Sleep-wake behaviors associated with cognitive performance in middle-aged participants of the Hispanic Community Health Study/Study of Latinos.

OBJECTIVES: Many sleep-wake behaviors have been associated with cognition. We examined a panel of sleep-wake/activity characteristics to determine which are most robustly related to having low cognitive performance in midlife. Secondarily, we evaluate the predictive utility of sleep-wake measures to screen for low cognitive performance. METHODS: The outcome was low cognitive performance defined as being >1 standard deviation below average age/sex/education internally normalized composite cognitive performance levels assessed in the Hispanic Community Health Study/Study of Latinos. Analyses included 1006 individuals who had sufficient sleep-wake measurements about 2years later (mean age=54.9, standard deviation= 5.1; 68.82% female). We evaluated associations of 31 sleep-wake variables with low cognitive performance using separate logistic regressions. RESULTS: In individual models, the strongest sleep-wake correlates of low cognitive performance were measures of weaker and unstable 24-hour rhythms; greater 24-hour fragmentation; longer time-in-bed; and lower rhythm amplitude. One standard deviation worse on these sleep-wake factors was associated with ∼20%-30% greater odds of having low cognitive performance. In an internally cross-validated prediction model, the independent correlates of low cognitive performance were: lower Sleep Regularity Index scores; lower pseudo-F statistics (modellability of 24-hour rhythms); lower activity rhythm amplitude; and greater time in bed. Area under the curve was low/moderate (64%) indicating poor predictive utility. CONCLUSION: The strongest sleep-wake behavioral correlates of low cognitive performance were measures of longer time-in-bed and irregular/weak rhythms. These sleep-wake assessments were not useful to identify previous low cognitive performance. Given their potential modifiability, experimental trials could test if targeting midlife time-in-bed and/or irregular rhythms influences cognition.

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.

Light in ecological settings: Entrainment, circadian disruption, and interventions.

Light is the predominant signal for the human circadian clock to synchronize to the solar 24-h day through an active process called entrainment. Modern light profiles are characterized by exposure to both natural daylight and artificial lighting. A mismatch between these self-selected light profiles and the solar day-night alternation can disrupt the circadian system, resulting in acute and chronic effects for health and safety. In this chapter, we describe (i) how entrainment works in the real world, illustrating the major role of light for this process; (ii) ways in which the circadian system can be disrupted by (external) factors such as irregular sleep, shift work, daylight saving time, and longitudinal position in a time zone; and (iii) how field studies have used light interventions to reduce direct and indirect effects of circadian disruption in ecological settings.

Sex differences and sex bias in human circadian and sleep physiology research.

Growing evidence shows that sex differences impact many facets of human biology. Here we review and discuss the impact of sex on human circadian and sleep physiology, and we uncover a data gap in the field investigating the non-visual effects of light in humans. A virtual workshop on the biomedical implications of sex differences in sleep and circadian physiology led to the following imperatives for future research: i) design research to be inclusive and accessible; ii) implement recruitment strategies that lead to a sex-balanced sample; iii) use data visualization to grasp the effect of sex; iv) implement statistical analyses that include sex as a factor and/or perform group analyses by sex, where possible; v) make participant-level data open and available to facilitate future meta-analytic efforts.

Chronotypes in the US: Influence of longitude position in a time zone.

The availability of electrical light has altered modern light exposure, affecting the synchronization process ('entrainment') of the circadian clock to the natural light-dark cycle. The discrepancy between the natural light-dark cycle and self-selected light exposure has raised the question whether humans entrain to sun time (as most organisms do) vs. social time. None of the studies addressing this question have been conducted in the US in a large-scale, nationally representative sample. In this brief report, we aimed at estimating the relationship between individual chronotype (the result of the entrainment process) and longitude position in a time zone, using 12 years (2003-2014) of pooled diary data (n = 50,753) from the American Time Use Survey (ATUS). Chronotype was estimated based on mid-sleep time on weekends (MSFWe), a proxy that was previously shown to replicate known age and sex differences in chronotype in the ATUS. Longitude position was derived from state-level information (e.g., average state border outline). Regression results showed a progressive delay in MSFWe from east to west within three of the four US continental time zones (delay per degree of longitude): Eastern, 1.8 min; Central, 1.2 min; Mountain, 2.4 min (all p < .01). The findings suggest that humans entrain to sun time, leading to an increasing discrepancy between social time and biological time ("circadian misalignment") towards the west of a time zone. Such a misalignment induced by where people live within a time zone may affect a large share of the population, with implications for health and safety.

Measuring sleep regularity: theoretical properties and practical usage of existing metrics.

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

Chronotype-specific Sleep in Two Versus Four Consecutive Shifts.

The study aimed to explore chronotype-specific effects of two versus four consecutive morning or night shifts on sleep-wake behavior. Sleep debt and social jetlag (a behavioral proxy of circadian misalignment) were estimated from sleep diary data collected for 5 weeks in a within-subject field study of 30 rotating night shift workers (29.9 ± 7.3 years, 60% female). Mixed models were used to examine whether effects of shift sequence length on sleep are dependent on chronotype, testing the interaction between sequence length (two vs. four) and chronotype (determined from sleep diaries). Analyses of two versus four morning shifts showed no significant interaction effects with chronotype. In contrast, increasing the number of night shifts from two to four increased sleep debt in early chronotypes, but decreased sleep debt in late types, with no change in intermediate ones. In early types, the higher sleep debt was due to accumulated sleep loss over four night shifts. In late types, sleep duration did not increase over the course of four night shifts, so that adaptation is unlikely to explain the observed lower sleep debt. Late types instead had increased sleep debt after two night shifts, which was carried over from two preceding morning shifts in this schedule. Including naps did not change the findings. Social jetlag was unaffected by the number of consecutive night shifts. Our results suggest that consecutive night shifts should be limited in early types. For other chronotypes, working four night shifts might be a beneficial alternative to working two morning and two night shifts. Studies should record shift sequences in rotating schedules.

Stability of the timing of food intake at daily and monthly timescales in young adults.

Cross-sectional observations have shown that the timing of eating may be important for health-related outcomes. Here we examined the stability of eating timing, using both clock hour and relative circadian time, across one semester (n = 14) at daily and monthly time-scales. At three time points ~ 1 month apart, circadian phase was determined during an overnight in-laboratory visit and eating was photographically recorded for one week to assess timing and composition. Day-to-day stability was measured using the Composite Phase Deviation (deviation from a perfectly regular pattern) and intraclass correlation coefficients (ICC) were used to determine individual stability across months (weekly average compared across months). Day-to-day clock timing of caloric events had poor stability within individuals (~ 3-h variation; ICC = 0.12-0.34). The timing of eating was stable across months (~ 1-h variation, ICCs ranging from 0.54-0.63), but less stable across months when measured relative to circadian timing (ICC = 0.33-0.41). Our findings suggest that though day-to-day variability in the timing of eating has poor stability, the timing of eating measured for a week is stable across months within individuals. This indicates two relevant timescales: a monthly timescale with more stability in eating timing than a daily timescale. Thus, a single day's food documentation may not represent habitual (longer timescale) patterns.

Reference values of physiological 18F-FET uptake: Implications for brain tumor discrimination.

PURPOSE: The aim of this study was to derive reference values of 18F-fluoro-ethyl-L-tyrosine positron emission tomography (18F-FET-PET) uptake in normal brain and head structures to allow for differentiation from tumor tissue. MATERIALS AND METHODS: We examined the datasets of 70 patients (median age 53 years, range 15-79), whose dynamic 18F-FET-PET was acquired between January 2016 and October 2017. Maximum standardized uptake value (SUVmax), target-to-background standardized uptake value ratio (TBR), and time activity curve (TAC) of the 18F-FET-PET were assessed in tumor tissue and in eight normal anatomic structures and compared using the t-test and Mann-Whitney U-test. Correlation analyses were performed using Pearson or Spearman coefficients, and comparisons between several variables with Pearson's chi-squared tests and Kruskal-Wallis tests as well as the Benjamini-Hochberg correction. RESULTS: All analyzed structures showed an 18F-FET uptake higher than background (threshold: TBR > 1.5). The venous sinuses and cranial muscles exhibited a TBR of 2.03±0.46 (confidence interval (CI) 1.92-2.14), higher than the uptake of caudate nucleus, pineal gland, putamen, and thalamus (TBR 1.42±0.17, CI 1.38-1.47). SUVmax, TBR, and TAC showed no difference in the analyzed structures between subjects with high-grade gliomas and subjects with low-grade gliomas, except the SUVmax of the pineal gland (t-tests of the pineal gland: SUVmax: p = 0.022; TBR: p = 0.411). No significant differences were found for gender and age. CONCLUSION: Normal brain tissue demonstrates increased 18F-FET uptake compared to background tissue. Two distinct clusters have been identified, comprising venous structures and gray matter with a reference uptake of up to SUVmax of 2.99 and 2.33, respectively.

Later school start times in a flexible system improve teenage sleep.

Sleep deprivation in teenage students is pervasive and a public health concern, but evidence is accumulating that delaying school start times may be an effective countermeasure. Most studies so far assessed static changes in schools start time, using cross-sectional comparisons and one-off sleep measures. When a high school in Germany introduced flexible start times for their senior students-allowing them to choose daily between an 8 am or 9 am start (≥08:50)-we monitored students' sleep longitudinally using subjective and objective measures. Students (10-12th grade, 14-19 y) were followed 3 weeks prior and 6 weeks into the flexible system via daily sleep diaries (n = 65) and a subcohort via continuous wrist-actimetry (n = 37). Satisfaction and perceived cognitive outcomes were surveyed at study end. Comparisons between 8 am and ≥9 am-starts within the flexible system demonstrated that students slept 1.1 h longer when starting school later-independent of gender, grade, chronotype, and frequency of later starts; sleep offsets were delayed but, importantly, onsets remained unchanged. Sleep quality was increased and alarm-driven waking reduced. However, overall sleep duration in the flexible system was not extended compared to baseline-likely because students did not start later frequently enough. Nonetheless, students were highly satisfied with the flexible system and reported cognitive and sleep improvements. Therefore, flexible systems may present a viable alternative for implementing later school starts to improve teenage sleep if students can be encouraged to use the late-option frequently enough. Flexibility may increase acceptance of school start changes and speculatively even prevent delays in sleep onsets through occasional early starts.

Irregular sleep and event schedules are associated with poorer self-reported well-being in US college students.

STUDY OBJECTIVES: Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric-the Composite Phase Deviation (CPD)-to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. METHODS: Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy-Alert, Sad-Happy, Sluggish-Energetic, Sick-Healthy, and Stressed-Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. RESULTS: CPD for sleep was a significant predictor of average well-being (e.g. Stressed-Calm: b = -6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed-Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. CONCLUSIONS: Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. CLINICAL TRIAL REGISTRATION: NCT02846077.

The impact of weather, road surface, time-of-day, and light conditions on severity of bicycle-motor vehicle crash injuries.

BACKGROUND: This study examined whether environmental variables including weather, road surface, time-of-day, and light conditions were associated with the severity of injuries resulting from bicycle-motor vehicle crashes. METHODS: Using log-binomial regressions, we analyzed 113 470 police reports collected between 2000 and 2014 in four U.S. states with environmental and injury severity information. "Severe" injuries included fatal and incapacitating injuries, and "non-severe" included non-incapacitating, possible or no-injuries. RESULTS: Light condition was significantly associated with the injury severity to the bicyclist with more severe injuries at dawn (RR = 1.62 [95%CI 1.35-1.94]) and during darkness (both lighted and unlighted roads: 1.32 [1.24-1.40], respectively, 1.57 [1.41-1.76]) as compared to daylight. In these conditions of low visibility, risk was further increased during early morning hours before 7 am (1.61 [1.22-2.13]). CONCLUSIONS: Crashes in low light conditions and during early morning hours are more likely to result in higher injury severity.

Dynamics and Ultradian Structure of Human Sleep in Real Life.

The temporal dynamics that characterize sleep are difficult to capture outside the sleep laboratory. Therefore, longitudinal studies and big-data approaches assessing sleep dynamics are lacking. Here, we present the first large-scale analysis of human sleep dynamics in real life by making use of longitudinal wrist movement recordings of >16,000 sleep bouts from 573 subjects. Through non-linear conversion of locomotor activity to "Locomotor Inactivity During Sleep" (LIDS), movement patterns are exposed that directly reflect ultradian sleep cycles and replicate the dynamics of laboratory sleep parameters. Our current analyses indicate no sex differences in LIDS-derived sleep dynamics, whereas especially age but also shift work have pronounced effects, specifically on decline rates and ultradian amplitude. In contrast, ultradian period and phase emerged as remarkably stable across the tested variables. Our approach and results provide the necessary quantitative sleep phenotypes for large field studies and outcome assessments in clinical trials.

Updating the "Risk Index": A systematic review and meta-analysis of occupational injuries and work schedule characteristics.

Fatigue is a major risk factor for occupational 'accidents' and injuries, and involves dimensions of physical, mental, and muscular fatigue. These dimensions are largely influenced by temporal aspects of work schedules. The "Risk Index" combines four fatigue-related components of work schedules to estimate occupational 'accident' and injury risk based on empirical trends: shift type (morning, afternoon/evening, night), length and consecutive number, and on-shift rest breaks. Since its first introduction in 2004, several additional studies have been published that allow the opportunity to improve the internal and external validity of the "Risk Index". Thus, we updated the model's estimates by systematically reviewing the literature and synthesizing study results using meta-analysis. Cochrane Collaboration directives and MOOSE guidelines were followed. We conducted systematic literature searches on each model component in Medline. An inverse variance approach to meta-analysis was used to synthesize study effect sizes and estimate between-studies variance ('heterogeneity'). Meta-regression models were conducted to explain the heterogeneity using several effect modifiers, including the sample age and sex ratio. Among 3,183 initially identified abstracts, after screening by two independent raters (95-98% agreement), 29 high-quality studies were included in the meta-analysis. The following trends were observed: Shift type. Compared to morning shifts, injury risk significantly increased on night shifts (RR = 1.36 [95%CI = 1.15-1.60], n = 14 studies), while risk was slightly elevated on afternoon/evening shifts, although non-significantly (RR = 1.12 [0.76-1.64], n = 9 studies). Meta-regressions revealed worker's age as a significant effect modifier: adolescent workers (≤ 20 y) showed a decreased risk on the afternoon/evening shift compared to both morning shifts and adult workers (p < 0.05). Number of consecutive shifts. Compared to the first shift in a block of consecutive shifts, risk increased exponentially for morning shifts (e.g., 4th: RR = 1.09 [0.90-1.32]; n = 6 studies) and night shifts (e.g., 4th: RR = 1.36 [1.14-1.62]; n = 8 studies), while risk on afternoon/evening shifts appeared unsystematic. Shift length. Injury risk rose substantially beyond the 9th hour on duty, a trend that was mirrored when looking at shift lengths (e.g., >12 h: RR = 1.34 [1.04-1.51], n = 3 studies). Rest breaks. Risk decreased for any rest break duration (e.g., 31-60 min: RR = 0.35 [0.29-0.43], n = 2 studies). With regards to time between breaks, risk increased with every additional half hour spent on the work task compared to the first 30 min (e.g., 90-119 min: RR = 1.62 [1.00-2.62], n = 3 studies). Rest break duration and interval seem to interact such that with increasing duration, the time between breaks becomes irrelevant. The updated "Risk Index". All four components were combined to form the updated model and the relative risk values estimated for a variety of work schedules. The resulting "Risk Map" shows regions of highest risk when rest breaks are not taken frequently enough (i.e. <4 h) or are too short (i.e. <30 min), when shift length exceeds 11 h, and when work takes place during the night (particularly for >3 consecutive night shifts). The "Risk Index" is proposed as an empirical model to predict occupational 'accident' and injury risk based on the most recent data in the field, and can serve as a tool to evaluate hazards and maximize safety across different work schedules.

Chronotypes in the US - Influence of age and sex.

An individual's chronotype reflects how the circadian system embeds itself into the 24-h day with rhythms in physiology, cognition and behavior occurring accordingly earlier or later. In view of an increasing number of people working at unusual times and linked health and safety risks, the wide range in human chronotypes may provide opportunities to allow people to work (and sleep) at times that are in synch with their circadian physiology. We aimed at estimating the distribution of chronotypes in the US population by age and sex. Twelve years (2003-2014) of pooled diary data from the American Time Use Survey were used to calculate chronotype based on mid-point of sleep on weekends (MSFWe, n = 53,689). We observed a near-normal distribution overall and within each age group. The distribution's mean value is systematically different with age, shifting later during adolescence, showing a peak in 'lateness' at ~19 years, and shifting earlier thereafter. Men are typically later chronotypes than women before 40, but earlier types after 40. The greatest differences are observed between 15 and 25 for both sexes, equaling more than 50% of the total chronotype difference across all age groups. The variability in chronotype decreases with age, but is generally higher in males than females. This is the first study to estimate the distribution and prevalence of individual chronotypes in the US population based on a large-scale, nationally representative sample. Our finding that adolescents are on average the latest chronotypes supports delaying school start times to benefit their sleep and circadian alignment. The generally wide range in chronotypes may provide opportunities for tailored work schedules by matching external and internal time, potentially decreasing long- and short-term health and safety risks.

A novel method to visualise and quantify circadian misalignment.

The circadian clock governs virtually all processes in the human body, including sleep-wake behaviour. Circadian misalignment describes the off-set between sleep-wake cycles and clock-regulated physiology. This strain is predominantly caused by external (societal) demands including shift work, early school start times and fast travels across time zones. Sleeping at the 'wrong' internal time can jeopardise health and safety, and we therefore need a good quantification of this phenomenon. Here, we propose a novel method to quantify the mistiming of sleep-wake rhythms and demonstrate its versatility in day workers and shift workers. Based on a single time series, our Composite Phase Deviation method unveils distinct, subject- and schedule-specific geometries ('islands and pancakes') that illustrate how modern work times interfere with sleep. With increasing levels of circadian strain, the resulting shapes change systematically from small, connected forms to large and fragmented patterns. Our method shows good congruence with published measures of circadian misalignment (i.e., Inter-daily Stability and 'Behavioural Entrainment'), but offers added value as to its requirements, e.g., being computable for sleep logs and questionnaires. Composite Phase Deviations will help to understand the mechanisms that link 'living against the clock' with health and disease on an individual basis.

Are chronotype, social jetlag and sleep duration associated with health measured by Work Ability Index?

The present study investigates the impact of chronotype, social jetlag and sleep duration on self-perceived health, measured by Work Ability Index (WAI), within an industrial setting. Between 2011 and 2013, 2474 day and shift workers participated in a health check offered by an occupational health promotion program and filled out the Munich ChronoType Questionnaire (adapted to the rotational 12-h schedule for shift workers) and the WAI. We computed sleep duration on work and free days, chronotype, and social jetlag. We used linear regression models to examine chronotype, sleep duration and social jetlag for association with the WAI sum score, and proportional odds models to estimate the combined effect of social jetlag and sleep duration. Participants reported an average daily sleep duration of 7.35 h (SD: 1.2 h), had an average chronotype of 3:08 a.m. (SD: 1 h), and the average social jetlag corresponded to 1.96 h (SD: 2.05 h). Increasing social jetlag and shorter sleep duration were independently associated with a decreasing WAI, while chronotype per se was not associated with WAI. Short sleep duration combined with high social jetlag significantly increased the risk of poor WAI (OR = 1.36; 95% CI: 1.09-1.72), while long sleep duration and high social jetlag were not associated with poor WAI (OR = 1.09; 95% CI: 0.88-1.35). Our results add to a growing body of literature, suggesting that circadian misalignment, but not chronotype per se, may be critical for health. Our results indicate that longer sleep may override the adverse effects of social jetlag on WAI.

A unique, fast-forwards rotating schedule with 12-h long shifts prevents chronic sleep debt.

Sleep debt--together with circadian misalignment--is considered a central factor for adverse health outcomes associated with shift work. Here, we describe in detail sleep-wake behavior in a fast-forward rotating 12-h shift schedule, which involves at least 24 hours off after each shift and thus allows examining the role of immediate recovery after shift-specific sleep debt. Thirty-five participants at two chemical plants in Germany were chronotyped using the Munich ChronoType Questionnaire for Shift-Workers (MCTQ(Shift)) and wore actimeters throughout the two-week study period. From these actimetry recordings, we computed sleep and nap duration, social jetlag (a measure of circadian misalignment), and the daily timing of activity and sleep (center of gravity and mid-sleep, respectively). We observed that the long off-work periods between each shift create a fast alternation between shortened (mean ± standard deviation, 5h 17min ± 56min) and extended (8h 25min ± 72min) sleep episodes resulting in immanent reductions of sleep debt. Additionally, extensive napping of early chronotypes (up to 3 hours before the night shift) statistically compensated short sleep durations after the night shift. Partial rank correlations showed chronotype-dependent patterns of sleep and activity that were similar to those previously described in 8-h schedules; however, sleep before the day shift did not differ between chronotypes. Our findings indicate that schedules preventing a build-up of chronic sleep debt may reduce detrimental effects of shift work irrespective of shift duration. Prospective studies are needed to further elucidate the relationship between sleep, the circadian system, and health and safety hazards.