between-person and within-person levels. An earlier start time of the first-reported activity predicted earlier sleep onset (between and within: p<.001) and shorter total sleep time (within: p<.001) for the previous night, as well as earlier wake onset on the corresponding day (between and within: p<.001). A later end time of the last-reported activity predicted later sleep onset (within: p=.002) and shorter total sleep time (within: p=.02) on that night. A more intense daily schedule (i.e., greater total duration of reported activities) predicted an earlier wake onset time (between: p=.003, within: p<.001), a later sleep onset time (within: p<.001), a shortened total night-time sleep duration (between: p=.03, within: p<.001), and greater sleep efficiency (within: p<.001). These results indicate that college students may organize their sleep and wake times based on their daily schedule.

Internal circadian phase assessment is increasingly acknowledged as a critical clinical tool for the diagnosis, monitoring, and treatment of circadian rhythm sleep-wake disorders and for investigating circadian timing in other medical disorders. The widespread use of in-laboratory circadian phase assessments in routine practice has been limited, most likely because circadian phase assessment is not required by formal diagnostic nosologies, and is not generally covered by insurance. At-home assessment of salivary dim light melatonin onset (DLMO, a validated circadian phase marker) is an increasingly accepted approach to assess circadian phase. This approach may help meet the increased demand for assessments and has the advantages of lower cost and greater patient convenience. We reviewed the literature describing at-home salivary DLMO assessment methods and identified factors deemed to be important to successful implementation. Here, we provide specific protocol recommendations for conducting at-home salivary DLMO assessments to facilitate a standardized approach for clinical and research purposes. Key factors include control of lighting, sampling rate, and timing, and measures of patient compliance. We include findings from implementation of an optimization algorithm to determine the most efficient number and timing of samples in patients with Delayed Sleep-Wake Phase Disorder. We also provide recommendations for assay methods and interpretation. Providing definitive criteria for each factor, along with detailed instructions for protocol implementation, will enable more widespread adoption of at-home circadian phase assessments as a standardized clinical diagnostic, monitoring, and treatment tool.

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.

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 ± SDage=16.92 ± 0.87; 263 emerging adults, 71.9% female, M±SDage=21.29 ± 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.

BackgroundDuring 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.MethodsApproximately 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.DiscussionThe 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 disseminationEthical 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 datesRecruitment occurred between October 2019 and September 2021, data collection from 2019 to 2023.

Abstract Introduction Large inter-individual differences exist in how sensitive the circadian system is to light. Circadian light sensitivity can be affected by medications, such as antidepressants, and varies as a function of age and some mood disorders. Using a computational model, we investigated how differences in an individual’s light sensitivity can be offset by changes in their light environment to maintain stable circadian timing. Methods A previously validated computational model was used to simulate sleep and circadian timing under realistic assumptions about light and sleep schedules in day workers across two weeks. The model predicted circadian phase (dim light melatonin onset) and sleep onset/offset times for each day. Simulations were repeated varying two parameters: (i) light sensitivity, representing changes in the dose-response curve to light, and (ii) evening illuminance, representing home lighting levels after sunset. Results Higher light sensitivity and higher evening illuminance levels resulted in systematically later predicted sleep and circadian timing. The effects of increasing light sensitivity could be offset by reducing the level of evening illuminance, with this relationship holding over a wide range of light sensitivity values. Low light sensitivity combined with high evening illuminance produced a non-entrained (non-24) phenotype; decreasing evening illuminance from this state resulted in stable entrainment. Discussion We are only beginning to understand the influence of medications and health conditions on circadian light sensitivity. Our results show how modifications to evening light levels can be used to offset impacts of variable light sensitivity on sleep and circadian timing.

Abstract Background Circadian phase and sleep-wake timing delays are hallmark adolescent development features. These delays contribute to shorter sleep duration in combination with early school schedules. Both delayed circadian phase and insufficient sleep are associated with increased prevalence/severity of mood problems. This study examined associations between circadian phase, sleep duration, and mood in early adolescence. Methods 157 Year 7 Australian students (M±SD=12.81±0.40 years, 56.7% female) wore actiwatches and completed sleep diaries for two weeks in school term. Circadian phase was measured via salivary dim light melatonin onset (DLMO) on the final Friday of sleep monitoring. PROMIS Paediatric Depression and Anxiety measured depression/anxiety symptom severity. Sleep duration/efficiency were calculated using actigraphy and self-reports. Multiple linear regression quantified the relationships between sleep duration and efficiency and circadian phase and mood (depression/anxiety) associations, adjusting for sex. Results DLMO occurred at the expected time for early adolescents [continuous decimal time; M±SD=20.52±1.20; range:17.52-24.03]. Sleep duration (p=.03), but not sleep efficiency (p=.89), moderated associations between DLMO and depression (but not anxiety; p-values>.08). Simple slope analyses showed that in individuals with shorter average sleep duration, a one-hour circadian phase delay was associated with 1.84 t-score increase in depressive symptom severity (p=.03). These associations were non-significant in individuals with longer average sleep duration (p=.44). Conclusion Although delayed circadian phase is a developmental norm, its association with increased depressive symptom severity was ameliorated in young adolescents who obtained longer sleep duration. Addressing sleep duration, a modifiable protective factor (e.g., by delaying school start times), may yield mood-related benefits for adolescents.

Abstract Background Over the past 15 years, there has been substantial growth in web-based psychological interventions. We summarize evidence regarding the efficacy of web-based self-directed psychological interventions on depressive, anxiety and distress symptoms in people living with a chronic health condition. Method We searched Medline, PsycINFO, CINAHL, EMBASE databases and Cochrane Database from 1990 to 1 May 2019. English language papers of randomized controlled trials (usual care or waitlist control) of web-based psychological interventions with a primary or secondary aim to reduce anxiety, depression or distress in adults with a chronic health condition were eligible. Results were assessed using narrative synthases and random-effects meta-analyses. Results In total 70 eligible studies across 17 health conditions [most commonly: cancer (k = 20), chronic pain (k = 9), arthritis (k = 6) and multiple sclerosis (k = 5), diabetes (k = 4), fibromyalgia (k = 4)] were identified. Interventions were based on CBT principles in 46 (66%) studies and 42 (60%) included a facilitator. When combining all chronic health conditions, web-based interventions were more efficacious than control conditions in reducing symptoms of depression g = 0.30 (95% CI 0.22–0.39), anxiety g = 0.19 (95% CI 0.12–0.27), and distress g = 0.36 (95% CI 0.23–0.49). Conclusion Evidence regarding effectiveness for specific chronic health conditions was inconsistent. While self-guided online psychological interventions may help to reduce symptoms of anxiety, depression and distress in people with chronic health conditions in general, it is unclear if these interventions are effective for specific health conditions. More high-quality evidence is needed before definite conclusions can be made.

Abstract Using light-emitting devices before bed is a modifiable behaviour that may affect adolescent sleep. We investigated the daily associations between device use before bed and sleep-wake timing, duration, and quality in early adolescence. Participants were 168 Year 7 students (M±SD=12.82±0.42 years, 56% females) in Australia. Sleep-wake timing, sleep quality, and device use in the hour before bed (device type and media content) were measured using daily diaries for two weeks during school term. Linear mixed models were used to examine the association between device use in the hour before bed and sleep outcomes. Nearly all (99%) participants used devices before bed on at least one night, with 58% using devices before bed every night during the two-week monitoring period. Using devices to access social media predicted later reported sleep onset, longer sleep onset latency, and shorter sleep duration (all p<.05). Similarly, playing games, watching videos, or using a game console all predicted later sleep onset that night (all p<.05), and watching television predicted more wake after sleep onset (p<.01). In contrast, using devices for homework predicted earlier sleep onset (p<.05), while reading on devices predicted better sleep quality (p<.05). Our findings indicate that the type of device, and what they are used for before bed, may have different effects on sleep, potentially due to differences in light exposure levels and/or their impact on arousal systems. These findings may help inform existing guidelines for healthy pre-bedtime device use in adolescents.

Abstract High-level cognitive function is essential for academic performance in adolescents. While obtaining sufficient sleep duration has been shown to support cognitive function less is known about the role of sleep regularity in cognitive function. We investigated how sleep regularity relates to self-report cognitive function in 179 Year 7 students (M±SD=12.81±0.41 years, 56% females) in Australia. Sleep/wake timing was measured via wrist actigraphy and daily sleep diaries over two-school-weeks. Sleep regularity was measured using the Sleep Regularity Index (SRI) calculated via actigraphy measured sleep (SRI range = 58-95). Self-report cognitive function was measured using the PROMIS Paediatric Cognitive Function questionnaire, which requires participants to self-report cognitive performance over the last four-weeks. Academic skills were measured using two-subtests (reading comprehension and numerical operations) from the Weschler Individual Achievement Test – Third Edition (WIAT-III). We found that adolescents with more regular sleep self-reported better subjective cognitive function (β = .39, p = .001), even when controlling for age, sex, circadian phase assessed using DLMO, and total sleep time. In contrast, average total sleep time (range = 5.78-11.30 hours) alone was not associated with subjective cognitive function (β = .06, p = .99). Higher self-reported cognitive function was also associated with improved reading and numerical ability on the WIAT-III (β = .42, p =.04; β = .37, p =.02, respectively). The SRI did not predict performance on the WIAT (p >.05). Our findings suggest regular sleep may be important in supporting optimal cognitive functioning. These results have important implications for learning adolescence.

Abstract Study Objectives Light is the main time cue for the human circadian system. Irregular sleep/wake patterns are associated with poor health outcomes, which could be mediated by irregular patterns of light exposure. The relationship between sleep and light regularity has not been directly explored. We investigated the relationship between sleep and light regularity in adolescents, across school-term and vacation, using novel metrics for measuring light regularity. Methods Daily sleep and light patterns were measured via wrist actigraphy in 104 adolescents (54% male, age M±SD = 17.17±0.80 years) over two weeks of school-term and a subsequent two-week vacation. The Sleep Regularity Index (SRI) was computed for each two-week block. Stability of daily light exposure was assessed using variation of mean daily light timing (MLiT), variation in daily photoperiod, and the Light Regularity Index. Associations between SRI and each light regularity metric were examined, and within-individual changes in metrics were examined between school and vacation. Results More regular sleep was significantly associated with more regular scores for each light variability metric, during school and vacation. Between school and vacation sleep regularity decreased and nuanced changes in light patterns were observed. Variability measured by the MLiT variable increased, whereas variability measured by the LRI and photoperiod variable decreased. Conclusions Adolescents with irregular sleep also have irregular patterns of light exposure. These findings suggest sleep regularity may be a useful proxy for variability in the main circadian time cue, meaning that irregular light exposure may carry implications for the developing adolescent circadian system.

Abstract During the COVID-19 pandemic, schools rapidly transitioned from in-person to remote learning. We examined sleep- and mood-related changes in early adolescents, before and after this transition to assess the impact of in-person vs. remote learning. Sleep-wake timing was measured using wrist-actigraphy and sleep diaries over 1–2 weeks in Year 7 students (age M±SD =12.79±0.42 years) during in-person learning (n=28) and remote learning (n=58; n=27 were studied in both conditions). 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 min later and woke 49 min later, resulting in 22 min longer sleep duration (all p<0.0001); (ii) DLMO time did not differ significantly between conditions, although participants woke at a later relative circadian phase (43 minutes, p=0.03) during remote learning; (iii) participants reported significantly lower sleepiness (p=0.048) and lower anxiety symptoms (p=0.006). Depressive symptoms did not differ between conditions. Changes in mood symptoms were not mediated by changes in sleep timing. Although remote learning had the same school start times as in-person learning, 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 duration and improve some subjective symptoms in adolescents.

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 (r(2) = 0.16, p =.006) and during the four-week RCI (r(2) = 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 (r(2) = 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.

Abstract 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.

Abstract Introduction Sleep and affect are closely related. Late adolescence and emerging adulthood are associated with unique sleep patterns and risk for mood disturbances. This daily study examined whether dysfunctional beliefs and attitudes about sleep (DBAS), a modifiable cognitive vulnerability factor, moderated daily sleep-affect associations. Methods 421 community adolescents (n=205, 54.1% females, M±SDage=16.9±0.87) and emerging adults (n=216, 73.1% females, M±SDage=21.31±1.73) self-reported sleep and affect (adapted 12-item PANAS) and wore an actigraphy device for 7–28 days, providing >5000 daily observations. Linear mixed models tested whether DBAS moderated daily associations between self-reported and actigraphic sleep duration (total sleep time), 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, gender, ethnicity, day of week, and previous-day affect. Results DBAS significantly moderated associations between average sleep and next-day positive, but not negative, affect. Individuals with higher DBAS had significantly lower high arousal positive affect as average sleep duration (actigraphic: p=.002; self-reported: p=.014) and efficiency (actigraphic: p=.014) decreased. Similar moderation was found for average self-reported sleep duration and low arousal positive affect (p=.032). No significant results emerged on the within-person level. Previous-day affect significantly predicted next-day affect across models and outcomes (all p<.001). Discussion Adolescents and emerging adults with more negative views about sleep may experience dampened positive affect in shorter, or poorer, sleep periods. DBAS may constitute a modifiable factor increasing affective vulnerability on a global but not day-to-day level, and a therapeutic target for sleep-related affect disturbances in youths.

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.

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.

Existing models of the human circadian clock accurately predict phase at group-level but not at individual-level. Interindividual variability in light sensitivity is not currently accounted for in these models and may be a practical approach to improving individual-level predictions. Using the gold-standard predictive model, we (i) identified whether varying light sensitivity parameters produces meaningful changes in predicted phase in field conditions; and (ii) tested whether optimizing parameters can significantly improve accuracy of circadian phase prediction. Healthy participants (n=12, 7 women, aged 18-26) underwent continuous light and activity monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim light melatonin onset (DLMO) was measured each week. A model of the human circadian clock and its response to light was used to predict the three weekly DLMO times using the individual’s light data. A sensitivity analysis was performed varying three model parameters within physiological ranges: (i) amplitude of the light response [p]; (ii) advance vs. delay bias of the light response [K]; and (iii) intrinsic circadian period [tau]. These parameters were then fitted using least squares estimation to obtain optimal predictions of DLMO for each individual. Accuracy was compared between optimized parameters and default parameters. The default model predicted DLMO with mean absolute error of 1.02h. Sensitivity analysis showed the average range of variation in predicted DLMOs across participants was 0.65h for p, 4.28h for K and 3.26h for tau. Fitting parameters independently, we found mean absolute error of 0.85h for p, 0.71h for K and 0.75h for tau. Fitting p and K together reduced mean absolute error to 0.57h. Light sensitivity parameters capture similar or greater variability in phase as intrinsic circadian period, indicating they are a viable option for individualising circadian phase predictions. Future prospective work is needed using measures of light sensitivity to validate this approach. N/A

Knowledge of circadian phase is critical for timing interventions for circadian rhythm disorders, medications, or predicting alertness. Current gold-standard measures of circadian phase are impractical for continuous or real-time tracking. Mathe-matical modeling offers an alternative, whereby ambulatory monitoring of environmental, behavioral, and/or physiological variables can be used to predict circadian phase. This review examines available approaches for predicting circadian phase, ranging from statistical models to machine learning and dynamical systems models, and evaluates their readiness for individual phase predictions. Multiple models predicted circa-dian phase with similar accuracy when individuals were stably entrained. However, most models did not generalize, or were not tested, under more challenging conditions (e.g., circadian misalignment). One model performed similarly under a range of conditions: a limit-cycle oscillator model. Most models had been designed to predict circadian phase using group-level assumptions. Future work should focus on model individuali-zation and improved wearables to capture more accurate ambulatory signals.