Sleep Profiles of Elite Swimmers During Different Training Phases

Energy intake, hydration status, and sleep of world-class male archers during competition

BACKGROUND

Nutritional intake and sleep, play an important role for recovery and performance in elite sport but little work has been undertaken in archery. The present study aimed to assess energy intake (EI), hydration status, and sleep parameters in world-class male archers over the course of a four-day competition.

METHODS

Results, Conclusions Six male, elite-standard archers participated in the study and measurements of hydration status, EI, competition load, and sleep were recorded throughout each day of competition.

RESULTS

Daily energy, carbohydrate, and protein intake ranged between 2,563 and 3,986 kcal, 4 and 7.1 g/kg BM, 2.2 and 3.6 g/kg BM per day, respectively. Thus, archers practiced elements of periodized nutrition such that energy and carbohydrate intake was greater on the high-volume competition days (i.e. days 1 and 3; more numbers of arrows, longer duration, and walking distance) in comparison to low-volume days (days 2 and 4) over the tournament (all p > 0.01). Additionally, urine specific gravity was higher after waking, compared to pre- and post-competition, and before bed (all p < 0.05). This indicates that archers were euhydrated pre- and post-competition and before bedtime, while they were slightly hypohydrated after waking up. Sleep data show that disturbances were kept to a minimum.

CONCLUSIONS

Collectively, archers appear capable of periodizing their nutritional intake according to daily physical loading during a tournament whilst, staying euhydrated and maintaining sleep quality. In part, such data can help to explain why these archers experience a sustained level of success.

To Sleep Dreaming Medals: Sleep Characteristics, Napping Behavior, and Sleep-Hygiene Strategies in Elite Track-and-Field Athletes Facing the Olympic Games of Tokyo 2021

PURPOSE

Few data are available on sleep characteristics of elite track-and-field athletes. Our study aimed to assess (1) differences in sleep between sexes and among different track-and-field disciplines, (2) the effect of individualized sleep-hygiene strategies on athletes' sleep parameters, and (3) daytime nap characteristics in track-and-field athletes.

METHODS

Sleep characteristics of 16 elite Olympic-level track-and-field athletes (male: n = 8; female: n = 8) were assessed during the preseason period, at baseline (T0), and during the in-season period, after the adoption of individualized sleep-hygiene strategies (T1). Sleep parameters were objectively monitored by actigraphy for a minimum of 10 days, for each athlete, at both T0 and T1. A total of 702 nights were analyzed (T0 = 425; T1 = 277).

RESULTS

Female athletes displayed better sleep efficiency (88.69 [87.69-89.68] vs 91.72 [90.99-92.45]; P = .003, effect size [ES]: 0.44), lower sleep latency (18.99 [15.97-22.00] vs 6.99 [5.65-8.32]; P < .001, ES: 0.65), higher total sleep time (07:03 [06:56-07:11] vs 07:18 [07:10-07:26]; P = .030, ES: 0.26), earlier bedtime (00:24 [00:16-00:32] vs 00:13 [00:04-00:22]; P = .027, ES: 0.18), and lower nap frequency (P < .001) than male athletes. Long-distance runners had earlier bedtime (00:10 [00:03-00:38] vs 00:36 [00:26-00:46]; P < .001, ES: 0.41) and wake-up time (07:41 [07:36-07:46] vs 08:18 [08:07-08:30]; P < .001, ES: 0.61), higher nap frequency, but lower sleep efficiency (88.79 [87.80-89.77] vs 91.67 [90.95-92.38]; P = .013, ES: 0.44), and longer sleep latency (18.89 [15.94-21.84] vs 6.69 [5.33-8.06]; P < .001, ES: 0.67) than athletes of short-term disciplines. Furthermore, sleep-hygiene strategies had a positive impact on athletes' total sleep time (429.2 [423.5-434.8] vs 451.4 [444.2-458.6]; P < .001, ES: 0.37) and sleep latency (14.33 [12.34-16.32] vs 10.67 [8.66-12.68]; P = .017, ES: 0.19).

CONCLUSIONS

Sleep quality and quantity were suboptimal at baseline in Olympic-level track-and-field athletes. Large differences were observed in sleep characteristics between sexes and among different track-and-field disciplines. Given the positive effect of individualized sleep-hygiene strategies on athlete's sleep, coaches should implement sleep education sessions in the daily routine of top-level athletes.

SLEEP RESPONSES OF YOUNG SWIMMERS TO TRAINING LOAD AND RECOVERY DURING TAPERING

ABSTRACT Introduction: Tapering is a decisive phase in planning a young swimmer's preparation for competition. During this period, not only training loads, but also recovery, which includes sleep quality, must be constantly monitored. Objective: This study aims to investigate sleep behavior as a variable directly influenced by training load during the tapering phase of training for young swimmers. Methods: A polysomnography test was performed at the beginning of the tapering phase, as a baseline for sleep variables. In each daily training session, the overload and recovery variables were measured. The internal training load was evaluated in the PSE session and the external load by quantifying the training. The recovery variables were divided into the recovery variable, assessed using the TQR questionnaire, and sleep variables, which included sleepiness, scored using the Karolinska scale, and sleep behavior, using an actigraph and a sleep diary. Conclusion: It is concluded that there is no significant influence between training loads and sleep variables. However, a strong association was observed between TST and EL (External load), in the irregular sleep group. Level of evidence III, Study of behavior.

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.

Training and academic demands are associated with sleep quality in high-performance "dual career" student swimmers

Dual career collegiate athletes, who combine sporting endeavours with academic studies, are commonplace in high-performance sport. Sleep is an important aspect of physical and psychological recovery for athletes, plays a role in memory and learning and is associated with academic achievement. The aim of this study was to assess variations in sleep characteristics of collegiate swimmers to understand the incidence of poor sleep, and which stressors might contribute towards this. A total of 22 (male n = 13, female n = 9) high-performance student swimmers (mean ± SD; age 20 ± 2 years) participated in this study. Sleep/wake behaviour was assessed using the Pittsburgh Sleep Quality Index each month over a 12-month period. Additionally, academic and sporting commitments were quantified on a monthly basis and summarised as 4 stressors: average weekly training hours, total number of competitive races, total number of academic assessments, and average weekly learning hours. On average across the 12-month period, 41.7% of athletes reported poor sleep. The latest bedtimes, wake times and longest sleep durations were found in the months where academic and training demands were lowest. A statistically significant positive association was identified between median sleep quality scores and mean number of academic assessments (ρ₍₁₂₎ = 0.71, p=0.005). Hierarchal linear modelling analysis determined that number of academic assessments best predicted sleep quality within this cohort. The present study highlights the need for those working with athletes who have competing academic demands to consider sleep quality, and its potential impact on performance and wellbeing.

To Nap or Not to Nap? A Systematic Review Evaluating Napping Behavior in Athletes and the Impact on Various Measures of Athletic Performance

PURPOSE

The objective of this systematic review was to 1) determine how studies evaluated napping behavior in athletes (frequency, duration, timing and measurement); 2) explore how napping impacted physical performance, cognitive performance, perceptual measures (eg, fatigue, muscle soreness, sleepiness and alertness), psychological state and night-time sleep in athletes.

METHODS

Five bibliographic databases were searched from database inception to 11 August 2020. Observational and experimental studies comprising able-bodied athletes (mean age ≥12 years), published in English, in peer-reviewed journal papers were included. The Downs and Black Quality Assessment Checklist was used for quality appraisal.

RESULTS

Thirty-seven studies were identified of moderate quality. Most studies did not include consistent information regarding nap frequency, duration, and timing. Napping may be beneficial for a range of outcomes that benefit athletes (eg, physical and cognitive performance, perceptual measures, psychological state and night-time sleep). In addition, napping presents athletes with the opportunity to supplement their night-time sleep without compromising sleep quality.

CONCLUSION

Athletes may consider napping between 20 to 90 min in duration and between 13:00 and 16:00 hours. Finally, athletes should allow 30 min to reduce sleep inertia prior to training or competition to obtain better performance outcomes. Future studies should include comprehensive recordings of nap duration and quality, and consider using sleep over a 24 hour period (daytime naps and night-time sleep period), specifically using objective methods of sleep assessment (eg, polysomnography/actigraphy).

Sleep quality monitoring in individual sports athletes: parameters and definitions by systematic review

In the present review, we identify which instruments and parameters are used for sleep quality monitoring in individual sport athletes and which definitions were used for sleep quality parameters in this literature field. Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews. The initial search returned 3316 articles. After the removal of duplicate articles, eligibility assessment, 75 studies were included in this systematic review. Our main findings were that the most widely used measurement instruments were Actigraphy (25%), Rating Likert Scales (16%) and Sleep Diary (13%). On sleep quality parameters (Sleep duration = 14%; Wake after sleep onset = 14%; Sleep Quality = 12%; Sleep Effciency = 11% and Sleep Latency = 9%), the main point is that there are different definitions for the same parameters in many cases reported in the literature. We conclude that the most widely used instruments for monitoring sleep quality were Actigraphy, Likert scales and Sleep diary. Moreover, the definitions of sleep parameters are inconsistent in the literature, hindering the understanding of the sleep-sport performance relationship.

Get sleep or get stumped: sleep behaviour in elite South African cricket players during competition

Despite evidence supporting the positive affect sleep has on sport performance, there is limited application of sleep research in cricket. This study investigated the sleep behaviours of 26 elite South African cricket players (28.6 ± 4.0 years) during home and away competition. Players completed an altered version of the Core Consensus Sleep Diary every morning post-travel, pre-match and post-match. Linear mixed model regression was used to compare differences in sleep between time-periods, venues and formats. Spearman's correlations (r_s) assessed the relationship of match performance and sleep. Post-match total sleep time (06:31 ± 01:09) was significantly (p < 0.05) shorter compared to post-travel (07:53 ± 01:07; g = 1.19) and pre-match (08:43 ± 01:03; g = 1.98). Post-travel sleep onset latency and sleep efficiency were significantly shorter (-20; g = 1.35) and higher (+10.4%; g = 0.74) at home than away respectively. Longer sleep onset latencies and shorter total sleep times were significantly associated with poorer One-Day International (r_s = -0.57) and Test (r_s = 0.59) batting performances respectively. The poor post-match sleep behaviour, and the sleep and performance correlations, provide motive for future interventions to focus on recovery and the use of sleep monitoring as a competitive advantage.

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.