A Complex Relationship: Sleep, External Training Load, and Well-Being in Elite Australian Footballers

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.

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 relationship between wellness and training and match load in professional male soccer players

The aims of this study were to: (i) analyse the within-microcycle variations in professional soccer players; (ii) analyse the relationships between wellness and training and match load demands; (iii) analyse the relationships between match-day (MD) demands and wellness during the following day (MD+1); and (iv) analyse the relationships between MD and wellness during the day before match-play (MD-1). Thirteen professional soccer players (age: 24.85±3.13 years) were monitored daily over 16-weeks for wellness and training and match-play intensity. The daily wellness measures included fatigue, quality of sleep, muscle soreness, mood and stress using a 1-5 scale. Internal intensity was subjectively measured daily using the rating of perceived exertion (RPE) and the multiplication of RPE by session duration (s-RPE). While external intensity was quantified utilising high-speed running, sprinting, and acceleration and deceleration metrics. Data was analysed from each training session before (i.e., MD-5) or after the match (i.e., MD+1). Repeated measures ANOVA or Friedman ANOVA was used to analyse the aims (i) where Spearman correlation was applied to analyse the relationships between the aims (ii) and (iii) between sleep quality and training intensity. The main results for aim (i) showed that MD+1 presented the lowest values for wellness variables (p < 0.05). While MD-1 presented the lowest internal and external load values (for all variables), with MD presenting the highest values (p < 0.05). Regarding aim (ii), the main result showed significant large negative correlations between fatigue and s-RPE (r = -0.593; p = 0.033). Considering aim (iii), significant small to very large negative correlations were found for sleep quality, fatigue and muscle soreness with all internal and external variables (p < 0.05). Lastly, the main results for aim (iv) showed large negative correlations for fatigue and session duration; fatigue and s-RPE; muscle soreness and session duration; muscle soreness and s-RPE; and muscle soreness and decelerations (p < 0.05, for all). The main conclusions were that MD had an influence on wellness and internal and external training intensity, notably MD-1 and MD+1 were most affected. In this regard, a tendency of higher internal and external intensity on MD was associated with lower wellness measures of sleep quality, muscle soreness and fatigue on MD+1.

Association Between Variations in Training Load, Sleep, and the Well-Being of Professional Hockey Players

PURPOSE

To evaluate whether sleep quantity and quality of professional hockey players is affected by external training load (TL), their perception of well-being, and contextual factors associated with match participation.

METHODS

Fifty male athletes were monitored daily during the 28 weeks of the regular season using well-being and sleep surveys. On-ice external TL was monitored using portable inertial measurement units during practices and matches. Linear mixed-effects models were applied to evaluate whether well-being perception (ie, pain, nutrition, stress, and rest) and external TL may affect sleep quality and quantity.

RESULTS

High levels of well-being positively affected sleep duration and quality (P < .001), whereas high-intensity TL had a detrimental impact on sleep duration (P = .007). Moreover, away and evening matches had a detrimental effect on sleep quantity and quality (P < .001). Finally, a high match on-ice load per minute had a negative association with sleep quality (P = .04).

CONCLUSIONS

Findings indicate that well-being and high-intensity trainings can impair sleep duration and quality. In addition, high-intensity match load, away matches, and evening matches can impair postmatch sleep. Therefore, monitoring well-being in conjunction with TL is essential to understand sleep disturbances in athletes. Practitioners should also implement sleep hygiene strategies that facilitate longer time in bed after high-intensity, away, and evening matches to help athletes recover.

The Influence of Different Training Load Magnitudes on Sleep Pattern, Perceived Recovery, and Stress Tolerance in Young Soccer Players

ABSTRACT

Ferreira, ABdM, Ribeiro, BLL, Batista, EdS, Dantas, MP, and Mortatti, AL. The influence of different training load magnitudes on sleep pattern, perceived recovery, and stress tolerance in young soccer players. J Strength Cond Res 37(2): 351-357, 2023-The aim of this study was to analyze the influence of 3 weeks on sleep parameters, perceived recovery, and stress tolerance in young soccer players using different training load magnitudes. A total of 13 young male soccer athletes (15.9 ± 0.5 years; 68.7 ± 6.1 kg; 170 ± 7 cm) who performed 3 typical training weeks with different workloads were analyzed. The external training load (ETL) was verified by the PlayerLoad method, and the internal training load (ITL) was determined using the session rating of perceived exertion method. Sleep was monitored using a wrist-actigraphy monitor. Sleep variables, including total time in bed (TTB), total sleep time (TST), sleep latency (SL), wake after sleep onset (WASO), and sleep efficiency (SE), were evaluated across all nights of sleep. The recovery status was assessed with the perceived recovery status (PRS) scale, and the stress tolerance was monitored using the "daily analysis of life demands of athletes" questionnaire. There was an increase in sleep time during the week with the highest training load (week 2) (TTB: +35 minutes, TST: +46 minutes, SL: -5 minutes, SE: +3%). There was no difference in the PRS or in the stress tolerance during the evaluation weeks. A very large within-individual correlation was observed between ITL and ETL ( r = 0.78) and moderate within-individual correlation between ETL and TST ( r = 0.34), between ITL and TST ( r = 0.45), and between ITL and SE ( r = 0.359). These results showed that there was an increase in TST during a microcycle with intensified loads, without impairing bedtime and resulting in maintenance of the perceived recovery or stress tolerance values.

Sleep Regularity and Predictors of Sleep Efficiency and Sleep Duration in Elite Team Sport Athletes

Background

Many elite athletes have suboptimal sleep duration and efficiency, potentially due to factors that may impact sleep onset and offset times. Variability in sleep onset and offset may negatively influence sleep. The sleep regularity index (SRI) is a novel metric for sleep regularity, however there are no published descriptions of SRI in elite athletes. Further, contributors to sleep efficiency and duration in elite athletes using objective measures have not been explored.

Methods

Sleep was monitored over a minimum of seven consecutive days (7 to 43)—in 203 elite team sport athletes (age range = 19–36 years; female, n = 79; male, n = 124, total sleep nights = 1975) using activity monitoring and sleep diaries. The sleep regularity index (SRI) was calculated to reflect the night-to-night shifts in sleep by accounting for changes in sleep onset and sleep offset. Sleep characteristics were compared between regular and irregular sleepers and important contributors to sleep efficiency and total sleep time were assessed using multiple linear regression models.

Results

The median sleep regularity index and interquartile range were 85.1 (81.4 to 88.8). When compared to irregular sleepers, regular sleepers demonstrated (1) significantly greater sleep efficiency (p = 0.006; 0.31 medium effect size [ES]), (2) significantly less variability in total sleep time (− p ≤ 0.001; − 0.69, large ES) and sleep efficiency (− 0.34, small ES), (3) similar total sleep time and (4) significantly less variation in sleep onset (p ≤ 0.001; − 0.73, large ES) and offset (p ≤ 0.001; − 0.74, large ES) times. Sleep characteristics explained 73% and 22% of the variance in total sleep time and sleep efficiency, respectively. The most important contributor to total sleep time was a later sleep offset time, while the most important contributors to sleep efficiency were an earlier bedtime and less variable sleep onset times.

Conclusions

Bedtime and a consistent sleep onset time are important factors associated with sleep efficiency in athletes, while sleep offset is an important factor for total sleep time. Coaches and staff can assist their athletes by providing training schedules that allow for both regularity and sufficiency of time in bed where possible.

Validierung des Fragebogens zum Schlafverhalten von Sportlerinnen und Sportlern (FSVS)

Zusammenfassung. Der Athlete Sleep Behavior Questionnaire ist ein englischsprachiger Fragebogen zur Erhebung des individuellen Schlafverhaltens von Sportler_innen. Ziel dieser Studie war die Übersetzung des Athlete Sleep Behavior Questionnaire ins Deutsche sowie die wissenschaftliche Validierung dieser deutschen 18-Item-Version, dem Fragebogen zum Schlafverhalten von Sportlerinnen und Sportlern (FSVS). Die Validierungsstichprobe umfasste 450 Teilnehmende. Mittels eines dichotomen Items zur regelmäßigen Wettkampfteilnahme (ja/nein) wurde in Wettkampfsportler_innen ( n = 209) und Kontrollgruppe ( n = 241) unterteilt. Cronbach-α des FSVS-Gesamtwerts ergab .66. Die Pearson-Produkt-Moment-Korrelationsanalysen ergaben moderate Korrelationen zwischen FSVS-Gesamtwert, der Epworth-Sleepiness-Scale ( r = .33) und dem Pittsburgh-Sleep-Quality-Index ( r = .30). Es wurden signifikante Gruppenunterschiede für den FSVS-Gesamtwert gefunden, t‍(448) = 4.47, p < .001, d = 0.42, bei deskriptiv höheren Werten der Gruppe der Wettkampfsportler_innen. In einer separaten Stichprobe, die 13 Ruderer_innen der deutschen Junioren-Nationalmannschaft umfasste, wurden die Fragebogenwerte anschließend mit objektiven Schlafparametern korreliert. Dabei ließ sich eine hohe Korrelation zwischen FSVS-Gesamtwert und Einschlaflatenz ( rs = .68, p = .011) erkennen. Zusammenfassend ist der FSVS ein schnelles und valides Werkzeug, um dysfunktionale Schlafverhaltensweisen bei Wettkampfsportler_innen zu identifizieren. In Anlehnung an die Originalversion wird die Ableitung von Interventionen anhand einzelner Items empfohlen.

Sleep Quality and Training Intensity in Soccer Players: Exploring Weekly Variations and Relationships

The aim of this study was twofold: it (i) analyzed the weekly variations of sleep quality and training intensity of youth soccer players and (ii) analyzed the relationships between sleep quality and training intensity. This study followed an observational design. Twenty men youth players (age: 18.81 ± 0.41 years) were monitored daily over two weeks for sleep quality and training intensity. Sleep quality was measured daily using the 15-item consensus sleep diary. The training intensity was measured daily using the CR-10 Borg’s scale as a measure of rate of perceived exertion (RPE); a heart rate (HR) sensor was used to measure minimum, average and peak; a global positioning system (GPS) was used for measuring the total distance covered and distances covered at different speed thresholds. Repeated measures ANOVA was used to analyze the weekly variations of sleep quality and training intensity. The Pearson correlation test was executed to analyze the relationships between sleep quality and training intensity. Repeated measures ANOVA revealed significant within-week variations in sleep duration (hours) (p = 0.043), quality of sleep (p = 0.035), RPE (p = 0.007), session-RPE (p = 0.011), HRminimum (p = 0.027), HRpeak (p = 0.005), total distance (p < 0.001), pace (p < 0.002), distance covered at 3.00–6.99 km/h (p < 0.001), distance covered at 7.00–10.99 km/h (p < 0.001), distance covered at 11.00–14.99 km/h (p < 0.001), distance covered at 15.00–18.99 km/h (p < 0.001) and distance covered at >19.00 km/h (p < 0.001). Significant small correlations were found between sleep duration before training and session-RPE (r = 0.252), total distance (r = 0.205), distance covered at 3.00–6.99 km/h (r = 0.209) and distance covered at 7.00–10.99 km/h (r = 0.265). Significant small correlations were found between session-RPE and sleep duration after (r = 0.233), total distance and quality of sleep after (r = 0.198), distance at 3.00–6.99 km/h and quality of sleep after training (r = 0.220), distance covered at >19.00 km/h and quality of sleep after training (r = 0.286), session duration and rested feeling after training (r = 0.227), total distance and rested feeling after training (r = 0.202), distance covered at 11.00–14.99 km/h and rested feeling after training (r = 0.222) and distance covered at >19.00 km/h and rested feeling after training (r = 0.214). In conclusion, sleep duration was longer in the training sessions during the middle of the week; the training intensity was also greater (485.8 ± 56.8 A.U.). Moreover, sleep outcomes after training were slightly correlated with both physiological and locomotor demands.

In-Season Nutrition Strategies and Recovery Modalities to Enhance Recovery for Basketball Players: A Narrative Review

Basketball players face multiple challenges to in-season recovery. The purpose of this article is to review the literature on recovery modalities and nutritional strategies for basketball players and practical applications that can be incorporated throughout the season at various levels of competition. Sleep, protein, carbohydrate, and fluids should be the foundational components emphasized throughout the season for home and away games to promote recovery. Travel, whether by air or bus, poses nutritional and sleep challenges, therefore teams should be strategic about packing snacks and fluid options while on the road. Practitioners should also plan for meals at hotels and during air travel for their players. Basketball players should aim for a minimum of 8 h of sleep per night and be encouraged to get extra sleep during congested schedules since back-to back games, high workloads, and travel may negatively influence night-time sleep. Regular sleep monitoring, education, and feedback may aid in optimizing sleep in basketball players. In addition, incorporating consistent training times may be beneficial to reduce bed and wake time variability. Hydrotherapy, compression garments, and massage may also provide an effective recovery modality to incorporate post-competition. Future research, however, is warranted to understand the influence these modalities have on enhancing recovery in basketball players. Overall, a strategic well-rounded approach, encompassing both nutrition and recovery modality strategies, should be carefully considered and implemented with teams to support basketball players' recovery for training and competition throughout the season.

Business Class Travel Preserves Sleep Quality and Quantity and Minimizes Jet Lag During the ICC Women's T20 World Cup

PURPOSE

To determine the impact of the quality and quantity of sleep during an international flight on subsequent objective sleep characteristics, training and match-day load, self-reported well-being, and perceptions of jet lag of elite female cricketers during an International Cricket Council Women's T20 World Cup.

METHODS

In-flight and tournament objective sleep characteristics of 11 elite female cricketers were assessed using activity monitors. Seated in business class, players traveled west from Melbourne, Australia, to Chennai, India. The outbound flight departed Melbourne at 3:30 AM with a stopover in Dubai for 2 hours. The arrival time in Chennai was 8:10 PM local time (1:40 AM in Melbourne). The total travel time was 19 hours 35 minutes. Perceptual ratings of jet lag, well-being, and training and competition load were collected. To determine the impact of in-flight sleep on tournament measures, a median split was used to create subsamples based on (1) in-flight sleep quantity and (2) in-flight sleep quality (2 groups: higher vs lower). Spearman correlation coefficients were calculated to assess the bivariate associations between sleep measures, self-reported well-being, perceptual measures of jet lag, and internal training and match-day load.

RESULTS

Mean duration and efficiency of in-flight sleep bouts were 4.72 hours and 87.45%, respectively. Aggregated in-flight sleep duration was 14.64 + 3.56 hours. Players with higher in-flight sleep efficiency reported higher ratings for fatigue (ie, lower perceived fatigue) during the tournament period. Tournament sleep duration was longer, and bed and wake times were earlier compared with habitual. Compared with other nights during the tournament, sleep duration was shorter following matches.

CONCLUSIONS

Maximizing in-flight sleep quality and quantity appears to have implications for recovery and sleep exhibited during competition. Sleep duration was longer than habitual except for the night of a match, which suggests that T20 matches may disrupt sleep duration.

The First-Night Effect in Elite Sports: An Initial Glance on Polysomnography in Home-Based Settings

Self-applied portable polysomnography is considered a promising tool to assess sleep architecture in field studies. However, no findings have been published regarding the appearance of a first-night effect within a sport-specific setting. Its absence, however, would allow for a single night sleep monitoring and hence minimize the burden on athletes while still obtaining the most important variables. For this reason, the aim of the study was to assess whether the effect appears in home-based sleep monitoring of elite athletes. The study sample included eight male and 12 female German elite athletes from five different sports. Participants slept with a portable polysomnography for two nights, which they self-applied at night before going to bed. Time in bed and wake-up time in the morning were freely chosen by each individual athlete without any restrictions regarding time or sleeping environment. Participants were asked to keep the same location and time frame during the two days of monitoring and stick to their usual sleeping schedules. Sleep stages were manually scored using 30-s epochs. Sleep parameters and stages were later compared with the help of linear mixed models to investigate the factor time. Significant differences between the two nights were found for percentage of Non-REM sleep [T(19) = -2,10, p < 0.05, d = -0.47, 95%-CI (-7.23, -0.01)] with small effect size, Total Wake Time [T(19) = 2.30, p = 0.03, d = 0.51, 95%-CI (1.66, 35.17)], Sleep Efficiency [T(19) = -2.48, p = 0.02, d = -0.55, 95%-CI (-7.43, -0.63)], and Wake percentage [T(19) = 2.47, p = 0.02, d = 0.55, 95%-CI (0.61, 7.43)] with moderate effect sizes, and N3 Sleep Onset Latency [T(19) = 3.37, p < 0.01, d = 0.75, 95%-CI (7.15, 30.54)] with large effect size. Confidence Intervals for all other indices range from negative to positive values and hence specify, that parameters were not systematically negatively affected in the first night. Findings suggest that some individuals are more affected by the first-night effect than others. Yet, in order to keep the measurement uncertainties to a minimum, a more conservative approach with at least two monitoring nights should be used whenever possible, if no other supporting information on the athletes says otherwise.

Assessment of sleep quality and daytime sleepiness in German national ice hockey players preparing for the world championship

Objectives

Objectives were to examine subjective sleep quality and daytime sleepiness of the German ice hockey junior national team prior to the world championship to identify athletes of concern and areas of optimization with the intention of equally preventing injury and enhancing performance.

Methods

Twenty-one athletes (Mage = 18.5 ± 0.6 years, Mheight = 181.7 ± 4.3 cm, Mweight = 81.4 ± 7.1 kg), playing for national (n = 13) and international (n = 8) home clubs, answered the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) before training camp (T1, day 1) and prior to tournament (T2, day 11).

Results

Overall, 9 players at T1 and 7 at T2 were identified as bad sleepers (PSQI > 5), while high sleepiness (ESS > 10) was found for 6 athletes at each measurement time. Group means and standard deviations reduced descriptively for PSQI (T1 = 5.38 ± 2.31, T2 = 4.57 ± 2.36) and ESS (T1 = 9.24 ± 3.74, T2 = 8.48 ± 3.28). Tendential differences were visible for PSQI in international-based players (Z = −1.7, p = 0.09) and ESS in first-national-league players (Z = −1.73, p = 0.08) over time. Higher PSQI values for international-based players (6.25 ± 2.6) were found compared to first-national-league (5.83 ± 1.60) and lower-league players (4.00 ± 2.08), with large effect sizes for lower-league compared to international (d = 0.95) and national players (d = 0.98) at T1 and small effect sizes compared to first-league players (d = 0.24) at T2.

Conclusion

Findings emphasize great vulnerability and individuality and underline the importance of intraindividual sleep monitoring to meet the requirements needed to equally obtain health and enhance overall performance.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Evidence-based post exercise recovery in combat sports: a narrative review

INTRODUCTION

Some methods such as ergo nutritional aids, cooling or massage among others could improve recovery in combat sports (CS). The effects, doses, duration, and timing of these methods remains unknown. Nowadays, there is no clear consensus regarding the recovery strategies and it is necessary to understand the type of fatigue induced in CS and its underlying mechanisms. The main aim of this article is to review the update literature related to recovery strategies in CS.

EVIDENCE ACQUISITION

A literature search was conducted following preferred reporting items for review statement on the topic of: "combat sports," "recovery," "nutrition," "fatigue," "ergogenic aids," "weight cutting" and "hydration."

EVIDENCE SYNTHESIS

The initial search of the literature detected 369 articles about CS. Later, 307 were excluded after being determined unrelated to recovery or after failure to fulfill the inclusion criteria. Of the 80 included articles, 19 satisfied the final inclusion criteria.

CONCLUSIONS

To optimize CS performance, adequate recovery is required during training and competition processes. Traditional ergo nutritional supplementation of carbohydrates and proteins combined. Besides, the consumption of evidence supported supplementation (green tea, beetroot gels, creatine or alkaline water) improve recovery processes. Further methods of recovery including physical (cold water immersion, massage or photobiomodulation) and physiological (types of active recovery, sleep and rest) therapies have also been shown useful. This narrative review elucidates the important role of recovery techniques in CS.

Season Suspension and Summer Extension: Unique Opportunity for Professional Team-Sport Athletes and Support Staff During and Following the COVID-19 Crisis

Given the emergence of the COVID-19 outbreak, the official declaration of a global pandemic by the World Health Organization, and its consequential disruptions on the professional team sports landscape, it is the main objective of this brief opinion to help ensure that professional team-sport athletes and support staff remain aware as a society of some of the potential pitfalls - illustrated via negative but plausible detrimental scenarios. Finally, new ideas are introduced and evidence-based hypotheses are presented on the following five themes: periodization, exploration, virtual (at-home) training, player monitoring, and continued education, including return-to-competition preparation strategies following these exceptional times of uncharted territory.