The night before night shift: Chronotype impacts total sleep and rapid eye movement sleep during a strategically delayed sleep.

Transition to night shift may be improved by strategically delaying the main sleep preceding a first night shift. However, the effects of delayed timing on sleep may differ between chronotypes. Therefore, the study aim was to compare the impacts of chronotype on sleep quality and architecture during a normally timed sleep opportunity and a delayed sleep opportunity. Seventy-two (36 female, 36 male) healthy adults participated in a laboratory study. Participants were provided with a normally timed sleep opportunity (23:00-08:00) and a delayed sleep opportunity (03:00-12:00) over two consecutive nights in a sleep laboratory. Sleep was monitored by polysomnography (PSG), and chronotype was determined from dim light melatonin onset (DLMO). A tertile split of DLMO defined early (20:24 ± 0:42 h), intermediate (21:31 ± 0:12 h), and late chronotype (22:56 ± 0:54 h) categories. Although there was no main effect of chronotype on any sleep measure, early chronotypes obtained less total sleep with delayed sleep than with normally timed sleep (p = 0.044). Intermediate and late chronotypes obtained more rapid eye movement (REM) sleep with delayed sleep than with normally timed sleep (p = 0.013, p = 0.012 respectively). Wake was more elevated for all chronotypes in the later hours of the delayed sleep opportunity than at the start of the sleep opportunity. Strategically delaying the main sleep preceding a first night shift appears to benefit intermediate and late chronotypes (i.e., more REM sleep), but not early chronotypes (i.e., less total sleep). Circadian processes appear to elevate wakefulness for all chronotypes in the later stages of a delayed sleep opportunity.

No Effect of Chronotype on Hunger or Snack Consumption during a Night Shift with Acute Sleep Deprivation.

Night shift workers experience circadian misalignment and sleep disruption, which impact hunger and food consumption. The study aim was to assess the impact of chronotype on hunger and snack consumption during a night shift with acute sleep deprivation. Seventy-two (36f, 36m) healthy adults participated in a laboratory study. A sleep opportunity (03:00-12:00) was followed by a wake period (12:00-23:00) and a simulated night shift (23:00-07:00). Subjective measures of hunger, prospective consumption, desire to eat fruit, and desire to eat fast food were collected before (12:20, 21:50) and after (07:20) the night shift. Snack opportunities were provided before (15:10, 19:40) and during (23:50, 03:30) the night shift. A tertile split of the dim light melatonin onset (DLMO) distribution defined early (20:24 ± 0:42 h), intermediate (21:31 ± 0:12 h), and late chronotype (22:56 ± 0:54 h) categories. There were no main effects of chronotype on any subjective measure (p = 0.172-0.975), or on snack consumption (p = 0.420), and no interactions between chronotype and time of day on any subjective measure (p = 0.325-0.927) or on snack consumption (p = 0.511). Differences in circadian timing between chronotype categories were not associated with corresponding differences in hunger, prospective consumption, desire to eat fruit, desire to eat fast food, or snack consumption at any measurement timepoint.

Concordance of Chronotype Categorisations Based on Dim Light Melatonin Onset, the Morningness-Eveningness Questionnaire, and the Munich Chronotype Questionnaire.

Chronotype reflects circadian timing and can be determined from biological markers (e.g., dim light melatonin onset; DLMO), or questionnaires (e.g., Morningness-Eveningness Questionnaire; MEQ, or Munich Chronotype Questionnaire; MCTQ). The study's aim was to quantify concordance between chronotype categorisations based on these measures. A total of 72 (36f) young, healthy adults completed the MEQ and MCTQ and provided saliva samples hourly in dim light during the evening in a laboratory. The corrected midpoint of sleep on free days (MSFsc) was derived from MCTQ, and tertile splits were used to define early, intermediate and late DLMO-CT, MEQ-CT and MSFsc-CT chronotype categories. DLMO correlated with MEQ score (r = -0.25, p = 0.035) and MSFsc (r = 0.32, p = 0.015). For early, intermediate and late DLMO-CT categories, mean(SD) DLMO were 20:25(0:46), 21:33(0:10) and 23:03(0:53). For early, intermediate and late MEQ-CT categories, mean(SD) MEQ scores were 60.5(5.3), 51.4(2.9) and 40.8 (5.0). For early, intermediate and late MSFsc-CT categories, mean(SD) MSFsc were 03:23(0:34), 04:37(0:12) and 05:55(0:48). Low concordance of categorisations between DLMO-CT and MEQ-CT (37%), and between DLMO-CT and MSFsc-CT (37%), suggests chronotype categorisations depend on the measure used. To enable valid comparisons with previous results and reduce the likelihood of misleading conclusions, researchers should select measures and statistical techniques appropriate to the construct of interest and research question.

No Effect of Chronotype on Sleepiness, Alertness, and Sustained Attention during a Single Night Shift.

The study's aim was to examine the effect of chronotype on cognitive performance during a single night shift. Data were collected from 72 (36f) young, healthy adults in a laboratory study. Participants had a 9 h sleep period (03:00-12:00) followed by an 8 h night shift (23:00-07:00). During the night shift, participants completed five test sessions, which included measures of subjective sleepiness, subjective alertness, and sustained attention (i.e., psychomotor vigilance task; PVT). Dim light melatonin onset (DLMO) was derived from saliva samples taken during the evening preceding the night shift. A tertile split of DLMO was used to determine three chronotype categories: earlier (DLMO = 20:22 ± 0:42), intermediate (DLMO = 21:31 ± 0:13), and later (DLMO = 22:54 ± 0:54). There were (a) significant main effects of test session (all p < 0.001); (b) no main effects of chronotype; and (c) no interaction effects between chronotype and test session on sleepiness, alertness, PVT response time, and PVT lapses. The results indicate that under controlled sleeping conditions, chronotype based on dim light melatonin onset did not affect nighttime performance. Differences in performance during night shift between chronotypes reported by field studies may be related to differences in the amount and/or timing of sleep before or between night shifts, rather than circadian timing.

Finding DLMO: estimating dim light melatonin onset from sleep markers derived from questionnaires, diaries and actigraphy.

Determination of circadian phase is required to diagnose and treat circadian abnormalities, but the measurement of dim light melatonin onset (DLMO), the most common phase marker, is laborious. As sleep timing reflects circadian phase, measurement of sleep markers (e.g., sleep onset, sleep midpoint, sleep offset) provides a simple way to estimate DLMO. The study aim was to compare methods to estimate DLMO from markers derived from the Pittsburgh Sleep Quality Index (PSQI), Munich Chronotype Questionnaire (MCTQ), sleep diaries, and actigraphy. PSQI, MCTQ, and 1 week of diary and actigraphy data were collected from 72 (36 f, 36 m) healthy adults aged 23.1 (± 3.6) y prior to a laboratory sleep study. Saliva samples were collected hourly in dim light during the second evening of the study. The sleep markers most strongly associated with DLMO from each source were PSQI onset, MCTQ average midpoint, 7-d diary midpoint, and 7-d actigraphy midpoint. Estimates of DLMO as a fixed interval before the sleep marker exhibited proportional bias. DLMO estimated from regression models based on sleep midpoint from 7 d of diary or 7 d of actigraphy showed the narrowest limits of agreement with measured DLMO without proportional bias (±1.8 h and ±1.9 h, respectively). Our findings indicate none of the methods provided precise estimates of DLMO from sleep markers. The best estimates were from linear regressions on sleep midpoints from 7 d of diary or actigraphy, and these estimates of DLMO may be suitable for limited research purposes.

Finger Twitches are More Frequent in REM Sleep Than in Non-REM Sleep.

INTRODUCTION: Abnormal rapid eye movement (REM) sleep is often symptomatic of chronic disorders, however polysomnography, the gold standard method to measure REM sleep, is expensive and often impractical. Attempts to develop cost-effective ambulatory systems to measure REM sleep have had limited success. As elevated twitching is often observed during REM sleep in some distal muscles, the aim of this study was to assess the potential for a finger-mounted device to measure finger twitches, and thereby differentiate periods of REM and non-REM (NREM) sleep. METHODS: One night of sleep data was collected by polysomnography from each of 18 (3f, 15m) healthy adults aged 23.2 ± 3.3 (mean ± SD) years. Finger movement was detected using a piezo-electric limb sensor taped to the index finger of each participant. Finger twitch densities were calculated for each stage of sleep. RESULTS: Finger twitch density was greater in REM than in NREM sleep (p < 0.001). Each sleep stage had a unique finger twitch density, except for REM and stage N1 sleep which were similar. Finger twitch density was greater in late REM than in early REM sleep (p = 0.005), and there was a time-state interaction: the difference between finger twitch densities in REM and NREM sleep was greater in late sleep than in early sleep (p = 0.007). CONCLUSION: Finger twitching is more frequent in REM sleep than in NREM sleep and becomes more distinguishable as sleep progresses. Finger twitches appear to be too infrequent to make definitive 30-second epoch determinations of sleep stage. However, an algorithm informed by measures of finger twitch density has the potential to detect periods of REM sleep and provide estimates of total REM sleep time and percentage.

The likelihood of crashing during a simulated post-work commute decreases across a week of consecutive night shifts.

The aim of this study was to assess the effect of working multiple, consecutive night shifts on crash risk during the morning commute. Participants (36 F, 36 M, aged 23.1 ± 3.6 y) completed a laboratory-based shiftwork protocol with seven consecutive night shifts (23:00-07:00 h) that each started and ended with a 20 min simulated commute. Compared to the corresponding pre-work commutes, the likelihood of crashing during the post-work commutes was 11.0-, 8.5-, and 5.6-fold higher at the start, middle, and end of the week, respectively. The results of this simulation study indicate that crash risk is relatively high during the morning commute but declines throughout a week of night work.