The impact of training schedules on the sleep and fatigue of elite athletes

The training times of athletes could play a role in clinical sleep problems due to their associations with sleep difficulty scores

OBJECTIVES

Sleep is a key component of athletic recovery, yet training times could influence the sleep of athletes. The aim of the current study was to compare sleep difficulties in athletes across different training time groups (early morning, daytime, late evening, early morning plus late evening) and to investigate whether training time can predict sleep difficulties.

METHODS

Athletes from various sports who performed at a national-level (n = 273) answered the Athlete Sleep Screening Questionnaire (ASSQ) along with several other questionnaires related to demographics, exercise training, and mental health. From the ASSQ, a Sleep Difficulty Score (SDS) was calculated. Transformed SDS (tSDS) was compared across different training time categories using multiple one-way ANOVAs. A stepwise regression was then used to predict tSDS from various sleep-related factors.

RESULTS

SDSs ranged from none (31%), mild (38%), moderate (22%), and severe (9%). However, the one-way ANOVAs revealed training earlier or later vs. training daytime shifted the tSDS in a negative direction, a trend toward increased sleep difficulty. In particular, athletes training in the late evening (>20:00 or >21:00) had a significantly higher tSDS when compared to daytime training (p = .03 and p < .01, respectively). The regression model (p < .001) explained 27% of variance in the tSDS using depression score, age, training time, and chronotype score.

CONCLUSION

Among a heterogeneous sample of national-level athletes, 31% displayed moderate to severe SDSs regardless of their training time. However, when athletes trained outside daytime hours there was a tendency for the prevalence of sleep difficulties to increase.

Habitual Nocturnal Sleep, Napping Behavior, and Recovery Following Training and Competition in Elite Water Polo: Sex-Related Effects

PURPOSE

To examine nocturnal sleep patterns, napping behaviors, and subjective wellness responses of elite water polo players within an in-season week and to identify whether sleeping patterns differ between men and women.

METHODS

Sleep characteristics of 10 male and 17 female professional water polo players were objectively assessed during 1 week of the in-season period, including 5 training days, 1 match day, and 1 day of rest. Internal load (rating of perceived exertion × duration of training or match) was assessed 30 minutes posttraining or postmatch, and the total quality of recovery was recorded every morning. A series of multilevel models were used to analyze the data.

RESULTS

Time in bed and wake-up time were earlier on both training (P < .001) and rest days (P < .001) than on the day of the match. Internal workload did not predict any of the players' sleeping patterns. Midday naps predicted less time in bed (P = .03) and likely less sleep time (P = .08). The total quality of recovery was predicted only by the total sleep time (P < .01). Women exhibited higher sleep efficiency (P < .001), less waking after sleep onset (P = .01), and a lower number of awakenings (P = .02) than men.

CONCLUSIONS

The current results indicate that the nocturnal sleep patterns of elite water polo players are not associated with internal load and that women display better nocturnal sleep quality compared with men. As long naps interfere with nocturnal sleep, and total nocturnal sleep time predicts total quality of recovery, we suggest that athletes follow hygiene sleep strategies to facilitate adequate nocturnal sleep and next-day recovery.

Current Aspects of Selected Factors to Modulate Brain Health and Sports Performance in Athletes

This review offers a comprehensive evaluation of current aspects related to nutritional strategies, brain modulation, and muscle recovery, focusing on their applications and the underlying mechanisms of physiological adaptation for promoting a healthy brain, not only in athletes but also for recreationally active and inactive individuals. We propose that applying the rule, among others, of good sleep, regular exercise, and a properly balanced diet, defined as "SPARKS", will have a beneficial effect on the function and regeneration processes of the gut-brain-muscle axis. However, adopting the formula, among others, of poor sleep, stress, overtraining, and dysbiosis, defined as "SMOULDER", will have a detrimental impact on the function of this axis and consequently on human health as well as on athletes. Understanding these dynamics is crucial for optimizing brain health and cognitive function. This review highlights the significance of these factors for overall well-being, suggesting that adopting the "SPARKS" approach may benefit not only athletes but also older adults and individuals with health conditions.

Health status of university football athletes through multidimensional screening

OBJECTIVE

Adequate conditioning results from various physical, environmental, and psychological factors in sports activities. In this study, we aimed to clarify the concurrence and relevance of injuries, psychological problems, and sleep disturbance in university football (soccer) players. Biomechanical characteristics and risk factors for those injuries were also investigated.

METHODS

Overall, 1,084 university football players participated in this cross-sectional study using a web-based health history questionnaire. Assessments were made via the presence of injuries for physical problems, the Athlete Psychological Strain Questionnaire (APSQ) for psychological problems, and the Athlete Sleep Screening Questionnaire (ASSQ) for sleep difficulties.

RESULTS

There were 200 (19%) athletes with injuries; 413 (38%) of these were APSQ-positive, 246 (23%) were ASSQ-positive, and 468 (43%) athletes had no physical, psychological, or sleep problems. APSQ-positive respondents were significantly more likely to have a current injury than those who were APSQ-negative. No significant difference was observed between the number of ASSQ-positive and -negative respondents and current injuries. Those with sleep problems were significantly more likely to have psychological problems.

CONCLUSION

More than 60% of university football players had one or more physical, psychological, or sleep problems. Players may have co-occurring problems, and it is important to address them for these athletes to reach peak performance.

Sleep duration is associated with overnight changes in perceived fatigue in elite women soccer players

The current study aimed to describe sleep habits and analyze the associations between sleep indices, changes in perceived fatigue (Δ Fatigue) and external training load measures in women soccer players during a 7-day training camp. Sixteen elite women soccer players (age: 25.4 ± 3.6 years; mean ± SD) from the Portuguese Women's National Team participated in the study. Sleep indices (sleep duration and efficiency) were measured using a wrist-worn accelerometer. External training loads measures were measured using GPS devices. Players also reported perceived fatigue using a Likert scale (1 - very, very low to 7 - very, very high) before and immediately after sleep. Players' within-subject coefficient of variation for sleep duration was 5.6%, and 4.6% for sleep efficiency. Individually, 8 players (50%) slept less than 7 h per night throughout the training camp, and the same number of athletes had a sleep efficiency lower than 85%. Similar values (p<0.05) were measured for sleep duration and efficiency between training and match days. A moderate negative within-subjects correlation was found between Δ Fatigue and sleep duration (adjusted for pre-sleep fatigue) [r = -0.32; 95% Confidence Interval (-0.51 - -0.08); p = 0.04]. These findings reinforce the importance of sleep in the recovery process of elite women soccer players, showing that more sleep may help to attenuate fatigue.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Sleep and Ultramarathon: Exploring Patterns, Strategies, and Repercussions of 1,154 Mountain Ultramarathons Finishers

BACKGROUND

Sleep and physical performance are strongly related and mutually influence each other. Athletes, particularly in disciplines like offshore sailing and ultra-endurance sports, often suffer from sleep deprivation due to factors like irregular training times, travel, and the extended duration of events like 100-mile mountain races. Despite growing interest in sleep's role in sports science, few studies have specifically investigated the sleep patterns of ultramarathon runners. This study aimed to investigate sleep patterns and sleep management strategies in ultramarathons, and the repercussions of sleep deprivation during and after races.

METHODS

This cross-sectional study using e-survey was conducted on 1154 runners from two ultramarathons (a 165 km race with 9,576 m positive elevation; 2018 finish time [23:18:48-66:04:00], and a 111 km race with 6,433 m elevation; [15:34:56 - 41:54:16]).

RESULTS

The results revealed that 58% of the runners reported implementing sleep management strategies before or during the race. Most runners began the race with some level of sleep debt (-50 min a week before the race). During the races, 77% of runners slept, with the cumulative sleep duration varying based on race duration and the number of nights spent on the race (76 min at 165 km and 27 min at 111 km). Short naps lasting less than 30 min were the most popular strategy. The prevalence of symptoms attributed to sleep deprivation during the race was high (80%), with reported falls and hallucinations. After the race, runners reported recovering a normal state of wakefulness relatively quickly (within two days); 22% believed that sleep deprivation during the race increased the risk of accidents in everyday life.

CONCLUSION

This study provides valuable insights into sleep patterns and strategies in ultramarathon running and emphasizes the importance of adequate sleep management for performance and post-race recovery.

Sleep architecture of elite soccer players surrounding match days as measured by WHOOP straps

This study aimed to quantify and compare sleep architecture before and after home and away matches in elite soccer players from the English Premier League. Across two seasons, 6 male players (age 28 ± 5 y; body mass 85.1 ± 9.5 kg; height 1.86 ± 0.09 m) wore WHOOP straps to monitor sleep across 13 matches that kicked off before 17:00 h. For each, sleep was recorded the night before (MD-1), after (MD) and following the match (MD +1). Across these 3 days total sleep time (TST), sleep efficiency (SE), sleep disturbances, wake time, light sleep, deep sleep, REM sleep, sleep and wake onsets, alongside external load, were compared. TST was reduced after MD versus MD +1 (392.9 ± 76.4 vs 459.1 ± 66.7 min, p = 0.003) but no differences existed in any other sleep variables between days (p > 0.05). TST did not differ after home (386.9 ± 75.7 min) vs. away matches (401.0 ± 78.3 min) (p = 0.475), nor did other sleep variables (p > 0.05). GPS-derived external load peaked on MD (p < 0.05). In conclusion, despite reduced TST on MD, sleep architecture was unaffected after matches played before 17:00 h, suggesting sleep quality was not significantly compromised.

Sleep Quality in Team USA Olympic and Paralympic Athletes

Adequate sleep is crucial for elite athletes' recovery, performance readiness, and immune response. Establishing reference ranges for elite athletes enables appropriate contextualization for designing and targeting sleep interventions.

PURPOSE

To establish sleep-quality reference ranges for Olympic and Paralympic cohorts using the Pittsburgh Sleep Quality Index (PSQI) and explore differences based on sex and sport types.

METHODS

Team USA athletes (men = 805, women = 798) completed the PSQI as part of a health-history questionnaire. Descriptive statistics were used to create reference ranges and linear models, and χ2 test of independence determined differences in PSQI global and component scores between sex, games, season, and participation.

RESULTS

Six hundred thirty-two (39.43%) athletes reported poor sleep (PSQIGlobal ≥ 5). Men displayed later bedtimes (P = .006), better global PSQI scores, shorter sleep latency, less sleep disturbance, and less use of sleep medication than women (all P < .001). Winter Games participants had later bedtime (P = .036) and sleep offset time (P = .028) compared with Summer Games athletes. Team-sport athletes woke earlier than individual-sport athletes (P < .001). Individual-sport athletes were more likely to have low (P = .005) and mild (P = .045) risk for reduced sleep duration than team-sport athletes.

CONCLUSION

These data provide PSQI-specific reference ranges to identify groups at greatest risk for poor sleep, who may benefit most from targeted sleep interventions.

Early morning sport scheduling is associated with poorer subjective sleep characteristics in British student-athletes

This study presents the sleep characteristics of British student-athletes and examines the relationships between sport scheduling and time demands on sleep outcomes. Student-athletes (n = 157, 51% male) completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and the Sleep Hygiene Index (SHI). Self-reported sleep characteristics on weekdays and weekends, weekly frequencies of early morning and late evening sport sessions, and academic-related and sport-related time demands were also collected. Questionnaires revealed a high prevalence of undesired sleep characteristics including poor sleep quality (global PSQI >5 in 49.0%) and low sleep durations on weekdays (25% reporting <7 h). Paired t-tests revealed significant differences in bedtime, waketime, sleep duration, and sleep onset latency between weekdays and weekends (all p < 0.01). Hierarchical regression analyses indicated that early morning sport frequency was a significant predictor of PSQI (β = 0.30) and SHI (β = 0.24) global scores, weekday waketimes (β = -0.17), and weekday sleep durations (β = -0.25; all p < 0.05) in models adjusted for participant characteristics. Late evening sport frequency, and academic-related and sport-related time demands, were not significant predictors of any sleep outcome. Adjusting sport scheduling to avoid early start times could provide a means to improve sleep outcomes and may improve sporting performance and academic attainment.

Poor quality of sleep and musculoskeletal pains among highly trained and elite athletes in Senegal

BACKGROUND

Previous studies reported that poor sleep quality (PSQ) was associated with musculoskeletal pains (MSP) and poor physical performance in athletes.

OBJECTIVE

The current study aimed at determining PSQ and its associations with MSP in some sub-Saharan athletes.

METHODS

A cross sectional study was conducted among 205 highly trained and 115 elite athletes (aged: 25 ± 2 years, Body mass index: 22.8 ± 0.9 kg/m2) in Dakar, Senegal, during a competitive season in a variety of sport disciplines including athletics, basketball, football, rugby, wrestling, tennis. Quality of sleep and MSP were assessed using the French version Pittsburgh Sleep Quality Index (PSQI) and French version of Nordic questionnaire respectively. Pain on body joints during a week was defined as seven-day MSP (MSP-7d) and PSQ for a PSQI > 5.

RESULTS

27.8% (95%CI: 23.2-32.9) of the overall sample suffered PSQ, with 33.7% (95%CI: 24.7-44.0) in basketball and 24.7% (95%CI: 16.9-34.6) in football. According to athletic status and gender, PSQ was more prevalent among highly trained (66.3; 95%CI: 55.9-75.3) and men (69.7%; 95%CI: 59.5-78.7). Among athletes with PSQ 43.8% (95%CI: 33.9-54.2) suffered MSP-7d, with 36.6%; highly trained (95%CI: 23.7-42.9) and 28.1% female. Considering body region, hips/thigh (14.6%; 95% CI: 8.74-23.4) and upper back (13.5%; 95%CI: 7.88 -21, 1) were more affected. Basketball players were more affected from MSP (MSP-7d = 38.5%; 95%CI: 24. 9-54.1) on high on wrists/hands (MSP-7d = 44.4%; 95%CI: 18.9 -73.3; P = 0.04). Based on athletic status, MSP-7d were higher on highly trained necks (100%; 95%CI: 56.1-100; p = 0.04). PSQ was associated with basketball (OR: 3.062, 95%CI: 1.130-8.300, p = 0.02) compared to Athletic. PSQ and MSP-7d were associated on Wrist/hands (OR: 3.352, 95%CI: 1.235-9.099, p = 0.01), and at the upper back (OR: 5.820, 95%CI: 2.096-16.161, p = 0.0007).

CONCLUSION

These results indicate that PSQ is considerable among Senegalese athletes and is associated with MSP during a week. Hence, we recommend to look for strategies optimizing good quality of sleep in order to reduce pains, to improve health.

The impact of strategic napping on peak expiratory flow and respiratory function in young elite athletes

Respiratory health is a critical determinant of athletic performance, and the utilization of restorative strategies, such as strategic napping, may offer a competitive edge to athletes. This study investigates the effects of nap duration on the respiratory function of young elite athletes who have achieved top rankings national competitions. Participants engage in three test sessions with varying nap durations: no nap (N0), a 25-minute nap (N25), and a 45-minute nap (N45), with a minimum 72-hour interval between sessions. Respiratory parameters including Forced Vital Capacity (FVC), Forced Expiratory Volume in one second (FEV1), FEV1/FVC ratio, Peak Expiratory Flow rate (PEF), Forced Expiratory Flow at 25-75% of FVC (FEF25-75%), and Forced Expiratory Time (FET) are assessed. Results reveal a significant enhancement in PEF values following a 45-minute nap (N45) compared to the no-nap control (N0) [F1 - 11=7.356, p =.004, ηp2 = 0.401, (95% CI for difference: -1.56 to - 0.056)], indicating a potential positive influence of napping on maximum expiratory flow rate and, consequently, athletes' respiratory performance. While no significant changes are observed in other respiratory parameters across different nap durations, these findings underscore the potential benefits of strategic napping in optimizing respiratory health in young elite athletes.

Circadian rhythm in sportspersons and athletic performance: A mini review

Circadian rhythms in the physiological and behavioral processes of humans play a crucial role in the quality of living and also in the magnitude of success and failure in various endeavors including competitive sports. The rhythmic activities of the body and performance in sportspersons do have a massive impact on their every cutthroat competition. It is essential to schedule sports activities and training of players according to their circadian typology and time of peak performance for improved performance and achievement. In this review, the focus is on circadian rhythms and diurnal variations in peak athletic performance in sportspersons. Accuracy and temporal variability in peak performance in an individual could be attributed to various factors, namely chronotype, time of the day, body temperature, jetlag, hormones, and prior light exposure. Circadian rhythm of mood, alertness, T-core, and ultimately athletic performance is not only affected by sleep but also by circadian variations in hormones, such as cortisol, testosterone, and melatonin. There are, however, a few reports that are not consistent with the conclusions drawn in this review. Nevertheless, circadian rhythm and performance among sportspersons and athletes are important areas of research. This review might be useful to the managers and policymakers associated with competitive sports and athletic events.

Delaying early morning workouts to protect sleep in two-a-day athletes

Two-a-day training is common for endurance athletes with training sessions typically beginning at 6 a.m. and 3 p.m. or 4 p.m. However, the early morning workouts could contribute to significant sleep loss, especially for night owls. Chronic sleep loss over a season could result in impaired performance, as well as an increased risk of physical and mental illness. It is hypothesized that shifting the early morning workout to later in the day could have beneficial effects for these athletes. A number of obstacles could make this hypothesis difficulty to test and implement. However, such a change could have dramatic benefits for some athletes.

Daytime naps improve afternoon power and perceptual measures in elite rugby union athletes-a randomized cross-over trial

Daytime naps are used by elite athletes in both training and match-day settings. Currently, there are limited interventional studies on the efficacy of napping on physical performance in elite team-sport athletes. Therefore, the objective was to investigate the effect of a daytime nap (<1 hour) on afternoon performance of peak power, reaction time, self-reported wellness, and aerobic performance in professional rugby union athletes. A randomized cross-over design was carried out among 15 professional rugby union athletes. Athletes performed nap (NAP) and no nap (CON) conditions on two occasions, separated by 1 week. Baseline testing of reaction time, self-reported wellness, and a 6-second peak power test on a cycle ergometer were completed in the morning, followed by 2 × 45-minute training sessions, after which athletes completed the NAP or CON condition at 1200 hours. Following the nap period, baseline measures were retested in addition to a 30-minute fixed-intensity interval cycle and a 4-minute maximal effort cycling test. A significant group × time interaction was determined for 6-second peak power output (+157.6 W, p < 0.01, d = 1.53), perceived fatigue (-0.2 AU, p = 0.01, d = 0.37), and muscle soreness (-0.1 AU, p = 0.04, d = 0.75) in favor of the NAP condition. A significantly lower perceived exertion rating (-1.2 AU, p < 0.01, d = 1.72) was recorded for the fixed-intensity session in favor of NAP. This study highlights that utilizing daytime naps between training sessions on the same day improved afternoon peak power and lowered perceptions of fatigue, soreness, and exertion during afternoon training in professional rugby union athletes.

Sleep patterns before and after competition: A real-world examination of elite athletes

Good sleep before and after competitions is crucial to cognitive, physiological performance and recovery. Yet, elite athletes face a unique set of challenges when acquiring good sleep before and after competitions, and indeed commonly report sleep problems when it matters most. This study examined the sleep of elite athletes before and after competition compared to before and after free days. A total of 1808 unique nights of actigraphy (n = 1495) and sleep diary (n = 1335) data from elite Australian Football League and National Rugby League male athletes (N = 85, M-age = 24.4 ± 3.6) were collected and analysed using multi-level mixed models. On nights before competitions, athletes advanced sleep timings (p < .001, d = 0.63) and increased total sleep time (p < .001, d = 0.65) compared to nights before free days. On nights after competitions, athletes delayed sleep timings (p < .001, d = 1.64), reduced total sleep time (p < .001, d = 1.28), and had significantly worse quality sleep (p < .001, d = 0.71) compared to nights after free days. Sleep was especially worse following night competitions. While elite athletes and organisations may be implementing effective sleep strategies to optimise sleep the night before competitions, strategies to improve sleep after competitions may be lacking. We discuss potential factors contributing to this asymmetry and propose areas for research moving forward.

The Impact of Multisession Sleep-Hygiene Strategies on Sleep Parameters in Elite Swimmers

PURPOSE

Short sleep duration and poor sleep quality are common in swimmers. Sleep-hygiene strategies demonstrated beneficial effects on several sleep parameters. The present study assessed the impact of a multisession sleep-hygiene training course on sleep in elite swimmers.

METHODS

Twenty-eight elite swimmers (17 [2] y) participated. The sleep-hygiene strategy consisted of 3 interventions. Sleep was measured by actigraphy for 7 days before the beginning of the intervention (baseline), after the first collective intervention (postintervention), after the second collective intervention (postintervention 2), and, finally, after the individual intervention (postintervention 3). The Epworth Sleepiness Scale (ESS) was completed concurrently. Swimmers were classified into 2 groups: nonsomnolent (baseline ESS score ≤ 10, n = 13) and somnolent (baseline ESS score ≥ 11, n = 15).

RESULTS

All swimmers had a total sleep time of <8 hours per night. Sixty percent of swimmers were moderately morning type. Later bedtime, less time in bed, and total sleep time were observed in the somnolent group compared with the nonsomnolent group at baseline. An interaction between training course and group factors was observed for bedtime, with a significant advance in bedtime between baseline, postintervention 2, and postintervention 3 for the somnolent group.

CONCLUSIONS

The present study confirms the importance of implementing sleep-hygiene strategies, particularly in athletes with an ESS score ≥11. A conjunction of individual and collective measures (eg, earlier bedtime, napping, and delaying morning training session) could favor the total sleep time achieved.

Is implementing a post-lunch nap beneficial on evening performance, following two nights partial sleep restriction?

We have investigated the effects that partial-sleep-restriction (PSR0, 4-h sleep retiring at 02:30 and waking at 06:30 h for two consecutive nights) have on 07:30 and 17:00 h cognitive and submaximal weightlifting; and whether this performance improves at 17:00 h following a 13:00 h powernap (0, 30 or 60-min). Fifteen resistance-trained males participated in this study. Prior to the experimental protocol, one repetition max (1RM) bench press and back squat, normative habitual sleep and food intake were recorded. Participants were familiarised with the testing protocol, then completed three experimental conditions: (i) PSR with no nap (PSR0); (ii) PSR with a 30-min nap (PSR30) and (iii) PSR with a 60-min nap (PSR60). Conditions were separated by 7 days with trial order counterbalanced. Intra-aural temperature, Profile of Mood Scores, word-colour interference, alertness and tiredness values were measured at 07:30, 11:00, 14:00, 17:00 h on the day of exercise protocol. Following final temperature measurements at 07:30 h and 17:00 h, participants completed a 5-min active warm-up before performing three repetitions of left and right-hand grip strength, followed by three repetitions at each incremental load (40, 60 and 80% of 1RM) for bench press and back squat, with a 5-min recovery between each repetition. A linear encoder was attached perpendicular to the bar used for the exercises. Average power (AP), average velocity (AV), peak velocity (PV), displacement (D) and time-to-peak velocity (tPV) were measured (MuscleLab software) during the concentric phase of the movements. Data were analysed using general linear models with repeated measures. The main findings were that implementing a nap at 13:00 h had no effect on measures of strength (grip, bench press or back squat). There was a main effect for time of day with greatest performance at 17:00 h for measures of strength. In addition to a significant effect for "load" on the bar for bench press and back squat where AP, AV, PV, D values were greatest at 40% (P < 0.05) and decreased with increased load, whereas tPV and RPE values increased with load; despite this no interaction of "load and condition" were present. A post lunch nap of 30- and 60-minute durations improved mood state, with feelings of alertness, vigour and happiness highest at 17:00 h, in contrast to confusion, tiredness and fatigue (P < 0.05), which were greater in the morning (07:30 h). The word-colour interference test, used as an indicator of cognitive function, reported significant main effect for condition, with the highest total test score in PSR60 condition (P = 0.015). In summary, unlike strength measures the implementation of a 30 or 60-minute nap improved cognitive function when in a partially sleep restricted state, compared to no nap.

Napping behaviour, daytime sleepiness, and arousal in high performance athletes and non-athlete controls

Napping offers a strategy to manage sleep, aid recovery and enhance performance in elite sport. However, relatively little research attention has focussed on the natural history of athlete napping or tested the widely held assumption that athlete napping is mainly a consequence of degraded night-time sleep. Within a sample of 158 team (n = 76) and individual (n = 80) sport athletes, and 82 non-athlete controls, we analysed napping behaviour in relation to sleep quality, daytime sleepiness, and pre-sleep somatic and cognitive arousal. There was no significant association between athlete/non-athlete status and the prevalence, frequency, or duration of naps. Comparisons of athlete nappers and non-nappers found no significant differences in sleep quantity or quality. While nap propensity was significantly related to higher daytime sleepiness, this influence was moderated or augmented by levels of pre-sleep cognitive arousal. For some nappers, those with higher levels of arousal may need to be sleepier than those with lower levels of arousal in order to successfully initiate daytime sleep. Approximately 50% of athletes did not nap. If the benefits of athlete napping are to be fully exploited, the needs of this substantial group for whom napping may be problematic should be recognised and addressed.

Narrative review: The role of circadian rhythm on sports performance, hormonal regulation, immune system function, and injury prevention in athletes

Objectives

This study was a narrative review of the importance of circadian rhythm (CR), describes the underlying mechanisms of CR in sports performance, emphasizes the reciprocal link between CR, endocrine homeostasis and sex differences, and the unique role of the circadian clock in immune system function and coordination.

Method

As a narrative review study, a comprehensive search was conducted in PubMed, Scopus, and Web of Science (core collection) databases using the keywords "circadian rhythm", "sports performance", "hormonal regulation", "immune system", and "injury prevention". Inclusion criteria were studies published in English and peer-reviewed journals until July 2023. Studies that examined the role of CR in sports performance, hormonal status, immune system function, and injury prevention in athletes were selected for review.

Results

CR is followed by almost all physiological and biochemical activities in the human body. In humans, the superchiasmatic nucleus controls many daily biorhythms under solar time, including the sleep-wake cycle. A body of literature indicates that the peak performance of essential indicators of sports performance is primarily in the afternoon hours, and the evening of actions occurs roughly at the peak of core body temperature. Recent studies have demonstrated that the time of day that exercise is performed affects the achievement of good physical performance. This review also shows various biomarkers of cellular damage in weariness and the underlying mechanisms of diurnal fluctuations. According to the clock, CR can be synchronized with photonic and non-photonic stimuli (i.e., temperature, physical activity, and food intake), and feeding patterns and diet changes can affect CR and redox markers. It also emphasizes the reciprocal links between CR and endocrine homeostasis, the specific role of the circadian clock in coordinating immune system function, and the relationship between circadian clocks and sex differences.

Conclusion

The interaction between insufficient sleep and time of day on performance has been established in this study because it is crucial to balance training, recovery, and sleep duration to attain optimal sports performance.

Sleep and Sport Performance

SUMMARY

Elite athletes and coaches believe sleep is the most important recovery strategy and widely consider it critical to optimal performance. Despite this perceived importance, there are numerous circumstances that can reduce sleep quantity and quality in athletic populations. Because of the effects of sleep loss on various physical, neurophysiological, and cognitive parameters, such perturbations can have consequences for performance and recovery outcomes. Although peer-reviewed literature examining the interaction between sleep, performance, and recovery in athletes is increasing, understanding of these issues remains equivocal. Perhaps most pertinently, the effect of sleep on sport performance does not align with a one-size-fits-all approach and rather depends on numerous factors such as type of sport, scheduling, time of the season, and the intraindividual requirements for sleep. The relationship between brain plasticity and memory, which in turn can influence learning processes and long-term memory consolidation, suggests that sleep may play an important role in learning new skills and tactics for both elite and developing athletes. The aim of this special issue review was to analyze the evidence of sleep loss on sport performance and recovery, with a specific focus on elite athletes. An assessment of these sleep-compromising situations that elite athletes may face during a typical season and practical considerations for alleviating these issues is also provided to further the understanding for medical professionals, scientists, and applied sporting practitioners alike.

The Effects of Napping on Wakefulness and Endurance Performance in Athletes: A Randomized Crossover Study

Background: Athletes often experience poor sleep quality due to stress, altitude exposure, travel across different time zones, and pre-competition nervousness. Coaches use daytime naps to counteract the negative effects of fragmented nighttime sleep. Napping before competitions has also been used to enhance performance in athletes without sleep problems, with mixed results in previous studies, particularly for endurance performance. Thus, we investigated the effects of napping after partial sleep deprivation (PSD) on endurance performance and wakefulness in athletes. Methods: We recruited 12 healthy and trained participants (seven female and five male) for a randomized crossover study design. The participants underwent two test sessions: a five-hour night of sleep without a nap (noNap) and a five-hour night of sleep with a 30-min nap opportunity (Nap30). Participants recorded their sleep-wake rhythm one week before and during the study using the Consensus Sleep Diary-Core and the Morningness-Eveningness Questionnaire to examine their circadian rhythm type. We quantified PSD and the nap with pupillography (pupil unrest index, PUI), a subjective level of sleepiness questionnaire (Karolinska Sleepiness Scale, KSS), and polysomnography. After each night, participants performed a maximal cycling ergometry test to determine time to exhaustion (TTE) and maximal oxygen uptake (VO _2max). Results: Participants had an average sleep duration of 7.2 ± 0.7 h and were identified as moderately morning types (n = 5), neither type (n = 5), and moderately evening types (n = 2). There was a significant difference in both sleepiness parameters between the two conditions, with the PUI (p = 0.015) and KSS (p ≤ 0.01) significantly decreased at 5 h and nap compared with only 5 h of sleep. The PUI (p ≤ 0.01) and KSS (p ≤ 0.01) decreased significantly from before to after the nap. However, there was no significant difference in physical exercise test results between the conditions for TTE (p = 0.367) or VO _2max (p = 0.308). Conclusions: Our results suggest that napping after light PSD does not significantly influence endurance performance. We conclude that aerobic performance is a multidimensional construct, and napping after PSD may not enhance it. However, napping is an effective method to increase wakefulness and vigilance, which can be beneficial for sports competitions.

The Impact of Kiwifruit Consumption on the Sleep and Recovery of Elite Athletes

BACKGROUND

Poor sleep and resultant under-recovery can negatively impact training adaptations, increase the risk of injury and reduce subsequent performance. Due to the 'food first' approach adopted by many athletes, there is scope for investigation of 'functional food' based interventions (i.e., kiwifruit contains melatonin which plays a role in circadian rhythm regulation) designed to promote athlete recovery and/or enhance sleep quality and quantity.

METHODS

Following the baseline assessment (Week 1) all subjects began the intervention (Weeks 2-5). During the 4-week intervention, participants were asked to consume 2 medium-sized green kiwifruit (Actinidia Deliciosa) an hour before bed. Participants completed a questionnaire battery at baseline and post-intervention, and a daily sleep dairy for the duration of the study.

RESULTS

The results demonstrated a positive impact of kiwifruit consumption on key aspects of sleep and recovery in elite athletes. From baseline to post-intervention, there were clinically significant improvements in sleep quality (i.e., improved PSQI global scores and sleep quality component scores) and improvements in recovery stress balance (reduced general stress and sports stress scales). Moreover, the intervention improved sleep as evidenced by significant increases in total sleep time and sleep efficiency % and significant reductions in number of awakenings and wake after sleep onset.

CONCLUSION

The findings broadly suggested that kiwifruit does impact positively on sleep and recovery in elite athletes.

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.

Which Dark Personality Traits Could Predict Insomnia? The Mediated Effects of Perceived Stress and Ethical Judgments

This study aimed to investigate the relationship between dark personality traits and insomnia by considering the mediated effects of perceived stress and ethical judgments. This descriptive and correlational study was conducted with 464 university athlete students from individual and team sports. Dark Triad Scale (DTS), Insomnia Severity Index (ISI), Perceived Stress Scale (PSS), and Moral Content Judgment in Sport Questionnaire (MCJSQ) were used. Significant correlation coefficients were observed between the dark personality traits and other variables. Regression analysis showed that psychopathy (about 19%) and ethical judgments (about 16%) could predict insomnia. It was shown that among dark personality traits, psychopathy along with ethical judgments could predict insomnia.

Intensified Olympic Preparation: Sleep and Training-Related Hormonal and Immune Responses in Water Polo

PURPOSE

To investigate whether sleeping activity, hormonal responses, and wellness are altered in elite water polo players during their preparation toward the Tokyo Olympics.

METHODS

Eight elite-level water polo players participated in 3 consecutive training phases: (1) before the commencement of a residential-based conditioning camp (PRE-CAMP; 3 d), (2) residential-based conditioning camp (5 d), and (3) a congested period of training and competition (POST-CAMP; 8 d). Nocturnal sleep was monitored for 14 consecutive days in PRE-CAMP (2 d), CAMP (5 d), and POST-CAMP (7 d). Postawakening salivary cortisol, immunoglobulin-A, and subjective wellness were measured during PRE-CAMP, CAMP, and POST-CAMP, and internal training/match load (ITL) was calculated daily. The averaged values for dependent variables were compared among training phases and analyzed using linear mixed models.

RESULTS

At CAMP compared with PRE-CAMP, ITL was higher (P < .01), and sleep onset and offset were earlier (P < .01). At this period, sleep interruptions and salivary cortisol were higher (P < .01, d = 1.6, d = 1.9, respectively), and subjective wellness was worsened (P < .01, d = 1.3). At POST-CAMP, the reduction of workload was followed by increased sleep efficiency, reduced sleep interruptions, and moderately affected salivary cortisol, yet overall wellness remained unaltered. In POST-CAMP, 2 of the players demonstrated severe symptoms of illness.

CONCLUSIONS

At the highest level of the sport and prior to the Olympics, large increments in workload during a training camp induced meaningful sleep interruptions and salivary cortisol increases, both of which were reversed at POST-CAMP. We suggest that the increased workload alongside the inadequate recovery affects sleep patterns and may increase the risk of infection.

Case Report: Effect of low energy availability and training load on sleep in a male combat sport athlete

Purpose

The aim of this case report was to describe the sleep responses in a male combat sport athlete, who was engaging in both chronic (CWL) and acute (AWL) weight loss practices in order to reduce body mass for a national competition.

Methods

During the first seven weeks of training (Phases 1 and 2), the athlete adhered to a daily energy intake (EI) equating to their resting metabolic rate (1700 kcal·day^-1) followed by a reduction in EI (915-300 kcal·day^-1) in the 5 days before weighing in (Phase 3). Nocturnal sleep was monitored throughout the 8-week training period using wristwatch actigraphy and frequent measurements of body mass/composition, daily exercise energy expenditure and training load (TL) were taken.

Results

The athlete was in a state of low energy availability (LEA) during the entire training period. There was a very large decrease in LEA status during phase 3 compared with phases 1 and 2 (3 vs. 20 kcal·kgFFM·day^-1) and there was a small decrease in TL during phase 3 compared with phase 2 (410 vs. 523 AU). The athlete's sleep efficiency increased throughout the training period, but total sleep time displayed a small to moderate decrease in phase 3 compared with phases 1 and 2 (386 vs. 429 and 430 min). However, correlational analysis demonstrated trivial to small, non-significant relationships between sleep characteristics and the athlete's LEA status and TL.

Conclusion

These findings suggest that CWL and AWL practices that cause fluctuations in LEA and TL may be implemented without compromising the sleep of combat sport athletes.

Sleep in German female youth national ice hockey athletes

Background

Adequate sleep is of great importance in recovering from and preparing for training and competition.

Objective

This study aimed to investigate subjective sleep quality and daytime sleepiness of the German women’s junior national ice hockey team before and after a training camp immediately preceding the World Championship Division IA tournament.

Materials and methods

Twenty athletes (16.40 ± 0.68 years) completed German paper–pencil versions of the Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) before the training camp (T1, day 1) and on the final day of the training camp (T2, day 7).

Results

Paired t-tests indicated a significant decrease in PSQI global scores from pre- to post-training camp assessments (t = 2.33, p = 0.031, df = 19), with a medium effect size (d = 0.52). Regarding ESS global scores, results of the paired t-test indicated no significant pre- to post-training camp differences (t = 0.67, p = 0.510, df = 19) and the effect size was trivial (d = 0.15). Mean scores were reduced for both PSQI (T1 = 5.90 ± 2.36, T2 = 4.65 ± 2.18) and ESS (T1 = 9.00 ± 3.58, T2 = 8.60 ± 4.04) after the training camp. When analyzed according to the position of the players, no statistically significant intergroup differences were found.

Conclusion

This study underlines the need for monitoring and screening youth athletes’ sleep before special sports events in order to identify a potential need for intervention as early as possible, to prevent serious consequences for athletes’ performance capability and well-being.

Effect of sleep and fatigue on cardiovascular performance in young, healthy subjects

Physical performance could be negatively affected by sleep deficiency and fatigue. The present study assesses the role of sleep quality, fatigue and motivation on cardiovascular performance (VO₂peak, Wmax, and HRmax) in a sample of active young subjects. The current study is a cross-sectional design. Ninety-six university students (males 54.2%; 21.5 ± 2.9 yrs) completed an incremental exercise test on a bicycle ergometer. Sleep, fatigue, and motivation were assessed with the Pittsburgh Sleep Quality Index and two visual analogue scales, respectively. Differences in VO₂peak, Wmax, HRmax, self-perceived fatigue and motivation were compared between good and bad sleepers and sleep duration >/<7.5 h, while regression analysis defined the predictors of VO₂peak, Wmax, and HRmax_. In the male sample, good and bad sleepers' differences were significant only for self-perceived fatigue (p = 0.04). The female sample showed no statistically significant differences between good and bad sleepers and different sleep durations. In the male sample, linear regression analysis showed a significant inverse correlation between Wmax and the PSQI score (-0.4, p = 0.004). The stepwise regression model indicated that sleep (β = -0.3, p = 0.02) was a significant predictor of VO₂peak in males accounting for 20% of the variance, whereas physical performance seems more affected by fatigue (β = -0.4, p = 0.03) in females. In conclusion, chronic inadequate and self-reported sleep quality seems to be one of the factors compromising cardiovascular performance in males.

Sleep habits of high school student-athletes and nonathletes during a semester

STUDY OBJECTIVES

Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between US high school student-athletes and nonathletes during a semester of school and competition.

METHODS

Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years, and 38 nonathletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8-14 consecutive days and nights, measuring total sleep time, sleep efficiency, bedtimes, wake times, and steps counted.

RESULTS

Student-athletes and nonathletes did not differ in total sleep time (440.4 ± 46.4 vs 438.1 ± 41.7 min, P = .82) and sleep efficiency (93.6 ± 2.3 vs 92.9 ± 2.3%, P = .20). Fitbit data revealed that 79% of student-athletes and 87% of nonathletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs 8,418 ± 2,489, P < .01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased total sleep time (P < .01). Earlier wake times were significantly correlated to increased steps per day (P < .01).

CONCLUSIONS

Participation in high school sports may not have a detrimental effect on a student's sleep habits. High school students are not meeting the recommended 8-10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health.

CITATION

Ungaro CT, De Chavez PJD. Sleep habits of high school student-athletes and nonathletes during a semester. J Clin Sleep Med. 2022;18(9):2189-2196.

Sleep behavior and training load in adolescent elite basketball players during COVID-19 pandemic development

The COVID-19 pandemic caused strong restrictions in organized sports. Specifically, adolescent athletes, whose daily routine is usually determined by school and practice schedules, experienced a sudden lifestyle change. The aim of this study was to examine sleep and training patterns of German elite adolescent basketball players (N = 115, 15.70 ± 1.22 years, female = 32.17%) during different phases of the 2021 lockdown. Sleep and training behavior were documented by a subjective monitoring over 10 days at three different lockdown phases in February, April, and June/July. A linear mixed model approach was conducted for the statistical analyses. Sleep duration and time in bed decreased between phase 1 (prohibition of organized sports) and phase 3 (normal training conditions), while sleep efficiency and quality increased. There was no change in training duration over time, but in training intensity, which peaked in the third phase. This study contains meaningful evidence to analyze the lockdown-related return to practice process. Athletes were able to maintain training duration with alternative protocols during pandemic conditions. Interestingly, athletes slept less under non-lockdown conditions, but reported a better sleep quality. It is recommended to consider athletes' individual preferences when planning training and recovery schedules.

Sleep and Athletic Performance: Impacts on Physical Performance, Mental Performance, Injury Risk and Recovery, and Mental Health: An Update

Sleep health is an important consideration for athletic performance. Athletes are at high risk of insufficient sleep duration, poor sleep quality, daytime sleepiness and fatigue, suboptimal sleep schedules, irregular sleep schedules, and sleep and circadian disorders. These issues likely have an impact on athletic performance via several domains. Sleep loss and/or poor sleep quality can impair muscular strength, speed, and other aspects of physical performance. Sleep issues can also increase risk of concussions and other injuries and impair recovery after injury. Cognitive performance is also impacted in several domains, including vigilance, learning and memory, decision making, and creativity.

The Impact of Sleep Inertia on Physical, Cognitive, and Subjective Performance Following a 1- or 2-Hour Afternoon Nap in Semiprofessional Athletes

PURPOSE

This study examined the impact of sleep inertia on physical, cognitive, and subjective performance immediately after a 1- or 2-hour afternoon nap opportunity.

METHODS

Twelve well-trained male athletes completed 3 conditions in a randomized, counterbalanced order-9 hours in bed overnight without a nap opportunity the next day (9 + 0), 8 hours in bed overnight with a 1-hour nap opportunity the next day (8 + 1), and 7 hours in bed overnight with a 2-hour nap opportunity the next day (7 + 2). Nap opportunities ended at 4:00 PM. Sleep was assessed using polysomnography. Following each condition, participants completed four 30-minute test batteries beginning at 4:15, 4:45, 5:15, and 5:45 PM. Test batteries included a warm-up, self-ratings of readiness to perform, motivation to perform and expected performance, two 10-m sprints, 2 agility tests, a 90-second response-time task, and 5 minutes of seated rest.

RESULTS

Total sleep time was not different between conditions (P = .920). There was an effect of condition on readiness (P < .001), motivation (P = .001), and expected performance (P = .004)-all 3 were lower in the 8 + 1 and 7 + 2 conditions compared with the 9 + 0 condition. There was no effect of condition on response time (P = .958), sprint time (P = .204), or agility (P = .240), but a large effect size was observed for agility.

CONCLUSIONS

After waking from a nap opportunity, agility may be reduced, and athletes may feel sleepy and not ready or motivated to perform. Athletes should schedule sufficient time (∼1 h) after waking from a nap opportunity to avoid the effects of sleep inertia on performance.

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.

Sleep Quality and Well-Being in Junior Elite Athletes

Abstract. Little cross-sectional and even less longitudinal research has been conducted on sleep quality and well-being in junior elite athletes. This study analyzed the relationship between subjective sleep quality and both psychological and physiological well-being in junior elite athletes using both a cross-sectional ( N = 625 athletes; 49.9 % = female; M = 16.4 years, SD = 3.5 years) and a longitudinal approach ( n = 79; 44.3 % = female; M = 16.4 years, SD = 3.5 years) over a period of M = 1.66 years ( SD = 0.87 years). The results demonstrated that both samples showed that subjective sleep quality is correlated with well-being in junior elite athletes. In addition, the results showed that athletes of prepuberty age reported significantly better sleep quality than athletes during and after puberty, and that the relationship between sleep quality and psychological well-being is stronger in female junior athletes than in male athletes. This study emphasizes the need to ensure quality sleep in junior elite athletes.

Sleep Quality and Nutrient Intake in Japanese Female University Student-Athletes: A Cross-Sectional Study

Objectives: This study aimed to characterize the sleep quality and nutrient intake of Japanese female college athletes to provide specific conditioning support. Methods: A cross-sectional survey conducted between December 2019 and January 2020 during the regular training season in Niigata City, located at 139°02′ E longitude and 37°55′ N latitude. Overall, 120 female university student-athletes from eight clubs were selected. All sports were at the national level of competition. The Pittsburgh Sleep Quality Index (PSQI) assessed sleep quality. Nutrient intake was assessed using Excel nutrition software through the Food Intake Frequency Questionnaire. Results: The mean PSQI score was 4.5 ± 2.5, with 29% of participants having a PSQI score ≥ 5.5. The sleep duration was 6.8 ± 1.1 h, with 45% of participants sleeping <7 h. The energy intake was 1800 ± 419 kcal, with no correlation between PSQI score and nutrient intake. Conclusions: PSQI scores were higher compared with other studies, but many participants had shorter sleep duration and lower nutrient intake, these data suggest that there is the possibility of improving the habits of Japanese athletes by increasing the subjects’ amount of sleep time and food intake.

Der Zusammenhang zwischen Schlaf und Training bei jugendlichen Elite-Mannschaftsathleten in Österreich

Zusammenfassung. In dieser Studie wurde das Schlaf-Wach-Verhalten jugendlicher Elite-Mannschaftsathleten in Österreich im Zusammenhang mit deren Training untersucht. Dafür wurden 104 Athleten ( M=14.52 Jahre, SD=0.40), die jeweils Mitglied eines U15/U16 Teams im Fußball ( n=69) oder Eishockey ( n =35) waren, über mehrere Wochen (14 – 42 Tage) per Aktigraphie und Schlafprotokoll untersucht. Zusätzlich wurden der Chronotyp (D-MEQ) und die allgemeine Schlafqualität (PSQI) erhoben. Es wurde angenommen, dass die Schlafqualität vor Trainings- und Spieltagen geringer ist als vor Ruhetagen und dass sich eine geringe Schlafqualität negativ auf die Trainingsintensität auswirkt. Es konnte gezeigt werden, dass die Athleten an Ruhetagen eine längere Schlafdauer aufweisen. Es hat sich außerdem bestätigt, dass eine kürzere Schlafdauer und längere Einschlaflatenz einen negativen Einfluss auf die wahrgenommene Trainingsintensität am folgenden Tag haben. Ein stärkerer Einbezug der Schlafqualität bei Leistungsanalysen und in der Gestaltung von Trainingsplänen wird empfohlen.

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.

Prevalence and Risk Factors of Poor Sleep Quality in Collegiate Athletes during COVID-19 Pandemic: A Cross-Sectional Study

The COVID-19 pandemic has changed our lifestyle, sleep and physical activity habits. This study evaluated the prevalence of poor sleep quality, its disrupters, and the impact of the pandemic in collegiate athletes. We performed a cross-sectional study of collegiate athletes (N = 339, median age: 20 (IQR,19−21) years old, 48.5% female, 47% individual sports) who received a web-based questionnaire in April 2021. This survey included subject characteristics, chronotype, sleep disrupters, the changes due to the pandemic and sleep quality (Pittsburg Sleep Quality Index [PSQI]). A multivariate linear regression was performed to assess the relationship between sleep quality, gender, chronotype, sleep disrupters and the changes to training volume or sleep. Results showed a disrupted sleep quality in 63.7%. One in five students had a total sleep time under 6.5 h per night. Poor sleep quality was significantly correlated with nocturnal concerns related to the pandemic, evening chronotype, female gender, third year of study, caffeine consumption and lack of sleep routine (all p < 0.05). To conclude, poor sleep quality is common in collegiate athletes. Sleep disrupters remain prevalent in the lifestyle habits of this population and may have been exacerbated by changes related to the COVID-19 pandemic. Sleep hygiene should become a major aspect of sports education during the return to post-covid normality.

The Relationships Between Sport Specialization, Sleep, and Quality of Life in Female Youth Volleyball Athletes

BACKGROUND

Although sport specialization may be associated with stress and burnout among youth athletes, the relationship with quality of life (QOL) remains unknown. The purpose of this study was to evaluate the relationship between sport specialization, sleep, and QOL in female youth athletes.

HYPOTHESIS

Higher levels of specialization are associated with increased daytime sleepiness and worse QOL.

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 4.

METHODS

Female high school volleyball players completed preseason surveys to determine sport specialization (low, moderate, high), injury history, QOL, sleep duration, and daytime sleepiness. QOL and sleep variables were compared across specialization groups. Multivariable linear regression models were developed to evaluate the associations between sleepiness, QOL, specialization, grade in school, and injury history.

RESULTS

Of 1482 participants, 591 (40%), 436 (29%), and 455 (31%) were categorized as low, moderate, and high specialization, respectively. Highly specialized athletes demonstrated worse QOL (median 91.3 [interquartile range 86-96]) than low (92.4 [88-97], P = 0.05) and moderate (93.5 [88-99], P = 0.05) specialization groups and greater daytime sleepiness (11 [7-15]) than low (10 [6-14], P < 0.001) and moderate (10 [6-14], P < 0.001) specialization groups. In the multivariable model, QOL was negatively associated with prior injury occurrence (β = -1.1 ± 0.5, P = 0.02), but not grade in school (β = -0.08 ± 0.2, P = 0.71) or specialization (moderate: β = 0.08 ± 0.5, P = 0.88; high: β = -0.70 ± 0.5, P = 0.18). Daytime sleepiness increased with high specialization (β = 1.12 ± 0.3, P < 0.001) and grade (β = 0.76 ± 0.1, P < 0.001), but not prior injury (β = 0.51 ±0.3, P = 0.10).

CONCLUSION

Highly specialized female volleyball athletes demonstrate decreased QOL, perhaps because of higher rates of prior injury. Specialization is also associated with increased daytime sleepiness.

CLINICAL RELEVANCE

Sport participation patterns and injury may have implications for QOL in youth athletes.

Relationship between sleep quality, mood state, and performance of elite air-rifle shooters

Background

To evaluate the impact of pre-competition sleep quality on the mood and performance of elite air-rifle shooters.

Methods

Elite shooters who participated in an air-rifle shooting-competition from April 2019 to October 2019 were evaluated using actigraphy, including Total Sleep Time (TST), Sleep Efficiency (SE), Sleep Latency (SL), Wake-time after Sleep Onset (WASO). Sleep quality was assessed by Pittsburgh sleep quality index (PSQI) and Profile of Mood State (POMS). Mood state was assessed by Competitive State Anxiety Inventory-2.

Results

Study included 23 shooters, of them 13 male and 10 female with the mean age 23.11 ± 4.82 years. The average time to fall asleep was 20.6 ± 14.9 min, TST was 7.0 ± 0.8 h and SE was 85.9 ± 5.3%. Average sleep quality was 5.2 ± 2.2 and tended to decrease as the competition progressed. Pre-competition sleep time in female athletes was significantly higher compared to the competition day (P = 0.05). Pre-competition SL was significantly longer in women than in men (P = 0.021). During training and pre-competition, the tension, fatigue, depression, and emotional disturbance were significantly lower in athletes with good sleep quality. Athletes with good sleep quality had significantly more energy. The PSQI total score positively correlated with cognitive anxiety (r = 0.471, P < 0.01), and somatic anxiety (r = 0.585, P < 0.01), and negatively correlated with energy (− 0.504, P < 0.01) and self-confidence scores (r = − 0.523, P < 0.01).

Conclusion

Poor sleep quality negatively impacted the mood of athletes; however, sleep indices and competition performance of athletes during competitions were not significantly correlated.

Effects of Acute Sleep Loss on Physical Performance: A Systematic and Meta-Analytical Review

BACKGROUND

Sleep loss may influence subsequent physical performance. Quantifying the impact of sleep loss on physical performance is critical for individuals involved in athletic pursuits.

DESIGN

Systematic review and meta-analysis.

SEARCH AND INCLUSION

Studies were identified via the Web of Science, Scopus, and PsycINFO online databases. Investigations measuring exercise performance under 'control' (i.e., normal sleep, > 6 h in any 24 h period) and 'intervention' (i.e., sleep loss, ≤ 6 h sleep in any 24 h period) conditions were included. Performance tasks were classified into different exercise categories (anaerobic power, speed/power endurance, high-intensity interval exercise (HIIE), strength, endurance, strength-endurance, and skill). Multi-level random-effects meta-analyses and meta-regression analyses were conducted, including subgroup analyses to explore the influence of sleep-loss protocol (e.g., deprivation, restriction, early [delayed sleep onset] and late restriction [earlier than normal waking]), time of day the exercise task was performed (AM vs. PM) and body limb strength (upper vs. lower body).

RESULTS

Overall, 227 outcome measures (anaerobic power: n = 58; speed/power endurance: n = 32; HIIE: n = 27; strength: n = 66; endurance: n = 22; strength-endurance: n = 9; skill: n = 13) derived from 69 publications were included. Results indicated a negative impact of sleep loss on the percentage change (%_Δ) in exercise performance (n = 959 [89%] male; mean %_Δ =  - 7.56%, 95% CI - 11.9 to - 3.13, p = 0.001, I² = 98.1%). Effects were significant for all exercise categories. Subgroup analyses indicated that the pattern of sleep loss (i.e., deprivation, early and late restriction) preceding exercise is an important factor, with consistent negative effects only observed with deprivation and late-restriction protocols. A significant positive relationship was observed between time awake prior to the exercise task and %_Δ in performance for both deprivation and late-restriction protocols (~ 0.4% decrease for every hour awake prior to exercise). The negative effects of sleep loss on different exercise tasks performed in the PM were consistent, while tasks performed in the AM were largely unaffected.

CONCLUSIONS

Sleep loss appears to have a negative impact on exercise performance. If sleep loss is anticipated and unavoidable, individuals should avoid situations that lead to experiencing deprivation or late restriction, and prioritise morning exercise in an effort to maintain performance.

An Individualized Intervention Increases Sleep Duration in Professional Athletes

ABSTRACT

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

Predicting Youth Athlete Sleep Quality and the Development of a Translational Tool to Inform Practitioner Decision Making

BACKGROUND

Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes.

HYPOTHESIS

Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality.

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 3.

METHODS

A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set.

RESULTS

Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality.

CONCLUSION

The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits.

CLINICAL RELEVANCE

Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

How Do the Habitual Sleep Patterns of Elite Female Basketball and Soccer Athletes Compare With the General Population?

PURPOSE

To compare the habitual sleep of female basketball and soccer athletes to age- and sex-matched controls and to characterize the sleep of basketball and soccer athletes at different competition locations and on the days surrounding competition.

METHODS

Using an observational case-control design, 41 female participants were recruited to participate, consisting of 11 basketball athletes (mean [SD]: age = 24.1 [4.9] y), 10 soccer athletes (24.8 [6.4] y), and 20 nonathletic controls (24.2 [2.8] y). Sleep was monitored using actigraphy for four 7-day periods throughout the preseason and subsequent competition season. Generalized linear models were used to analyze the effect of group and competition situation (eg, Home or Away) on sleep.

RESULTS

During habitual conditions, basketball athletes had longer sleep durations (7.4 [1.5] h) than soccer athletes (7.0 [1.2] h, P < .001) and controls (7.3 [1.2] h, P = .002). During competition, basketball and soccer athletes had longer sleep durations following home (7.7 [1.7] and 7.2 ± 1.3 h) compared with away games (6.8 [1.8] and 7.0 [1.3] h). In addition, basketballers went to bed earlier (23:49 [01:25]) and woke earlier (07:22 [01:59]) following away games compared with soccer athletes (00:10 [01:45] and 08:13 [01:45]).

CONCLUSIONS

Basketballers had longer habitual sleep durations compared with soccer athletes and nonathletic controls. During competition, basketballers had earlier bed and wake times compared with soccer athletes following away games, highlighting the need for individualized sleep strategies.

Can the training regimen influence night time physical activity in racehorses?

Physical activity has been widely investigated in horses to elucidate locomotion characteristics and behavior. However, research in real environment of training stables is limited. The objective of this study was to evaluate the effect of training regimen on night time physical activity of racehorses. Physical activities of twenty animals were monitored during the night time using accelerometers. The animals were compared in terms of training regimen: horses subjected to training on continual days, and horses subjected to training on intermittent days; age and sex were also considered. The variables analyzed were: counts per minute, percentage of time in (sedentary, light, moderate and vigorous) physical activity. Statistical analysis was performed by the PROCGLM procedure (ANOVA) and the means were calculated by Tukey's test. The training regimen impacts the physical activity of thoroughbreds. The mean counts per minute showed a higher physical activity in the intermittent trained animals in relation with continuously trained animals. The continuously trained animals presented a greater percentage of time in sedentary physical activity than those intermittent trained. However, intermittently trained horses spent a larger percentage of time in moderate and vigorous physical activity, in comparison with continuously trained animals. In conclusion, racehorses subject to training on continual days have lower physical activity in the night time than those that train on intermittent days.

The Effect of Sleep Quality and Quantity on Athlete's Health and Perceived Training Quality

University athletes are unique because they not only have to cope with the normal psycho-physiological stress of training and playing sport, but they also need to accommodate the stress associated with their academic studies along with considerable stress from their social environment. The ability to manage and adapt to stress ultimately helps improve athletic performance, but when stress becomes too much for the athlete, it can result in maladaptation's including sleep disruption which is associated with performance loss, negative mood changes, and even injury or illness. This research aimed to determine if sleep quantity and quality were associated with maladaptation in university athletes. We examined subjective measures of sleep duration and sleep quality along with measures of mood state, energy levels, academic stress, training quality and quantity, and frequency of illness and injury in 82 young (18–23 years) elite athletes over a 1 year period in 2020. Results indicate sleep duration and quality decreased in the first few weeks of the academic year which coincided with increased training, academic and social stress. Regression analysis indicated increased levels of perceived mood (1.3, 1.1–1.5, Odds Ratio and 95% confidence limits), sleep quality (2.9, 2.5–3.3), energy levels (1.2, 1.0–1.4), training quality (1.3, 1.1–1.5), and improved academic stress (1.1, 1.0–1.3) were associated with ≥8 h sleep. Athletes that slept ≥8 h or had higher sleep quality levels were less likely to suffer injury/illness (0.8, 0.7–0.9, and 0.6, 0.5–0.7 for sleep duration and quality, respectively). In conclusion, university athletes who maintain good sleep habits (sleep duration ≥8 h/night and high sleep quality scores) are less likely to suffer problems associated with elevated stress levels. Educating athletes, coaches, and trainers of the signs and symptoms of excessive stress (including sleep deprivation) may help reduce maladaptation and improve athlete's outcomes.