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Female soccer has grown extensively in recent years, however differences in gender-specific physiology have rarely been considered. The female reproductive hormones which rise and fall throughout the menstrual cycle, are known to affect numerous cardiovascular, respiratory, thermoregulatory and metabolic parameters, which in turn, may have implications on exercise physiology and soccer performance. Therefore, the main aim of the present study was to investigate potential effects of menstrual cycle phase on performance in soccer specific tests. Nine sub elite female soccer players, all of whom have menstrual cycles of physiological length; performed a series of physical performance tests (Yo-Yo Intermittent endurance test (Yo-Yo IET), counter movement jump (CMJ) and 3x30 m sprints). These were conducted at distinct time points during two main phases of the menstrual cycle (early follicular phase (FP) and mid luteal phase (LP)) where hormones contrasted at their greatest magnitude. Yo-Yo IET performance was considerably lower during the mid LP (2833±896 m) as compared to the early FP (3288±800 m). A trend towards significance was observed (p = 0.07) and the magnitude based inferences suggested probabilities of 0/61/39 for superiority/equality/inferiority of performance during the mid LP, leading to the inference of a possibly harmful effect. For CMJ (early FP, 20.0±3.9 cm; mid LP 29.6±3.0 cm, p = 0.33) and sprint (early FP, 4.7±0.1 s; mid LP, 4.7±0.1 s, p = 0.96) performances the results were unclear (8/24/68, 48/0/52, respectively). The results of this study are in support of a reduction in maximal endurance performance during the mid LP of the menstrual cycle. However, the same effect was not observed for jumping and sprint performance. Therefore, consideration of cycle phase when monitoring a player's endurance capacity may be worthwhile.

Although its true function remains unclear, sleep is considered critical to human physiological and cognitive function. Equally, since sleep loss is a common occurrence prior to competition in athletes, this could significantly impact upon their athletic performance. Much of the previous research has reported that exercise performance is negatively affected following sleep loss; however, conflicting findings mean that the extent, influence, and mechanisms of sleep loss affecting exercise performance remain uncertain. For instance, research indicates some maximal physical efforts and gross motor performances can be maintained. In comparison, the few published studies investigating the effect of sleep loss on performance in athletes report a reduction in sport-specific performance. The effects of sleep loss on physiological responses to exercise also remain equivocal; however, it appears a reduction in sleep quality and quantity could result in an autonomic nervous system imbalance, simulating symptoms of the overtraining syndrome. Additionally, increases in pro-inflammatory cytokines following sleep loss could promote immune system dysfunction. Of further concern, numerous studies investigating the effects of sleep loss on cognitive function report slower and less accurate cognitive performance. Based on this context, this review aims to evaluate the importance and prevalence of sleep in athletes and summarises the effects of sleep loss (restriction and deprivation) on exercise performance, and physiological and cognitive responses to exercise. Given the equivocal understanding of sleep and athletic performance outcomes, further research and consideration is required to obtain a greater knowledge of the interaction between sleep and performance.

Evidence-based practice (EBP) is the collated integration of practitioner expertise, athlete values and research evidence aimed to optimise the decision-making process surrounding sport performance. Despite the academic interest afforded to sport science research, our knowledge of how this research is applied in elite sport settings is limited. This current opinion examines the existing evidence of the translation of sport science research into the field, with a tailored focus on the current perceptions of practitioners, researchers and coaches. Recent studies show that practitioners and researchers report they ascertain sport science knowledge differently, with coaches preferring personal interactions compared with coaching courses or scientific journals. The limited peer-reviewed research shows that coaches perceive their knowledge is greater in fields such as tactical/technical areas, rather than physical fitness or general conditioning. This likely explains coaches’ greater perceived value in research dedicated to technical and tactical expertise, as well as mental training and skill acquisition. Practitioners place a large emphasis on the need for research in physical fitness areas, which is likely due to their occupational focus. There are many perceived barriers of sport science research application, including funding, time, coach/player/staff ‘buy in’ and research questions that may not apply to the setting. We contend that researchers and practitioners may benefit in producing research, ascertaining knowledge and disseminating findings in alternative methods that better align with coaches’ needs. In addition, educational strategies that focus on real-world context and promote social interaction between coaches, practitioners, organisational personnel and researchers would likely benefit all stakeholders.

As part of both training and active service, military members can be exposed to prolonged periods of sleep loss. Given the extent of physical and cognitive performances viewed as critical to successful military performance, such sleep disruption may present risk to health and performance. The primary aim of this narrative review was to investigate evidence on the effect of inadequate sleep on measures of aerobic capacity, anaerobic capacity, muscular strength and muscular endurance in military personnel. Sleep loss appears to have the greatest negative impact on aerobic capacity, muscular endurance and military-specific performance in military populations. The findings showed varied results for handgrip strength and anaerobic capacity, with sleep loss inducing a decrease in mean power of the upper body. In comparison to other measures of performance, lower-body muscular strength appeared to be resilient to sleep restriction. However, due to the limited evidence and inter-individual variability in results there is no clear consensus on the specific volume of sleep loss that induces significant or meaningful performance decrements. The difficulties of conducting well-designed and -controlled interventions in military populations are appreciated. However, due to the low quality of reporting and lack of control for confounders (i.e. physical activity, load carriage, prior sleep debt, motivation and energy intake) in the majority of studies, it is difficult to establish the relationship between sleep loss and physical performance in military populations.

Elite athletes are particularly susceptible to sleep inadequacies, characterised by habitual short sleep (<7 hours/night) and poor sleep quality (eg, sleep fragmentation). Athletic performance is reduced by a night or more without sleep, but the influence on performance of partial sleep restriction over 1–3 nights, a more real-world scenario, remains unclear. Studies investigating sleep in athletes often suffer from inadequate experimental control, a lack of females and questions concerning the validity of the chosen sleep assessment tools. Research only scratches the surface on how sleep influences athlete health. Studies in the wider population show that habitually sleeping <7 hours/night increases susceptibility to respiratory infection. Fortunately, much is known about the salient risk factors for sleep inadequacy in athletes, enabling targeted interventions. For example, athlete sleep is influenced by sport-specific factors (relating to training, travel and competition) and non-sport factors (eg, female gender, stress and anxiety). This expert consensus culminates with a sleep toolbox for practitioners (eg, covering sleep education and screening) to mitigate these risk factors and optimise athlete sleep. A one-size-fits-all approach to athlete sleep recommendations (eg, 7–9 hours/night) is unlikely ideal for health and performance. We recommend an individualised approach that should consider the athlete’s perceived sleep needs. Research is needed into the benefits of napping and sleep extension (eg, banking sleep).

Athletes are increasingly required to travel domestically and internationally, often resulting in travel fatigue and jet lag. Despite considerable agreement that travel fatigue and jet lag can be a real and impactful issue for athletes regarding performance and risk of illness and injury, evidence on optimal assessment and management is lacking. Therefore 26 researchers and/or clinicians with knowledge in travel fatigue, jet lag and sleep in the sports setting, formed an expert panel to formalise a review and consensus document. This manuscript includes definitions of terminology commonly used in the field of circadian physiology, outlines basic information on the human circadian system and how it is affected by time-givers, discusses the causes and consequences of travel fatigue and jet lag, and provides consensus on recommendations for managing travel fatigue and jet lag in athletes. The lack of evidence restricts the strength of recommendations that are possible but the consensus group identified the fundamental principles and interventions to consider for both the assessment and management of travel fatigue and jet lag. These are summarised in travel toolboxes including strategies for pre-flight, during flight and post-flight. The consensus group also outlined specific steps to advance theory and practice in these areas.

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.

Rugby league is a physically demanding sport that necessitates considerable nutritional intake, focusing on quality and type, in order to optimize training and competition demands. However, rugby league athletes are reported to have inadequate nutrition intake to match these demands. Some factors that may determine an athlete's nutrition intake have been reported in other sports, including (but not limited to, knowledge, time, cooking skills, food costs, income, belief in the importance of nutrition, body composition goals, and family/cultural support). However, these potential factors are relatively unexplored in rugby league, where a range of personal (age, body composition) or social (ancestry) influences could affect nutritional intake. Further exploration of these factors is warranted to understand the knowledge, attitudes and behavior underlying rugby league athletes' nutritional intake that can provide practitioners with a more detailed understanding of how to approach nutrition behaviors and attitudes in rugby league athletes. The primary aim was to describe the nutrition behaviors and knowledge of rugby league athletes. A secondary aim was to compare nutrition knowledge and behavior based on age, body composition and self-identified ancestry. Fifty professional rugby league athletes anonymously completed a seventy-six-question online survey. The survey consisted of three sections : 1) sports nutrition knowledge, 2) attitudes toward nutrition on performance , and 3) nutrition behaviors. All participants completed the online survey without assistance using their own personal device, with data entered via REDCap during pre-season. Nutrition knowledge was compared based on age (years), body composition (body fat percentage (%)) and ancestral groups (Pasifika, Aboriginal and/or Torres Strait Islander (ATSI) and Anglo- European).Pearson correlation was used for the relationship between nutrition knowledge, age and body composition. An Analysis of Covariance (ANCOVA) was used to determine nutrition knowledge differences between ancestral groups with age and body composition as covariates. Attitudes and behaviors were compared based on age groups (<20, 20-24 and >25 y), ancestry and body composition. Attitudes and behaviors were analyzed by Pearson correlation for body composition, one-way ANOVA for age groups and ANCOVA for ancestry with covariates age and body composition. Overall athletes' nutrition knowledge score was reported as 40 ± 12% (overall rating \"poor\"). Nutritional behaviors were significant for body composition, as those with lower body fat percentage had higher intakes of vegetables and dairy products (  = 0.046,  = 0.009), and ate more in the afternoon (lunch  = 0.048, afternoon snack  = 0.036). For ancestry, after adjustment for both age and body composition, Pasifika athletes were more inclined to miss breakfast and lunch compared to their Anglo-European (  = 0.037,  = 0.012) and ATSI (  = 0.022,  = 0.006) counterparts and ate more fruit than Anglo-Europeans (  = 0.006,  = 0.016). After adjustment for body composition, ATSI athletes also viewed the impact of nutrition on mental health and well-being significantly lower than Pasifika (  = 0.044). These findings suggest differences exist within rugby league athletes based on ancestral backgrounds and body composition for nutrition attitudes, behaviors and knowledge. Such outcomes could be used when designing nutrition education interventions, with consideration given to these factors to optimize long-term positive behavior change.

Subjective assessment of athletes’ movement quality is widely used by physiotherapists and other applied practitioners within many sports. One of the beliefs driving this practice is that individuals who display ‘poor’ movement patterns are more likely to suffer an injury than those who do not. The aim of this review was to summarize the reliability of the movement screens currently documented within the scientific literature and explore the evidence surrounding their association with injury risk. Ten assessments with accompanying reliability data were identified through the literature search. Only two of these ten had any evidence directly related to injury risk. A number of methodological issues were present throughout the identified studies, including small sample sizes, lack of descriptive rater or participant information, ambiguous injury definitions, lack of exposure time reporting and risk of bias. These factors, combined with the paucity of research on this topic, make drawing conclusions as to the reliability and predictive ability of movement screens difficult. None of the movement screens that appear within the scientific literature currently have enough evidence to justify the tag of ‘injury prediction tool’.

This study described the relationship of age, body composition and ancestry on sleep behavior, knowledge, and attitudes in rugby league athletes. Fifty rugby league athletes completed the Sleep Practices and Attitudes Questionnaire alongside demographic information (age, body composition, ancestry). The results were compared based on age (<20 years old, 20−24 years old and >25 years), body composition (body fat percentage (%)) and ancestral groups (Pasifika, Aboriginal and/or Torres Strait Islander (ATSI) and Anglo-European). Spearman's correlation determined the associations between body composition and sleep knowledge, beliefs and attitudes. An ANCOVA compared differences between ancestral groups with age and body composition as covariates, while a one-way ANOVA was used for age group comparisons. Younger athletes had higher scores for eating/drinking in bed (p = 0.039), while those with higher body fat percentage read less in bed (p = 0.022) and reported lower sleep quality (p = 0.027). For ancestry, significantly lower sleep difficulty scores were reported for Anglo-European compared to both ATSI and Pasifika (p < 0.05) athletes. Furthermore, significantly higher self-reported sleep durations existed between Anglo-Europeans and Pasifika (p = 0.030). Ancestry affected coping with chronic insomnia (prioritize sleep, reduce caffeine), activities in bed (eat/drink, work/thinking) and the physical environment (dark, physically comfortable, comfortable temperature). These findings underscore the importance of accounting for cultural and physiological variation when designing sleep education and interventions in rugby leagues. Future research needs to consider ancestry, body composition and age when assisting sleep educational programs during rugby league.