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).

Sleep is an essential component for athlete recovery due to its physiological and psychological restorative effects, yet few studies have explored the habitual sleep/wake behaviour of elite athletes. The aims of the present study were to investigate the habitual sleep/wake behaviour of elite athletes, and to compare the differences in sleep between athletes from individual and team sports. A total of 124 (104 male, 20 female) elite athletes (mean ± s: age 22.2 ± 3.0 years) from five individual sports and four team sports participated in this study. Participants' sleep/wake behaviour was assessed using self-report sleep diaries and wrist activity monitors for a minimum of seven nights (range 7-28 nights) during a typical training phase. Mixed-effects analyses of variances were conducted to compare the differences in the sleep/wake behaviour of athletes from two sport types (i.e. individual and team). Overall, this sample of athletes went to bed at 22:59 ± 1.3, woke up at 07:15 ± 1.2 and obtained 6.8 ± 1.1 h of sleep per night. Athletes from individual sports went to bed earlier, woke up earlier and obtained less sleep (individual vs team; 6.5 vs 7.0 h) than athletes from team sports. These data indicate that athletes obtain well below the recommended 8 h of sleep per night, with shorter sleep durations existing among athletes from individual sports.

In any sport, successful performance requires a planned approach to training and recovery. While sleep is recognized as an essential component of this approach, the amount and quality of sleep routinely obtained by elite athletes has not been systematically evaluated. Data were collected from 70 nationally ranked athletes from seven different sports. Athletes wore wrist activity monitors and completed self-report sleep/training diaries for 2 weeks during normal training. The athletes also recorded their fatigue level prior to each training session using a 7-point scale. On average, the athletes spent 08:18 ± 01:12 h in bed, fell asleep at 23:06 ± 01:12 h, woke at 6:48 ± 01:30 h and obtained 06:30 ± 01:24 h of sleep per night. There was a marked difference in the athletes' sleep/wake behaviour on training days and rest days. Linear mixed model analyses revealed that on nights prior to training days, time spent in bed was significantly shorter (p = 0.001), sleep onset and offset times were significantly earlier (p < 0.001) and the amount of sleep obtained was significantly less (p = 0.001), than on nights prior to rest days. Moreover, there was a significant effect of sleep duration on pre-training fatigue levels (p ≤ 0.01). Specifically, shorter sleep durations were associated with higher levels of pre-training fatigue. Taken together, these findings suggest that the amount of sleep an elite athlete obtains is dictated by their training schedule. In particular, early morning starts reduce sleep duration and increase pre-training fatigue levels. When designing schedules, coaches should be aware of the implications of the timing of training sessions for sleep and fatigue. In cases where early morning starts are unavoidable, countermeasures for minimizing sleep loss - such as strategic napping during the day and correct sleep hygiene practices at night - should be considered.

This investigation examined precompetitive sleep behaviour of 103 athletes and how it relates to precompetitive mood and subsequent performance. Results revealed that on the night before competition athletes slept well under the recommended target of eight hours of sleep for healthy adults, with almost 70% of athletes experiencing poorer sleep than usual. It was found that anxiety, noise, the need to use the bathroom and early event times were amongst the most commonly reported causes of disrupted sleep in athletes on the night prior to competition. The negative moods of fatigue and tension were both significantly negatively correlated with precompetitive relative sleep quality (r = -0.28, P = 0.004, r = -0.21, P = 0.030, respectively) and total sleep time (r = -0.23, P = 0.023, r = -0.20, P = 0.044, respectively). Additionally, tension was positively correlated with number of awakenings (r = -0.20, P = 0.045). Vigour was seen to be significantly positively associated with relative sleep quality (r = 0.24, P=0.013). The relationships between relative sleep quality and fatigue, tension and vigour accounted for approximately 4 - 5% of the variance in mood scores. Disrupted sleep did not demonstrate any significant relationship with relative sporting performance. Conclusions from the present investigation are that athletes may be at particular risk of disrupted sleep on the night prior to competition, and this disruption can negatively relate to an athlete's precompetitive mood states.

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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.

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 infected with something other than COVID-19). 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.

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.