The Variability of Sleep Among Elite Athletes

Physiological Parameters of Sleep and the Risk of Obstructive Sleep Apnea in Competitive Athletes with Poor Sleep Quality

This study aimed to explore the sleep conditions and obstructive sleep apnea (OSA) risk in athletes with poor sleep quality. Athletes with poor sleep quality before competition were recruited. Cardiopulmonary coupling analysis, the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Insomnia Severity Index were used to assess and compare athletes at risk of OSA (apnea-hypopnea index (AHI) ≥ 5 events per hour) with those not at risk (AHI < 5 events per hour). Comparisons were made between the non-OSA group (n = 23) and the OSA risk group (n = 19, AHI = 10.79 ± 4.47 events per hour). The OSA risk group exhibited a significantly higher percentage of Stage 1 (S1) and Stage 2 (S2) sleep and greater heart rate variability (HRV) (p < 0.05). Positive correlations were found between AHI and the percentage of S1 and S2 sleep, low-frequency (LF), and the LF/HF ratio (p < 0.05). Conversely, significant negative correlations were observed between AHI and the percentage of Stage 3 (S3) and Stage 4 (S4) sleep, as well as HRV (p < 0.05). Athletes with poor sleep quality and high OSA risk demonstrated reduced parasympathetic activity, increased sympathetic activity, and affected sympathovagal balance during nocturnal HRV.

Effects of sleep deprivation on sports performance and perceived exertion in athletes and non-athletes: a systematic review and meta-analysis

Background

Sleep deprivation can significantly affect sports performance and the perception of fatigue. However, the impact of sleep deprivation on sports performance remains a subject of ongoing debate across different populations.

Objectives

This study aimed to investigate the effects of sleep deprivation on sports performance and ratings of perceived exertion (RPE) in different groups, as well as how different types of sleep deprivation affect these aspects.

Methods

This systematic review followed the PRISMA guidelines (PROSPERO CRD42023492792). Randomized controlled trials (RCTs) and randomized crossover studies published in any language or up to any date were eligible based on the P.I.C.O.S. criteria. The systematic search included databases such as PubMed, Cochrane, Embase, Web of Science, and EBSCO, covering studies up to September 2024. The Cochrane RoB 2 tool was used to assess the risk of bias. Meta-analysis was conducted using either a fixed-effect model or a random-effects model. This study conducted subgroup analyses based on different populations, types of sleep deprivation, and testing times.

Results

This review includes 45 studies, comprising 16 on aerobic endurance (AE) performance, 8 on anaerobic endurance (AnE) performance, 23 on explosive power (EP), 10 on maximum force (MF), 4 on speed, 4 on skill control, and 12 on rating of perceived exertion (RPE). The results indicate that sleep deprivation significantly impaired AE in athletes [SMD = -0.66; 95% CI (-1.28, -0.04); P = 0.04], as well as EP [SMD = -0.63; 95% CI (-0.94, -0.33); P < 0.00001], MF [SMD = -0.35; 95% CI (-0.56, -0.14); P = 0.001], speed [SMD = -0.52, 95% CI (-0.83, -0.22); P = 0.0008], skill control [SMD = -0.87; 95% CI (-1.7, -0.04); P = 0.04], and RPE [SMD = 0.39; 95% CI (0.11, 0.66); P = 0.006]. Additionally, AE was also reduced in healthy non-athletes [SMD = -1.02; 95% CI (-1.84, -0.21); P = 0.01]. During the sleep deprivation process, early sleep deprivation (PSDE) significantly reduced EP [SMD = -1.04; 95% CI (-1.58, -0.5); P = 0.0002], MF [SMD = -0.57; 95% CI (-0.94, -0.19); P = 0.003], speed [SMD = -0.78; 95% CI (-1.35, -0.2); P = 0.008], and RPE [SMD = 0.6; 95% CI (0.17, 1.02); P = 0.006]. Late sleep deprivation (PSDB) impacted speed [SMD = -0.57; 95% CI (-1.15, 0.01); P = 0.05], skill control [SMD = -2.12; 95% CI (-3.01, -1.24); P < 0.00001], and RPE [SMD = 0.47; 95% CI (0.02, 0.92); P = 0.04]. Overall, total sleep deprivation primarily affected AE [SMD = -0.56; 95% CI (-1.08, -0.05); P = 0.03]. In terms of testing phases, p.m. tests had a significant impact on AE [SMD = -1.4; 95% CI (-2.47, -0.34); P = 0.01], EP [SMD = -0.68; 95% CI (-1.06, -0.31); P = 0.0004], MF [SMD = -0.3; 95% CI (-0.51, -0.09); P = 0.005], skill control [SMD = -2.12; 95% CI (-3.01, -1.24); P < 0.00001], and RPE [SMD = 0.72; 95% CI (0.20, 1.24); P = 0.007]. In contrast, a.m. tests primarily affected speed [SMD = -0.81; 95% CI (-1.52, -0.1); P = 0.03] and RPE [SMD = 0.44; 95% CI (0.01, 0.86); P = 0.04].

Conclusion

Sleep deprivation significantly impairs athletes' performance across various domains, including AE, MF, speed, and skill control, while also exacerbating RPE. In contrast, although sleep deprivation also negatively affects the AE of healthy non-athletes. Furthermore, PSDE appears to have a more pronounced effect on sports performance overall. Additionally, performance assessments conducted in the p.m. have been shown to further impact sports performance. These findings are crucial for understanding how sleep deprivation impacts both athletes and non-athletes, particularly in the context of training and competitive settings.

Sleep architecture is altered with travel and matches in professional rugby union players

Field-based athlete research has primarily focused on sleep-wake patterns, but technological advancements now enable detailed assessment of sleep architecture. This study compared sleep quantity, quality, and architecture during home (no travel; HOME) and away (eastbound travel across three time zones; AWAY) matches in professional male rugby union. The sleep of twenty athletes was assessed using home-based polysomnography over three nights per condition: two nights preceding the match (MD-2), match night (MD), and the following night after (MD + 1). Sleep duration, efficiency, onset/offset, latency, wake after sleep onset, awakenings, and sleep stages (as light, deep and rapid eye movement [REM] sleep, evaluated as proportion [%] and time [min]) were monitored. Compared to HOME, AWAY increased sleep onset latency (+12 ± 23 min; p < 0.01) and reduced light sleep (-6.2 ± 7.1%; p < 0.01). Match days (MD) incurred delayed bedtime (+71 ± 135 min; p < 0.001), reduced sleep duration (-94 ± 120 min; p < 0.05), and altered sleep architecture (deep: +9.8 ± 10.0%; REM: -6.9 ± 8.3%; both p < 0.05) compared to MD-2. On MD + 1, sleep duration (+96 ± 147 min; p < 0.01) and light and REM sleep time rebounded (+52 ± 44 min and + 39 ± 52 min, respectively; both p < 0.05). Travel and matches altered sleep architecture in professional rugby. Training and flight schedules should ensure sufficient sleep opportunity after travel and matchday to optimise recovery.

Chronotype, sleep hygiene, and sleep characteristics among athletes of different disciplines

The objective of the study was to assess chronotype, sleep hygiene, and sleep characteristics of Italian athletes in order to identify differences among sport disciplines (team- vs individual-sports), level of competition (Elite vs Non-Elite) and sexes. Moreover, correlations between chronotype, sleep hygiene and sleep quality were evaluated. Two hundred eighty Italian athletes (Females:113; Males:167) participated in the study. They completed the Pittsburgh Sleep Quality Index (PSQI), the Sleep Hygiene Index (SHI) and the MorningnessEveningness Questionnaire (MEQ). Among the athletes, 66.4% were intermediate-type, 21.8% were morning-type and 11.8% were evening-type. 55.4% of the sample was classified as good sleeper (PSQI<5), while 16.1% had poor sleep hygiene (SHI≥35). Females had higher total sleep time (p=0.022) than males. Individual-sports showed more morning-oriented score (p=0.001) and lower PSQI (p=0.006) and SHI (p<0.001) scores, with earlier timing of sleep (bedtime:p<0.001; wake-up time:p<0.001) compared to teamsports. Elite athletes exhibited greater sleep efficiency (p=0.009), and lower sleep latency (p=0.018) and wake after sleep onset (p=0.043) than Non-Elite athletes. Additionally, significant correlations were found between PSQI and SHI (r=0.43, p<0.001), and between MEQ and SHI (r=-0.35, p<0.001). This study yields crucial insights into the sleep behaviors of Italian athletes, revealing that nearly half of the sample were bad sleepers.

Irregular sleep/wake patterns in student-athletes exposed to early morning training

This study aimed to examine the sleep parameters and sleep/wake regularity of a cohort of student-athletes who start training between 06:30 and 07:00. Twenty-one male Rugby Union players, aged 21 ± 2 years and competing at a national level, were assessed using actigraphy over two weeks, and the Athlete Sleep Screening Questionnaire (ASSQ). Sleep/wake regularity was calculated using the Sleep Regularity Index (SRI). Wilcoxon signed-rank tests showed that nocturnal sleep preceding morning training had a significantly shorter sleep duration (1.8 hr, r = .67), and advanced sleep onset (0.9 hr, r = .50) and sleep offset times (3.2 hr, r = .85) compared to nocturnal sleep preceding free days. The variability of training demands resulted in an inconsistent sleep pattern between consecutive days, resulting in a median SRI score of 67.0 (interquartile range: 17.0). Pearson correlations revealed that lower SRI was significantly associated with a higher daily sleep duration including naps (r = -.62), delayed sleep onset (r = -.50) and sleep offset (r = -.60), and a later chronotype assessed using the ASSQ (r = .52). These findings indicate that early morning training is a factor contributing to irregular sleep/wake patterns in student-athletes, and where feasible should be scheduled at an alternative time.

Increased training load promotes sleep propensity and slow-wave sleep in endurance runners: Can a high-heat-capacity mattress topper modulate this effect?

The present study aimed to: (1) investigate sleep architecture in response to an overload training and taper periods among endurance runners; and (2) assess the sleep benefits of a high-heat-capacity mattress topper. Twenty-one trained male endurance runners performed a 2-week usual training regimen (baseline) followed by 2-week overload and taper periods. From overload to the end of the taper period, they were assigned into two groups based on the mattress topper used: high-heat-capacity mattress topper (n = 11) or low-heat-capacity mattress topper (n = 10). Training load was assessed daily using the session rating of perceived exertion. Following each period, sleep was monitored by polysomnography, and nocturnal core body temperature was recorded throughout the night. Irrespective of the group, awakening episodes > 5 min decreased following overload compared with baseline (-0.48, p = 0.05). Independently of mattress topper, each 100 A.U. increase in 7-day training load prior to polysomnographic recording was associated with higher slow-wave sleep proportion (β = +0.13%; p = 0.05), lower sleep-onset latency (β = -0.49 min; p = 0.05), and a reduction in the probability of transition from N1 sleep stage to wakefulness (β = -0.12%; p = 0.05). Sleeping on a high-heat-capacity mattress topper did not affect any sleep variable compared with a low-heat-capacity mattress topper. Increased training loads promote slow-wave sleep and sleep propensity, highlighting the adaptative nature of sleep to diurnal activity and the role of sleep in physiological recovery. Further studies are required on the potential benefits of high-heat-capacity mattress toppers on sleep architecture among athletes.

Social jetlag affects jump skills in sub-elite volleyball players

Social jetlag (SJL), resulting from misalignment between biological rhythms and social schedules, has emerged as a prevalent phenomenon in modern society, particularly among young athletes. However, the effect of SJL on performance is poorly studied. Jump and dynamic balance are two key skills in volleyball, as the first allows the player to perform better both during the offense and defense phase, and the second is fundamental in landing and in injury prevention. Therefore, our aim was to investigate the effect of SJL on jump skill performance and balance in female volleyball players. Thirty female volleyball players (mean age: 17.3 ± 0.88 years) participated in the study. SJL was assessed using the Munich ChronoType Questionnaire (MCTQ), integrated with Jankowsky's sleep-corrected formula. Jump skill performance was evaluated using a standardized jump test, the Vertec Jump Test, while balance was assessed with the Y Balance Test. The tests were performed at 09:00 a.m. and at 06:00 p.m. The results revealed that players with greater SJL exhibited decreased jump performance, characterized by lower vertical jump height (p = 0.02). Furthermore, players with lower SJL showed the typical difference between morning and afternoon performance (p = 0.001), demonstrating their synchronization between biological rhythms and social commitments, while no statistically significant difference between the two sessions was shown in players with higher SJL. Regarding balance, no significant association with SJL was found, but the morning session yielded lower results than the afternoon one (p = 0.01). These findings highlight the detrimental impact of SJL on jump skill performance, underscoring the importance of optimizing sleep-wake schedules and circadian alignment to enhance athletic performance. Future research should explore targeted interventions, such as sleep hygiene education, to minimize social jetlag and promote optimal performance in adolescent athletes.

Exploring the impact of sleep on emotional and physical well-being in professional cricketers: a cohort study over an in-season training period

Background

Professional athletes navigate a multitude of unique challenges associated to sport-specific factors (e.g., training, travel and competition) and non-sport factors (e.g., performance pressure, stress and anxiety) that can interfere with healthy sleep behaviors. Sleep plays a key role in proper biopsychosocial development as well as short- and long-term biological, physical, psychological, and cognitive health. As poor sleep quality is known to impair proper brain function, this study aimed to investigate the effect of sleep quality on a professional athlete's ability to train, recover, and perform, as well as their overall emotional and physical well-being.

Methods

A cohort study was performed in 40 professional male cricket athletes from the Dutch national cricket team (mean age 26.5 ± 5.1 years). The athletes were monitored across a 22 weeks in-season training period. Sleep quality and overall emotional and physical well-being were assessed using daily sleep diaries and questionnaires which scored the readiness to train, stress levels, fatigue, muscle soreness and flu symptoms respectively. Quality of sleep and subsequent association with the consecutive elements of the well-being questionnaire were assessed through statistical using the student t-test and clinical differences with the methodology of Osoba and colleagues: <5% "no change", 5%-10% "little change"; 10%-20% "moderate change"; and >20% "very much change".

Results

The results demonstrated that the professional athletes assessed their sleep quality as average with a mean score of 3.4 out of 5. Lower perceived quality of sleep (<75th percentile) was correlated with a decreased readiness to train (mean score 3.2 [IQR: 3.0-4.0] vs. 3.5 [IQR: 3.0-5.0]; P < 0.001) and increased extent of muscle soreness (2.7 [IQR: 2.0-3.0] vs. 2.3 [IQR: 2-3]; P < 0.001), stress level (mean score 2.3 [IQR: 2.0-3.0] vs. 1.9 [IQR: 1.0-2.0]; P < 0.001) and perceived fatigue (mean score 2.9 [IQR: 2.0-3.0] vs. 2.3 [IQR: 2.0-3.0]; P < 0.001). Likewise, in patients with lower perceived quality of sleep, the proportion of players presenting with flu symptoms increased over 4-fold (4.1% vs. 17%; P < 0.001).

Conclusions

This study highlights that good sleep quality positively influences the overall emotional and physical well-being of professional athletes. Our results emphasize the importance of targeted sleep interventions to improve sleep quality and subsequently optimize psychological and physiological wellness.

Sleep Quality in Greek Adolescent Swimmers

The aim of our study was to investigate the relationship between sleep quality and functional indices, swimming distance and gender in adolescent competitive swimmers. Forty-eight adolescent swimmers (boys, n = 22, 15.7 ± 1.0 years and girls, n = 26, 15.1 ± 0.8 years) were included in our study. They were assessed for handgrip strength, respiratory muscle strength and pulmonary function, answered a Pittsburg Sleep Quality Index questionnaire (PSQI), and recorded their anthropometric and morphological characteristics and training load for the last four weeks. The results showed differences between swimming distance and chest circumference difference, between maximal inhalation and exhalation (Δchest) (p = 0.033), PSQI score (p < 0.001), and sleep quality domains for "cannot breathe comfortably" (p = 0.037) and "have pain" (p = 0.003). Binary logistic regression (chi-square = 37.457, p = 0.001) showed that the variables Δchest (p = 0.038, 95% CI: 1.05-6.07) and PSQI score (p = 0.048, 95% CI: 0.1-1.07) remained independent predictors of the swim distance groups. Girls had a lower percentage of predicted values for the maximal inspiratory pressure (p < 0.001), maximal expiratory pressure (p = 0.027), forced expiratory volume within the first second (p = 0.026), forced vital capacity (p = 0.008) and sleep quality domains for "cough or snore loudly" (p = 0.032) compared to boys. A regression analysis showed that the sleep quality score was explained by the six independent variables: respiratory muscle strength (t = 2.177, β = 0.164, p = 0.035), Δchest (t = -2.353, β = -0.17, p = 0.023), distance (t = -5.962, β = -0.475, p < 0.001), total body water (t = -7.466, β = -0.687, p < 0.001), lean body mass (t = -3.120, β = -0.434, p = 0.003), and handgrip (t = 7.752, β = 1.136, p < 0.001). Our findings demonstrate that sleep quality in adolescent swimmers is a multifactorial result of morphometric characteristics, strength and respiratory function.

Routine, Routine, Routine: Sleep Regularity and its Association with Sleep Metrics in Professional Rugby Union Athletes

BACKGROUND

Maintaining a consistent sleep and wake time is often reported as a key component of circadian rhythmicity and quality sleep. However, the impact of sleep onset and offset time variability on overall sleep outcomes are underreported in elite athlete populations. This study investigated the relationship between sleep onset and offset time variability using the sleep regularity index (SRI) and measures of sleep and well-being in professional rugby union athletes. Twenty-three professional male rugby union athletes (mean ± SD, age: 23 ± 3 y) underwent sleep monitoring via wrist actigraphy for three weeks during a pre-season phase of training and completed a daily wellness questionnaire. Median SRI was calculated and used to stratify the trainees into two quantile groups: >76.4 SRI (Regular, n = 11) and < 76.4 SRI (Irregular, n = 12).

RESULTS

The regular sleep group showed significantly longer total sleep duration (p = 0.02, d = 0.97) compared to the irregular group (7:42 ± 0:29 vs. 7:18 ± 0:20 h: min per night, respectively). Furthermore, while not statistically significant, the regular sleep group showed greater sleep efficiency and less wake episodes compared to irregular sleepers, as demonstrated by moderate effect sizes (d = 0.71 and 0.69, respectively).

CONCLUSIONS

The results from this study indicate that minimizing variability in sleep onset and offset time is beneficial for increasing sleep duration and may improve sleep efficiency during pre-season training in elite male rugby union athletes. This study provides evidence for the importance of including sleep-wake routines as a key component of sleep education interventions.

Night-to-night variability of objective sleep outcomes in youth Middle Eastern football players

OBJECTIVE

To describe components of night-to-night variation in objective measures of sleep.

METHODS

We conducted a secondary data analysis of consecutive and chronologically ordered actigraphy-based measurements for time in bed (min), time asleep (min), and wake-after-sleep onset (min). This investigation examined 575 individual night-based measures available for a sub-sample of fifty-two, male youth Middle Eastern football players tracked over a 14-day surveillance period (chronological age range: 12.1 to 16 years). Distinct multivariable-adjusted generalized additive models included each objective sleep outcome measure as dependent variable and disaggregated components of variation for night measurement-by-sleep period interaction, week part (weekday or weekend), and study participant random effects from within-subject night-to-night sleep variation.

RESULTS

The within-subject standard deviation (SD) of ±98 min (95% confidence interval [CI], 92 to 104 min) for time in bed, ±87 min (95%CI, 82 to 93 min) for time asleep, and ±23 min (95%CI, 22 to 25 min) for wake-after-sleep-onset overwhelmed other sources of variability and accounted for ∼44% to 53% of the overall night-to-night variation. The night measurement-by-fragmented sleep period interaction SD was ±83 min (95%CI, 44 to 156 min) for time in bed, ±67 min (95%CI, 34 to 131 min) for time asleep, and ±15 min (95%CI, 7 to 32 min) for wake-after-sleep-onset that accounted for ∼22% to 32% of each sleep outcome measure overall variability.

CONCLUSIONS

Substantial random night-to-night within-subject variability poses additional challenges for strategies aiming to mitigate problems of insufficient and inconsistent sleep that are detrimental to school learning and youth athlete development processes.

Associations of Training and Academic Stress with Sleep in Dual-Career Collegiate Badminton Athletes: A Preliminary Study

Introduction

Poor sleep negatively impacts cognitive and physical functioning and affects athletic and academic achievement. "Dual-career" athletes emphasize the pursuit of academic excellence along with athletic performance.

Purpose

The study aimed to assess sleep characteristics and sleep quality in dual-career collegiate badminton athletes. Furthermore, the study explored associations between training and academic stress and sleep, providing a theoretical basis for better training and sleep programs for dual-career athletes.

Participants and Methods

In this study, 15 dual-career collegiate badminton athletes were recruited, and 12 subjects (male n = 8, female n = 4, mean age 20.3 ± 1.7) completed the questionnaire. Repeated measurements were taken monthly in the spring semester from March to August 2021. The questionnaire assessed sleep quality and daytime sleepiness by the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Score (ESS). Moreover, we collected average training, study time per week, and monthly sports competitions and academic tests to quantify participants' training and academic stress.

Results

An average of 36.1% of dual-career athletes reported poor sleep and 25.0% had excessive daytime sleepiness. Overall, a significant positive correlation existed between PSQI scores and weekly study hours (r = 0.308, p = 0.009). Significant positive correlations were found between the four stressors and PSQI (August: r = 0.868, p < 0.001; July: r = 0.573, p = 0.026) or ESS scores (March: r = -0.678, p = 0.015; August: r = 0.598, p = 0.040) for specific months. Hierarchical linear modeling (HLM) analysis identified that lower study and training hours predict better sleep quality.

Conclusion

Dual-career collegiate badminton athletes had a higher prevalence of poor sleep and daytime sleepiness, and daytime sleepiness did not result in better sleep quality; study and training hours had the greatest effect on the sleep quality of dual-career collegiate badminton athletes.

Athlete Sleep Behavior Questionnaire in Japanese (ASBQ-J): An Adaptation and Validation Study

Purpose

The Athlete Sleep Behavior Questionnaire (ASBQ) is an 18-item self-reported questionnaire that can be used to screen for sleep disorders in athletes and assess environmental, behavioral, and sports-related factors along with sleep disorders. This study aimed to analyze the reliability and validity of the ASBQ translated into Japanese (ASBQ-J).

Patients and Methods

111 female collegiate athletes completed both the initial test and retest. Independent variables were what kind of sports they perform, level as an athlete, grade (freshman, sophomore, junior, senior), and age. The reliability of the survey was assessed using Cronbach's alpha and intraclass correlation, and Pearson's correlation coefficient was calculated to determine the correlation between the total ASBQ scores and three components which were routine/environmental, behavioral, and sports factors.

Results

A total of 37.8% of athletes had ASBQ scores equal or less than 36 indicating good sleep. However, 19.8% of athletes scored more than 42, indicating poor sleep. Cronbach alpha was 0.62 and 0.65 for test and retest, respectively. Intraclass correlation was 0.78 (P < 0.01), indicating sufficient internal consistency and test-retest validity. Mean ASBQ scores were 38.5 ± 5.0 and 37.0 ± 6.5 for test and retest, respectively, and it was not significantly different between the two groups (P = 0.37). The correlation coefficients between the ASBQ-J and component scores were all statistically significant (all P < 0.01).

Conclusion

ASBQ-J is a reliable questionnaire for assessing sleep disorders in athletes whose primary language is Japanese.

Chronotype characteristics of professional athletes in China: a comprehensive descriptive study

BACKGROUND

Chronotype has gained recognition as a significant factor in enhancing athletic performance. This study aimed to deepen our understanding of athletes' sleep chronotypes and provide a foundation for developing evidence-based training and competition programs. By comprehensively describing and analyzing the chronotype characteristics of Chinese professional athletes, considering individual and sports factors, sleep quality and habits, and mental energy, this research aimed to contribute valuable insights to the field.

METHODS

A sample of 1069 professional athletes from sports teams in Shanghai completed the Athlete Sleep Screening Questionnaire and the Athlete Mental Energy Scale to assess chronotype, sleep quality, sleep-influencing habits, and mental energy.

RESULTS

Among athletes, sleep typology fell within the intermediate range, slightly leaning toward morningness. Male athletes and those who engaged in static sports displayed a greater propensity for morningness. Age correlated with a preference for eveningness. High-level athletes exhibited a stronger inclination toward eveningness. Sleep quality issues were associated with an inclination toward eveningness. Daily caffeine intake and the habit of using electronic devices before bedtime are also linked to eveningness. Chronotype demonstrated the ability to predict various dimensions of athletes' mental energy. It was the strongest predictor of vigor, but the loadings were smaller than those of sleep quality.

CONCLUSION

Chinese athletes' chronotypes primarily exhibit distinct characteristics related to individual factors such as gender, sports discipline, and ranking, as well as habits like caffeine consumption and electronic device use. Moreover, these sleep patterns demonstrate predictive capabilities across all dimensions of athletes' mental energy. This study sheds light on Chinese athletes' unique sleep chronotype attributes, enriching our understanding of sleep patterns among professional athletes under various systems. These insights offer an initial basis for enhancing the effectiveness of athlete scheduling and training management.

The relationship between sleep, perfectionistic strivings, perfectionistic concerns, and academic and sports performance in young athletes

Perfectionism is associated with sleep as well as with academic and sports performance. Given the importance of sleep for performance and the sleep changes that occur during adolescence, the present study aimed to explore the relationship between sleep, two dimensions of perfectionism (perfectionistic strivings and perfectionistic concerns), with academic and sports performance in young athletes. The occurrence of perfectionistic strivings was identified as a tendency to set high personal standards for oneself, while the occurrence of perfectionistic concerns was identified as a tendency to feel pressure to be perfect and have concerns about imperfections. A total of 32 athletes aged 13 to 16 years old participated in this study. Perfectionism, sleep, and course grades were measured at the end of the school year. Sleep was measured by actigraphy, and sports performance improvement was obtained by comparing individual performance at the beginning and the end of the school year. The moderating effect of both dimensions of perfectionism on the relationships between sleep and academic and sports performance improvement was measured. First, results showed no significant relationship between sleep and academic/sports performance. Further analyses revealed that perfectionistic strivings moderated the relationship between certain aspects of sleep with academic and sports performance improvement. Delayed and suboptimal sleep habits were associated with lower academic and sports performance improvement when low levels of perfectionistic strivings were present. Results with perfectionistic concerns showed that this dimension did not moderate the relationship between sleep and academic and sports performance improvement. This study supports the notion that high levels of perfectionistic strivings in young athletes may mitigate the adverse effects of poor sleep habits in young athletes.

The impact of long haul travel on the sleep of elite athletes

In order to manage and implement strategies to alleviate the symptoms of jet lag it is essential to assess the impact of jet lag in athletes. The aim of this study was to assess the impact of long haul eastward travel on elite athletes' (n = 7 elite national track cyclists; male n = 3, and female n = 4) sleep. The athletes' sleep was monitored before, during and after travel using both actigraphy and self-report measures. Participants wore an activity monitor for 5 days prior to travel, during the long haul travel and 5 days upon arrival at their destination and completed a daily online sleep diary Actigraphy highlighted significant reductions in time in bed, total sleep time and sleep efficiency (%) due to long haul eastward travel, particularly in the 48 h after travel. Sleep diary data exhibited significant reductions in time in bed, total sleep time, sleep efficiency, sleep quality and a significant increase in fatigue going to bed as a result of long haul eastward travel. In order to facilitate the development of interventions to reduce the symptoms and severity of jet lag objective and subjective assessments of sleep should be coupled with assessments of chronotype and perceived sleep need.

Effect of training load on sleep parameters and biochemical fatigue markers in elite swimmers

The effect of strenuous exercise on sleep patterns in swimmers is equivocal. Therefore, the purpose of the study was to describe possible changes in sleep parameters among elite swimmers subjected to different training loads (TL). Methods: Eighteen elite swimmers (8 females) were monitored across two high-volume preparatory 1-wk periods (P1, P2) and a lower-volume tapering 1-wk period (P3) before a major competition. Internal (the session rating of perceived exertion [sRPE]) and external TL (training duration and volume) were measured, along with several sleep indices (e.g., bedtime, get-up time, sleep time, wake after sleep onset [WASO]). Serum measurements of urea, creatine kinase (CK), testosterone and cortisol were taken before and after training sessions at the beginning (Mondays) and end (Fridays) of each micro cycle. Athlete TL decreased significantly in a stepwise manner from P1 to P2 and from P2 to P3. Of all sleep parameters, only significant differences in bedtime and get-up time emerged (P3 > P1 and/or P2). Sleep duration (~6.3 h) or quality (WASO: 41-45 min) were also unaffected by TL. CK levels declined from P1 to P3 (d = -0.8), and from P2 to P3 (d = -0.6). Positive exercise-induced changes in CK were also seen in each training period. The other biomarkers did not show the same temporal or acute patterns. Irrespective of the TL, the monitored swimmers experienced insufficient and fragmented sleep across this study. Neither sleep quality nor quantity were affected by different magnitudes of TL. Among the biochemical markers of fatigue, baseline plasma CK activity best reflected the physiological response to TL.

Effects of a Sleep Hygiene Strategy on Parameters of Sleep Quality and Quantity in Youth Elite Rugby Union Players

PURPOSE

To assess the effects of a sleep hygiene strategy on parameters of sleep quality and quantity in youth elite rugby union players.

METHOD

Eleven male players (age: 19.0 [1.4] y) undertook a sleep hygiene strategy composed of 2 theoretical sessions and 3 practical sessions over a 4-week period. Sleeping time, time in bed, total sleep time, sleep latency (SL), sleep efficiency (SE), wake after sleep onset, and wake bouts were recorded with an actigraphic device during the 4-week sleep hygiene strategy (baseline) and during 4 weeks after the last intervention (postintervention).

RESULTS

At baseline, the overall group reported poor sleep quantity (total sleep time = 6:27 [0:30] min), but sleep quality was considered acceptable (SL = 0:18 [0:08] min and SE = 77.8% [5.8%]). Postintervention, the overall group showed a small improvement in SL (d = -0.23 [-0.42 to -0.04], P = .003) and SE (d = 0.30 [0.03 to 0.57], P = .0004). For individual responses, sleeping time, time in bed, and total sleep time were positively influenced in only 4, 3, and 5 players, respectively. For parameters of sleep quality, SL and SE were positively influenced in a majority of players (n = 7 and 8, respectively). The magnitude of difference between baseline and postintervention was strongly associated with baseline values in SE (r = -.86; P = .0005) and wake after sleep onset (r = -.87; P = .0007).

CONCLUSION

A sleep hygiene strategy is efficient to improve sleep quality but not sleep quantity in young rugby union players. The strategy was more efficient in players with lower initial sleep quality and should be implemented prior to a high cumulative fatigue period.

A bespoke sleep monitoring and sleep hygiene intervention improves sleep in an U18 professional football player: A case study

This case study reports on a professional football player (age: 17.6 years) who was referred for sleep monitoring and intervention after reporting excessive night-time awakenings. The player undertook a series of subjective sleep assessments and objective sleep monitoring (activity monitor). Based on the data presented, a sleep hygiene intervention was prescribed. Numerical comparisons were made between pre-intervention (Pre) and post-intervention (Post) values. Objective values were also compared to reference data from a similarly aged professional cohort from the same club (n = 11). Wake episodes per night (Pre: 7.9 ± 3, Post: 4.5 ± 1.9; -43%) and wake after sleep onset (WASO; Pre: 74.3 ± 31.8 mins, Post: 50.0 ± 22.8 mins, -33%) were improved from Pre to Post. Compared to the reference data, mean wake episodes per night (Pre: 7.9 ± 3.0, reference: 4.6 ± 2.6; -42%) and WASO (Pre: 74.3 ± 31.8 mins, reference: 44.3 ± 36.5 mins; -40%) were all lower compared to Pre levels. Whilst causality cannot be proven, we observed multiple sleep metrics improving following an intervention. This provides a potential framework for practitioners looking to provide targeted sleep assessment and intervention.

Habitual sleep and intraindividual variability of sleep in gifted children: an actigraphy study

STUDY OBJECTIVES

Giftedness is a multidimensional condition. It is increasingly put forward that gifted children (GC) could be a population at high risk for sleep problems. The current study investigated GC and typically developing children for their habitual sleep, night-to-night sleep variability, and parental reports of child sleep.

METHODS

The sample consisted of 62 GC (31 girls; mean age = 9.63 ± 1.71 years) and 62 typically developing children (31 girls; mean age = 9.68 ± 1.68 years). Groups were age and sex matched. Giftedness was identified using Renzulli's 3-factor definition of giftedness. Sleep duration, quality, and night-to-night variability were assessed using actigraphy. Parents were asked to complete the short-form version of the Children's Sleep Habits Questionnaire to report on their child's sleep. Groups were compared with independent sample t-tests and chi-square analyses.

RESULTS

GC displayed lower sleep efficiencies, more wake time after sleep onset, and more night-to-night sleep variability than typically developing children. GC were found to experience less social jetlag compared to typically developing children, and they also showed more clinically significant sleep problems as reported by parents.

CONCLUSIONS

Sleep maintenance and stability tend to be challenged in GC. While there is growing evidence that greater sleep variability is associated with poorer physical and emotional health, studies have yet to examine these associations in GC specifically to get a better understanding of giftedness. Overall, there is a need for research focused on both predictors and consequences of sleep patterns and sleep variability in GC.

CITATION

Bastien L, Théoret R, Bernier A, Godbout R. Habitual sleep and intraindividual variability of sleep in gifted children: an actigraphy study. J Clin Sleep Med. 2023;19(5):925-934.

Tracking sleep in the field: sleep quality and sleep behaviours of elite track and field athletes during preparation and competition

Elite athletes are susceptible to inadequate sleep, which may peak during competition and be exacerbated by poor sleep behaviours. This study sought to characterise and compare the sleep quality and sleep behaviours of elite track and field athletes during preparation and major competitions. Forty elite international track and field athletes (50% female, aged 25.1 ± 3.9 years) completed the Athlete Sleep Screening Questionnaire and the Athlete Sleep Behaviour Questionnaire on three separate occasions: during habitual training, during a pre-meet training camp and during a major international competition. Overall, 62.5% of athletes reported at least mild sleep difficulty during competition. Athletes reported higher sleep difficulty and poorer sleep behaviour during major competitions and the pre-meet training camp compared to habitual training (P = .001-.025). No significant differences were observed between the training camp and major competition. Global sleep behaviour scores were underpinned by unique characteristics at each timepoint. Sleep behaviour (R² = .330, P = .017), injury status (R² = .253, P = .003) and major championship experience (R² = .113, P = .034) were associated with sleep difficulty during competition. Sleep quality and behaviours vary according to stage of the track and field season, providing a foundation for targeted intervention.

The Sleep Parameters of Paralympic Athletes: Characteristics and Assessment Instruments

CONTEXT

Sleep serves many important functions for athletes, particularly in the processes of learning, memory, recovery, and cognition.

OBJECTIVES

Define the sleep parameters of Paralympic athletes and identify the instruments used to assess and monitor sleep Paralympic athletes.

EVIDENCE ACQUISITION

This systematic review was carried out based on the PRISMA guidelines. The survey was conducted in April 2020, the searches were carried out again in September 2021 to check whether there were new scientific publications in the area of sleep and Paralympic sport, searches were performed in the following databases: PubMed, Web of Science, Scopus, SPORTDiscus, Virtual Health Library (BIREME), and SciELO. This systematic review has included studies that investigated at least one of the following sleep parameters: total sleep time, sleep latency, sleep efficiency, number of awakenings, quality of sleep, daytime sleepiness, and chronotype; the participants were comprised of athletes with disabilities. Studies published at any time in English, Portuguese, and Spanish, were included.

EVIDENCE SYNTHESIS

Data extraction and study selection were performed by 2 researchers independently, and a third author was consulted as necessary. The search returned a total of 407 studies. Following the screening based on exclusion and inclusion criteria, a total of 13 studies were considered. Paralympic athletes have a low amount (7.06 h) of sleep with poor quality and sleep latency (28.05 min), and 57.2% have daytime sleepiness, with the majority belonging to the indifferent chronotype (53, 5%). Moreover, 11 studies assess sleep using subjective instruments (questionnaires), and 2 studies used an objective instrument (actigraphy).

CONCLUSIONS

Sleep disorders are common among Paralympic athletes, poor sleep quality and quantity, and high rates of daytime sleepiness. Subjective methods are most commonly used to assess sleep.

Sleep and Performance in Professional Athletes

Purpose of Review

Sleep is an essential human behavior that plays a key role in proper biopsychosocial development as well as short- and long-term biological, physical, psychological, and cognitive health. Sleep plays a key role in athletic performance, influencing an athlete's ability to train, recover, and perform, as well as their overall wellness. Over the recent decade, the awareness of sleep's import has penetrated just about every professional sport domain. The purpose of the review was to identify and synthesize the literature published within the past 5 years (2018-2022) that relates to sleep and performance in professional athletes. Literature related to nonprofessional, high-level athletes (e.g., collegiate; Olympic) was omitted as well as those associated with non-traditional professional sports (e.g., eSports).

Recent Findings

Results from 38 articles were incorporated into this review, which covered (1) the sleep's role in the training, physical injury prevention and recovery, competitive performance, and mental health of professional athletes, (2) common sleep problems and disorders in professional athletes, and (3) the impact of unique challenges from training, travel, competition, and other factors on sleep health. Additionally, we provide an orientation to utilized strategies and interventions to assist with sleep health in professional athletes, as well as conclude with a commentary on critical steps forward.

Summary

Sleep plays a critically important role in the training, recovery, performance, and overall wellness of professional athletes. Professional athletes are vulnerable to a variety of sleep-related problems and disorders, due to unique factors related to training, travel, and competition, among other factors. Improved, standardized research methodology and partnerships between professional athletes, coaches, teams, and organizations and researchers are necessary to advance the knowledge of sleep and performance in professional athletes, including identifying sport-specific differences and variation across individual characteristics, as well as developing individualizable, dynamic, and appropriate interventions for improving sleep health among professional athletes.

The Athlete's Paradox: Adaptable Depression

We proposed that an athlete's depressive symptoms may be different from the general population in etiology if considered from the context of a depressive disorder. By shifting focus from a limited notion of symptoms onto a comprehensive model of depression, the full scope of the phenomenon becomes clearer. This paper investigated the relationship between neurotransmitters and allostatic load to explain the incidence of depression among elite athletes. This literature review extensively analyzed exercise-induced neurohormonal imbalance resulting in depressive states among athletes. The research revealed that 5-HTTLPR polymorphism, brain-derived neurotrophic factor (BDNF), extensive psychological demands, social stigma, and overtraining syndrome (OTS) may all contribute to a unique version of depression. The research revealed that the biological standards of athletes differ from those of non-athletes, to the point that the new model may be useful, thereby introducing the new term "Adaptable Depression (AD)" to the literature. This framework suggests a new direction for future research to precisely measure the neurotransmitter-related brain changes that result in "Adaptable Depression" in athletes and to establish a better understanding of the depressive tipping point.

Lockdown Duration and Training Intensity Affect Sleep Behavior in an International Sample of 1,454 Elite Athletes

Objective: To investigate the effect of 1) lockdown duration and 2) training intensity on sleep quality and insomnia symptoms in elite athletes.

Methods: 1,454 elite athletes (24.1 ± 6.7 years; 42% female; 41% individual sports) from 40 countries answered a retrospective, cross-sectional, web-based questionnaire relating to their behavioral habits pre- and during- COVID-19 lockdown, including: 1) Pittsburgh sleep quality index (PSQI); 2) Insomnia severity index (ISI); bespoke questions about 3) napping; and 4) training behaviors. The association between dependent (PSQI and ISI) and independent variables (sleep, napping and training behaviors) was determined with multiple regression and is reported as semi-partial correlation coefficient squared (in percentage).

Results: 15% of the sample spent < 1 month, 27% spent 1–2 months and 58% spent > 2 months in lockdown. 29% self-reported maintaining the same training intensity during-lockdown whilst 71% reduced training intensity. PSQI (4.1 ± 2.4 to 5.8 ± 3.1; mean difference (MD): 1.7; 95% confidence interval of the difference (95% CI): 1.6–1.9) and ISI (5.1 ± 4.7 to 7.7 ± 6.4; MD: 2.6; 95% CI: 2.3–2.9) scores were higher during-compared to pre-lockdown, associated (all p < 0.001) with longer sleep onset latency (PSQI: 28%; ISI: 23%), later bedtime (PSQI: 13%; ISI: 14%) and later preferred time of day to train (PSQI: 9%; ISI: 5%) during-lockdown. Those who reduced training intensity during-lockdown showed higher PSQI (p < 0.001; MD: 1.25; 95% CI: 0.87–1.63) and ISI (p < 0.001; MD: 2.5; 95% CI: 1.72–3.27) scores compared to those who maintained training intensity. Although PSQI score was not affected by the lockdown duration, ISI score was higher in athletes who spent > 2 months confined compared to those who spent < 1 month (p < 0.001; MD: 1.28; 95% CI: 0.26–2.3).

Conclusion: Reducing training intensity during the COVID-19-induced lockdown was associated with lower sleep quality and higher insomnia severity in elite athletes. Lockdown duration had further disrupting effects on elite athletes’ sleep behavior. These findings could be of relevance in future lockdown or lockdown-like situations (e.g., prolonged illness, injury, and quarantine after international travel).

Sleep and Mental Health Issues in Current and Former Athletes: A Mini Review

Sleep and mental health are important aspects of human health that work concurrently. However, sleep and mental health disorders are often overlooked and undiagnosed in sport due to the negative stigma associated with them. Evidence suggests that athletes are disproportionately affected by mental health issues and sleep problems. Internal and external pressures contribute to psychological distress. Variable competition times, travel and stress are detrimental to sleep quality. Retirement from sport can deteriorate sleep and psychological wellbeing, particularly for those who retired involuntarily and identify strongly with their athletic role. When untreated, these issues can manifest into a range of clinical disorders. This is concerning, not only for compromised athletic performance, but for general health and wellbeing beyond sport. Previous research has focussed on sleep and health independently among currently competing, or former, athletes. To date, no research has comprehensively assessed and compared sleep complaints and mental health issues between these two cohorts. Moreover, research has failed to obtain data across a variety of different competition levels, sports, and genders, leaving the current scope of the literature narrow. Comorbid conditions (e.g., concussion history, obesity), ex-college athletes, and mental health has been the focus of existing literature post-retirement. Future research would benefit from employing both quantitative and qualitative methodologies to comprehensively assess the prevalence and severity of sleep and mental health disorders across current and retired athletes. Research outcomes would inform education strategies, safeguarding athletes from these issues by reducing negative stigmas associated with help-seeking in sport and ultimately increase self-guided treatment.

Potential Prevalence, Pattern, and Risk Factors of Insomnia Symptoms in Adolescent Elite Athletes

Abstract. Sleep disorders are an important issue among elite athletes. In this study, we used data from two studies to examine the prevalence, pattern, and risk factors of insomnia symptoms among adolescent elite athletes. We assessed insomnia symptoms using the Insomnia Severity Index. The prevalence of moderate-to-severe insomnia varied between 5 – 11 %, with 36 – 54 % of the athletes reporting subthreshold insomnia and 61 – 73 % perceiving at least one insomnia symptom. Female participants were more prone to report insomnia symptoms. Dysfunctional sleep-related cognitions were the most critical risk factor. We identified three groups with distinct insomnia symptom patterns via latent class analyses: 43 % of the participants were characterized as healthy sleepers, 45 % reported medium sleep without heightened concerned about sleep problems, and 12 % reported poor sleep and were substantially worried/distressed about their impaired sleep. More information is needed on how dysfunctional sleep-related cognitions can be influenced to improve athletes’ sleep and to foster their well-being.

The Relationships of Sleep Duration and Inconsistency With the Athletic Performance of Collegiate Soft Tennis Players

We evaluated the relationships of daily sleep duration and inconsistency with soft tennis competitive performance among 15 healthy collegiate soft tennis players (13 male, 2 female, mean age = 19.7 ± 0.8 years, height = 170.8 ± 7.3 cm, weight = 60.3 ± 5.6 kg, soft tennis experience = 8.7 ± 2.0 years). Sleep duration and inconsistency were determined by a 50-day sleep diary, which recorded sleep and wake times of sleep. Soft tennis athletic performance was evaluated by a service and baseline stroke accuracy test and the spider run test. Mean sleep duration was 7.4 ± 1.7 h. No correlation was found between long-term mean sleep duration and athletic performance. But inconsistency in sleep duration (SD of sleep duration) was inversely correlated with service score after controlling for soft tennis experience and sex (r = -0.56, p = 0.046). There was no significant relationship between sleep inconsistency and other athletic performance. This result indicates that reducing the instability of sleep duration (i.e., sleep regular hours) in the long-term may have a positive effect on soft tennis players' service performance. Although participants' current mean sleep duration (7.4 h) was not as sufficient as the recommendation in sleep extension experiments (9-10 h), it revealed the importance for athletes to maintain regular sleep in daily life.

Effects of sleep self-monitoring via app on subjective sleep markers in student athletes

As sleep problems are highly prevalent among university students and competitive athletes, and the application of commercial sleep technologies may be either useful or harmful, this study investigated the effects of a 2-week sleep self-monitoring on the sleep of physically active university students (n = 98, 21 ± 1.7 years). Two intervention groups used a free sleep app (Sleep Score; SleepScore Labs™, Carlsbad, CA, USA: n = 20 or Sleep Cycle; Sleep Cycle AB, Gothenburg, Sweden: n = 24) while answering online sleep diaries. They used the app analysis function in week 1 and the 'smart alarm' additionally in week 2. As controls, one group answered the online sleep diary without intervention (n = 21) and another the pre-post questionnaires only (n = 33). Facets of subjective sleep behaviour and the role of bedtime procrastination were analysed. Multilevel models did not show significant interactions, indicating intervention effects equal for both app groups. Sleep Cycle users showed trends toward negative changes in sleep behaviour, while the online sleep diary group showed more, tendentially positive, developments. Bedtime procrastination was a significant predictor of several variables of sleep behaviour and quality. The results indicate neither benefits nor negative effects of app-based sleep self-tracking. Thus, student athletes do not seem to be as susceptible to non-validated sleep technologies as expected. However, bedtime procrastination was correlated with poor sleep quality and should be addressed in sleep intervention programmes.

Evening Whey Protein Intake, Rich in Tryptophan, and Sleep in Elite Male Australian Rules Football Players on Training and Nontraining Days

OBJECTIVES

To investigate the effect of evening whey protein supplementation, rich in tryptophan, on sleep in elite male Australian Rules Football players.

DESIGN

Double-blinded, counterbalanced, randomized, cross-over study.

METHODS

Sleep was assessed using wrist activity monitors and sleep diaries in 15 elite male Australian Football League players on two training and nontraining days following evening consumption of an isocaloric whey protein supplement or placebo in preseason. A 5-day preintervention period was implemented to determine habitual dietary intake and baseline sleep measures. These habitual data were used to inform the daily dietary intake and timing of ingestion of the evening whey protein supplement or placebo on the intervention days. The whey protein supplement or placebo was consumed 3 hr prior to habitual bedtime.

RESULTS

Separate one-way repeated-measures analyses of covariance revealed no differences between the whey protein supplement and the placebo on sleep duration, sleep onset latency, sleep efficiency, or wake after sleep onset on either training or nontraining days.

CONCLUSIONS

Evening whey protein supplementation, rich in tryptophan, does not improve acute sleep duration or quality in elite male Australian Football League players. However, elite athletes may be able to ingest a high protein/energy intake close to bedtime without impairing sleep, which is important for athlete recovery. Future research should investigate the effect of evening protein intake, high in tryptophan, on sleep duration and quality, including sleep staging during periods of restricted sleep and in poor-sleeping athletes.

Sleep, health, and well-being in elite athletes from different sports, before, during, and after international competition

Objectives: Limited research has been conducted on sleep problems in elite athletes at international competition, and how this relates to their general health and well-being. Methods: Sixty-five elite international athletes (37 males, 28 females, 21.8 ± 2.1 years) from different sports completed validated sleep (Athlete Sleep Screening Questionnaire), health (Subjective Health Complaints Inventory) and well-being (Sports Profile of Mood States) questionnaires; 1 month pre-competition, at the end of international competition, and 1 month post-competition. Results: Twenty-three percent of the elite athletes were identified as having a moderate or severe clinically significant sleep problem during competition, with 82% reporting less than 8 h of sleep per night. Athletes with a moderate or severe clinically significant sleep problem during competition had significantly greater general health complaints (p = 0.002), mood disturbance (p = 0.001) and poorer sleep hygiene (p = 0.002). Swimmers had more sleep difficulty pre and during competition compared to athletics and soccer (p = 0.009). Conclusion: Sleep disturbance during international competition is common and associated with poorer health and lower mood. Swimmers may be more at risk of sleep difficulty pre and during competition compared to those competing in athletics and soccer. Sleep services may be required to support elite athletes at international competition.

High sleep quality can increase the performance of CrossFit® athletes in highly technical- and cognitive-demanding categories

BACKGROUND

In current sports science, the important role of sleep quality for health and peak performance is well acknowledged. More precisely, it is evident that the negative combination of stressful factors, low resources, and bad sleep habits causes short-term performance losses as well as long-term health consequences. As the maximization of human performance consisting of multiple fixed workloads is the main motivation in CrossFit® (CF), the aim of the present study was to investigate the influence of high sleep quality on performance in CrossFit® athletes (CFA) and the different training categories that are addressed in CF.

METHODS

In total, 149 CFA (81 females, 68 males; 32.01 ± 7.49 years old, 2.56 ± 1.77 active years in CF) filled in the online survey comprising the Pittsburgh Sleep Quality Index (PSQI) and CF performance-specific questions.

RESULTS

It was found that CFA with high sleep quality reported higher values in all performance-related outcomes. Especially in Hero-/Girl-Workouts (χ2 = (1, n = 83) = 9.92, p = .002, φ = 0.37) and Gymnastics (χ2 = (1, n = 129) = 8.74, p = .003, φ = 0.28), performance differed significantly between good and poor sleeping CFA. Since those CF categories are highly technical- and cognitive-demanding high sleep quality seems to play a fundamental role in complex motor skill learning and performance development.

CONCLUSIONS

These results reveal for the first time the positive effects of high sleep quality for performance in CFA and could be used as basis for future studies. Future research should also develop and empirically test suitable interventions to foster high sleep quality in CFA.

TRIAL REGISTRATION

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of University of the Federal Armed Forces Munich, Germany (06/04/2018).

On the usability of digital sleep interventions in sports

High sleep quality is highly related to better health and peak performance. Nowadays, multiple applications and platforms are available to track activity, to monitor heart rate, or to evaluate sleep quality. While activity tracking and heart rate monitoring are widely used, little is known about the potentials of digital tools to optimize sleep quality. Especially among athletes, who often suffer from reduced sleep quality because of full schedules and high competition performance pressure, interventions to maximize performance by optimizing recovery and sleep quality seem to be promising. In the present paper, we give an overview on existing research focusing on the potentials of digital interventions to enhance sleep quality among athletes. In particular, mindfulness-based digital interventions seem to be promising as they evidently foster high sleep quality and related health and performance patterns. Further, athletes can time- and cost-effectively integrate them into their daily routines. Future research is needed to empirically test the usability of digital features and suitable interventions to optimize sleep.

When Much Is Too Much-Compared to Light Exercisers, Heavy Exercisers Report More Mental Health Issues and Stress, but Less Sleep Complaints

BACKGROUND

Physical inactivity has become a global somatic and mental health issue. To counterbalance, promoting regular physical activity appears plausible, above all among adults, where physical inactivity is particularly high. However, some, but sparse, research also indicates that excessive exercising might be associated with unfavorable mental health dimensions. Here, we tested the hypothesis that excessive exercising was associated with more mental health issues. To this end, we assessed mental health issues, stress, mental toughness, and sleep disturbances among heavy and light adult exercisers.

METHODS

A total of 200 adults (mean age: 35 years; 62% females) took part in the study. Of those, 100 were heavy exercisers (18-22 h/week), and 100 were light exercisers (1-6 h/week). Participants completed questionnaires covering sociodemographic information, mental health issues, perceived stress, mental toughness, and sleep disturbances.

RESULTS

Compared with light exercisers, heavy exercisers reported higher mental health issues, more stress, but also higher mental toughness scores and less sleep disturbances. Higher age, lower mental toughness scores, heavy exerciser-status, and more sleep disturbances predicted higher mental health complaints.

CONCLUSIONS

Compared with light exercising, heavy exercising might be associated with more mental health issues. As such, it appears that the association between exercise frequency, intensity, and duration and psychological well-being might be related to an optimum point, but not to a maximum point. In a similar vein, heavily exercising athletes, their coaches, parents, and representatives of sports associations should get sensitized to possible adverse psychological effects of excessive physical activity patterns.

The effects of evening high-intensity exercise on sleep in healthy adults: A systematic review and meta-analysis

Moderate-intensity exercise is generally recommended for improving sleep, whereas, high-intensity exercise (HIE) prior to bedtime is often discouraged. We conducted a systematic review and meta-analysis to determine if acute or regular (chronic) HIE performed before bedtime disrupts nighttime sleep of healthy adult, good sleepers compared with a no-exercise control. Six databases (PubMed, EMBASE, Scopus, Web of Science, CENTRAL, and PsycINFO) were searched from inception to 31st May, 2021. Studies were experimental trials published in English language, objectively (polysomnography, actigraphy) and/or subjectively assessed sleep after evening HIE in sedentary and physically fit, good sleepers (aged 18-50 y old). The revised Cochrane risk of bias tool for randomized trials was used to assess risk of bias in the included studies. The random-effects model was used for the meta-analyses. We included 15 acute evening HIE studies in the meta-analysis with a total of 194 participants. Acute evening HIE ending 0.5-4 h before bedtime decreased rapid eye movement sleep (-2.34%; p = 0.002) compared with a no-exercise control. No other significant sleep changes occurred. A regular evening HIE did not disrupt nighttime sleep. Overall, acute evening HIE performed 2-4 h before bedtime does not disrupt nighttime sleep of healthy, young and middle-aged adults. PROSPERO, protocol registration number: CRD42020218299.

Sleep-Wake Behavior in Elite Athletes: A Mixed-Method Approach

Purpose

Using a mixed-method approach, this investigation aimed to examine athletes’ sleeping patterns along with the socio-physiological acute and chronic stressors affecting their sleep.

Methods

Fifteen elite athletes (M_age = 21.7 years; SD = 2.2) were monitored daily during a typical in-season training period (20 ± 1 days) and completed individual semi-structured interviews. Their sleep was analyzed using actigraphy and sleep diaries. A content analysis method was used to assess qualitative interviews.

Results

Three factors influencing sleep emerged from the qualitative interview analyses, i.e., sleep environment, training and study requirements. Half the athletes (8/15) complained about their sleeping environment with noise and/or bedroom temperature and/or mattress quality and/or light exposure presented as an issue. “Complainers” notably exhibited impaired (p < 0.05) sleep efficiency and wake after sleep onset compared with “non-complainers.” Daily training load showed correlations (p < 0.05) with wake after sleep onset and sleep onset latency. “Student athletes” exhibited later bedtime and earlier wake-up time compared with “non-student athletes,” leading to a reduced total sleep time (6:50 ± 1:13 vs. 7:06 ± 0:53; p < 0.05; d = 0.20).

Conclusion

An individualized assessment of sleep using actigraphy and interviews, with consideration to various socio-physiological factors, is recommended. Individualized sleep interventions with consideration to physiological (e.g., training load), behavioral (e.g., screen use) and environmental factors (e.g., room temperature, noise, mattress), can then be provided to each athlete.

Contemporary Variables that Impact Sleep and Development in Female Adolescent Swimmers and Gymnasts

The effects of sleep on elite athletes in late adolescence and early adulthood have been well documented in a myriad of sports. However, there is underrepresentation of pre-pubertal and young female adolescent athlete research between the ages of 11-17, and specifically female gymnast and swimmers. Neglecting to understand how high energy demand at a young age relates to sleep and restoration may lead to developmental ramifications for this group, as they display physiological dysfunctions like delayed puberty, amenorrhea and are at risk for the female athlete triad or components of the triad. This review aims to summarize the contemporary variables of blue light emitting screens, social media, and caffeine on quality and quantity of sleep in young athletes while identifying gaps in the literature on how these factors impact the target group of young female swimmers and gymnasts.  The implications of this work include sleep hygiene recommendations for increasing duration and quality of sleep, as well as future research with respect to electronic device usage, social media participation, caffeine consumption, and sport engagement in female early adolescent athletes.

Causes and Consequences of Interindividual Response Variability: A Call to Apply a More Rigorous Research Design in Acute Exercise-Cognition Studies

The different responses of humans to an apparently equivalent stimulus are called interindividual response variability. This phenomenon has gained more and more attention in research in recent years. The research field of exercise-cognition has also taken up this topic, as shown by a growing number of studies published in the past decade. In this perspective article, we aim to prompt the progress of this research field by (i) discussing the causes and consequences of interindividual variability, (ii) critically examining published studies that have investigated interindividual variability of neurocognitive outcome parameters in response to acute physical exercises, and (iii) providing recommendations for future studies, based on our critical examination. The provided recommendations, which advocate for a more rigorous study design, are intended to help researchers in the field to design studies allowing them to draw robust conclusions. This, in turn, is very likely to foster the development of this research field and the practical application of the findings.

How does sleep help recovery from exercise-induced muscle injuries?

OBJECTIVES

Athletes and military personnel may experience sleep disturbances due to conditions of training and competitions or military missions/field operations. The risk of muscle injuries is greater for them when sleep duration decreases, and training load increases simultaneously, which can be exacerbated by fatigue. Accumulating evidence demonstrates that sleep extension improved performance, pain sensitivity and GH/IGF-I anabolic responses, which may be beneficial in accelerating recovery from muscle injuries.

DESIGN & METHODS

This narrative review describes the importance of sleep for the recovery/prevention of exercise-induced muscle injuries and provides perspectives on the transferability of currently available scientific evidence to the field.

RESULTS

The first part presents the role of sleep and its interaction with the circadian system for the regulation of hormonal and immune responses, and provides information on sleep in athletes and soldiers and its relationship to injury risk. The second part is an overview of muscle injuries in sport and presents the different phases of muscle regeneration and repair, i.e. degeneration, inflammation, regeneration, remodeling and maturation. Part three provides information on the deleterious effects of sleep deprivation on muscle tissue and biological responses, and on the benefits of sleep interventions. Sleep extension could potentially help and/or prevent recovery from exercise-induced muscle-injuries through increasing local IGF-I and controlling local inflammation.

CONCLUSIONS

Although the science of sleep applied to sport is still an emerging field, the current scientific literature shows many potential physiological pathways between sleep and exercise-related muscle injuries. More direct studies are needed to establish clear guidelines for medical personnel and coaches.

Complete sleep evaluation of top professional cross-country mountain bikers athletes

BACKGROUND

Despite growing interest in athletes' sleep, few studies have focused on professional athletes, especially in individual sports. Moreover, limited investigations included female athletes. This study aims to evaluate sleep chronotype, as well as objective and subjective sleep characteristics in male and female professional cross-country mountain bikers.

METHODS

Thirteen athletes (7 males and 6 females) of the French national team took part in this study. The Chronotype was assessed by the Horne and Östberg Morningness-Eveningness Questionnaire and sleep by actigraphy for one month, by ambulatory polysomnography (PSG) for one night and by the Pittsburgh Sleep Quality Index.

RESULTS

Most athletes (77 %) are classified as moderately morning type and a minority of athletes (23 %) are intermediate type. Athletes sleep on average 8 hours per night and during the night recorded by PSG, N3 and REM sleep stages represented 21.2 ± 3.4 % and 20.9 ± 3.1 % of the total sleep time, respectively. These good sleep parameters were confirmed by subjective data with 77 % good sleepers. Except the poorer subjective sleep quality in female athletes (5.7 ± 1.6) compared to male athletes (2.6 ± 1.7, p < 0.05), no significant sex difference was found for all characteristics evaluated.

CONCLUSIONS

The professional status of these athletes and the organization of mountain bike calendar may explain their good sleep characteristics.

Monitoring Individual Sleep and Nocturnal Heart Rate Variability Indices: The Impact of Training and Match Schedule and Load in High-Level Female Soccer Players

PURPOSE

To describe individual sleep habits and nocturnal heart rate variability (HRV) responses, and to explore intra-individual associations of workload with sleep and nocturnal HRV indices in high-level female soccer players throughout a 2-week competitive period.

MATERIALS AND METHODS

The study followed a descriptive, observational design. Thirty-four high-level female soccer players (aged 20.6 ± 2.3 years) wore wrist actigraph units and heart rate (HR) monitors during night-sleep to record objective sleep and HRV data throughout 14 days [six evening-time training sessions (ET), six rest-days (RD), and two match-days (MD)]. During each ET and MD, exercise HR (HRexe), %HRpeak, training impulse (TRIMP), session rating of perceived exertion (s-RPE) and perceived ratings of wellbeing were monitored.

RESULTS

After ET, a higher number of players (17-22) slept less than 7 h/night, in contrast to the remaining days (i.e., MD and RD), but only 1-6 players had a sleep efficiency < 75%. The coefficient of variation (CV) for sleep duration and sleep efficiency ranged between 9-22% and 2-11%, respectively. A small negative within-subject correlation was found between TRIMP and sleep duration [r = -0.25 (-0.36; -0.12); P < 0.001] and sleep efficiency [r = -0.20 (-0.32; -0.08); P = 0.004]. A moderate and small negative within-subject correlation was found between s-RPE and sleep duration [r = -0.43 (-0.53; -0.32); P < 0.001] and sleep efficiency [r = -0.17 (-0.30; -0.05); P = 0.02]. Nocturnal HRV for the time-domain analyses ranged from 4.1 (3.9; 4.3) to 4.4 (4.1; 4.6) ln[ms], and for the frequency-domain analyses ranged from 6.3 (5.9; 6.7) to 7.5 (7.1; 7.9) ln[ms2]. CV for time-domain HRV ranged from 3 to 23%, and from 4 to 46% for the frequency-domain. Higher CV fluctuations in time- and frequency-domain HRV were particularly observed in four players.

CONCLUSION

Overall, this study highlights the individual variability of sleep and nocturnal HRV indices, indicating that sleep duration may be affected by training and match schedules and workloads. Training and matches workload were not associated with nocturnal HRV in high-level female soccer players.

Evidence That Sleep Is an Indicator of Overtraining during the Competition Phase of Adolescent Sprinters

Although sleep disturbance is a common complaint in overtrained athletes, the role of sleep in the overtraining process is not clear. This study aimed (i) to compare sleep efficiency/quantity at the start of a competition phase in elite adolescent sprinters who adapted to prior training with that in those who maladapt and (ii) to examine the influence of prior training, fatigue, and sleep on performance through a moderated mediation model. Fatigue (via Profile of Mood State) and internal training load (via session rating of perceived exertion and duration of training as volume) were measured in 20 sprinters (mean age: 15.9 ± 1.7 years) across 4 mesocycles (baseline (T1); preparatory (T2); precompetitive (T3); and competitive (T4) phases), over 26 weeks. Performances were assessed during the competitive period (T3, T4), while sleep was monitored (via actigraphy) for a week during T4. It was inferred that sprinters who had increasingly greater fatigue and concomitant decrements in performance (35%) were maladapted to training and the remaining sprinters who improved fatigue and performance (65%) were adapted to training. Sleep efficiency (91 ± 3% vs. 82 ± 3%, p < 0.001) and quantity (425 ± 33 min vs. 394 ± 20 min, p < 0.001) at the start of T4 were significantly greater in sprinters who adapted. Moreover, higher prior training volume (mean of T1 to T3 training volume) was associated with lower sleep efficiency at the start of T4 (R² = 0.55, p < 0.001) which was associated with poorer performance (R² = 0.82, p < 0.001). Fatigue moderated the indirect effect of prior training volume on performance through its moderation of the effect of sleep efficiency on performance (R² = 0.89, p < 0.001). Impaired sleep as a result of greater prior training volume may be related to performance decrements through fatigue. Athletes should improve sleep during periods of higher training volume to reduce fatigue for better adaptation to training.

Chronotypes in elite Japanese athletes: assessed by the reduced Morningness-Eveningness and Munich ChronoType Questionnaires

This study aimed to test in elite athletes the correlation between the chronotype determined by the reduced version of the Morningness-Eveningness Questionnaire (rMEQ) and that determined by the Munich ChronoType Questionnaire (MCTQ). In total, 351 elite athletes filled out the rMEQ questionnaire, 59 athletes filled out the MCTQ questionnaire, and 39 athletes filled out both questionnaires. The rMEQ score and the corrected midpoint of sleep from the MCTQ correlated weakly (|r| = 0.360, p < .05); however, some cases were mismatched. The MSFsc also weakly correlated with age (|r| = 0.374, p < .05), while rMEQ did not. Our results may suggest that the choice of questionnaire to determine chronotype should depend on the purpose and the type of sport athlete.

The Relationships Between Training Load, Type of Sport, and Sleep Among High-Level Adolescent Athletes

PURPOSE

To evaluate the effects of sporting activities, training loads, and athletes' characteristics on sleep among high-level adolescent athletes, in a controlled training and academic environment.

METHODS

A total of 128 high-level adolescent athletes (age = 15.2 [2.0] y), across 9 different sports, completed common sleep questionnaires and were monitored daily (7.3 [2.7] d) during a typical in-season training period. Sleep was analyzed using actigraphy and sleep diaries, whereas training load was evaluated using the session rating of perceived exertion, and muscle soreness and general fatigue were reported with the aid of visual analog scales. Separate linear mixed-effects models were fitted, including the athlete as a random effect and the following variables as fixed effects: the sport practiced (categorical predictor), daily training load, age, and sex. Different models were used to compare sleep variables among sports and to assess the influence of training load, age, and sex.

RESULTS

The mean total sleep time was 7.1 (0.7) hours. Swimmers presented increased sleep fragmentation, training loads, perceived muscle soreness, and general fatigue compared with athletes who engaged in other sports. Independent of any sport-specific effects, a higher daily training load induced an earlier bedtime and reduced total sleep time and perceived sleep quality, with higher sleep fragmentation. Moreover, female athletes experienced increased total sleep time and worse sleep quality in response to stress compared with those in males.

CONCLUSION

In a controlled training and academic environment, high-level adolescent athletes did not achieve the recommended sleep duration. Impaired sleep quality and quantity could be partially explained by increased training loads.

Urgent wake up call for the National Basketball Association

NONE

Frequent air travel and the condensed game schedule typical of a National Basketball Association (NBA) team during the season, often results in accompanying sleep disturbances related to sleep length, sleep quality, and sleep timing (with highly harmful impacts on health, both physical and mental). These issues are not only problematic for NBA players, but also the coaches, training staff, and management support. In this narrative review, we summarize the detrimental effects that this travel and game schedule could have on NBA team members' sleep, as well as their physical and mental health. Multiple peer-reviewed articles address the role of sleep in athletic performance and health; however, to date, the literature focused on sleep-related issues that are unique to the NBA schedule is scarce. Firstly, this review addresses the impact of the NBA schedule, outlining the number of games and the travel involved (number of flights, the timing of flights, timings of arrival at destination and hotel); we also outline a typical daily NBA travel schedule, providing the reader a glimpse of what this encompasses. Secondly, we provide a brief overview of sleep science and discuss specific applications related to the NBA. Finally, we provide comment on the unique current situation of the NBA "bubble". Based on this review, there appears to be considerable scope for further investigation of the acute and chronic effects of sleep disturbances concerning the NBA travel and game schedule. Sleep science recommendations need to inform practice, target sleep interventions, and personalized protocols designed to enhance sleep health that can be incorporated at the organizational level.

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).