Sleep, performance and well-being in adults and adolecents

Preceedings of the 10th annual meeting of the Australasian Chronobiology Society

Plasma lactate accumulation is reduced during incremental exercise in untrained women compared with untrained men

The lactate threshold (LT) is commonly reported as not different between sexes, yet lower blood lactate concentrations have been reported in women during submaximal exercise. The purpose of the present study was to measure the changes in plasma lactate concentration [La−1] in men and women during incremental cycle ergometer exercise using the same protocol and compare the data using several different methods of analysis. A group of untrained men (n = 21) and women (n = 22) were studied and venous blood drawn at regular intervals during and after exercise for assay of plasma [La−1]. Plasma [La−1] increased during exercise in both sexes, reaching higher values in men, both at exhaustion (men 8.6 ± 2.3 mmol l−1; women 6.2 ± 2.3 mmol l−1; P = 0.01) and post-exercise (men 11.8 ± 2.1 mmol l−1; women 10.2 ± 2.4 mmol l−1; P = 0.03). Logarithmic transformation of the data yielded LT values that were not different between sexes (men 44.2 ± 12.9; women 50.2 ± 12.6; %V˙O2peak; P=0.45), yet both the 2 and 4 mmol l−1 fixed concentration LT occurred at lower relative intensities in men (2 mmol l−1: men 50.9 ± 12.9; women 66.9 ± 11.1; %VO2peak;P=0.01. 4 mmol l−1: men 75.7 ± 11.0; women 90.6 ± 9.2; V˙O2peak;P=0.01). However, when the plasma [La−1] was examined in both sexes throughout exercise, using a single exponential function, plasma [La−1] was significantly lower in women (P < 0.05) at all relative intensities between 30 and 100%V˙O2peak. While the basis of this sex difference is unknown, reduced plasma [La−1] during submaximal exercise in women may offset to some degree the endurance performance disadvantage of their lower VO2peak

Interventions to minimize jet lag after westward and eastward flight

Air travel across several time zones, i.e., transmeridian flight, causes negative effects—some of which occur during flight and some of which occur in the days after flight. Anecdotally, these effects are often referred to collectively as jet lag, but they are actually two separate phenomena—travel fatigue and jet lag—each with their own causes and consequences (Waterhouse et al., 2004). Travel fatigue refers to a collection of symptoms that occur during or immediately after long flights. These symptoms include fatigue, disorientation, and headache (Waterhouse et al., 2004)—primarily caused by the sleep loss, dehydration, hypoxia, and discomfort associated with being in an aircraft with confined space, recline-restricted seats, low air pressure, low humidity, etc., for 8–14 h (Brown et al., 2001; Roach et al., 2018). In contrast, jet lag refers to a collection of symptoms that occur in the days after flight across three or more time zones. These symptoms include headache, irritability, daytime sleepiness, difficulty sleeping at night, poor mental and physical performance, and poor gastrointestinal function (Waterhouse et al., 2004)—primarily caused by the mismatch between the circadian system, or internal body clock, which is synchronized to time cues in the departure time zone, and the desired timing of sleep and wake, which are typically synchronized to time cues in the destination time zone. In August 2020, the Olympic Games will be held in Tokyo, Japan. Athletes will travel from all over the world to compete in the Games, and many will have to travel across several time zones. For example, athletes traveling to Japan from North America and Western Europe will face time zone changes of 8–11 h west and 6–8 h east, respectively. Some athletes will travel to Japan, or nearby countries, weeks before their events, while others will arrive in Japan in the days prior to competition. In either case, athletes will want to adjust to the new time zone as quickly as possible so that they can prepare well and/or compete at the highest level. The purpose of this manuscript is to discuss the causes and consequences of jet lag and to provide examples of how to use judiciously timed light exposure/avoidance and/or exogenous melatonin ingestion to adapt the circadian system to a new time zone after transmeridian flight. These guides could be applied by athletes competing in the Tokyo 2020 Olympic Games, but they could also be applied by athletes traveling to other countries for training or competition, or by non-athletes traveling for business or pleasure

Sleep of different populations

9th Annual Meeting of the Australasian Chronobiology Society

Sleep duration is reduced in elite athletes following night-time competition.

This study examined the impact of competition on the sleep/wake behaviour of elite athletes. The sleep/wake behaviour of Australian Rules Football players was assessed with wrist activity monitors on the night immediately before, and the night immediately after, a day game and an evening game. The time of day that a game occurred had a marked influence on sleep/wake behaviour later that night. After the evening game, sleep onset was later, time in bed was shorter and total sleep obtained was less than after the day game. It is yet to be determined whether a reduction in sleep after evening games impairs recovery

The evidence that cyclic alternating pattern subtypes affect cognitive functioning is very weak

In their recent paper, Ferri et al. [1] examined the relationships between cognitive functioning and three subtypes of the cyclic alternating pattern (CAP) in non-REM sleep. They concluded that ‘‘CAP A1 subtypes are associated with higher [i.e., better] cognitive functioning, whereas CAP A3 subtypes are associated with lower [i.e., poorer] cognitive functioning” (p. 378). For the reasons summarised below, we contend that In their recent paper, Ferri et al. [1] examined the relationships between cognitive functioning and three subtypes of the cyclic alternating pattern (CAP) in non-REM sleep. They concluded that ‘‘CAP A1 subtypes are associated with higher [i.e., better] cognitive functioning, whereas CAP A3 subtypes are associated with lower [i.e., poorer] cognitive functioning” (p. 378). For the reasons summarised below, we contend that this conclusion is not warranted based on the data presented

The duration of light exposure in the morning and early-afternoon affects adaptation to night work

Introduction : The aim of the study was to examine how the tim-ing of daytime sleep in the dark, and thus the timing of daytime light exposure, affects circadian adaptation to a week of simulated night shifts. It was hypothesised that night work would delay the circadian system – and the size of the delay would increase as the duration of exposure to morning and early- afternoon light (MAL) decreased. Methods : So far, 43 adults (21F, 22M) have been randomly assigned to one of four conditions in a laboratory- based simulated shiftwork protocol. Each condition included 7 consecutive 8- h night shifts (23:00–07:00 hr). The only difference between conditions was in the timing of the 7- h sleep opportunities in breaks between shifts – morning (08:30–15:30 hr, shortest MAL), split#1 (08:30–13:30 hr & 19:30–21:30 hr, short MAL), split#2 (08:30–10:30 hr & 16:30–21:30 hr, long MAL), and afternoon/evening (14:30–21:30 hr, longest MAL). Circadian phase was assessed using salivary dim light mela-tonin onset (DLMO) on the nights immediately before and after the week of night work. Light intensity was 75 lux during night shifts, < 0.03 lux during sleep, < 10 lux during DLMO assessments, and 350 lux at other times. Results : The DLMO data were analysed using a mixed- design ANOVA with one within- subjects factor (time: pre/post) and one between- subjects factor (condition). There was a significant interaction (F = 10.6; df = 3,39; p < .0001) – the type and size of the phase shift dif-fered between the conditions, i.e., morning (delay = 5.06 ± 2.11 hr), split#1 (delay = 2.58 ± 2.46 hr), split#2 (delay = 1.30 ± 2.62 hr), and afternoon/evening (advance = 0.71 ± 2.84 hr). Discussion : These data indicate that the timing of daytime sleep, and thus the amount of exposure to morning and early- afternoon light (MAL), substantially affects the degree of circadian adaptation to a week of night work. In situations where a shiftworker wishes to maximise adaptation to night work, the most sleep should be taken in the morning. To minimise adaptation, sleep should occur in the later afternoon and evening

Can a watch tell body clock time? Phase relationships between dim light melatonin onset and sleep markers determined from actigraphy, sleep diaries and the Munich Chronotype Questionnaire

Measurement of circadian phase (body clock timing) is often required for diagnosis and treatment of circadian rhythm disorders. However measurement of Dim Light Melatonin Onset (DLMO), the gold standard measure of circadian phase, is expensive and often impractical. As preferred timing of sleep reflects body clock timing, measurement of sleep markers provides a simple way to derive circadian phase. DLMO has been estimated from markers determined subjectively by questionnaires or sleep diaries; however actigraphy now provides the potential for objective and more accurate measurement of sleep markers. The aim of this study is to compare the phase differences between DLMO and sleep markers determined objectively by actigraphy, and subjectively by sleep diaries and the Munich ChronoType Questionnaire (MCTQ)

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. © 2020 Taylor & Francis Group, LLC

Mild to moderate sleep restriction does not affect the cortisol awakening response in healthy adult males

The cortisol awakening response (CAR) is a distinct rise in cortisol that occurs upon awakening that is thought to contribute to arousal, energy boosting, and anticipation. There is some evidence to suggest that inadequate sleep may alter the CAR, but the relationship between sleep duration and CAR has not been systematically examined. Healthy males (n = 111; age: 23.0 ± 3.6 yrs) spent 10 consecutive days/nights in a sleep laboratory. After a baseline night (9 h time in bed), participants spent either 5 h (n = 19), 6 h (n = 23), 7 h (n = 16), 8 h (n = 27), or 9 h (n = 26) in bed for seven nights, followed by a 9 h recovery sleep. The saliva samples for cortisol assay were collected at 08:00 h, 08:30 h and 08:45 h at baseline, on experimental days 2 and 5 and on the recovery day. The primary dependent variables were the cortisol concentration at awakening (08:00 h) and the cortisol area under the curve (AUC). There was no effect of time in bed on either the cortisol concentration at awakening or cortisol AUC. In all the time in bed conditions, the cortisol AUC tended to be higher at baseline and lower on experimental day 5. Five consecutive nights of mild to moderate sleep restriction does not appear to affect the CAR in healthy male adults