Stephanie Centofanti

A 30-Minute, but Not a 10-Minute Nighttime Nap is Associated with Sleep Inertia

STUDY OBJECTIVES To assess sleep inertia following 10-min and 30-min naps during a simulated night shift. METHODS Thirty-one healthy adults (aged 21-35 y; 18 females) participated in a 3-day laboratory study that included one baseline (BL) sleep (22:00-07:00) and one experimental night involving randomization to either: total sleep deprivation (NO-NAP), a 10-min nap (10-NAP) or a 30-min nap (30-NAP). Nap opportunities ended at 04:00. A 3-min psychomotor vigilance task (PVT-B), digit-symbol substitution task (DSST), fatigue scale, sleepiness scale, and self-rated performance scale were undertaken pre-nap (03:00) and at 2, 17, 32, and 47 min post-nap. RESULTS The 30-NAP (14.7 ± 5.7 min) had more slow wave sleep than the 10-NAP (0.8 ± 1.5 min; P < 0.001) condition. In the NO-NAP condition, PVT-B performance was worse than pre-nap (4.6 ± 0.3 1/sec) at 47 min post-nap (4.1 ± 0.4 1/sec; P < 0.001). There was no change across time in the 10-NAP condition. In the 30-NAP condition, performance immediately deteriorated from pre-nap (4.3 ± 0.3 1/sec) and was still worse at 47 min post-nap (4.0 ± 0.5 1/sec; P < 0.015). DSST performance deteriorated in the NO-NAP (worse than pre-nap from 17 to 47 min; P < 0.008), did not change in the 10-NAP, and was impaired 2 min post-nap in the 30-NAP condition (P = 0.028). All conditions self-rated performance as better than pre-nap for all post-nap test points (P < 0.001). CONCLUSIONS This study is the first to show that a 10-min (but not a 30-min) nighttime nap had minimal sleep inertia and helped to mitigate short-term performance impairment during a simulated night shift. Self-rated performance did not reflect objective performance following a nap.

Do night naps impact driving performance and daytime recovery sleep

Short, nighttime naps are used as a fatigue countermeasure in night shift work, and may offer protective benefits on the morning commute. However, there is a concern that nighttime napping may impact upon the quality of daytime sleep. The aim of the current project was to investigate the influence of short nighttime naps (<30min) on simulated driving performance and subsequent daytime recovery sleep. Thirty-one healthy subjects (aged 21-35 y; 18 females) participated in a 3-day laboratory study. After a 9-h baseline sleep opportunity (22:00h-07:00h), subjects were kept awake the following night with random assignment to: a 10-min nap ending at 04:00h plus a 10-min nap at 07:00h; a 30-min nap ending at 04:00h; or a no-nap control. A 40-min driving simulator task was administered at 07:00h and 18:30h post-recovery sleep. All conditions had a 6-h daytime recovery sleep opportunity (10:00h-16:00h) the next day. All sleep periods were recorded polysomnographically. Compared to control, the napping conditions did not significantly impact upon simulated driving lane variability, percentage of time in a safe zone, or time to first crash on morning or evening drives (p>0.05). Short nighttime naps did not significantly affect daytime recovery total sleep time (p>0.05). Slow wave sleep (SWS) obtained during the 30-min nighttime nap resulted in a significant reduction in SWS during subsequent daytime recovery sleep (p<0.05), such that the total amount of SWS in 24-h was preserved. Therefore, short naps did not protect against performance decrements during a simulated morning commute, but they also did not adversely affect daytime recovery sleep following a night shift. Further investigation is needed to examine the optimal timing, length or combination of naps for reducing performance decrements on the morning commute, whilst still preserving daytime sleep quality.

The impact of short night-time naps on performance, sleepiness and mood during a simulated night shift

ABSTRACT Short naps on night shift are recommended in some industries. There is a paucity of evidence to verify the sustained recovery benefits of short naps in the last few hours of the night shift. Therefore, the current study aimed to investigate the sustained recovery benefits of 30 and 10-min nap opportunities during a simulated night shift. Thirty-one healthy participants (18F, 21–35 y) completed a 3-day, between-groups laboratory study with one baseline night (22:00–07:00 h time in bed), followed by one night awake (time awake from 07:00 h on day two through 10:00 h day three) with random allocation to: a 10-min nap opportunity ending at 04:00 h, a 30-min nap opportunity ending at 04:00 h or no nap (control). A neurobehavioral test bout was administered approximately every 2 h during wake periods. There were no significant differences between nap conditions for post-nap psychomotor vigilance performance after controlling for pre-nap scores (p > 0.05). The 30-min nap significantly improved subjective sleepiness compared to the 10-min nap and no-nap control (p < 0.05). The 10-min nap significantly worsened negative mood compared to the 30-min nap and no-nap control (p < 0.01). Contrary to some evidence suggesting “power naps” can help to alleviate performance decrements, a 30-min nap opportunity at approximately 04:00 h was found to improve subjective, but not objective sleepiness. A 10-min nap may lead to increased negative mood in the hours following the nap due to a “short nap aversion” effect.

Sleep inertia associated with a 10-min nap before the commute home following a night shift: A laboratory simulation study

Night shift workers are at risk of road accidents due to sleepiness on the commute home. A brief nap at the end of the night shift, before the commute, may serve as a sleepiness countermeasure. However, there is potential for sleep inertia, i.e. transient impairment immediately after awakening from the nap. We investigated whether sleep inertia diminishes the effectiveness of napping as a sleepiness countermeasure before a simulated commute after a simulated night shift. N=21 healthy subjects (aged 21-35 y; 12 females) participated in a 3-day laboratory study. After a baseline night, subjects were kept awake for 27h for a simulated night shift. They were randomised to either receive a 10-min nap ending at 04:00 plus a 10-min pre-drive nap ending at 07:10 (10-NAP) or total sleep deprivation (NO-NAP). A 40-min York highway driving task was performed at 07:15 to simulate the commute. A 3-min psychomotor vigilance test (PVT-B) and the Samn-Perelli Fatigue Scale (SP-Fatigue) were administered at 06:30 (pre-nap), 07:12 (post-nap), and 07:55 (post-drive). In the 10-NAP condition, total pre-drive nap sleep time was 9.1±1.2min (mean±SD), with 1.3±1.9min spent in slow wave sleep, as determined polysomnographically. There was no difference between conditions in PVT-B performance at 06:30 (before the nap). In the 10-NAP condition, PVT-B performance was worse after the nap (07:12) compared to before the nap (06:30); no change across time was found in the NO-NAP condition. There was no significant difference between conditions in PVT-B performance after the drive. SP-Fatigue and driving performance did not differ significantly between conditions. In conclusion, the pre-drive nap showed objective, but not subjective, evidence of sleep inertia immediately after awakening. The 10-min nap did not affect driving performance during the simulated commute home, and was not effective as a sleepiness countermeasure.

Sleep inertia during a simulated 6-h on/6-h off fixed split duty schedule

ABSTRACT Sleep inertia is a safety concern for shift workers returning to work soon after waking up. Split duty schedules offer an alternative to longer shift periods, but introduce additional wake-ups and may therefore increase risk of sleep inertia. This study investigated sleep inertia across a split duty schedule. Sixteen participants (age range 21–36 years; 10 females) participated in a 9-day laboratory study with two baseline nights (10 h time in bed, [TIB]), four 24-h periods of a 6-h on/6-h off split duty schedule (5-h TIB in off period; 10-h TIB per 24 h) and two recovery nights. Two complementary rosters were evaluated, with the timing of sleep and wake alternating between the two rosters (2 am/2 pm wake-up roster versus 8 am/8 pm wake-up roster). At 2, 17, 32 and 47 min after scheduled awakening, participants completed an 8-min inertia test bout, which included a 3-min psychomotor vigilance test (PVT-B), a 3-min Digit-Symbol Substitution Task (DSST), the Karolinska Sleepiness Scale (KSS), and the Samn–Perelli Fatigue Scale (SP-Fatigue). Further testing occurred every 2 h during scheduled wakefulness. Performance was consistently degraded and subjective sleepiness/fatigue was consistently increased during the inertia testing period as compared to other testing times. Morning wake-ups (2 am and 8 am) were associated with higher levels of sleep inertia than later wake-ups (2 pm and 8 pm). These results suggest that split duty workers should recognise the potential for sleep inertia after waking, especially during the morning hours.

Coping with shift work-related circadian disruption: A mixed-methods case study on napping and caffeine use in Australian nurses and midwives

ABSTRACT Introduction: Two of the most ubiquitous fatigue countermeasures used by shift-working nurses are napping and caffeine. This mixed-methods case study investigated the ways nurses and midwives utilised napping and caffeine countermeasures to cope with shift work, and associated sleep, physical health and psychological health outcomes. Materials and Methods: N = 130 Australian shift-working nurses and midwives (mean age = 44 years, range = 21–67, 115F, 15M) completed the Standard Shiftwork Index. A sub-set of 22 nurses and midwives completed an in-depth interview. Results: Nearly 70% of participants reported napping. Those who napped during night shifts had significantly less total sleep time before (F2,75 = 5.5, p < 0.01) and between days off (F2,82 = 3.9, p < 0.05). By the end of the night shift, average hours of time awake were significantly less for prophylactic and on-shift nappers compared to non-nappers (F2,85 = 97.2, p < 0.001). Since starting shift work, the percentage of high caffeine consumers (>400 mg/day) increased from 15% to 33% of the sample and an average of 4 (SD = 2) caffeinated beverages per day was reported. Increased caffeine consumption was associated with greater sleep disturbance (r = 0.26, p < 0.05), psychological distress (r = 0.37, p < 0.001), abdomen pain (r = 0.27, p < 0.05) and weight gain since starting shift work (r = 0.25, p < 0.05). Interviews confirmed these relationships and revealed that caffeine consumption on night shift was common, whereas napping on night shift was dependent on a number of factors including ability to sleep during the day. Conclusion: This study identified reasons shift workers chose to engage in or abstain from napping and consuming caffeine, and how these strategies related to poor sleep and health outcomes. Further research is required to help develop recommendations for shift workers regarding napping and caffeine consumption as fatigue countermeasures, whilst taking into account the associated hazards of each strategy.

Timing of Australian flight attendant food and beverage while crewing: a preliminary investigation

Flight attendants experience circadian misalignment and disrupted sleep and eating patterns. This survey study examined working time, sleep, and eating frequency in a sample (n=21, 4 males, 17 females) of Australian flight attendants (mean age=41.8 yr, SD=12.0 yr, mean BMI=23.8 kg/m2, SD=4.1 kg/m2). Respondents indicated frequencies of snack, meal, and caffeine consumption during their last shift. Reported sleep duration on workdays (mean=4.6 h, SD=1.9 h) was significantly lower than on days off (M=7.2 h, SD=1.2 h, p<0.001), and significantly lower than perceived sleep need (M=8.1 h, SD=0.8 h, p<0.001). Food intake was distributed throughout shifts and across the 24 h period, with eating patterns incongruent with biological eating periods. Time available, food available, and work breaks were the most endorsed reasons for food consumption. Caffeine use and reports of gastrointestinal disturbance were common. Working time disrupts sleep and temporal eating patterns in flight attendants and further research into nutritional and dietary-related countermeasures may be beneficial to improving worker health and reducing circadian disruption.

Establishing norms for mental well-being in young people (7-19 years) using the General Health Questionnaire-12

Objective This study investigated the reliability and factor structure of the General Health Questionnaire‐12 (GHQ‐12) in children and adolescents and examined whether the GHQ‐12 is sensitive to expected mental well‐being differences across age and sex. Method Here, N = 180,700 Australian students (7–19 years of age) completed the GHQ‐12 as part of a larger survey, the Resilience Survey (Resilient Youth Australia Limited). Exploratory factor analysis (EFA) was conducted, and internal consistency was assessed with Cronbachs alpha. Linear mixed model ANOVAs were conducted to investigate differences in GHQ‐12 scores between females and males. Results EFA revealed a two‐factor model which was consistent across all age bands—Factor A: General Dysphoria (depression and anxiety), and Factor B: General Functioning (ability to cope with day‐to‐day activities). Internal consistency was good (Cronbachs α > 0.7) in all age bands for total GHQ‐12 and factor scores. Confirmatory factor analysis with a two‐factor correlated structure supported EFA results. Bifactor modelling suggested a unidimensional structure. Males aged 7–9 years had significantly higher (more problematic) total GHQ‐12 scores, General Dysphoria scores and General Functioning scores than females (p < 0.001), and females aged 12–19 years had significantly higher scores than males (p < 0.001). Conclusions Our results support the use of the GHQ‐12 for the measurement of mental well‐being symptoms in children from 7 to 19 years of age. Overall, psychometric properties including sensitivity, suggest that the GHQ‐12 provides a robust indicator of short‐term mental state in children and adolescents.

Eating on nightshift: A big vs small snack impairs glucose response to breakfast

Shift work is a risk factor for chronic diseases such as Type 2 diabetes. Food choice may play a role, however simply eating at night when the body is primed for sleep may have implications for health. This study examined the impact of consuming a big versus small snack at night on glucose metabolism. N = 31 healthy subjects (21–35 y; 18 F) participated in a simulated nightshift laboratory study that included one baseline night of sleep (22:00 h-07:00 h) and one night awake with allocation to either a big snack (2100 kJ) or small snack (840 kJ) group. The snack was consumed between 00:00–00:30 h and consisted of low fat milk, a sandwich, chips and fruit (big snack) or half sandwich and fruit (small snack). Subjects ate an identical mixed meal breakfast (2100 kJ) at 08:30 h after one full night of sleep and a simulated nightshift. Interstitial glucose was measured continuously during the entire study using Medtronic Continual Glucose Monitors. Only subjects with identical breakfast consumption and complete datasets were analysed (N = 20). Glucose data were averaged into 5-minute bins and area under the curve (AUC) was calculated for 90 min post-breakfast. Pre-breakfast, glucose levels were not significantly different between Day1 and Day2, nor were they different between snack groups (p > 0.05). A snack group by day interaction effect was found (F1,16 = 5.36, p = 0.034) and post-hocs revealed that in the big snack group, AUC response to breakfast was significantly higher following nightshift (Day2) compared to Day1 (p = 0.001). This translated to a 20.8% (SEM 5.6) increase. AUC was not significantly different between days in the small snack group. Consuming a big snack at 00:00 h impaired the glucose response to breakfast at 08:30 h, compared to a smaller snack. Further research in this area will inform dietary advice for shift workers, which could include recommendations on how much to eat as well as content.