Deconstructing athletes’ sleep: A systematic review of the influence of age, sex, athletic expertise, sport type, and season on sleep characteristics

Vlahoyiannis, Angelos; Aphamis, George; Bogdanis, Gregory C.; Sakkas, Giorgos K.; Andreou, Elena; Giannaki, Christoforos D.

doi:10.1016/j.jshs.2020.03.006

Cited by 55 publications

(38 citation statements)

References 113 publications

(133 reference statements)

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“…Interestingly the PSQI mean TST (<8 h) was lower than that reported in the CSD-C (>8 h), it has been suggested that athletes tend to overestimate their sleep [ 104 , 105 ]. A recent review suggested that sleep in athletes is limited to 7.2 h per night, with all studies reporting <8 h per night and mean SE was 86.3 ± 6.8% [ 106 ], which is in line with the PSQI and CSD-C data from the current study. The PSQI mean TST for both groups in the current study is adequate according to current sleep recommendations (7–9 h) [ 48 ].…”

Section: Discussionsupporting

confidence: 88%

The Sleep and Recovery Practices of Athletes

et al. 2021

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Background: Athletes maintain a balance between stress and recovery and adopt recovery modalities that manage fatigue and enhance recovery and performance. Optimal TST is subject to individual variance. However, 7–9 h sleep is recommended for adults, while elite athletes may require more quality sleep than non-athletes. Methods: A total of 338 (elite n = 115, 74 males and 41 females, aged 23.44 ± 4.91 years; and sub-elite n = 223, 129 males and 94 females aged 25.71 ± 6.27) athletes were recruited from a variety of team and individual sports to complete a battery of previously validated and reliable widely used questionnaires assessing sleep, recovery and nutritional practices. Results: Poor sleep was reported by both the elite and sub-elite athlete groups (i.e., global PSQI score ≥5—elite 64% [n = 74]; sub-elite 65% [n = 146]) and there was a significant difference in sport-specific recovery practices (3.22 ± 0.90 vs. 2.91 ± 0.90; p < 0.001). Relatively high levels of fatigue (2.52 ± 1.32), stress (1.7 ± 1.31) and pain (50%, n = 169) were reported in both groups. A range of supplements were used regularly by athletes in both groups; indeed, whey (elite n = 22 and sub-elite n = 48) was the most commonly used recovery supplement in both groups. Higher alcohol consumption was observed in the sub-elite athletes (12%, n = 26) and they tended to consume more units of alcohol per drinking bout. Conclusion: There is a need for athletes to receive individualised support and education regarding their sleep and recovery practices.

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Section: Discussionsupporting

confidence: 88%

The Sleep and Recovery Practices of Athletes

et al. 2021

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“…As training load could further affect sleep, this must be stressed as a limitation to the study. 15 However, elite athletes often face training camp or competition phases in which they do not sleep at home, have inconsistent food intake, and do not have perfectly controllable training and sleeping environment. Consequently, it is of great interest to examine whether portable PSG is reasonable in these particularly high demanding situations as diagnostics will often be applied in these circumstances.…”

Section: Discussionmentioning

confidence: 99%

“…2 Yet, in-laboratory PSG is not feasible in sport-scientific field studies; hence, most of the sleep data in a population of athletes is obtained by using subjective questionnaires or wrist actigraphy as they are portable, non-invasive, and no specialist is needed to secure the recording. 11,[14][15][16] However, as no EEG measures are recorded, it is not possible to score sleep stages with these methods. Thus, they cannot measure physiological aspects of sleep.…”

Section: Introductionmentioning

confidence: 99%

Portable polysomnography for sleep monitoring in elite youth rowing: An athlete's gain or the sleep's thief?

et al. 2020

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Sleep is identified as a reoccurring behavioral state of reduced movement and responsiveness, allowing rest from prior periods of wakefulness, and is considered a precious resource for both, psychological and physiological well-being. 1,2 It follows a specific architecture within a circadian and ultra-circadian rhythm, and is divided into five stages, with three sleep stages (N1-N3), rapid eye movement (REM) sleep, and the waking state. Sleep stages split into light (N1-N2) and slow-wave (N3) sleep. A healthy sleeper starts a sleep cycle with N1, followed by more robust sleep (N2) and deep sleep (N3). REM as the last stage completes one sleep cycle. Ideally, a sleep cycle repeats three to seven

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“…The required sleep quantity varies among individuals and its adequacy is reflected by the absence of sleep-induced or sleep-related health issues, daytime dysfunction or sleepiness [4]. Insufficient sleep traits have been increased over the last years [5,6]. Its association with numerous diseases [7] underlined the necessity to examine practical and effective approaches, including manipulation of nutritional aspects to optimize sleep [8].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review, Meta-Analysis and Meta-Regression on the Effects of Carbohydrates on Sleep

et al. 2021

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This study aimed to assess the effects of quantity, quality and periodization of carbohydrates consumption on sleep. PubMed, SCOPUS and Cochrane Library were searched through October 2020. Data were pooled using random-effects meta-analysis. Eleven articles were included in the meta-analysis which consisted of 27 separate nutrition trials, resulting in 16 comparison data sets (sleep quantity n = 11; sleep quality n = 5). Compared to high carbohydrate (HCI), low carbohydrate intake (LCI) moderately increased duration and proportion of N3 sleep stage (ES = 0.37; 95% CI = 0.18, 0.56; p < 0.001 and ES = 0.51; 95% CI = 0.33, 0.69; p < 0.001, respectively). HCI prolonged rapid eye movement (REM) stage duration (ES = −0.38; 95% CI = 0.05, −8.05; p < 0.001) and proportion (ES = −0.46; 95% CI = −0.83, −0.01; p < 0.001), compared to LCI. The quality of carbohydrate intake did not affect sleep stages. Meta-regression showed that the effectiveness of carbohydrate quantity and quality in sleep onset latency was significantly explained by alterations of carbohydrate intake as a percentage of daily energy intake (R2 = 25.87, p = 0.018) and alterations in the glycemic load (R2 = 50.8, p = 0.048), respectively. Alterations in glycemic load partially explained the variance of the effectiveness of carbohydrate quality in sleep efficiency (R2 = 89.2, p < 0.001) and wake after sleep onset (R2 = 64.9, p = 0.018). Carbohydrate quantity was shown to affect sleep architecture, and especially N3 and REM sleep stages. Alterations in both quantity and quality of carbohydrate intake showed a significant effect on sleep initiation. Variations in carbohydrate quality significantly affected measures of sleep continuation. Further studies are needed to assess the effect of long-term carbohydrate interventions on sleep.

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Deconstructing athletes’ sleep: A systematic review of the influence of age, sex, athletic expertise, sport type, and season on sleep characteristics

Cited by 55 publications

References 113 publications

The Sleep and Recovery Practices of Athletes

The Sleep and Recovery Practices of Athletes

Portable polysomnography for sleep monitoring in elite youth rowing: An athlete's gain or the sleep's thief?

A Systematic Review, Meta-Analysis and Meta-Regression on the Effects of Carbohydrates on Sleep

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