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

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

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

Sleep Behaviors and Nutritional Knowledge in Amateur and Professional Combat Sport Athletes

ABSTRACT

Dunican, IC, Galpin, A, Turner, M, and Reale, R. Sleep behaviors and nutritional knowledge in amateur and professional combat sport athletes. J Strength Cond Res XX(X): 000-000, 2024-Combat sports is a term applied to various competitive contact sports involving 2 competitors engaging in a bout or contest under a specific ruleset. Little is known about combat sports athletes' sleep and nutritional knowledge. This study aimed to assess (a) sleep behaviors, (b) alcohol and shiftwork disorders, and (c) the nutritional knowledge of combat sports athletes. Subjects completed various demographic questions and estimations of their weight management practices and validated surveys, including the Athlete Sleep Behavior Questionnaire, Shiftwork Disorder (SWD) Screening Questionnaire, Alcohol Use Disorders Identification Test, and the Abridged Nutrition for Sports Knowledge Questionnaire. A total of 844 combat sports athletes completed the survey, 79% male, height of 177 ± 10 (cm), and body mass of 79 ± 15 (kg). Subjects were categorized into their specific combat sport type (e.g., boxing or Brazilian jiu-jitsu [BJJ]) and by their level (professional or amateur). Sleep quality was average for all combat sport types and risk prevalence of shiftwork disorder at 11%. Mann-Whitney U tests revealed that amateurs, compared with professional strikers (p = 0.046, CLES = 0.80), boxers (p = 0.01, CLES = 0.78), and BJJ (p = 0.046, CLES = 0.61) athletes, consumed significantly more alcohol. A Friedman test showed significant (p < 0.001, W = 0.51) differences in weight between time points, with most combat sports athletes reducing weight for competition. An overall nutritional knowledge average of 55% was reported. This study highlights a critical gap in the knowledge and practices of combat sports athletes in relation to sleep behaviors and disorders, alcohol consumption, and nutritional knowledge, emphasizing the need for targeted educational interventions.

Effect of Training Load Intensification on the Sleep Pattern of Young Soccer Players

Purpose: The aim of this study was to examine how intensifying training loads over a week affects the sleep patterns of young soccer players on the nights immediately following the intensified training sessions. Methods: Quasi-experimental study. Fifteen young athletes participants of a team engaged in national level competition, underwent two weeks of training with varying load magnitudes-Week 1: low accumulated training load and Week 2: intensified training loads [40% increase in external training load(ETL)]. To characterize the intensification of the workload, the methods PlayerLoad and RPE-Session were employed to measure ETL and internal training load(ITL), respectively. Total sleep time(TST), total time in bed(TTB), sleep efficiency(SE), sleep latency(SL), and wake after sleep onset(WASO) were obtained using actigraphy and daily sleep log. The variables were compared among the days of week (e.g. Monday of week 1 with Monday of week 2, and so forth). Results: Acute training intensification in week 2 led to significant increases in ETL and ITL on Monday and on Wednesday(p < .05), and ETL(p < .05) on Friday on the second week. Improvements in sleep were observed (Tuesday-TST:+80 min, WASO:-29.3 min, SL:-8 min, SE:+9%; Thursday-TST:+86 min, SL:-4 min, SE:+4%; Saturday-TST:+40 min, SL:+1 min) compared to the same day of the previous week. Correlations between ETL and ITL(r = 0.637), ITL and TST(r = 0.572), ITL and SE(r = 0.548) were found. Conclusion: Intensification of training loads results in alterations in sleep variables, notably an elevated TST and SE in the days subsequent to the acute load increment.

Adapting the athlete sleep behavior questionnaire (ASBQ) for Arabic-speaking populations: Translation, reliability testing, and validation using classical theory and item response theory

Sleep is essential for athletes' physical performance as well as their general health, well-being, and quality of life. To assess athletes' sleep behaviors, the Athlete Sleep Behavior Questionnaire (ASBQ) was developed in the English language. However, a validated Arabic-version of the ASBQ is lacking. This study aimed to translate the ASBQ into Arabic (ASBQ-AR) and evaluate its reliability and validity among Arabic-speaking athletes. A total of 458 participants (254 athletes, 202 non-athletes) from four Arabic countries completed the ASBQ-AR and the Insomnia Severity Index (ISI) questionnaires. The psychometric properties of the ASBQ-AR were examined using unidimensional reliability analysis, confirmatory factor analysis (CFA), Item Response Theory (IRT), and convergent validity. The ASBQ-AR had acceptable internal consistency (Cronbach's α = 0.723, McDonald's ω = 0.725) and a factorial structure, confirming its construct validity. CFA demonstrated improved model fit indices after the removal of two potentially misfitting items (items 4 and 13); however, the model's fit to the data remains suboptimal. IRT results indicated that the majority of items demonstrated a good model fit, suggesting effective measurement of the intended construct without significant interference, except for ASBQ-AR 4. Additionally, ASBQ-AR 4 appears to present the highest level of difficulty for respondents. In terms of convergent validity, the mean ASBQ-AR global score was correlated with the mean ISI global score (r = 0.5, p < 0.0001). The ASBQ-AR is a reliable and valid tool for assessing maladaptive sleep practices among Arabic-speaking athletes. Additional refinements to the ASBQ-AR are warranted to optimize its psychometric properties.

Assessing sleep quality in elite and junior cyclists

In the pursuit of optimal recovery, the significance of sleep cannot be overstated for elite cyclists, including high-level cyclists within the junior category. This study aims to assess the sleep quality of elite athletes of different categories and disciplines, including junior. The sleep quality of 112 high-level cyclists (males n = 80; females n = 32) participating in endurance and sprint disciplines was evaluated using the Pittsburgh Sleep Quality Index (PSQI). A noteworthy 41% of both elite and junior cyclists displayed poor sleep quality. No significant differences were observed between elite and junior cyclists in terms of sleep quality, but there was a medium effect size, indicating greater sleep efficiency in junior cyclists [0.36 (0.16, 0.53)]. Gender differences were found, with females exhibiting worse PSQI scores (males = 4.00 [2.25]; females 5.00 [3.00]; p = 0.035). Endurance cyclists spent more time in bed compared to cyclists from sprinting disciplines (8:30 [1.00] and 8:00 [1:03], respectively; p = 0.019). These findings reveal poor sleep habits, even among individuals classified as good sleepers by the PSQI, emphasizing the importance of preventing sleep disorders in cyclists. This study provides valuable insights into athlete sleep quality, encompassing various categories, genders, and cycling disciplines. In conclusion, elite cyclists demonstrate suboptimal sleep quality, suggesting the potential for interventions utilizing the PSQI. These findings advocate for the incorporation of sleep quality assessments into routine evaluations for athletes.

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

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

Athletic retirement: factors contributing to sleep and mental health problems

Introduction

The aim of this investigation was to determine which factors were associated with symptoms of sleep and mental health disorders in former athletes.

Methods

Former athletes (N = 173, 50% women) who retired from any competition level within the last 20 years participated in an online survey. The survey consisted of the Athlete Sleep Screening Questionnaire (ASSQ), Personal Wellbeing Index-Adult (PWI-A), Center for Epidemiologic Studies Depression Scale-Revised (CESD-R), and Generalised Anxiety Disorder Assessment (GAD-7).

Results

Binary logistic regressions revealed that both age (OR = 0.95 [95% CI:0.92, 0.99], p = 0.007) and gender (OR = 2.28 [95% CI:1.09, 4.79], p = 0.029) were associated with anxiety, with women and younger ex-athletes presenting greater risk of anxiety symptoms. Higher body mass was associated with an increased risk for sleep difficulty (OR = 1.13 [95% CI:1.03, 1.23], p = 0.008), sleep disordered breathing (OR = 1.20 [95% CI:1.10, 1.30], p < 0.001), and compromised wellbeing (OR = 0.89 [95% CI:0.83, 0.96], p = 0.001). Athletes who subjectively placed a lower priority on sport while competing presented greater risk of sleep disordered breathing (OR = 2.00[95% CI:1.05, 3.80], p = 0.035). No associations between recency retirement and any outcome measures were observed.

Discussion

Findings suggest potential predictive factors for difficulty transitioning out of sport. Future longitudinal research should consider the interplay between sport re-engagement and the incidence and chronicity of sleep and mental health disorders.

Disruptions to sleep and circadian rhythms are associated with poorer athlete mental health in female, but not male, elite Australian Rules footballers

Elite athletes are vulnerable to sleep and circadian disruption and associated mental health symptoms. This study aimed to investigate sex differences in sleep, circadian rhythms, and mental health, as well as the moderating role of sex in the prediction of mental health, among male professional and female semi-professional elite athletes. Participants were 87 elite Australian Rules football (ARF) athletes (43% female; mean [standard deviation] age 24.0 [4.1] years). Participants completed baseline questionnaires, 2 weeks of sleep/wake monitoring via actigraphy, and a circadian phase assessment (dim-light melatonin onset [DLMO]). Cross-sectional data were collected in training-only Australian Football League (AFL) Men's and Women's pre-season periods, with 53 providing data in two pre-seasons. Female athletes, relative to males, reported poorer mental health (a higher athlete psychological strain score), had a later mid-sleep time (by 28 min), reported a greater preference towards eveningness, and displayed a later circadian phase (by 33 min). For female athletes, lower sleep efficiency and lower sleep regularity were associated with poorer mental health. For female athletes, there were U-shaped relationships between both morningness-eveningness and phase angle (interval between sleep onset and DLMO time) and mental health. No significant relationships were found for male athletes. In summary, elite female ARF athletes reported poorer mental health, relative to males, especially when experiencing sleep or circadian disruption. Lifestyle factors associated with sex differences in ARF professionalism (scheduling, finances, supports) may contribute to these findings. Programmes to improve sleep, circadian alignment, and mental health among female semi-professional elite athletes should be strongly considered.

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

Introduction

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

Purpose

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

Participants and Methods

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

Results

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

Conclusion

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

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.

Evaluating the reliability of the athlete sleep behavior questionnaire (ASBQ): a meta-analysis of Cronbach's alpha and intraclass correlation coefficient

BACKGROUND

The Athlete Sleep Behavior Questionnaire (ASBQ) was designed to identify maladaptive sleep practices among athletes. The aim of this meta-analysis was to evaluate the internal consistency and the test-retest reliability coefficients of the ASBQ.

METHODS

A systematic search across 10 databases from inception of the ASBQ to August 2023 was performed. Publications that reported estimates of internal consistency and/or test-retest reliability of the ASBQ were included. A random-effects model was employed to estimate the overall reliability measures of the ASBQ.

RESULTS

Meta-analytic results demonstrated a good level of internal consistency within the ASBQ, evidenced by a Cronbach's alpha of 0.73 (95% CI: 0.63 to 0.80). This suggests a modest correlation among the questionnaire items, supporting its reliability as an effective measure of sleep behavior. In terms of test-retest reliability, our meta-analysis revealed a very good degree of consistency (ICC = 0.88; 95% CI: 0.87 to 0.89), suggesting that the ASBQ can serve as an instrument for monitoring and evaluating changes in athletes' sleep behavior over time. No evidence of publication bias was identified.

CONCLUSION

While the ASBQ demonstrates a moderate level of internal consistency, its test-retest reliability suggests that it can serve as an instrument for longitudinal assessments of athletes' sleep behavior. Future studies focusing on refining the ASBQ to optimize its internal consistency and validate its applicability across diverse athletic populations are warranted.

Waking Up to the Issue! Research Inattention and Sex-Related Differences Warrant More Sleep Studies in Female Athletes

Understanding sleep patterns and behaviors of athletes is essential for developing targeted sleep-based interventions for implementation in practice. However, more than double the number of sleep studies have examined male athletes compared with female athletes, making the current understanding of sleep patterns, behaviors, and interventions among athletes disproportionately indicative of men. Consequently, this review demonstrates the need for more female-specific sleep data among athlete populations due to research inattention and sex-related differences. Specifically, this review identifies variations in sleep patterns and behaviors between male and female athletes, as well as physiological and lifestyle factors that potentially affect sleep patterns and behaviors across the lifespan, specifically in female athletes. In this regard, evidence suggests some female athletes experience longer sleep durations and better objective sleep quality, but similar or worse subjective sleep quality compared with male athletes. Additionally, scheduling training in the morning or throughout the day may benefit sleep in some female athletes. Considering sleep disorders, women may be at greater risk for insomnia and restless legs syndrome compared with men, which may be attributed to pregnancy, as well as a higher prevalence of anxiety and depressive symptoms, iron deficiency without anemia, and use of psychotropic medication among women. Finally, the menstrual cycle, menstrual disorders, oral contraceptive use, and the postpartum period have been shown to exert detrimental effects on sleep patterns and behaviors and should theoretically be considered when monitoring and managing sleep in female athletes.

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

PURPOSE

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

METHOD

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

RESULTS

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

CONCLUSION

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

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.

High-intensity circuit training change serum myostatin but not myogenin in adolescents' soccer players: a quasi-experimental study

BACKGROUND

Skeletal muscle contractions due to exercise lead to the secretion of many proteins and proteoglycan peptides called myokines. Myostatin (MSTN) and Myogenin (MyoG) are two of the most important skeletal muscle growth regulatory factors related to myoblast differentiation and muscle hypertrophy. The present study aims at investigating the effects over eight weeks of high-intensity circuit training (HICT) on serum MyoG and MSTN in male soccer players.

METHOD

The present study is a quasi-experimental study on 21 male soccer players (Experimental group: n = 11, Control group: n = 10) (ages 15.0 ± 3.4 years, body mass 55.7 ± 7.8 kg, height 173.3 ± 8.0 cm, Body mass index 18.4 ± 1.9 kg m^-2, maximum oxygen uptake 61.89 ± 3.01 ml kg^-1 and the peak height velocity 14.5 ± 0.3 years). Participants were randomly divided into two groups: training group and a control group. The first resting blood samples were obtained in the morning-fasting state, and the second blood samples were obtained after the maximum aerobic test at pre- and post-HICT.

RESULTS

There were non-significant differences in resting serum values of MyoG (p = 0.309, p > 0.05) but significant differences in resting serum values of MSTN between the training and control groups after eight weeks of HICT (p = 0.003, p < 0.05). No significant differences were observed between groups in the acute response of serum values of MyoG (p = 0.413, p < 0.05) and MSTN (p = 0.465, p < 0.05) to the maximum aerobic test after eight weeks of HICT.

CONCLUSION

These results suggest that eight weeks of HICT can decrease the resting serum values of MSTN but not change the resting serum values of MyoG in male adolescent soccer players. Also, eight weeks of HICT does not affect the acute response of MSTN and MyoG after a maximum aerobic test.

Sleep Patterns Fluctuate Following Training and Games across the Season in a Semi-Professional, Female Basketball Team

Quantifying athlete sleep patterns may inform development of optimal training schedules and sleep strategies, considering the competitive challenges faced across the season. Therefore, this study comprehensively quantified the sleep patterns of a female basketball team and examined variations in sleep between nights. Seven semi-professional, female basketball players had their sleep monitored using wrist-worn activity monitors and perceptual ratings during a 13-week in-season. Sleep variables were compared between different nights (control nights, training nights, training nights before games, nights before games, non-congested game nights, and congested game nights), using generalized linear mixed models, as well as Cohen's d and odds ratios as effect sizes. Players experienced less sleep on training nights before games compared to control nights, training nights, nights before games, and congested game nights (p < 0.05, d = 0.43-0.69). Players also exhibited later sleep onset times on non-congested game nights compared to control nights (p = 0.01, d = 0.68), and earlier sleep offset times following training nights before games compared to all other nights (p < 0.01, d = 0.74-0.79). Moreover, the odds of players attaining better perceived sleep quality was 88% lower on congested game nights than on nights before games (p < 0.001). While players in this study attained an adequate sleep duration (7.3 ± 0.3 h) and efficiency (85 ± 2%) on average across the in-season, they were susceptible to poor sleep on training nights before games and following games. Although limited to a team-based case series design, these findings suggest basketball coaches may need to reconsider scheduling team-based, on-court training sessions on nights prior to games and consider implementing suitable psychological and recovery strategies around games to optimize player sleep.

A Delayed Evening Meal Enhances Sleep Quality in Young Rugby Players

The aim of this study was to examine the effect of delayed evening mealtime on sleep quality in young athletes. Twelve rugby players (age 15.8 ± 0.7 years) participated in a crossover within-participant design. Adolescents spent five consecutive days in each of two conditions, separated by a 2-week washout period: routine dinner (3.5 hr before bedtime) and late dinner (LD, 1.5 hr before bedtime). Other mealtimes as well as bedtime and wake-up time were usual and remained the same in both conditions. Their schedules, dietary intakes, and physical activity were controlled and kept constant throughout the study. Sleep was assessed using polysomnography on the first and the last nights in the individual rooms of the boarding school. An increase in total sleep time by 24 min (p = .001, d = 1.24) and sleep efficiency by 4.8% was obtained during LD (p = .001, d = 1.24). Improvement in sleep efficiency was mainly due to a lower wake after sleep onset (-25 min, p = .014, d = -3.20), a decrease of microarousals (-25%, p = .049, d = -0.64), and awakenings ≥90 s (-30%, p < .01, d = -0.97) in LD compared to routine dinner. There were no significant differences in sleep architecture except for a shorter slow-wave sleep (N3) latency (-6.9 min, p = .03, d = -0.778) obtained during LD. In this study, evening dinner 1.5 hr before bedtime leads to better quality and less fragmented sleep compared to evening dinner 3.5 hr before bedtime in young athletes.

Complex Association Among Diet Styles, Sleep Patterns, and Obesity in Patients with Diabetes

BACKGROUND

Health risk factors (HRFs), including the adjustment of disturbed sleep patterns (including disorders and duration) and improvement of dietary intake, have become relatively novel and critical strategies to prevent the development of diabesity and treat diabetic complications.

OBJECTIVE

We aimed to explore 1) whether there was an association between diet styles (including healthy and unhealthy diets) and diabesity; 2) whether sleep patterns could moderate this relationship; and 3) whether there was a complex interaction association between sleep patterns, diet styles, and diabesity.

METHODS

The study was based on a national survey conducted by the China National Diabetic Chronic Complications Study Group, this study extracted data from some cities in Anhui Province and obtained basic and lifestyle information using a detailed questionnaire for analysis. The primary exposure was diet styles, and the outcomes were body mass index (BMI) and waist circumference (WC), while HbA1c and FBG and sleep patterns were moderators. Data were pooled using logistic regression and moderation analysis.

RESULTS

The overall response rate was 92.0%. This study ultimately included 1765 participants. The mean age was 57.10 ± 10.0 years. Sociographically, participants with lower educational levels were more likely to have lower levels of WC (χ ² = 2.73) and BMI (χ ² = 3.47), were female (t=6.54), were more likely to have lower educational levels (F=13.78) and were older (F=23.75), were more likely to have higher detection rates of sleep disorders and were more likely to have other HRFs (walking, SSBs, HbA1c and TG). Additionally, diet styles were also significantly associated with BMI and WC. In the moderation analysis, SES (socioeconomic status) also affected the correlation between healthy diet style, sleep disorders and WC (β=0.20), neither in BMI nor unhealthy diet style in BMI and WC; the combination effect between diet styles, sleep patterns and HbA1c was associated with WC and BMI.

CONCLUSION

Complex associations and interactions were found between diet styles, sleep patterns, HbA1c, and diabesity. Therefore, it is necessary to understand the dietary pattern and other HRFS that cause diabesity, so as to strengthen further preventive measures. These results can provide some theoretical basis for the treatment of diabesity in the public health field.

A Strategy to Inform Athlete Sleep Support From Questionnaire Data and Its Application in an Elite Athlete Cohort

PURPOSE

Information from the Pittsburgh Sleep Quality Index (PSQI) and Athlete Sleep Behavior Questionnaire (ASBQ) provide the ability to identify the sleep disturbances experienced by athletes and their associated athlete-specific challenges that cause these disturbances. However, determining the appropriate support strategy to optimize the sleep habits and characteristics of large groups of athletes can be time-consuming and resource-intensive. The purpose of this study was to characterize the sleep profiles of elite athletes to optimize sleep-support strategies and present a novel R package, AthSlpBehaviouR, to aid practitioners with athlete sleep monitoring and support efforts.

METHODS

PSQI and ASBQ data were collected from a cohort of 412 elite athletes across 27 sports through an electronic survey. A k-means cluster analysis was employed to characterize the unique sleep-characteristic typologies based on PSQI and ASBQ component scores.

RESULTS

Three unique clusters were identified and qualitatively labeled based on the z scores of the PSQI components and ASBQ components: cluster 1, "high-priority; poor overall sleep characteristics + behavioral-focused support"; cluster 2, "medium-priority, sleep disturbances + routine/environment-focused support"; and cluster 3, "low-priority; acceptable sleep characteristics + general support."

CONCLUSIONS

The findings of this study highlight the practical utility of an unsupervised learning approach to perform clustering on questionnaire data to inform athlete sleep-support recommendations. Practitioners can consider using the AthSlpBehaviouR package to adopt a similar approach in athlete sleep screening and support provision.

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.

COVID-19 Lockdowns: A Worldwide Survey of Circadian Rhythms and Sleep Quality in 3911 Athletes from 49 Countries, with Data-Driven Recommendations

Objective

In a convenience sample of athletes, we conducted a survey of COVID-19-mediated lockdown (termed ‘lockdown’ from this point forward) effects on: (i) circadian rhythms; (ii) sleep; (iii) eating; and (iv) training behaviors.

Methods

In total, 3911 athletes [mean age: 25.1 (range 18–61) years, 1764 female (45%), 2427 team-sport (63%) and 1442 elite (37%) athletes] from 49 countries completed a multilingual cross-sectional survey including the Pittsburgh Sleep Quality Index and Insomnia Severity Index questionnaires, alongside bespoke questions about napping, training, and nutrition behaviors.

Results

Pittsburgh Sleep Quality Index (4.3 ± 2.4 to 5.8 ± 3.1) and Insomnia Severity Index (4.8 ± 4.7 to 7.2 ± 6.4) scores increased from pre- to during lockdown (p < 0.001). Pittsburgh Sleep Quality Index was predominantly influenced by sleep-onset latency (p < 0.001; + 29.8%), sleep efficiency (p < 0.001; − 21.1%), and total sleep time (p < 0.001; − 20.1%), whilst Insomnia Severity Index was affected by sleep-onset latency (p < 0.001; + 21.4%), bedtime (p < 0.001; + 9.4%), and eating after midnight (p < 0.001; + 9.1%). During lockdown, athletes reported fewer training sessions per week (− 29.1%; d = 0.99). Athletes went to bed (+ 75 min; 5.4%; d = 1.14) and woke up (+ 150 min; 34.5%; d = 1.71) later during lockdown with an increased total sleep time (+ 48 min; 10.6%; d = 0.83). Lockdown-mediated circadian disruption had more deleterious effects on the sleep quality of individual-sport athletes compared with team-sport athletes (p < 0.001; d = 0.41), elite compared with non-elite athletes (p = 0.028; d = 0.44) and older compared with younger (p = 0.008; d = 0.46) athletes.

Conclusions

These lockdown-induced behavioral changes reduced sleep quality and increased insomnia in athletes. Data-driven and evidence-based recommendations to counter these include, but are not limited to: (i) early outdoor training; (ii) regular meal scheduling (whilst avoiding meals prior to bedtime and caffeine in the evening) with appropriate composition; (iii) regular bedtimes and wake-up times; and (iv) avoidance of long and/or late naps.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40279-021-01601-y.

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

BACKGROUND

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

HYPOTHESIS

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

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 3.

METHODS

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

RESULTS

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

CONCLUSION

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

CLINICAL RELEVANCE

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

Does observance of Ramadan affect sleep in athletes and physically active individuals? A systematic review and meta-analysis

The purpose of this systematic review and meta-analysis is to provide an accurate description of the effect of Ramadan observance on sleep duration, sleep quality, daily nap duration, and daytime sleepiness in athletes and physically active individuals. Five electronic databases (PubMed, Web of Science, Scopus, Wiley, and Taylor and Francis) were used to search for relevant studies conducted with athletes or physically active individuals during Ramadan, published in any language, and available before May 23, 2021. Studies that included assessments of sleep quantity and/or quality, and/or daytime sleepiness, and/or daily naps in athletes and physically active individuals were included. The methodological quality of the studies was assessed using "QualSyst". Of the 18 papers included in this study (298 participants in total), 14 were of strong quality, two were moderate, and the remaining two were rated as weak. Individuals who continued to train during Ramadan experienced a decrease in sleep duration (number of studies, K = 17, number of participants, N = 289, g = -0.766, 95% confidence interval [CI] -1.199 to -0.333, p = 0.001). Additionally, the global score of the Pittsburgh Sleep Quality Index increased from 4.053 (K = 5, N = 65, 95% CI 3.071-5.034) pre-Ramadan, to 5.346 (95% CI 4.362-6.333) during Ramadan, indicating a decrease in sleep quality. The duration of daytime naps increased during compared to pre-Ramadan (K = 2, N = 31, g = 1.020, 95% CI 0.595-1.445, p = 0.000), whereas Epworth Sleepiness Scale scores remained unchanged during versus pre-Ramadan (K = 3, N = 31, g = 0.190, 95% CI -0.139-0.519, p = 0.257). In conclusion, individuals who continued to train during Ramadan experienced a decrease in sleep duration, impairment of sleep quality, and increase in daytime nap duration, with no change in daytime sleepiness levels.

Sex differences in sleep and influence of the menstrual cycle on women's sleep in junior endurance athletes

Previous research shows that female athletes sleep better according to objective parameters but report worse subjective sleep quality than male athletes. However, existing sleep studies did not investigate variations in sleep and sleep stages over longer periods and have, so far, not elucidated the role of the menstrual cycle in female athletes' sleep. To address these methodological shortcomings, we investigated sex differences in sleep and sleep stages over 61 continuous days in 37 men and 19 women and examined the role of the menstrual cycle and its phases in 15 women. Sleep was measured by a non-contact radar, and menstrual bleeding was self-reported. Associations were investigated with multilevel modeling. Overall, women tended to report poorer subjective sleep quality (p = .057), but objective measurements showed that women obtained longer sleep duration (p < .001), more light (p = .013) and rapid eye movement sleep (REM; hours (h): p < .001, %: p = .007), shorter REM latency (p < .001), and higher sleep efficiency (p = .003) than men. R2 values showed that sleep duration, REM and REM latency were especially affected by sex. Among women, we found longer time in bed (p = .027) and deep sleep (h: p = .036), and shorter light sleep (%: p = .021) during menstrual bleeding vs. non-bleeding days; less light sleep (h: p = .040), deep sleep (%: p = .013) and shorter REM latency (p = .011) during the menstrual than pre-menstrual phase; and lower sleep efficiency (p = .042) and more deep sleep (%: p = .026) during the follicular than luteal phase. These findings indicate that the menstrual cycle may impact the need for physiological recovery, as evidenced by the sleep stage variations. Altogether, the observed sex differences in subjective and objective sleep parameters may be related to the female athletes' menstrual cycle. The paper provides unique data of sex differences in sleep stages and novel insights into the role of the menstrual cycle in sleep among female athletes.

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.

A Systematic Review, Meta-Analysis and Meta-Regression on the Effects of Carbohydrates on Sleep

This study aimed to assess the effects of quantity, quality and periodization of carbohydrates consumption on sleep. PubMed, SCOPUS and Cochrane Library were searched through October 2020. Data were pooled using random-effects meta-analysis. Eleven articles were included in the meta-analysis which consisted of 27 separate nutrition trials, resulting in 16 comparison data sets (sleep quantity n = 11; sleep quality n = 5). Compared to high carbohydrate (HCI), low carbohydrate intake (LCI) moderately increased duration and proportion of N3 sleep stage (ES = 0.37; 95% CI = 0.18, 0.56; p < 0.001 and ES = 0.51; 95% CI = 0.33, 0.69; p < 0.001, respectively). HCI prolonged rapid eye movement (REM) stage duration (ES = −0.38; 95% CI = 0.05, −8.05; p < 0.001) and proportion (ES = −0.46; 95% CI = −0.83, −0.01; p < 0.001), compared to LCI. The quality of carbohydrate intake did not affect sleep stages. Meta-regression showed that the effectiveness of carbohydrate quantity and quality in sleep onset latency was significantly explained by alterations of carbohydrate intake as a percentage of daily energy intake (R² = 25.87, p = 0.018) and alterations in the glycemic load (R² = 50.8, p = 0.048), respectively. Alterations in glycemic load partially explained the variance of the effectiveness of carbohydrate quality in sleep efficiency (R² = 89.2, p < 0.001) and wake after sleep onset (R² = 64.9, p = 0.018). Carbohydrate quantity was shown to affect sleep architecture, and especially N3 and REM sleep stages. Alterations in both quantity and quality of carbohydrate intake showed a significant effect on sleep initiation. Variations in carbohydrate quality significantly affected measures of sleep continuation. Further studies are needed to assess the effect of long-term carbohydrate interventions on sleep.

Improving on Half-Lightweight Male Judokas' High Performance by the Application of the Analytic Network Process

Judo is a multifactorial sport where many variables or key performance indicators (KPIs) such as force-velocity profile, bioenergetic capacity, technical and tactical skills, and cognitive and emotional competence play a role and influence the final result. While there have been many academic studies of these variables, usually in isolation, none have examined KPIs holistically or analyzed their impact on strategic performance. The main objective of the present study, therefore, is to apply a novel and easily replicable methodology to identify and prioritize the main KPIs affecting performance in professional judo. Such a methodology was applied to the High-Performance Judo Centre of Valencia, using a multi-criteria decision aid technique: the analytic network process (ANP). The ANP is used to identify and quantify cause and effect relationships not only between KPIs but also between KPIs and performance objectives. Further, the ANP offers effective results when there is a lack of historical KPI data, because it is based on experts' opinions and judgments. A judo expert group (JEG) comprising elite judo coaches and half-lightweight (under 66 kg) male judokas applied the methodology to discriminate between the characteristics required when aiming to reach high-level strategic objectives (such as participating in the Olympic Games or winning a medal in a Grand Slam/Gran Prix). The JEG, which consisted of five elite judokas, national and international champions, and three Olympic coaches-including a former European champion and silver medalist in a world championship-provided high added value to the study. The main findings were that the KPIs that made the most difference were mostly psychological, specifically motivation, stress, and team cohesion. Of these, motivation was by far the most important KPI for success in our case study, so judokas should make sure that they analyze it properly. Motivation is usually intrinsic to the competitor and should be maintained at a high level, not only during tournaments but also during daily training and lifestyle activities. Physical and other specific forms of training, as well as lifestyle KPIs, are very important for the elite competitor but are not sufficient to reach high-level objectives. The most important of these KPIs were Kumi-Kata (grip work), dual career, focus and concentration level, scouting, nutrition, and basic technique. Power and strength were the most important physical KPIs. In general, these are essential for meeting strategic objectives, but they do not make the crucial difference. This suggests that professional psychological support should be provided in daily training and that international team composition and internships should be fostered.

Sleep pattern and staging in elite adolescent rugby players during the in-season competitive phase compared to an age matched non-athlete population

Young athletes must contend with the constraints of elite sports on top of school commitments and the physiological processes associated with adolescence. This study assessed week and weekend sleep and schedule of activities in elite adolescent rugby players during the in-season competitive phase compared with age-matched non-athlete controls. 32 adolescents (GR: 16 elite rugby players, GC: 16 controls) from the same boarding school filled out a daily schedule of activities and a sleep diary, and wore a multichannel electroencephalogram for 14 days. They later filled out questionnaires on their sleep quality, sleepiness, and perceived stress. Both groups showed insufficient sleep duration during the week (<7 h). Only GC caught up on their sleep debt during the weekend (increased TIB, TST and time spent in REM sleep, all p < 0.001). Weekend TIB, TST and, N3 sleep remained similar to that for weekdays in GR. However, GR experienced lower sleep quality (decreased SE, increased WASO, all p < 0.01) and a decrease in REM sleep (p < 0.01). Schedules of activities showed an increase in time spent on overall activities during the weekend, mainly due to competition and sport-specific travel, which resulted in a decrease in sleep opportunity time compared with GC (p < 0.001). Δ sleep opportunity time (weekend-week) was associated with Δ TST (weekend-week), and Δ TST (weekend-week) was associated with sleepiness and perceived stress. Busy schedules during the competitive season decreased sleep opportunity time and prevented elite adolescent rugby players from catching up on their sleep at weekends.HIGHLIGHTS Adolescent rugby players have insufficient sleep during both week and weekend of the in-season competitive phaseCongested schedule prevented elite adolescent rugby players from catching up on their sleep at weekends.Sleep considerations are necessary when planning the weekend sporting schedules.

The effect of evening cycling at different intensities on sleep in healthy young adults with intermediate chronobiological phenotype: A randomized, cross-over trial

This study investigated the effect of various cycling intensities on sleep-related parameters in healthy young adults with intermediate chronobiological phenotype. Ten recreationally trained male volunteers underwent an evening i) moderate-intensity continuous training (MICT; 45 min at 70% W_max), ii) high-intensity interval training (HIIT; 10 × 1 min at 90% W_max), iii) sprint interval training (SIT; 6 × 20 sec at 140% W_max) or iv) a non-exercise (CON) trial in randomized, counter-balanced and crossover order. At baseline, somatometric data, maximum oxygen uptake and chronotype were evaluated. Sleep-related indices and daily activity were recorded by a multi-sensor activity monitor. Total sleep time was longer after SIT compared to CON and MICT (p < 0.05). Sleep efficiency was higher in SIT than in CON (p < 0.05). Sleep onset latency did not differ among trials. Wake after sleep onset was decreased after SIT compared to CON (p= 0.049). No differences were found for bedtime among trials. Wake time was earlier in the MICT trial compared to CON (p = 0.026). Evening cycling exercise -independently of intensity- did not impair sleep of individuals with intermediate chronobiological phenotype. Furthermore, a single SIT session improved sleep quantity and continuation of individuals with this specific chronotype.

Is the Da Vinci Xi system a real improvement for oncologic transoral robotic surgery? A systematic review of the literature

In this paper, we reviewed the current literature about TORS procedures performed with Xi system and we discussed the pros and cons of this robotic system comparing it to the previous Si model. A systematic literature review was conducted using the PubMed, Scopus, Web of Science and Cochrane databases for adequate published studies about TORS procedure performed with da Vinci Si and Xi models. Five papers on the TORS-Xi procedures met our inclusion and exclusion criteria. These papers showed results based on 74 subjects (mean age 60.51 ± 4.55 years old). The most common TORS procedure was performed to address the oropharynx (65 procedures). One case (1.35%) of a tongue base tumor was converted into a transmandibular approach. One subject (1.35%) had a positive margin, while two subjects (2.7%) had close/uncertain margins at final histology. Nine papers on the TORS-Si procedures met our inclusion and exclusion criteria. These papers showed results based on 128 subjects (mean age 59.79 ± 5.93 years old). The most common TORS procedure was performed to address the oropharynx (90 procedures). Three procedures (2.34%) were aborted due to inadequate exposure of the tumor. None of the subject had positive margins, while three subjects (2.34%) had close/uncertain margins at final histology. Despite its improved vision, easier docking and narrower robotic arms, the da Vinci Xi system has structural peculiarities that limit its applications for certain TORS procedures.

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.