The Night-Time Sleep and Autonomic Activity of Male and Female Professional Road Cyclists Competing in the Tour de France and Tour de France Femmes

BACKGROUND

Sleep is a critical component of recovery, but it can be disrupted following prolonged endurance exercise. The objective of this study was to examine the capacity of male and female professional cyclists to recover between daily race stages while competing in the 2022 Tour de France and the 2022 Tour de France Femmes, respectively. The 17 participating cyclists (8 males from a single team and 9 females from two teams) wore a fitness tracker (WHOOP 4.0) to capture recovery metrics related to night-time sleep and autonomic activity for the entirety of the events and for 7 days of baseline before the events. The primary analyses tested for a main effect of 'stage classification'-i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females-on the various recovery metrics.

RESULTS

During baseline, total sleep time was 7.2 ± 0.3 h for male cyclists (mean ± 95% confidence interval) and 7.7 ± 0.3 h for female cyclists, sleep efficiency was 87.0 ± 4.4% for males and 88.8 ± 2.6% for females, resting HR was 41.8 ± 4.5 beats·min-1 for males and 45.8 ± 4.9 beats·min-1 for females, and heart rate variability during sleep was 108.5 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time was 7.2 ± 0.1 h for males and 7.5 ± 0.3 h for females, sleep efficiency was 86.4 ± 1.2% for males and 89.6 ± 1.2% for females, resting HR was 44.5 ± 1.2 beats·min-1 for males and 50.2 ± 2.0 beats·min-1 for females, and heart rate variability during sleep was 99.1 ± 4.2 ms for males and 114.3 ± 11.2 ms for females. For male cyclists, there was a main effect of 'stage classification' on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was a main effect of 'stage classification' on recovery, such that the percentage of light sleep (i.e., lower-quality sleep) was highest after mountain stages.

CONCLUSIONS

Some aspects of recovery were compromised after the most demanding days of racing, i.e., mountain stages. Overall however, the cyclists obtained a reasonable amount of good-quality sleep while competing in these physiologically demanding endurance events. This study demonstrates that it is now feasible to assess recovery in professional athletes during multiple-day endurance events using validated fitness trackers.

Hit the gym or hit the hay: can evening exercise characteristics predict compromised sleep in healthy adults?

Introduction: Recent sleep guidelines regarding evening exercise have shifted from a conservative (i.e., do not exercise in the evening) to a more nuanced approach (i.e., exercise may not be detrimental to sleep in circumstances). With the increasing popularity of wearable technology, information regarding exercise and sleep are readily available to the general public. There is potential for these data to aid sleep recommendations within and across different population cohorts. Therefore, the aim of this study was to examine if sleep, exercise, and individual characteristics can be used to predict whether evening exercise will compromise sleep. Methods: Data regarding evening exercise and the subsequent night's sleep were obtained from 5,250 participants (1,321F, 3,929M, aged 30.1 ± 5.2 yrs) using a wearable device (WHOOP 3.0). Data for females and males were analysed separately. The female and male datasets were both randomly split into subsets of training and testing data (training:testing = 75:25). Algorithms were trained to identify compromised sleep (i.e., sleep efficiency <90%) for females and males based on factors including the intensity, duration and timing of evening exercise. Results: When subsequently evaluated using the independent testing datasets, the algorithms had sensitivity for compromised sleep of 87% for females and 90% for males, specificity of 29% for females and 20% for males, positive predictive value of 32% for females and 36% for males, and negative predictive value of 85% for females and 79% for males. If these results generalise, applying the current algorithms would allow females to exercise on ~ 25% of evenings with ~ 15% of those sleeps being compromised and allow males to exercise on ~ 17% of evenings with ~ 21% of those sleeps being compromised. Discussion: The main finding of this study was that the models were able to predict a high percentage of nights with compromised sleep based on individual characteristics, exercise characteristics and habitual sleep characteristics. If the benefits of exercising in the evening outweigh the costs of compromising sleep on some of the nights when exercise is undertaken, then the application of the current algorithms could be considered a viable alternative to generalised sleep hygiene guidelines.

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.

The effect of mild to moderate sleep restriction on subjective hunger in healthy young men

There is good evidence to indicate severe sleep restriction increases subjective feelings of hunger, but the impact of mild to moderate sleep restriction (i.e., 5-7 h) on hunger has not been systematically evaluated. Healthy male participants (n = 116; 22.8 ± 2.1 years; 22.9 ± 3.7 kg⋅m^-2) were recruited to a ten-day laboratory study. In a between groups design, participants were allocated to one of five time in bed conditions (5 h, 6 h, 7 h, 8 h or 9 h) for seven consecutive nights. Participants were provided a eucaloric diet and ratings of hunger, nausea and desire to eat certain foods were collected using visual analogue scales prior to meals (breakfast, lunch, afternoon snack, dinner and evening snack) on four days during the study. Data were analysed using linear mixed models with time in bed, time of day and study day as fixed effects and participant as a random effect. There was no main effect of time in bed, and no interaction between time in bed and study day, on hunger, nausea, prospective hunger or desire to eat certain foods. However, post-hoc analyses indicated that participants in the 5-h condition had an elevated desire to consume sweet foods and fruit on the final morning of the protocol. There was a main effect of time of day and study day on hunger; participants were hungriest prior to lunch time and hunger decreased over consecutive days of the protocol. When provided with a eucaloric diet, only 5-h time in bed increased desire to consume sweet foods and fruit in healthy young men.

How Tired is Too Tired to Drive? A Systematic Review Assessing the Use of Prior Sleep Duration to Detect Driving Impairment

Driver fatigue is a contributory factor in approximately 20% of vehicle crashes. While other causal factors (eg, drink-driving) have decreased in recent decades due to increased public education strategies and punitive measures, similar decreases have not been seen in fatigue-related crashes. Fatigued driving could be managed in a similar way to drink-driving, with an established point (ie, amount of prior sleep) after which drivers are "deemed impaired". This systematic review aimed to provide an evidence-base for the concept of deemed impairment and to identify how much prior sleep may be required to drive safely. Four online databases were searched (PubMed, Web of Science, Scopus, Embase). Eligibility requirements included a) measurement of prior sleep duration and b) driving performance indicators (eg, lane deviation) and/or outcomes (eg, crash likelihood). After screening 1940 unique records, a total of 61 studies were included. Included studies were categorised as having experimental/quasi-experimental (n = 21), naturalistic (n = 3), longitudinal (n = 1), case-control (n = 11), or cross-sectional (n = 25) designs. Findings suggest that after either 6 or 7 hours of prior sleep, a modest level of impairment is generally seen compared with after ≥ 8 hours of prior sleep (ie, well rested), depending on the test used. Crash likelihood appears to be ~30% greater after 6 or 7 hours of prior sleep, as compared to individuals who are well rested. After one night of either 4 or 5 hours of sleep, there are large decrements to driving performance and approximately double the likelihood of a crash when compared with well-rested individuals. When considering the scientific evidence, it appears that there is a notable decrease in driving performance (and associated increase in crash likelihood) when less than 5h prior sleep is obtained. This is a critical first step in establishing community standards regarding the amount of sleep required to drive safely.

A Week of Sleep Restriction Does Not Affect Nighttime Glucose Concentration in Healthy Adult Males When Slow-Wave Sleep Is Maintained

The aim of this laboratory-based study was to examine the effect of sleep restriction on glucose regulation during nighttime sleep. Healthy males were randomly assigned to one of two conditions: 9 h in bed (n = 23, age = 24.0 year) or 5 h in bed (n = 18, age = 21.9 year). Participants had a baseline night with 9 h in bed (23:00-08:00 h), then seven nights of 9 h (23:00-08:00 h) or 5 h (03:00-08:00 h) in bed. Participants were mostly seated during the daytime but had three bouts of treadmill walking (4 km·h^-1 for 10 min) at ~14:40 h, ~17:40 h, and ~20:40 h each day. On the baseline night and night seven, glucose concentration in interstitial fluid was assessed by using continuous glucose monitors, and sleep was assessed by using polysomnography. On night seven, compared to the 9 h group, the 5 h group obtained less total sleep (292 min vs. 465 min) and less REM sleep (81 min vs. 118 min), but their slow-wave sleep did not differ (119 min vs. 120 min), and their glucose concentration during sleep did not differ (5.1 mmol·L^-1 vs. 5.1 mmol·L^-1). These data indicate that sleep restriction does not cause elevated levels of circulating glucose during nighttime sleep when slow-wave sleep is maintained. In the future, it will be important to determine whether increased insulin is required to maintain circulating glucose at a normal level when sleep is restricted.

Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSD_CV) and HR (HR_CV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSD_CV and HR_CV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3-13%) and HR (~10-11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.

A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults

The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.

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.

Timing of Sleep in the Break Between Two Consecutive Night-Shifts: The Effect of Different Strategies on Daytime Sleep and Night-Time Neurobehavioural Function

OBJECTIVE

The aim of this study was to examine whether the timing of sleep in the break between consecutive night-shifts affects the quantity and quality of sleep obtained during the daytime and/or neurobehavioural function and self-perceived capacity during the night-time.

METHODS

Participants (n = 12, all male, aged 22.9±5.2 y) completed three randomised, counterbalanced conditions in a sleep laboratory, consisting of two consecutive 12-hour night-shifts (18:00-06:00) with 7 hours in bed in the break between shifts. The three conditions differed only in the timing of the sleep opportunities - immediate (07:00-14:00), delayed (10:00-17:00), split (07:00-10:30 and 13:30-17:00). Neurobehavioural function (attention, memory, throughput) and self-perceived capacity (sleepiness, alertness, fatigue, mood) were assessed at 2-hour intervals during the night-shifts.

RESULTS

Condition did not affect total sleep time (p = 0.465), but it did affect sleep onset latency (p < 0.001; W = 0.780; large effect), wake after sleep onset (p = 0.018; W = 0.333; moderate effect) and the amount of Stage N3 sleep (p < 0.001; η²=0.510; small effect). Compared to the immediate and delayed sleep conditions, the split sleep condition had less wake after sleep onset and more Stage N3 sleep; and compared to the delayed condition, the split sleep condition had longer latency to sleep onset. There was no effect of condition on measures of neurobehavioural function or self-perceived capacity during the second night-shift.

CONCLUSION

None of the three sleep strategies examined here - immediate, delayed or split - are clearly superior or inferior to the others in terms of the capacity to sleep during the daytime or to work at night. Therefore, those who work consecutive night-shifts should employ the strategy that best suits their personal preferences and/or circumstances.

Glucose Concentrations from Continuous Glucose Monitoring Devices Compared to Those from Blood Plasma during an Oral Glucose Tolerance Test in Healthy Young Adults

Continuous glucose monitoring devices measure glucose in interstitial fluid. The devices are effective when used by patients with type 1 and 2 diabetes but are increasingly being used by researchers who are interested in the effects of various behaviours of glucose concentrations in healthy participants. Despite their more frequent application in this setting, the devices have not yet been validated for use under such conditions. A total of 124 healthy participants were recruited to a ten-day laboratory study. Each participant underwent four oral glucose tolerance tests, and a total of 3315 out of a possible 4960 paired samples were included in the final analysis. Bland-Altman plots and mean absolute relative differences were used to determine the agreement between the two methods. Bland-Altman analyses revealed that the continuous glucose monitoring devices had proportional bias (R = 0.028, p < 0.001) and a mean bias of -0.048 mmol/L, and device measurements were more variable as glucose concentrations increased. Ninety-nine per cent of paired values were in Zones A and B of the Parkes Error Grid plot, and there was an overall mean absolute relative difference of 16.2% (±15.8%). There was variability in the continuous glucose monitoring devices, and this variability was higher when glucose concentrations were higher. If researchers were to use continuous glucose monitoring devices to measure glucose concentrations during an oral glucose tolerance test in healthy participants, this variability would need to be considered.

An Individualized Intervention Increases Sleep Duration in Professional Athletes

ABSTRACT

Sargent, C, Lastella, M, Schwerdt, S, and Roach, GD. An individualized intervention increases sleep duration in professional athletes. J Strength Cond Res 35(12): 3407-3413, 2021-Athletes typically obtain less sleep than is generally recommended for healthy adults. The aim of this study was to determine whether individualized feedback could increase sleep duration in professional cricket players in the 3 weeks before the start of the domestic season. Players were randomly assigned to a control group (i.e., no individual feedback; n = 8) or an intervention group (i.e., individual feedback about bedtime, wake time, and sleep duration; n = 7). Night-time sleep and daytime naps were monitored using wrist activity monitors in conjunction with self-report sleep diaries for 1 week before, and 1 week after, the feedback intervention. Cumulative sleep duration was calculated as the sum of the sleep duration for a night-time sleep episode and any naps that occurred on the following day. Differences in cumulative sleep duration before and after the intervention were examined using a mixed-effects analysis of variance. There was an interaction between group and week for cumulative sleep duration (p = 0.039; η2 = 0.6; large). The average cumulative sleep duration was longer (+36 minutes) in the intervention group in week 2 compared with week 1. Individualized feedback can be used to increase sleep duration in professional cricket players. In future, it will be important to determine whether improvements in sleep duration can be maintained throughout the season.

Consecutive Nights of Moderate Sleep Loss Does Not Affect Mood in Healthy Young Males

Sleep loss causes mood disturbance in non-clinical populations under severe conditions, i.e., two days/nights of sleep deprivation or a week of sleep restriction with 4–5 h in bed each night. However, the effects of more-common types of sleep loss on mood disturbance are not yet known. Therefore, the aim of this study was to examine mood disturbance in healthy adults over a week with nightly time in bed controlled at 5, 6, 7, 8 or 9 h. Participants (n = 115) spent nine nights in the laboratory and were given either 5, 6, 7, 8 or 9 h in bed over seven consecutive nights. Mood was assessed daily using the Profile of Mood States (POMS-2). Mixed-linear effects models examined the effect of time in bed on total mood disturbance and subscales of anger-hostility, confusion-bewilderment, depression-dejection, fatigue-inertia, tension-anxiety, vigour-activity and friendliness. There was no effect of time in bed on total mood disturbance (F(4, 110.42) = 1.31, p = 0.271) or any of the subscales except fatigue-inertia. Fatigue-inertia was higher in the 5 h compared with the 9 h time in bed condition (p = 0.012, d = 0.75). Consecutive nights of moderate sleep loss (i.e., 5–7 h) does not affect mood but does increase fatigue in healthy males.

A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland-Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (-37.6 min); WHOOP-AUTO underestimated SWS (-15.5 min); and WHOOP-MANUAL underestimated wake (-16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep.

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Exercise mitigates sleep-loss-induced changes in glucose tolerance, mitochondrial function, sarcoplasmic protein synthesis, and diurnal rhythms

OBJECTIVE

Sleep loss has emerged as a risk factor for the development of impaired glucose tolerance. The mechanisms underpinning this observation are unknown; however, both mitochondrial dysfunction and circadian misalignment have been proposed. Because exercise improves glucose tolerance and mitochondrial function, and alters circadian rhythms, we investigated whether exercise may counteract the effects induced by inadequate sleep.

METHODS

To minimize between-group differences of baseline characteristics, 24 healthy young males were allocated into one of the three experimental groups: a Normal Sleep (NS) group (8 h time in bed (TIB) per night, for five nights), a Sleep Restriction (SR) group (4 h TIB per night, for five nights), and a Sleep Restriction and Exercise group (SR+EX) (4 h TIB per night, for five nights and three high-intensity interval exercise (HIIE) sessions). Glucose tolerance, mitochondrial respiratory function, sarcoplasmic protein synthesis (SarcPS), and diurnal measures of peripheral skin temperature were assessed pre- and post-intervention.

RESULTS

We report that the SR group had reduced glucose tolerance post-intervention (mean change ± SD, P value, SR glucose AUC: 149 ± 115 A.U., P = 0.002), which was also associated with reductions in mitochondrial respiratory function (SR: -15.9 ± 12.4 pmol O2.s-1.mg-1, P = 0.001), a lower rate of SarcPS (FSR%/day SR: 1.11 ± 0.25%, P < 0.001), and reduced amplitude of diurnal rhythms. These effects were not observed when incorporating three sessions of HIIE during this period (SR+EX: glucose AUC 67 ± 57, P = 0.239, mitochondrial respiratory function: 0.6 ± 11.8 pmol O2.s-1.mg-1, P = 0.997, and SarcPS (FSR%/day): 1.77 ± 0.22%, P = 0.971).

CONCLUSIONS

A five-night period of sleep restriction leads to reductions in mitochondrial respiratory function, SarcPS, and amplitude of skin temperature diurnal rhythms, with a concurrent reduction in glucose tolerance. We provide novel data demonstrating that these same detrimental effects are not observed when HIIE is performed during the period of sleep restriction. These data therefore provide evidence in support of the use of HIIE as an intervention to mitigate the detrimental physiological effects of sleep loss.

Analyzing changes in respiratory rate to predict the risk of COVID-19 infection

COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study’s aim was to determine if changes in respiratory rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 ± 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included– 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while negative for COVID-19 but experiencing symptoms). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n = 57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n = 24 people, 320 samples); (3) a validation dataset of individuals who tested negative for COVID-19 (n = 190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of respiratory rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in respiratory rate during night-time sleep. The model’s ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms.

Sleep and the athlete: narrative review and 2021 expert consensus recommendations

Elite athletes are particularly susceptible to sleep inadequacies, characterised by habitual short sleep (<7 hours/night) and poor sleep quality (eg, sleep fragmentation). Athletic performance is reduced by a night or more without sleep, but the influence on performance of partial sleep restriction over 1-3 nights, a more real-world scenario, remains unclear. Studies investigating sleep in athletes often suffer from inadequate experimental control, a lack of females and questions concerning the validity of the chosen sleep assessment tools. Research only scratches the surface on how sleep influences athlete health. Studies in the wider population show that habitually sleeping <7 hours/night increases susceptibility to respiratory infection. Fortunately, much is known about the salient risk factors for sleep inadequacy in athletes, enabling targeted interventions. For example, athlete sleep is influenced by sport-specific factors (relating to training, travel and competition) and non-sport factors (eg, female gender, stress and anxiety). This expert consensus culminates with a sleep toolbox for practitioners (eg, covering sleep education and screening) to mitigate these risk factors and optimise athlete sleep. A one-size-fits-all approach to athlete sleep recommendations (eg, 7-9 hours/night) is unlikely ideal for health and performance. We recommend an individualised approach that should consider the athlete's perceived sleep needs. Research is needed into the benefits of napping and sleep extension (eg, banking sleep).

A validation study of the WHOOP strap against polysomnography to assess sleep

The aim of the study was to compare the WHOOP strap - a wearable device that estimates sleep based on measures of movement and heart rate derived from actigraphy and photoplethysmography, respectively. Twelve healthy adults (6 females, 6 males, aged 22.9 ± 3.4 years) participated in a 10-day, laboratory-based protocol. A total of 86 sleeps were independently assessed in 30-s epochs using polysomnography and WHOOP. For WHOOP, bed times were entered by researchers and sleeps were scored by the company based on proprietary algorithms. WHOOP overestimated total sleep time by 8.2 ± 32.9 minutes compared to polysomnography, but this difference was non-significant. WHOOP was compared to polysomnography for 2-stage (i.e., wake, sleep) and 4-stage categorisation (i.e., wake, light sleep [N1 or N2], slow-wave sleep [N3], REM) of sleep periods. For 2-stage categorisation, the agreement, sensitivity to sleep, specificity for wake, and Cohen's kappa were 89%, 95%, 51%, and 0.49, respectively. For 4-stage categorisation, the agreement, sensitivity to light sleep, SWS, REM, and wake, and Cohen's kappa were 64%, 62%, 68%, 70%, 51%, and 0.47, respectively. In situations where polysomnography is impractical (e.g., field settings), WHOOP is a reasonable method for estimating sleep, particularly for 2-stage categorisation, if accurate bedtimes are manually entered.

Sleep–wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue

Study Objectives

To compare rail workers’ actual sleep–wake behaviors in normal operations to those predicted by a biomathematical model of fatigue (BMMF). To determine whether there are group-level residual sources of error in sleep predictions that could be modeled to improve group-level sleep predictions.

Methods

The sleep–wake behaviors of 354 rail workers were examined during 1,722 breaks that were 8–24 h in duration. Sleep–wake patterns were continuously monitored using wrist-actigraphy and predicted from the work–rest schedule using a BMMF. Rail workers’ actual and predicted sleep–wake behaviors were defined as split-sleep (i.e. ≥2 sleep periods in a break) and consolidated-sleep (i.e. one sleep period in a break) behaviors. Sleepiness was predicted from the actual and predicted sleep–wake data.

Results

Consolidated-sleep behaviors were observed during 1,441 breaks and correctly predicted during 1,359 breaks. Split-sleep behaviors were observed during 280 breaks and correctly predicted during 182 breaks. Predicting the wrong type of sleep–wake behavior resulted in a misestimation of hours of sleep during a break. Relative to sleepiness predictions derived from actual sleep–wake data, predicting the wrong type of sleep–wake behavior resulted in a misestimation of sleepiness predictions during the subsequent shift.

Conclusions

All workers with the same work–rest schedule have the same predicted sleep–wake behaviors; however, these workers do not all exhibit the same sleep–wake behaviors in real-world operations. Future models could account for this group-level residual variance with a new approach to modeling sleep, whereby sub-group(s) may be predicted to exhibit one of a number of sleep–wake behaviors.

The effect of sleep restriction, with or without high-intensity interval exercise, on myofibrillar protein synthesis in healthy young men

Key points

Sleep restriction has previously been associated with the loss of muscle mass in both human and animal models.

The rate of myofibrillar protein synthesis (MyoPS) is a key variable in regulating skeletal muscle mass and can be increased by performing high‐intensity interval exercise (HIIE), although the effect of sleep restriction on MyoPS is unknown.

In the present study, we demonstrate that participants undergoing a sleep restriction protocol (five nights, with 4 h in bed each night) had lower rates of skeletal muscle MyoPS; however, rates of MyoPS were maintained at control levels by performing HIIE during this period.

Our data suggest that the lower rates of MyoPS in the sleep restriction group may contribute to the detrimental effects of sleep loss on muscle mass and that HIIE may be used as an intervention to counteract these effects.

Abstract

The present study aimed to investigate the effect of sleep restriction, with or without high‐intensity interval exercise (HIIE), on the potential mechanisms underpinning previously‐reported sleep‐loss‐induced reductions to muscle mass. Twenty‐four healthy, young men underwent a protocol consisting of two nights of controlled baseline sleep and a five‐night intervention period. Participants were allocated into one of three parallel groups, matched for age, V˙O2peak, body mass index and habitual sleep duration; a normal sleep (NS) group [8 h time in bed (TIB) each night], a sleep restriction (SR) group (4 h TIB each night), and a sleep restriction and exercise group (SR+EX, 4 h TIB each night, with three sessions of HIIE). Deuterium oxide was ingested prior to commencing the study and muscle biopsies obtained pre‐ and post‐intervention were used to assess myofibrillar protein synthesis (MyoPS) and molecular markers of protein synthesis and degradation signalling pathways. MyoPS was lower in the SR group [fractional synthetic rate (% day^–1), mean ± SD, 1.24 ± 0.21] compared to both the NS (1.53 ± 0.09) and SR+EX groups (1.61 ± 0.14) (P < 0.05). However, there were no changes in the purported regulators of protein synthesis (i.e. p‐AKT^ser473 and p‐mTOR^ser2448) and degradation (i.e. Foxo1/3 mRNA and LC3 protein) in any group. These data suggest that MyoPS is acutely reduced by sleep restriction, although MyoPS can be maintained by performing HIIE. These findings may explain the sleep‐loss‐induced reductions in muscle mass previously reported and also highlight the potential therapeutic benefit of HIIE to maintain myofibrillar remodelling in this context.

Sleep restriction has previously been associated with the loss of muscle mass in both human and animal models.

The rate of myofibrillar protein synthesis (MyoPS) is a key variable in regulating skeletal muscle mass and can be increased by performing high‐intensity interval exercise (HIIE), although the effect of sleep restriction on MyoPS is unknown.

In the present study, we demonstrate that participants undergoing a sleep restriction protocol (five nights, with 4 h in bed each night) had lower rates of skeletal muscle MyoPS; however, rates of MyoPS were maintained at control levels by performing HIIE during this period.

Our data suggest that the lower rates of MyoPS in the sleep restriction group may contribute to the detrimental effects of sleep loss on muscle mass and that HIIE may be used as an intervention to counteract these effects.

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.

Exercise before bed does not impact sleep inertia in young healthy males

Sleep inertia is the transitional state marked by impaired cognitive performance and reduced vigilance upon waking. Exercising before bed may increase the amount of slow-wave sleep within the sleep period, which has previously been associated with increased sleep inertia. Healthy males (n = 12) spent 3 nights in a sleep laboratory (1-night washout period between each night) and completed one of the three conditions on each visit - no exercise, aerobic exercise (30 min cycling at 75% heart rate), and resistance exercise (six resistance exercises, three sets of 10 repetitions). The exercise conditions were completed 90 min prior to bed. Sleep was measured using polysomnography. Upon waking, participants completed five test batteries every 15 min, including the Karolinska Sleepiness Scale, a Psychomotor Vigilance Task, and the Spatial Configuration Task. Two separate linear mixed-effects models were used to assess: (a) the impact of condition; and (b) the amount of slow-wave sleep, on sleep inertia. There were no significant differences in sleep inertia between conditions, likely as a result of the similar sleep amount, sleep structure and time of awakening between conditions. The amount of slow-wave sleep impacted fastest 10% reciprocal reaction time on the Psychomotor Vigilance Task only, whereby more slow-wave sleep improved performance; however, the magnitude of this relationship was small. Results from this study suggest that exercise performed 90 min before bed does not negatively impact on sleep inertia. Future studies should investigate the impact of exercise intensity, duration and timing on sleep and subsequent sleep inertia.

Daytime naps can be used to supplement night-time sleep in athletes

This study examined the efficacy of daytime napping to supplement night-time sleep in athletes. Twelve well-trained male soccer players completed three conditions in a randomised, counterbalanced order: 9 h in bed overnight with no daytime nap (9 h + 0 h); 8 h in bed overnight with a 1-h daytime nap (8 h + 1 h); and 7 h in bed overnight with a 2-h daytime nap (7 h + 2 h). Sleep was assessed using polysomnography. The total amount of sleep obtained in the three conditions was similar, i.e. 8.1 h (9 h + 0 h), 8.2 h (8 h + 1 h), and 8.0 h (7 h + 2 h). Daytime napping may be an effective strategy to supplement athletes' night-time sleep.

23rd International Symposium on Shiftwork and Working Time: Towards a Global Consensus

The Working Time Society (WTS), and the International Commission on Occupational Health (ICOH) Scientific Committee on Shiftwork and Working Time, are twin organisations focused on conducting research, and informing practice, regarding the impact of work hours in general, and shiftwork in particular, on the efficiency, productivity, safety, well-being, health, and biological rhythms, of employees. Every 2-3 years since 1969, the WTS and ICOH have conducted a series of international symposia in Europe, Asia, Australia, North America, and South America. The purpose of these symposia is to provide a forum for the exchange of knowledge, and the discussion of contested issues, with researchers, employee representatives, regulators, and employers. The most recent symposium in this series - the 23rd International Symposium on Shiftwork and Working Time, entitled "Toward a Global Consensus" - was held on 19-23 June 2017, at Yulara, Australia, near Uluru. Since 2004, Chronobiology International has released a special issue after each symposium, and that tradition continues with a special issue that includes 17 contributions based on a selection of the 128 papers that were presented at the most recent symposium. Here, we provide an overview of the papers that comprise the special issue, and we briefly comment on the implications of the findings for shiftworkers and their employers.

Travel fatigue and sleep/wake behaviors of professional soccer players during international competition

OBJECTIVE

The magnitude of travel completed by professional Australian soccer teams during domestic competition is substantial. The inclusion of Australian soccer teams into the Asian Champions league has seen additional stress placed on soccer players' training and competition schedules. For management staff, the complexity of organizing training and travel schedules during domestic competition and the Asian Champions league is challenging.

DESIGN

Case study.

PARTICIPANTS

Seven male professional soccer players (mean ± SD: age 25.2 ± 3.2 years, height 182.8 ± 5.2 cm, body mass 84.6 ± 7.4 kg).

MEASUREMENTS

This study examined the sleep and fatigue levels of Australian soccer players during an intensive home and away travel schedule during the Asian Champions league. Seven male professional soccer players' (mean ± SD: age 25.2 ± 3.2 years, height 182.8 ± 5.2 cm, body mass 84.6 ± 7.4 kg) sleep/wake behavior was assessed using sleep diaries and wrist activity monitors for 19 days, including 9 days before, 5 days during, and 4 days after a home and away group stage match of the Asian Champions league. Analyses examined differences in sleep/wake behavior and fatigue levels between day type (training day, rest day, pregame, and postgame) and between sleep location (Adelaide, during flight, and Hiroshima).

RESULTS

Sleep/wake behavior and fatigue levels were poorest the night immediately after games compared to the night before games, training days, and rest days. Soccer players' sleep/wake behaviors were disrupted during flights such that they obtained 3.6 hours less sleep during flights compared to sleep in Adelaide (7.0 ± 1.6 hours) and Hiroshima (7.0 ± 2.1 hours).

CONCLUSION

The sleep/wake behaviors of professional soccer players are compromised when they are required to travel and compete in multiple matches within a short period of time.

Flat-out napping: The quantity and quality of sleep obtained in a seat during the daytime increase as the angle of recline of the seat increases

Some shiftwokers in the long-haul transportation industries (i.e. road, rail, sea, air) have the opportunity to sleep in on-board rest facilities during duty periods. These rest facilities are typically fitted with a seat with a maximum back angle to the vertical of 20°, 40°, or 90°. The aim of this study was to examine the impact of "back angle" on the quantity and quality of sleep obtained in a seat during a daytime nap. Six healthy adults (3 females aged 27.0 years and 3 males aged 22.7 years) each participated in three conditions. For each condition, participants had a 4-h sleep opportunity in a bed (02:00-06:00 h) followed by a 4-h sleep opportunity in a seat (13:00-17:00 h). The only difference between conditions was in the back angle of the seat to the vertical during the seat-based sleep periods: 20° (upright), 40° (reclined), and 90° (flat). Polysomnographic data were collected during all sleep episodes. For the seat-based sleep episodes, there was a significant effect of back angle on three of four measures of sleep quantity, i.e. total sleep time, slow-wave sleep, and rapid eye movement (REM) sleep, and three of four measures of sleep quality, i.e. latency to REM sleep, arousals, and stage shifts. In general, the quantity and quality of sleep obtained in the reclined and flat seats were better than those obtained in the upright seat. In particular, compared to the flat seat, the reclined seat resulted in similar amounts of total sleep and slow-wave sleep, but 37% less REM sleep; and the upright seat resulted in 29% less total sleep, 30% less slow-wave sleep, and 79% less REM sleep. There are two main mechanisms that may explain the results. First, it is difficult to maintain the head in a comfortable position for sleep when sitting upright, and this is likely exacerbated during REM sleep, when muscle tone is very low. Second, an upright posture increases sympathetic activity and decreases parasympathetic activity, resulting in a heightened level of physiological arousal.

Athletes underestimate sleep quantity during daytime nap opportunities

This study examined the difference between athletes' self-reported and objective sleep durations during two nap opportunities. Twelve well-trained male soccer players' sleep durations were assessed using polysomnography and a self-report question during a 60- and 120-min nap opportunity. Participants underestimated sleep compared to objective sleep assessments for both the 60-min nap opportunity (p = 0.004) and 120-min nap opportunity (p = 0.001). Soccer players underestimated their sleep duration by approximately 10 min per hour of nap opportunity. It is yet to be determined if athletes are likely to underestimate sleep duration during their main nighttime sleep period.

How well does a commercially available wearable device measure sleep in young athletes?

The validity of a commercially available wearable device for measuring total sleep time was examined in a sample of well-trained young athletes during night-time sleep periods and daytime naps. Participants wore a FitBit HR Charge on their non-dominant wrist and had electrodes attached to their face and scalp to enable polysomnographic recordings of sleep in the laboratory. The FitBit automatically detected 24/30 night-time sleep periods but only 6/20 daytime naps. Compared with polysomnography, the FitBit overestimated total sleep time by an average of 52 ± 152 min for night-time sleep periods, and by 4 ± 8 min for daytime naps. It is important for athletes and practitioners to be aware of the limitations of wearable devices that automatically detect sleep duration.

Using interstimulus interval to maximise sensitivity of the Psychomotor Vigilance Test to fatigue

There is some evidence that short interstimulus intervals (ISIs) on the Psychomotor Vigilance Test (PVT) are associated with longer and more varied reaction times (RTs). Preparation processes may impede RT following short ISIs, resulting in additional unexplained variance. The aims of this study were to investigate whether there is an effect of ISI on RT and errors within the PVT, and whether such an effect changes with three elements of fatigue: time of day, prior wake and time on task. Twelve male participants completed 49 PVTs across 7× 28h periods of forced desynchrony. For analysis, RTs, reciprocal reaction times (1/RT), false starts and lapse responses within each 10min session were assigned to a 1-s ISI group, a 2-min time of task group, a 2.5-h PW level and a 60° phase of the circadian rhythm of core body temperature (as a measure of time of day). Responses following short ISIs (2-5s) were significantly slower and more varied than responses following longer ISIs (5-10s). The likelihood of a lapse was also higher for short ISIs, while the probability of a false start increased as a function of ISI. These effects were independent of the influences of time of day, prior wake and time on task. Hence, mixed model ANOVAs comprising only long ISIs (5-10s) contained stronger effect sizes for fatigue than a model of all ISIs (2-10s). Including an ISI variable in a model improved the model fit and explained more variance associated with fatigue. Short ISIs resulted in long RTs both in the presence and absence of fatigue, possibly due to preparation processes or ISI conditioning. Hence, omitting short ISI trials from RT means or including an ISI variable in analysis can reduce unwanted variance in PVT data, improving the sensitivity of the PVT to fatigue.

Do split sleep/wake schedules reduce or increase sleepiness for continuous operations?

This study compared the impact of split and consolidated sleep/wake schedules on subjective sleepiness during the biological day and biological night. This was achieved using a between-group design involving two forced desynchrony protocols: consolidated sleep/wake and split sleep/wake. Both protocols included 7×28-h days with 9.33h in bed and 18.67h of wake each day. While the consolidated sleep/wake protocol had 1×9.33-h sleep opportunity and 1×18.67-h wake period each day, the split sleep/wake protocol had 2×4.67-h sleep opportunities and 2×9.33-h wake periods each day. For both protocols, subjective sleepiness was measured using the Karolinska Sleepiness Scale every 2.5h during wake. A total of 29 healthy adult males participated, with 13 in the consolidated sleep/wake group (mean age=22.5 yrs) and 16 in the split sleep/wake group (mean age=22.6 yrs). On average, subjective sleepiness during wake periods of the split condition was significantly higher than that during the first half of wake periods of the consolidated condition, but was similar to the level during the second half. These findings were observed for wake periods that occurred during both the biological day and biological night. Previous data have shown that cognitive impairment at night is lower for split schedules than consolidated schedules, but the current data indicate that feelings of sleepiness are greater for split schedules than consolidated schedules for at least half of the time awake. Thus, it should be explained to people operating split sleep/wake schedules that although they may perform well, they are likely to feel sleepy.

How should a bio-mathematical model be used within a fatigue risk management system to determine whether or not a working time arrangement is safe?

Bio-mathematical models that predict fatigue and/or sleepiness have proved a useful adjunct in the management of what has been typically referred to as fatigue-related risk. Codifying what constitutes appropriate use of these models will be increasingly important over the next decade. Current guidelines for determining a safe working time arrangement based on model outputs generally use a single upper threshold and are, arguably, over-simplistic. These guidelines fail to incorporate explicitly essential aspects of the risk assessment process - namely, the inherent uncertainty and variability in human sleep-wake behavior; the non-linear relationship between fatigue, task performance and safety outcomes; the consequence of a fatigue-related error and its influence on overall risk; and the impact of risk mitigation or controls in reducing the likelihood or consequence of a fatigue-related error. As industry and regulatory bodies increasingly move toward performance-based approaches to safety management, any fatigue risk management system that includes a bio-mathematical model should specify what exactly is measured by the model, and how the model can be used in the context of a safety management system approach. This will require significant dialog between the various parties with an interest in bio-mathematical models, i.e. developers, vendors, end-users, and regulators.

Are two halves better than one whole? A comparison of the amount and quality of sleep obtained by healthy adult males living on split and consolidated sleep-wake schedules

The aim of this study was to compare the quantity/quality of sleep obtained by people living on split and consolidated sleep-wake schedules. The study had a between-groups design, with 13 participants in a consolidated condition (all males, mean age of 22.5yr) and 16 participants in a split condition (all males, mean age of 22.6yr). Both conditions employed forced desynchrony protocols with the activity:rest ratio set at 2:1, but the consolidated condition had one sleep-wake cycle every 28h (9.33+18.67), while the split condition had one sleep-wake cycle every 14h (4.67+9.33). Sleep was assessed using polysomnography. Participants in the split and consolidated conditions obtained 4.0h of sleep per 14h and 7.6h of sleep per 28h, respectively. Some differences between the groups indicated that sleep quality was lower in the split condition than the consolidated condition: the split sleeps had longer sleep onset latency (9.7 vs. 4.3min), more arousals (7.4 vs. 5.7 per hour in bed), and a greater percentage of stage 1 sleep (4.1% vs. 3.1%), than the consolidated sleeps. Other differences between the groups indicated that sleep quality was higher in the split condition than the consolidated condition: the split sleeps had a lower percentage of wake after sleep onset sleep (11.7% vs. 17.6%), and a greater percentage of slow wave sleep (30.2% vs. 23.8%), than the consolidated sleeps. These results indicate that the split schedule was not particularly harmful, and may have actually been beneficial, to sleep. Split work-rest schedules can be socially disruptive, but their use may be warranted in work settings where shiftworkers are separated from their normal family/social lives (e.g., fly-in fly-out mining) or where the need for family/social time is secondary to the task (e.g., emergency response to natural disasters).

The efficacy of objective and subjective predictors of driving performance during sleep restriction and circadian misalignment

Fatigue is a significant contributor to motor-vehicle accidents and fatalities. Shift workers are particularly susceptible to fatigue-related risks as they are often sleep-restricted and required to commute around the clock. Simple assays of performance could provide useful indications of risk in fatigue management, but their effectiveness may be influenced by changes in their sensitivity to sleep loss across the day. The aim of this study was to evaluate the sensitivity of several neurobehavioral and subjective tasks to sleep restriction (SR) at different circadian phases and their efficacy as predictors of performance during a simulated driving task. Thirty-two volunteers (M±SD; 22.8±2.9 years) were time-isolated for 13-days and participated in one of two 14-h forced desynchrony protocols with sleep opportunities equivalent to 8h/24h (control) or 4h/24h (SR). At regular intervals during wake periods, participants completed a simulated driving task, several neurobehavioral tasks, including the psychomotor vigilance task (PVT), and subjective ratings, including a self-assessment measure of ability to perform. Scores transformed into standardized units relative to baseline were folded into circadian phase bins based on core body temperature. Sleep dose and circadian phase effect sizes were derived via mixed models analyses. Predictors of driving were identified with regressions. Performance was most sensitive to sleep restriction around the circadian nadir. The effects of sleep restriction around the circadian nadir were larger for simulated driving and neurobehavioral tasks than for subjective ratings. Tasks did not significantly predict driving performance during the control condition or around the acrophase during the SR condition. The PVT and self-assessed ability were the best predictors of simulated driving across circadian phases during SR. These results show that simple performance measures and self-monitoring explain a large proportion of the variance in driving when fatigue-risk is high.

The Chronotype of Elite Athletes

The aims of this study were (i) to compare the chronotype distribution of elite athletes to a young adult population and (ii) to determine if there was a tendency for athletes to select and/or participate in sports which suited their chronotype. A total of 114 elite athletes from five sports (cricket, cycling, hockey, soccer and triathlon) participated in this study. The participants' chronotype, sleepiness, sleep satisfaction and sleep quality were determined using the Horne and Östberg Morningness and Eveningness questionnaire, the Epworth Sleepiness Scale and questions concerning their sleep satisfaction and quality. All questionnaires were administered during a typical training phase that was not in the lead up to competition and/or post competition. No differences between chronotype group for sleepiness, sleep satisfaction or sleep quality were found. There was a significantly higher proportion of triathletes that were morning and intermediate types compared to the control group χ² (2) = 7.5, p = 0.02. A significant relationship between sport and chronotype group (χ²(4)=15.9, p = 0.04) was observed, with a higher frequency of morning types involved in sports that required morning training. There was a clear indication that athletes tended to select and pursue sports that suited their chronotype. This was evident by the amount of morning types involved in morning sports. Given that athletes are more likely to pursue and excel in sports which suit their chronotype, it is recommended that coaches consider the athlete's chronotype during selection processes or if possible design and implement changes to training schedules to either suit the athletes' chronotype or the timing of an upcoming competition.

Is it on? An algorithm for discerning wrist-accelerometer non-wear times from sleep/wake activity

The accuracy of sleep/wake estimates derived with actigraphy is often dependent on researchers being able to discern non-wear times from sleep or quiescent wakefulness when confronted by discrepancies in a sleep log. Without knowing when an accelerometer is being worn, non-wear could be inferred from periods of inactivity unlikely to occur while in bed. Data collected in our laboratory suggest that more than 50% of inactive periods during time in bed are <8 min in duration. This duration may be an appropriate minimum threshold for routine non-wear classification during self-reported wake. Higher thresholds could be chosen to derive non-wear definitions for self-reported bedtimes depending on the desired level of certainty. To determine non-wear at thresholds of 75%, 95% and 99%, for example, would require periods of inactivity lasting ≥18 min, ≥53 min and ≥85 min, respectively.

No first night shift effect observed following a nocturnal main sleep and a prophylactic 1-h afternoon nap

Neurobehavioural impairment on the first night shift is often greater than on subsequent night shifts due to extended wakefulness. The aim of the study was to determine whether a 1-h afternoon nap prior to the first night shift is sufficient to produce neurobehavioural performance at levels comparable to the second night shift. Twelve male volunteers (mean age 22.9 years) participated in a laboratory protocol that simulated two 12-h night shifts. A nap preceded the first shift and a 7-h daytime sleep was scheduled between shifts. Neurobehavioural performance and subjective sleepiness measured across each night did not significantly differ between first and second shifts.

Daily Rhythms of Hunger and Satiety in Healthy Men during One Week of Sleep Restriction and Circadian Misalignment

The impact of sleep restriction on the endogenous circadian rhythms of hunger and satiety were examined in 28 healthy young men. Participants were scheduled to 2 × 24-h days of baseline followed by 8 × 28-h days of forced desynchrony during which sleep was either moderately restricted (equivalent to 6 h in bed/24 h; n = 14) or severely restricted (equivalent to 4 h in bed/24 h; n = 14). Self-reported hunger and satisfaction were assessed every 2.5 h during wake periods using visual analogue scales. Participants were served standardised meals and snacks at regular intervals and were not permitted to eat ad libitum. Core body temperature was continuously recorded with rectal thermistors to determine circadian phase. Both hunger and satiety exhibited a marked endogenous circadian rhythm. Hunger was highest, and satiety was lowest, in the biological evening (i.e., ~17:00–21:00 h) whereas hunger was lowest, and satiety was highest in the biological night (i.e., 01:00–05:00 h). The results are consistent with expectations based on previous reports and may explain in some part the decrease in appetite that is commonly reported by individuals who are required to work at night. Interestingly, the endogenous rhythms of hunger and satiety do not appear to be altered by severe—as compared to moderate—sleep restriction.

Feedback has a positive effect on cognitive function during total sleep deprivation if there is sufficient time for it to be effectively processed

This study examined whether the provision of feedback and the interval between successive stimuli interact to affect performance on a serial simple reaction time test during sleep deprivation. Sixteen participants (9 female, 7 male, aged 18-27 yr) completed four versions of the 5-min psychomotor vigilance task for a handheld personal digital assistant (PalmPVT) every 2 h during 28 h of sustained wakefulness. The four versions differed in terms of whether or not they provided feedback immediately after each response, and whether the inter-stimulus intervals (ISIs) were long (2-10 s) or short (1-5 s). Cognitive function was assessed using reciprocal response time and percentage of responses that were lapses (i.e., had a response time ≥ 500 ms). Data were analysed using repeated measures ANOVA with three within-subjects factors: test session, feedback, and ISI. For both measures, the only significant interaction was between feedback and ISI. Cognitive function was enhanced by feedback when the ISIs were long because it provided motivation. Cognitive function was not affected by feedback when the ISIs were short because there was insufficient time to both attend to the feedback and prepare for the subsequent stimulus.