Does self-perceived sleep reflect sleep estimated via activity monitors in professional rugby league athletes?

Sink or Swim? Sleep Patterns in Highly Trained Adolescent Swimmers during the In-Season Phase of Training

Objectives  The aim of this study was to investigate the sleeping patterns of highly-trained adolescent swimmers. Further objectives were to compare subjective and objective measures of sleep and to evaluate the relationship between electronic device use and sleep in this cohort. Methods  Fifteen national-level swimmers (age: 16.4 ± 1.0 years) wore wrist actigraphs while completing sleep diaries over a two-week training period. Types of nights were classified as early morning training (≤7am - EARLY), day training (>7am - DAY), and no training (REST). Information on electronic device use before bed and other sleep behaviors was collected via questionnaires. Results  Total sleep time (TST) was significantly ( p  < 0.05) shorter on nights preceding EARLY (5:53 ± 1:06 hour:min) compared with nights preceding DAY (7:40 ± 1:12 hour:min) and REST (7:59 ± 1:19 hour:min). Participants overestimated subjective TST by ∼54 minutes when compared with objectively measured TST ( p  < 0.05). Discussion  Adolescent athletes obtained a greater amount of sleep prior to later training the next day or before rest days, when compared with early morning swim training the next day. Adolescents also overestimated their sleep duration by ∼1 hour per night. There were no significant relationships between electronic device use at night and measured sleep indices.

Does a self-reported sleep duration reflect actigraphy reported sleep duration in female football players?

Sleep is often compromised in female athletes, and the monitoring of female athletes' sleep is an important preventative and educational tool. With self-reporting of sleep common practice for athletes as part of a daily wellness assessment, there is a need to understand whether sleep indices are being reported accurately, and thus whether self-report data are useful. This study aimed to compare the agreement between self-reported and actigraphy reported sleep duration in female football players, with the intention of informing best practice for athlete monitoring. Twenty-two female footballers (mean age 19.5 ± 1.3 years) provided a daily self-report across 7 days, whilst also wearing an actigraph across the same testing period. Agreement between the two measures was assessed using Bland-Altman limits of agreement, with acceptable limits of agreement defined as <30 minutes. Results showed evident disagreement between the two methods, with a mean bias of -0.54 (32 min, 95% CI -0.66 to -0.43) and a potential disagreement range of over 2 h (Lower 95% limits of agreement -1.49 to upper 95% limits of agreement 0.40). Coaches using self-reported sleep durations as a monitoring tool for female footballers should interpret the results with caution and be aware of the potential for inaccuracies in this measure. As such, where possible, coaches should consider other methods of sleep monitoring, rather than solely relying on a self-report, to ensure they are operating with optimal practice within situational constraints.

Sleep Assessment in Competitive Athletes: Development and Validation of French Versions of the Athens Insomnia Scale and the Athlete Sleep Behavior Questionnaire

Objective  The purpose of this study was to develop and validate French versions of two questionnaires assessing competitive athletes' sleep: the Athens Insomnia Scale (AIS-FR) and the Athlete Sleep Behavior Questionnaire (ASBQ-FR). Methods  Four complementary studies were carried out, with a total sample of 296 French competitive athletes from different sports and expertise levels. The studies aimed to develop preliminary versions of the AIS-FR and the ASBQ-FR (study 1), and then to examine their respective dimensionality and reliability (study 2), temporal stability (study 3), and concurrent validity (study 4). The dimensionality was established using confirmatory factor analysis. Similar and correlated psychological factor scales were used to examine the concurrent validity (the Insomnia Severity Index, the Pittsburgh Sleep Quality Index, the State-Trait Anxiety Inventory and the Positive and Negative Affect Schedule). Results  The AIS-FR consists of eight items with two subfactors: nocturnal symptoms and diurnal symptoms, assessed by a uniformized 4-point Likert-type scale. The ASBQ-FR is composed of 15 items with three subfactors, which differs from the original English version: behaviors affecting sleep, behaviors related to anxiety, and sleep disturbances. Due to the Covid context and curfews, three items of the original scale were excluded from the statistical analyses because non-applicable. Both scales presented satisfactory psychometric properties. Discussion  The AIS-FR and ASBQ-FR appear to be valid and reliable tools that can be used with competitive athletes for everyday training and research purposes. An ASBQ-FR version that includes the three excluded items should undergo validation testing once pandemic restrictions are eased.

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.

Subjective Measures of Workload and Sleep in Australian Army Recruits; Potential Utility as Monitoring Tools

INTRODUCTION

Subjective measures may offer practitioners a relatively simple method to monitor recruit responses to basic military training (BMT). Yet, a lack of agreement between subjective and objective measures may presents a problem to practitioners wishing to implement subjective monitoring strategies. This study therefore aims to examine associations between subjective and objective measures of workload and sleep in Australian Army recruits.

MATERIALS AND METHODS

Thirty recruits provided daily rating of perceived exertion (RPE) and differential RPE (d-RPE) for breathlessness and leg muscle exertion each evening. Daily internal workloads determined via heart rate monitors were expressed as Edwards training impulse (TRIMP) and average heart rate. External workloads were determined via global positioning system (PlayerLoadTM) and activity monitors (step count). Subjective sleep quality and duration was monitored in 29 different recruits via a customized questionnaire. Activity monitors assessed objective sleep measures. Linear mixed-models assessed associations between objective and subjective measures. Akaike Information Criterion assessed if the inclusion of d-RPE measures resulted in a more parsimonious model. Mean bias, typical error of the estimate (TEE) and within-subject repeated measures correlations examined agreement between subjective and objective sleep duration.

RESULTS

Conditional R2 for associations between objective and subjective workloads ranged from 0.18 to 0.78, P < 0.01, with strong associations between subjective measures of workload and TRIMP (0.65-0.78), average heart rate (0.57-0.73), and PlayerLoadTM (0.54-0.68). Including d-RPE lowered Akaike Information Criterion. The slope estimate between objective and subjective measures of sleep quality was not significant. A trivial relationship (r = 0.12; CI -0.03, 0.27) was observed between objective and subjective sleep duration with subjective measures overestimating (mean bias 25 min) sleep duration (TEE 41 min).

CONCLUSIONS

Daily RPE offers a proxy measure of internal workload in Australian Army recruits; however, the current subjective sleep questionnaire should not be considered a proxy measure of objective sleep measures.

Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport

Technological advances in sleep monitoring have seen an explosion of devices used to gather important sleep metrics. These devices range from instrumented 'smart pyjamas' through to at-home polysomnography devices. Alongside these developments in sleep technologies, there have been concomitant increases in sleep monitoring in athletic populations, both in the research and in practical settings. The increase in sleep monitoring in sport is likely due to the increased knowledge of the importance of sleep in the recovery process and performance of an athlete, as well as the well-reported challenges that athletes can face with their sleep. This narrative review will discuss: (1) the importance of sleep to athletes; (2) the various wearable tools and technologies being used to monitor sleep in the sport setting; (3) the role that sleep tracking devices may play in gathering information about sleep; (4) the reliability and validity of sleep tracking devices; (5) the limitations and cautions associated with sleep trackers; and, (6) the use of sleep trackers to guide behaviour change in athletes. We also provide some practical recommendations for practitioners working with athletes to ensure that the selection of such devices and technology will meet the goals and requirements of the athlete.

COVID19 Pandemic and Physical Activity: An Observational Study on Sleep Quality and Anxiety

Mental alterations were described during the COVID-19 pandemic and sleep deprivation has been reported as a consequence of social isolation. In Italy, the COVID-19 pandemic spread out at the beginning of 2020 determining severe lockdown periods. The aim of our study was to observe the effects of lockdown on sleep and anxiety in trained non-professional subjects and professional athletes who continued to train during the lockdown period. Forty-six subjects (21 trained non-professional subjects and 25 professional athletes) were recruited from a variety of team and individual sports to complete a battery of previously validated and widely used questionnaires assessing psychometric and anthropometric parameters, physical activity levels, lifestyle habits, and sleep quality. Subjects were aged 27.0 ± 5.14. All items were evaluated as percentages and chi-square and Fisher’s exact tests were performed, as appropriate. Our data showed that the prevalence of the difficulty of falling asleep (over 30%), the tendency of nocturnal awakenings (over 30%), and moderate anxiety (over 38%) were at the same extent in the two groups. Of the professional athletes, 72.73% declared snoring during sleep vs 42.86% of non-professional subjects. No other significant differences were found between the two groups except for the perception of being constant in daily activity, significantly more reported by trained subjects (p < 0.005). Our data show a similar scenario of anxiety and sleep disturbances for the two groups, suggesting that lockdown by the COVID-19 pandemic has partially mitigated the known beneficial effects due to physical activity on mental health and sleep quality. Further analyses are necessary to define the associated risk factors.

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.

Mental Fatigue Over 2 Elite Netball Seasons: A Case for Mental Fatigue to be Included in Athlete Self-Report Measures

PURPOSE

Mental fatigue is emerging as an important consideration for elite sporting performance, yet it is rarely monitored. The present study assessed changes in mental fatigue in professional team-sport athletes across 2 seasons and examined the relationship between mental fatigue and other athlete self-report measures of well-being.

METHODS

Elite netballers contracted to all teams competing in Australia's premier professional netball competition during the 2018 and 2019 seasons (N = 154) participated. Using 5-point Likert scales, mental fatigue, fatigue (physical), tiredness, sleep quality, stress, mood, and motivation were assessed daily across 2 seasons composed of 14 round and finals series.

RESULTS

The ratings of mental fatigue significantly changed during both seasons. In 2018, lower ratings of mental fatigue were reported in round 1 versus 3, 4, 6, 8, and 14; round 7 versus 6; and round 6 versus 10 (P < .05). In 2019, lower ratings of mental fatigue were identified for round 1 versus 3, 9, 10 to 14, and semifinal; round 2 versus 10 to 13; and 5 versus 10 to 12 (P < .05). Ordinal regression revealed significant differences between mental fatigue and physical fatigue (P < .001), tiredness (P < .001), stress (P < .001), mood (P < .001), and motivation (P < .05).

CONCLUSIONS

The present study found mental fatigue to significantly fluctuate across a season in elite netballers. Moreover, perceived mental fatigue differed from physical fatigue, tiredness, stress, mood, and motivation. The data impress the need for mental fatigue to be included as an independent measure of athlete well-being. Monitoring of mental fatigue can allow practitioners to implement strategies to manage its influence on performance.

Decreased Sleep and Subjective Well-Being as Independent Predictors of Injury in Female Collegiate Volleyball Players

Background:

The relationship among sleep duration, subjective well-being, and injury risk in athletes is poorly defined.

Purpose:

To evaluate the independent effects of sleep duration, sleep quality, and subjective well-being on in-season injuries in collegiate female volleyball athletes.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

During a 9-month competitive season, 17 female National Collegiate Athletic Association (NCAA) Division I volleyball players reported mood, fatigue, stress, soreness, sleep duration (hours), and sleep quality every morning. Well-being measures were recorded from 0 (worst) to 5 (best), and all time-loss injuries were recorded by the team athletic trainer. Separate mixed-effects logistic regression models were used to evaluate the effects of sleep and subjective well-being on in-season injury. Each well-being variable was also included in a separate mixed-effects logistic regression model with sleep duration as a covariate.

Results:

A total of 54 injuries were recorded during the study period. Compared with days without an injury, mood, fatigue, stress, soreness, sleep quality, and sleep duration were significantly worse the day before an injury occurred. In the separate prediction models, in-season injury was significantly predicted by fatigue (odds ratio [OR], 0.56 [95% CI, 0.36-0.86]; P = .008), mood (OR, 0.52 [95% CI, 0.35-0.78]; P = .002), stress (OR, 0.63 [95% CI, 0.42-0.94]; P = .023), soreness (OR, 0.54 [95% CI, 0.38-0.79]; P = .001), sleep quality (OR, 0.49 [95% CI, 0.34-0.7]; P < .001), and sleep duration (OR, 0.69 [95% CI, 0.55-0.87]; P = .001). In the multivariable models, sleep duration remained a significant independent predictor in each of the subsequent multivariable models (OR, 0.72-0.74; P < .05 for all), as did mood (OR, 0.55 [95% CI, 0.36-0.83); P = .005) and soreness (OR, 0.57 [95% CI, 0.39-0.83]; P = .003), while fatigue (OR, 0.65 [95% CI, 0.42-1]; P = .054) and stress (OR, 0.68 [95% CI, 0.45-1]; P = .061) no longer reached statistical significance.

Conclusion:

Increased sleep duration, mood, and decreased soreness were independently associated with a reduced risk of in-season injury in this cohort of female NCAA volleyball players.

Does Caffeine Consumption Influence Postcompetition Sleep in Professional Rugby League Athletes? A Case Study

PURPOSE

To examine the sleep of rugby league athletes the night before and following an evening match and to investigate the association between caffeine intake and sleep on the night of competition.

METHODS

On the night prior to, night of, and night after a professional rugby league match, 15 athletes had their sleep monitored using wrist activity monitors. Additionally, saliva samples were collected 60 minutes before and 30 minutes after the competition to assess salivary caffeine concentration. Differences in sleep across the 3 nights were examined using linear mixed models, and changes were assessed using effect size (ES). Pearson correlation (r) assessed the relationship between salivary caffeine levels and sleep indices.

RESULTS

On the night of competition, athletes went to bed later than the night before (P = .00002, ES = 1.84) and night after (P = .0003, ES = 1.49) competition. Consequently, their sleep duration was reduced on the night of competition compared with the previous night (P < .0000003, ES = 2.36) and night after competition (P = .001, ES = 1.53). Postcompetition salivary caffeine concentration was substantially elevated in athletes when compared with precompetition measures (P < .00000001, ES = 4.44), and moderate, nonsignificant correlations were observed between changes in salivary caffeine concentration and delayed bedtime (r = .48, P = .07), increased sleep latency (r = .45, P = .09), decreased sleep duration (r = -.30, P = .28), and reduced sleep efficiency (r = -.34, P = .22).

CONCLUSIONS

These results demonstrate that evening competition results in sleep disturbance in rugby league athletes, and caffeine supplementation prior to and during competition leads to substantial increases in postcompetition salivary caffeine concentration.

Impact of shift work on sleep and fatigue in Maritime pilots

This study examined how objective measures of sleep change across shift-cycles, and the impact of this on sleep quality and fatigue. Forty maritime pilots were recruited from Australian ports. Sleep wake-behaviour (timing and length), and self-reported sleep quality and fatigue, were assessed to determine any impact of roster status and 'on-call' status. On-roster pilots experienced reduced night time sleep duration compared to those off-roster (57 ± 8.8 min), while working on-call also diminished night time sleep duration (126 ± 11.3 min) and quality, compared to workers not on-call. Fatigue scores indicated that participants were not fully recovered prior to commencing rostered night shift, while sleep quality was significantly worse following sleep that occurred after a night shift, compared to after a day shift. These findings potentially support workplace negotiations to change future shift cycles, and to adopt monitoring systems that may mitigate the risk of fatigue-related accidents and chronic health outcomes. Practitioner summary: Long and irregular work hours of maritime pilotage can compromise worker performance and safety. This observational study found that on-roster pilots experience reduced sleep duration compared to those off-roster, while working on-call further diminishes sleep duration and quality. Future workload/fatigue monitoring systems may mitigate fatigue-related accidents and adverse chronic health outcomes. Abbreviations: ANOVA: analysis of variance; ANCOVA: analysis of covariance; BMI: body mass index; CVD: cardiovascular disease; h: hours; mins: minutes; SE: standard error of the mean; SD: standard deviation; SO: sleep opportunities; TST: total sleep time; WASO: wake after sleep onset.

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

PURPOSE

This systematic review aimed to describe objective sleep parameters for athletes under different conditions and address potential sleep issues in this specific population.

METHODS

PubMed and Scopus were searched from inception to April 2019. Included studies measured sleep only via objective evaluation tools such as polysomnography or actigraphy. The modified version of the Newcastle-Ottawa Scale was used for the quality assessment of the studies.

RESULTS

Eighty-one studies were included, of which 56 were classified as medium quality, 5 studies as low quality, and 20 studies as high quality. A total of 1830 athletes were monitored over 18,958 nights. Average values for sleep-related parameters were calculated for all athletes according to sex, age, athletic expertise level, training season, and type of sport. Athletes slept on average 7.2 ± 1.1 h/night (mean ± SD), with 86.3% ± 6.8% sleep efficiency (SE). In all datasets, the athletes' mean total sleep time was <8 h. SE was low for young athletes (80.3% ± 8.8%). Reduced SE was attributed to high wake after sleep onset rather than sleep onset latency. During heavy training periods, sleep duration and SE were on average 36 min and 0.8% less compared to pre-season and 42 min and 3.0% less compared to in-season training periods, respectively.

CONCLUSION

Athletes' sleep duration was found to be short with low SE, in comparison to the general consensus for non-athlete healthy adults. Notable sleep issues were revealed in young athletes. Sleep quality and architecture tend to change across different training periods.

How Much Sleep Does an Elite Athlete Need?

PURPOSE

Anecdotal reports indicate that many elite athletes are dissatisfied with their sleep, but little is known about their actual sleep requirements. Therefore, the aim of this study was to compare the self-assessed sleep need of elite athletes with an objective measure of their habitual sleep duration.

METHODS

Participants were 175 elite athletes (n = 30 females), age 22.2 (3.8) years (mean [SD]) from 12 individual and team sports. The athletes answered the question "how many hours of sleep do you need to feel rested?" and they kept a self-report sleep diary and wore a wrist activity monitor for ∼12 nights during a normal phase of training. For each athlete, a sleep deficit index was calculated by subtracting their average sleep duration from their self-assessed sleep need.

RESULTS

The athletes needed 8.3 (0.9) hours of sleep to feel rested, their average sleep duration was 6.7 (0.8) hours, and they had a sleep deficit index of 96.0 (60.6) minutes. Only 3% of athletes obtained enough sleep to satisfy their self-assessed sleep need, and 71% of athletes fell short by an hour or more. Specifically, habitual sleep duration was shorter in athletes from individual sports than in athletes from team sports (F1,173 = 13.1, P < .001; d = 0.6, medium), despite their similar sleep need (F1,173 = 1.40, P = .24; d = 0.2, small).

CONCLUSIONS

The majority of elite athletes obtain substantially less than their self-assessed sleep need. This is a critical finding, given that insufficient sleep may compromise an athlete's capacity to train effectively and/or compete optimally.

The Sleep and Recovery Practices of Athletes

BACKGROUND

Athletes maintain a balance between stress and recovery and adopt recovery modalities that manage fatigue and enhance recovery and performance. Optimal TST is subject to individual variance. However, 7-9 h sleep is recommended for adults, while elite athletes may require more quality sleep than non-athletes.

METHODS

A total of 338 (elite n = 115, 74 males and 41 females, aged 23.44 ± 4.91 years; and sub-elite n = 223, 129 males and 94 females aged 25.71 ± 6.27) athletes were recruited from a variety of team and individual sports to complete a battery of previously validated and reliable widely used questionnaires assessing sleep, recovery and nutritional practices.

RESULTS

Poor sleep was reported by both the elite and sub-elite athlete groups (i.e., global PSQI score ≥5-elite 64% [n = 74]; sub-elite 65% [n = 146]) and there was a significant difference in sport-specific recovery practices (3.22 ± 0.90 vs. 2.91 ± 0.90; p < 0.001). Relatively high levels of fatigue (2.52 ± 1.32), stress (1.7 ± 1.31) and pain (50%, n = 169) were reported in both groups. A range of supplements were used regularly by athletes in both groups; indeed, whey (elite n = 22 and sub-elite n = 48) was the most commonly used recovery supplement in both groups. Higher alcohol consumption was observed in the sub-elite athletes (12%, n = 26) and they tended to consume more units of alcohol per drinking bout.

CONCLUSION

There is a need for athletes to receive individualised support and education regarding their sleep and recovery practices.

Functional Overreaching in Endurance Athletes: A Necessity or Cause for Concern?

There are variable responses to short-term periods of increased training load in endurance athletes, whereby some athletes improve without deleterious effects on performance, while others show diminished exercise performance for a period of days to months. The time course of the decrement in performance and subsequent restoration, or super compensation, has been used to distinguish between the different stages of the fitness-fatigue adaptive continuum termed functional overreaching (FOR), non-functional overreaching (NFOR) or overtraining syndrome. The short-term transient training-induced decrements in performance elicited by increases in training load (i.e. FOR) are thought be a sufficient and necessary component of a training program and are often deliberately induced in training to promote meaningful physiological adaptations and performance super-compensation. Despite the supposition that deliberately inducing FOR in athletes may be necessary to achieve performance super-compensation, FOR has been associated with various negative cardiovascular, hormonal and metabolic consequences. Furthermore, recent studies have demonstrated dampened training and performance adaptations in FOR athletes compared to non-overreached athletes who completed the same training program or the same relative increase in training load. However, this is not always the case and a number of studies have also demonstrated substantial performance super-compensation in athletes who were classified as being FOR. It is possible that there are a number of contextual factors that may influence the metabolic consequences associated with FOR and classifying this training-induced state of fatigue based purely on a decrement in performance may be an oversimplification. Here, the most recent research on FOR in endurance athletes will be critically evaluated to determine (1) if there is sufficient evidence to indicate that inducing a state of FOR is necessary and required to induce a performance super-compensation; (2) the metabolic consequences that are associated with FOR; (3) strategies that may prevent the negative consequences of overreaching.

Insufficient Sleep in Young Athletes? Causes, Consequences, and Potential Treatments

Sleep is essential in the preparation for, and the recovery from, training and competition. Despite being important for all individuals, young athletes are considered an at-risk group for reduced sleep duration and quality. The purpose of this review is to synthesise current literature relating to sleep duration and quality in young (14-25 years) athletes. Specifically, typical sleep and wake patterns, factors affecting sleep and wake patterns, and the consequences of altered sleep and wake patterns in young athletes are discussed. Scheduling training and competition in the afternoon or evening appears to result in reduced sleep duration due to less time in bed. Evidence suggests that young athletes who obtain less than 8 h of sleep per night are at a higher risk of musculoskeletal injury. An increase in sleep duration above habitual nightly sleep may be associated with favourable performance in young athletes; however, the associations between sleep quality and performance- and health-related outcomes remain unclear.

Influence of Perceptions of Sleep on Well-Being in Youth Athletes

ABSTRACT

Sawczuk, T, Jones, B, Scantlebury, S, and Till, K. Influence of perceptions of sleep on well-being in youth athletes. J Strength Cond Res 35(4): 1066-1073, 2021-To date, most research considering well-being questionnaires has only considered the training stress imposed on the athlete, without evaluating the questionnaire's relationship with a measure of recovery (e.g., sleep). This study aimed to assess the influence of sleep duration (Sduration), sleep quality (Squality), and sleep index (Sindex; Sduration × Squality) on well-being in youth athletes, while accounting for the known training stressors of training load and exposure to match play. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily questionnaire including well-being (DWBno-sleep; fatigue, muscle soreness, stress, and mood) measures, Perceived Recovery Status Scale (PRS), the previous day's training loads, Sduration, and Squality every day for 13 weeks. Linear mixed models assessed the impact of Sduration, Squality, and Sindex on DWBno-sleep, its individual subscales, and PRS. Sduration had a small effect on DWBno-sleep (d = 0.31; ±0.09), fatigue (d = 0.42; ±0.11), and PRS (d = 0.25; ±0.09). Squality had a small effect on DWBno-sleep (d = 0.47; ±0.08), fatigue (d = 0.53; ±0.11), stress (d = 0.35; ±0.07), mood (d = 0.41; ±0.09), and PRS (d = 0.37; ±0.08). Sindex had a small effect on DWBno-sleep (d = 0.44; ±0.08), fatigue (d = 0.55; ±0.11), stress (d = 0.29; ±0.07), mood (d = 0.37; ±0.09), and PRS (d = 0.36; ±0.09). The results indicate that an athlete's perceptions of sleep are associated with deviations in well-being measures and should be used as an input to the monitoring process rather than as part of the outcome well-being score. The sleep index is suggested as a potential input because it provides information on both the duration and quality of the sleep experienced.

The Association Between Sleep and In-Game Performance in Basketball Players

Purpose:To investigate the associations between sleep and competitive performance in basketball.Methods:A total of 7 semiprofessional, male players were monitored across the in-season. On nights prior to competition, sleep duration and quality were assessed using actigraphs and sleep diaries. The data were accumulated over 1 (night 1), 2 (nights 1–2 combined), 3 (nights 1–3 combined), and 4 (nights 1–4 combined) nights prior to competition. Performance was reported as player statistics (field goal and free-throw accuracy, rebounds, assists, steals, blocks, and turnovers) and composite performance statistics (offensive rating, defensive rating, and player efficiency). Linear regression analyses with cluster-robust standard errors using bootstrapping (1000 replications) were performed to quantify the association between sleep and performance.Results:The night before competition, subjective sleep quality was positively associated with offensive rating and player efficiency (P < .05).Conclusions:Strategies to increase subjective sleep quality the night before competition should be considered to increase the likelihood of successful in-game performance, given its association with composite performance metrics.

An objective description of routine sleep habits in elite youth football players from the Middle-East

BACKGROUND AND STUDY OBJECTIVES

Adequate sleep is essential to support preparation and recovery processes for training and competition in athletes. A limited number of studies have examined whether adolescents from the Middle-East meet the minimum age-specific recommendations ranging from 8 to 9 h of night sleep based on objective measurements. This study aimed to provide an objective description of routine sleep habits in elite youth football players from the Middle-East.

METHODS

Using wrist-worn actigraphy, we examined objective measures of sleep over a 14-day surveillance period from fifty-nine, male, Middle-Eastern elite youth football players (age range: 12.1 to 16 years).

RESULTS

The observed median sleep duration was approximately 5.5 to 6 h during weekdays and 6.5 to 7.5 h over weekend days. Sleep intermissions resulting in two or more periods of sleep accounted for 8% and 17% of the data during weekdays and weekends, respectively.

CONCLUSIONS

For the first time, we reported an objective quantification of sleep measures indicating that elite youth athletes from the Middle-East do not meet the age-specific sleep recommendations. Integration of sleep tracking into the routine training monitoring process can be valuable to inform decisions relevant to the adoption of potential multidisciplinary interventions to address sleep insufficiency and disorders in youth athletes.

No familiarization or 'first-night effect' evident when monitoring sleep using wrist actigraphy

Anecdotal reports suggest that the first night of sleep monitoring using a wrist-actigraphy monitor may result in impaired sleep when compared to subsequent nights, due to increased levels of anxiety and awareness of being monitored. This phenomenon has been seen in sleep laboratories with polysomnographic monitoring. However, this is yet to be established for wrist actigraphy monitoring in the research literature. A total of 240 healthy adult participants (177 male, 63 female; age range, 18-35 years) had their sleep monitored using wrist actigraphy over a period of five nights of 'normal' sleep (1,200 nights of data). Sleep variables including sleep latency, wake episodes, wake after sleep onset, awakenings per hour, time in bed, total sleep time and sleep efficiency % were evaluated across all nights of sleep. Comparisons were made using repeated measures ANOVAs, mean differences, range of mean differences, intraclass correlation coefficients (ICC) and Cohen's d effect sizes. There were no significant differences between the first night and subsequent nights for any of the measured sleep variables (p < .05) and all differences were associated with trivial effect sizes (d < 0.2). ICCs ranged from 0.35 to 0.62 (low to moderate). Despite claims of impaired sleep during the first night of sleep monitoring, our results indicate that a familiarization period may not be necessary when monitoring sleep in healthy participants using wrist actigraphy. However, the response is highly individual and further research is required to assess personality traits and responses to sleep monitoring.

Decreased Sleep Is an Independent Predictor of In-Season Injury in Male Collegiate Basketball Players

Background:

Although decreased sleep has been associated with decreased performance, increased illness risk, and impaired well-being in athletes, the relationship between sleep and injury risk in collegiate athletes is unknown.

Purpose/Hypothesis:

To evaluate the independent effects of sleep duration and subjective well-being on in-season injury in male collegiate basketball athletes. We hypothesized that decreased sleep would be associated with an increased risk of in-season injury.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

During 2 consecutive seasons, 19 male National Collegiate Athletic Association Division I basketball players reported mood, fatigue, stress, soreness, sleep duration (hours), and previous day’s training load every morning. Well-being measures were recorded on a scale from 0 (worst) to 5 (best), and all time-loss injuries were recorded by the team athletic trainer. Separate mixed-effects logistic regression models were used to evaluate the effects of sleep and subjective well-being on in-season injury, with adjustment for same-day training load and individual repeated measures. To determine the independence of sleep and well-being as predictors of injury, each well-being variable was included in a separate mixed-effects logistic regression model, with sleep and training load as covariates.

Results:

During the study period, 32 injuries were recorded. In the initial separate prediction models, in-season injury was significantly predicted by mood (odds ratio [OR], 0.50; 95% CI, 0.37-0.66), fatigue (OR, 0.44; 95% CI, 0.33-0.59), stress (OR, 0.57; 95% CI, 0.42-0.76), soreness (OR, 0.41; 95% CI, 0.32-0.54), and sleep duration (OR, 0.57; 95% CI, 0.49-0.66) (P < .001 for all). In the multivariable models, sleep duration remained a significant, independent predictor in each of the subsequent multivariable models (OR, 0.52-0.69; P < .001 for all) as did soreness (OR, 0.65; 95% CI, 0.44-0.95; P = .024), whereas mood (OR, 1.2; 95% CI, 0.76-1.9; P = .43), fatigue (OR, 1.1; 95% CI, 0.65-1.9; P = .68), and stress (OR, 1.1; 95% CI, 0.75-1.5; P = .69) were no longer significant.

Conclusion:

Increased sleep duration is independently associated with a reduced risk of in-season injury in male collegiate basketball players, even after adjustment for training load and subjective well-being. The effects of mood, fatigue, and stress on injury were no longer evident after adjustment for the effect of sleep duration.

A critical review on sleep assessment methodologies in athletic populations: factors to be considered

A growing body of research focus on athletes' sleep in order to investigate the effects of sleep in sports performance and recovery or the prevalence of sleep disorders in athletes. At the same time, several sleep monitoring tools have been developed and used in athletic populations for fulfilling these purposes. This review aimed to provide critical assessment to the most used by athletes' methodological approaches and compared them with the gold standard approach. Advantages and disadvantages of the various sleep monitoring tools were critically discussed. Literature related to aspects of athletes' sleep was reviewed. From the shortlisted studies, several factors that seem to affect sleep in athletes were identified using objective methods such as polysomnography/electroencephalography and actigraphy. These factors were associated to sleep (eg such as sleep environment, familiarization procedures and napping) and daily habits (eg nutrition, fluid consumption, alcohol and caffeine intake, tobacco use). The selected studies that evaluated sleep objectively were screened according the reporting rates of these variables. The majority of the screened studies were found to underreport these variables. Practical issues were addressed and recommendations about reporting sleep-related factors were made in order to improve studies' quality assessment and allow for more robust comparisons between studies.

Influence of Faster and Slower Recovery-Profile Classifications, Self-Reported Sleep, Acute Training Load, and Phase of the Microcycle on Perceived Recovery in Futsal Players

PURPOSE

To determine whether daily perceived recovery is explained from a multifactorial single-session classification of recovery (ie, faster vs slower) or other circumstantial factors (ie, previous training load, self-reported sleep, or phase of the microcycle).

METHODS

Nineteen elite male futsal players were initially allocated to a recovery-classification group (faster recovery, slower physiological, or slower perceptual) based on previous research using a multifactorial cluster-analysis technique. During 4 ensuing weeks of preseason, training loads were monitored via player load, training impulse, and session rating of perceived exertion. Before each day's training, players reported their perception of recovery (Total Quality of Recovery scale [TQR]) and the number of hours and perceived quality of sleep the night prior. A hierarchical linear mixed model was used to analyze the effect of the different recovery profiles, training load, sleep, and phase of the microcycle (ie, start, middle, end) on daily TQR.

RESULTS

The recovery classification of players (P = .20), training load (training impulse, P = .32; player load, P = .23; session rating of perceived exertion, P = .46), and self-reported hours slept the night before (P = .45) did not significantly influence TQR. However, perceived sleep quality (P < .01) and phase of the microcycle (P < .01) were significantly associated with TQR (r2 = .41).

CONCLUSIONS

Neither recovery classification nor prior training load influenced perceived recovery during the preseason. However, higher TQR was evident with better self-reported sleep quality, whereas lower values were associated with phases of the training week.

Sleep and Salivary Testosterone and Cortisol During a Short Preseason Camp: A Study in Professional Rugby Union

Purpose: To examine changes in, and relationships between, sleep quality and quantity, salivary testosterone, salivary cortisol, testosterone-to-cortisol ratio (T:C), and self-reported muscle soreness during a residential-based training camp in elite rugby players. Methods: Nineteen male rugby players age 26.4 (3.9) years, height 186.0 (9.4) cm, and weight 104.1 (13.4) kg (mean [SD]) participated in this study. Wrist actigraphy devices were worn for 8 nights around a 4-d training camp (2 nights prior, during, and 2 nights after). Sleep-onset latency, sleep duration, sleep efficiency, and waking time were measured. Participants provided saliva samples during camp on waking and again 45 min later, which were then assayed for testosterone and cortisol levels. They also rated their general muscle soreness daily. Results: Little variation was observed for sleep quality and quantity or testosterone. However, significant differences were observed between and within days for cortisol, T:C, and muscle soreness (P < .001). Few relationships were observed for sleep and hormones; the strongest, an inverse relationship for sleep efficiency and T:C (r = -.372, P < .01). Conclusions: There may be no clear and useful relationship between sleep and hormone concentration in a short-term training camp context, and measures of sleep and testosterone and cortisol should be interpreted with caution because of individual variation. Alterations in hormone concentration, particularly cortisol, may be affected by other factors including anticipation of the day ahead. This study adds to our knowledge that changes in hormone concentration are individual and context specific.

Observing Ramadan and sleep-wake patterns in athletes: a systematic review, meta-analysis and meta-regression

Objective

To evaluate the effect of observing Ramadan on athletes’ sleep patterns.

Design

Systematic review and meta-analysis.

Data sources

The entire content of PubMed/MEDLINE and Web of Science.

Eligibility criteria for selecting studies

Single-group, prepost and cross-over design studies conducted in athletes aged ≥18 years, training at least twice a week and published in English before 12 July 2018 were included. Studies assessing sleep quantity, quality, daytime sleepiness and/or daily naps based on objective or subjective methods were deemed eligible.

Study appraisal

The methodological quality was assessed using ‘QualSyst’.

Results

Of 13 selected articles, 7 were of strong quality, 3 were moderate and 3 were weak. 11 studies evaluated total sleep time (TST); this decreased during Ramadan in 4 studies, increased in 1 and remained unchanged in 6. Pooled TST findings indicated a moderate effect size (− 0.97, SE=0.37, 95% CI −1.69 to −0.25, t=−2.64, p=0.01) with significant heterogeneity but no publication bias. Meta-regressions showed no effects of study year, age, sample size, type of sport or competition level, but there were effects of country (with France and Tunisia being the most affected countries and Turkey the least affected, Q=32.14, p<0.0001) and study design (Q=7.74, p=0.02). Four studies measured self-reported sleep quality and it decreased in three studies. One study of sleep architecture reported more frequent waking and more light sleep during Ramadan. Daily nap duration was increased in two studies, but daytime sleepiness remained unchanged in four studies.

Conclusion

When athletes continue to train at least two times/week while observing Ramadan, TST is decreased compared with athletes’ baseline levels.

Evidence-based post-exercise recovery strategies in rugby: a narrative review

In the sport of rugby, athletes need a multitude of sport-specific skills along with endurance, power, and speed to optimize performance. Further, it is not unusual for athletes to play several competitive matches with insufficient recovery time. Rugby requires repeated bouts of high-intensity actions intermixed with brief periods of low-to-moderate active recovery or passive rest. Specifically, a match is characterized by repeated explosive activities, such as jumps, shuffles, and rapid changes of direction. To facilitate adequate recovery, it is necessary to understand the type of fatigue induced and, if possible, its underlying mechanisms. Common approaches to recovery may include nutritional strategies as well as active (active recovery) and passive recovery (water immersions, stretching, and massage) methods. However, limited research exists to support the effectiveness of each strategy as it related to recovery from the sport of rugby. Therefore, the main aim of the current brief review is to present the relevant literature that pertains to recovery strategies in rugby.

Sleep practices implemented by team sport coaches and sports science support staff: A potential avenue to improve athlete sleep?

OBJECTIVES

The primary aims of the present study were to assess the sleep hygiene knowledge of high performance team sport coaches and sports science support staff; the sleep practices these individuals implement with athletes; and the barriers to the more frequent use of these practices.

DESIGN

Cross-sectional observational study.

METHODS

A sample of 86 Australian coaches and sports science support staff working within high performance team sport volunteered to complete a four-part questionnaire, including the Sleep Beliefs Survey used to assess sleep hygiene knowledge.

RESULTS

Overall sleep hygiene knowledge was adequate (15.3±2.9, score range 0-20; mean±SD), however knowledge of sleep-wake cycle behaviours (score 4.9±1.6 out of 7) and thoughts and attitudes about sleep (3.6±1.0 out of 5) were inadequate. Over half (56%) of coaches and support staff had monitored athlete sleep, while 43% had promoted sleep hygiene. Lack of resources (response range 44-60%) and knowledge (16-41%) were the two main barriers to the implementation of sleep monitoring and sleep hygiene practices.

CONCLUSIONS

Team sport coaches and sports science support staff have adequate overall sleep hygiene knowledge, yet some specific areas (e.g. sleep-wake cycle behaviours) warrant improvement. There appear to be limited sleep practices implemented with athletes, particularly regarding the promotion of sleep hygiene. The development of educational sleep resources for coaches and support staff to implement with athletes may help address the identified barriers and improve sleep knowledge.

Evening electronic device use and sleep patterns in athletes

The present study aimed to investigate pre-sleep behaviours (including evening electronic device use) and sleep quantity in well-trained athletes. Seventy well-trained athletes (44 females, 26 males) aged 21 ± 4 y from a range of team and individual sports were asked to complete an online sleep diary for 7 days. The sleep diary included questions about pre-sleep behaviours (e.g. napping, caffeine intake), electronic device use in the 2 h prior to bedtime (e.g. type of device and duration of use) and sleep (e.g. time in bed, sleep onset latency). On average, athletes spent 8:20 ± 1:21 h in bed each night. Associations between age, time in bed and sleepiness suggested that younger athletes spent more time in bed (B = -0.05, p = 0.001) but felt sleepier (r = -0.32, p < 0.01) than older athletes. On average, athletes mostly used electronic devices for 0-30 min prior to sleep. The use of multiple devices in the evening was associated with more perceived difficulty in falling asleep (B = 0.22, p = 0.03), but no associations existed with other sleep variables. In summary, younger athletes may require later start times or improved sleep quality to resolve excessive sleepiness.

Effects of training and competition on the sleep of elite athletes: a systematic review and meta-analysis

Objectives

To characterise the sleep of elite athletes and to identify factors associated with training and competition that negatively affect sleep.

Design

Prognosis systematic review.

Data sources

Three databases (PubMed, SCOPUS and SPORTDiscus) were searched from inception to 26 February 2018.

Eligibility criteria for selecting studies

Included studies objectively reported total sleep time (TST) and/or sleep efficiency (SE) in elite athletes. Studies were required to be observational or to include an observational trial.

Results

Fifty-four studies were included. During training, many studies reported athletes were unable to achieve TST (n=23/41) and/or SE (n=16/37) recommendations. On the night of competition, most studies reported athletes were unable to achieve TST (n=14/18) and/or SE (n=10/16) recommendations. TST was shorter (60 min) the night of competition compared with previous nights. SE was lower (1%) the night of competition compared with the previous night. TST was shorter the night of night competition (start ≥18:00; 80 min) and day competition (20 min) compared with the previous night. SE was lower (3%–4%) the night of night competition but unchanged the night of day competition compared with previous nights. Early morning training (start <07:00), increases in training load (>25%), late night/early morning travel departure times, eastward air travel and altitude ascent impaired sleep.

Conclusion

Athletes were often unable to achieve sleep recommendations during training or competition periods. Sleep was impaired the night of competition compared with previous nights. Early morning training, increases in training load, travel departure times, jet lag and altitude can impair athletes’ sleep.

PROSPERO registration number

CRD42017074367.

Diurnal Variations in Physical Performance: Are there Morning-to-Evening Differences in Elite Male Handball Players?

The purposes of this study were to determine: 1) morning-to-evening differences in physical performance with and without a ball; and 2) associations between sleep outcomes (duration and quality) and physical performance in handball players. Sixteen elite, male handball players (25.4 ± 5.8 yr, 94.0 ± 7.4 kg, 193.5 ± 7.5 cm) completed physical performance tests without a ball (a zig-zag test assessing closed-skill agility, linear sprints, and countermovement (CMJ) and squat jumps) and with a ball (a zig-zag test and linear sprints) in the morning and evening. In addition, sleep quality and quantity during the night before testing were obtained using self-reported measures. Superior physical performance was evident in all tests during the evening compared to the morning hours (p < 0.003). Specifically, jump height was moderately (effect size (ES) = 0.73 to 1.02) higher during the evening. Similarly, moderate (ES = 1.17) and large (ES = 1.67) improvements in zig-zag test performance were apparent during the evening with and without the ball, respectively. Also, large to very large (ES = 1.29 to 2.09) increases in sprint performance with and without the ball were evident in the evening. No significant correlations (p > 0.05) were apparent between sleep duration and quality and physical performance in both the morning and evening sessions. Diurnal variations in physical performance were apparent in elite male handball players with enhanced performance with and without the ball in the evening compared to morning hours. These findings indicate that morning-to-evening differences in physical performance should be considered when developing conditioning plans or preparing for competition in handball.

From pillow to podium: a review on understanding sleep for elite athletes

Sleep is considered vital to human health and well-being, and is critical to physiological and cognitive functioning. Elite athletes experience high training and competition demands, and are often exposed to various factors, situations, and environments that can cause sleep impairments. Previous research has shown that athletes commonly experience sleep loss in the lead up to and following competition, which could have significant impacts on their preparation, performance, and recovery. In particular, the results from previous research show significant reductions in total sleep time (~1:40 h:min) and significant increases in sleep latency (~45 minutes) following evening competition. Napping is common in both the training and competition setting in athletes; however, research on the effect of napping on physiology and performance is limited. In contrast, research on strategies and interventions to improve sleep are increasing in the athletic population, with sleep hygiene research resulting in significant improvements in key sleep indices. This review investigates the physiological importance of sleep in athletes, current tools to monitor athletes' sleep, the role of sleep for cognitive functioning and athletic performance, the prevalence of sleep disturbances and the potential mechanisms causing sleep disturbances, the role of napping, and different intervention strategies to improve sleep.

The influence of training load, exposure to match play and sleep duration on daily wellbeing measures in youth athletes

This study assessed the influence of training load, exposure to match play and sleep duration on two daily wellbeing measures in youth athletes. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on the previous day's training loads (TL) and self-reported sleep duration (sleep) every day for 13 weeks (n = 2727). Linear mixed models assessed the effect of TL, exposure to match play and sleep on DWB and PRS. An increase in TL had a most likely small effect on muscle soreness (d = -0.43;± 0.10) and PRS (d = -0.37;± 0.09). Match play had a likely small additive effect on muscle soreness (d = -0.26;± 0.09) and PRS (d = -0.25;± 0.08). An increase in sleep had a most likely moderate effect on sleep quality (d = 0.80;± 0.14); a most likely small effect on DWB (d = 0.45;± 0.09) and fatigue (d = 0.42;± 0.11); and a likely small effect on PRS (d = 0.25;± 0.09). All other effects were trivial or did not reach the pre-determined threshold for practical significance. The influence of sleep on multiple DWB subscales and the PRS suggests that practitioners should consider the recovery of an athlete alongside the training stress imposed when considering deviations in wellbeing measures.

Individual differences in compliance and agreement for sleep logs and wrist actigraphy: A longitudinal study of naturalistic sleep in healthy adults

There is extensive laboratory research studying the effects of acute sleep deprivation on biological and cognitive functions, yet much less is known about naturalistic patterns of sleep loss and the potential impact on daily or weekly functioning of an individual. Longitudinal studies are needed to advance our understanding of relationships between naturalistic sleep and fluctuations in human health and performance, but it is first necessary to understand the efficacy of current tools for long-term sleep monitoring. The present study used wrist actigraphy and sleep log diaries to obtain daily measurements of sleep from 30 healthy adults for up to 16 consecutive weeks. We used non-parametric Bland-Altman analysis and correlation coefficients to calculate agreement between subjectively and objectively measured variables including sleep onset time, sleep offset time, sleep onset latency, number of awakenings, the amount of wake time after sleep onset, and total sleep time. We also examined compliance data on the submission of daily sleep logs according to the experimental protocol. Overall, we found strong agreement for sleep onset and sleep offset times, but relatively poor agreement for variables related to wakefulness including sleep onset latency, awakenings, and wake after sleep onset. Compliance tended to decrease significantly over time according to a linear function, but there were substantial individual differences in overall compliance rates. There were also individual differences in agreement that could be explained, in part, by differences in compliance. Individuals who were consistently more compliant over time also tended to show the best agreement and lower scores on behavioral avoidance scale (BIS). Our results provide evidence for convergent validity in measuring sleep onset and sleep offset with wrist actigraphy and sleep logs, and we conclude by proposing an analysis method to mitigate the impact of non-compliance and measurement errors when the two methods provide discrepant estimates.

Development of the athlete sleep behavior questionnaire: A tool for identifying maladaptive sleep practices in elite athletes

Introduction

Existing sleep questionnaires to assess sleep behaviors may not be sensitive in determining the unique sleep challenges faced by elite athletes. The purpose of the current study was to develop and validate the Athlete Sleep Behavior Questionnaire (ASBQ) to be used as a practical tool for support staff working with elite athletes.

Methods

564 participants (242 athletes, 322 non-athletes) completed the 18-item ASBQ and three previously validated questionnaires; the Sleep Hygiene Index (SHI), the Epworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality Index (PSQI). A cohort of the studied population performed the ASBQ twice in one week to assess test-retest reliability, and also performed sleep monitoring via wrist-actigraphy.

Results

Comparison of the ASBQ with existing sleep questionnaires resulted in moderate to large correlations (r=0.32 - 0.69). There was a significant difference between athletes and non-athletes for the ASBQ global score (44±6 vs. 41±6, respectively, p<0.01) and for the PSQI, but not for the SHI or the ESS. The reliability of the ASBQ was acceptable (ICC=0.87) when re-tested within 7 days. There was a moderate relationship between ASBQ and total sleep time (r=-0.42).

Conclusion

The ASBQ is a valid and reliable tool that can differentiate the sleep practices between athletes and non-athletes, and offers a practical instrument for practitioners and/or researchers wanting to evaluate the sleep behaviors of elite athletes. The ASBQ may provide information on areas where improvements to individual athletes’ sleep habits could be made.