The purpose of this literature review is to provide a summary of the current literature on sleep behaviours in team sport, and more specifically in Rugby League. This review will start with an outline of the demands of rugby league and ensuing need for recovery. The necessity of sleep for human function and methods to accurately quantify sleep quantity and quality will be explored. Finally, the review will summarise sleep findings in the athletic population and the various factors that influence sleep in athletes, and how these factors influence athletic recovery and performance.
A review of three databases was conducted to explore literature on sleep in team sports, particularly in rugby league. The databases included Medline, Google Scholar and EBSCO. The keyword search terms were a combination of “football”, “rugby league”, “sleep”, and “athletes”. In addition to the electronic search, secondary searching of reference lists of the identified articles occurred. All identified articles were viewed and read, which included viewing all titles and reading through abstracts. A basic search for rugby league located 513 articles and following a review of all titles and abstracts, 428 articles were excluded. A basic search of “sleep” retrieved 163,089 articles. More keywords were used such as “circadian rhythms”, “propensity” and “measures” to refine the search into more relevant areas. From this, 45 articles were used. A search of “sleep AND athlete” narrowed this search down to 116 articles, with “sleep, recovery and athlete” further narrowing the search to 39 articles. Studies were included if they examined sleep, the physical demands and recovery of rugby league, and/or the interaction of sleep on performance or recovery. Articles were excluded if the subjects were animals or had no relevance to the context (outlined in Figure 1). Articles were not included if they involved Ramadan, or were not specific to the athletic population.
Figure 1: Schematic representation of the literature search process.
Rugby league football is a collision based sport which is intermittent in nature (Twist & Highton, 2013). Match play consists of two 40 minute halves, involving frequent high intensity bouts of running, passing, colliding and tackling, which is interspersed with lower intensity activities, such as jogging, walking and standing (Gabbett, 2005). In the NRL, each professional rugby league team consists of 13 players on field with 4 interchange players. Each positional group partakes in unique roles within the game, and therefore individual match play demands are distinctively different (King, et al., 2009). The major objective in rugby league is to maintain possession whilst advancing the ball over the opposition’s goal line, and consequently scoring a “try” (Gibbs, 1993; Gissane et al., 2002). This must be done before the opposition has made a maximum of six tackles, at which point possession is turned over (Gibbs, 1993; Gissane et al., 2002). The same players are involved in both attacking and opposing sets and, therefore, need to be physiologically capable for the demands of attacking and defensive play (Gabbett, 2005).
The physiological demands of the sport are multifaceted and it is essential that players exhibit abilities in the areas of speed, agility, aerobic and anaerobic capacity, and muscular strength and power (Douge, 1987; Meir, 1994). Additionally, rugby league players require technical and tactical skills specific to match play (Kempton and Coutts, 2016). Thus, success in rugby league relies on a combination of physical, tactical (interaction with other individuals) and technical (individual) skills (Kempton and Coutts, 2016).
The physical demands of rugby league are complex, and predominantly involve running, accelerations, and collisions. Each positional group plays a different role in the game, and therefore match demands will vary between players. The game duration varies between forwards (average ~50 minutes) and backs (typically 80 minutes) (Waldron et al., 2011; Gabbett et al., 2012), and therefore the total distance covered also varies between ~3000 m (forwards) to ~8800 m (backs), respectively (King et al., 2009; Gabbett et al., 2012). Although forwards play for much shorter periods of time, the speed/distance that forwards (≈95 m/min) produce in that time are similar to backs (≈90 m/min) (Waldron et al., 2011; Gabbett et al., 2012). Players work at a variety of speeds, from walking (low intensity) to sprinting (very high intensity i.e. ≥20 km/h) (Sykes et al., 2011; Gabbett et al., 2012; Twist et al., 2013), with the majority of rugby league match play involving low speed activity (Gabbett, et., al 2012; Sirotic et al., 2011; Waldron et al., 2011). However, this is interspersed by brief high speed running efforts which are crucial during match play (Austin et al., 2011). Research has shown that teams who compete at a higher standard of rugby league produce greater high speed running distances than those that play in a sub-elite division (Gabbett, 2014; McLellan et al., 2013). This highlights the necessity for highly developed running capacities, particularly at high speed.
An athlete’s ability to quickly and repeatedly accelerate, decelerate and change direction is imperative in rugby league, as these actions commonly occur throughout a game. The metabolic cost of rapid accelerations is large and represents a significant element of match play (Osgnach et al., 2010). Additionally, a very large component of the physical demands associated with rugby league are collisions (Kempton and Coutts, 2016). An individual player’s and team’s ability to dominate in a tackle is imperative during match play (Gabbet et al., 2010). Tackling/collisions are arguably the most physiologically demanding component of the game and largely contributes to match-related fatigue (Johnston et al., 2014). The majority of injuries in rugby league occur during collisions (Gabbett, 2004), with forwards (1.0/min) being more frequently involved in tackles compared to backs (0.3/min) (Gabbett et al., 2012; Twist et al., 2012). This demanding physical profile of rugby league (i.e. collisions, high speed running, accelerations and decelerations) likely causes significant muscle damage, highlighting the need for sufficient post-match recovery (Johnston et al., 2013).
Although an essential part of rugby league is to be physically developed, the most fundamental aspect of the game is to maintain possession whilst advancing the ball to ultimately score points (Gerrard et al., 2007; Kempton and Coutts, 2016). Technical and tactical elements of match play may be difficult to measure; however, they play a crucial role in match play. Tactics in rugby league are dynamic and quick to change depending on the opposition and/or the score (Kempton and Coutts, 2016). Therefore, information on the vast situational occurrences for possession are paramount for teams (Kempton and Coutts, 2016), and therefore application of this information is necessary. Teams will often implement strategies for relevant attacking and defending situations within training, to improve in-game decision making and, ultimately, for each player to understand their role.
Recovery in Rugby League
The training and match loads in rugby league cause muscle damage and can result in fatigue (Twist et al., 2012; McLellan et al., 2011; McLellan et al., 2011b; Duffield et al., 2012); therefore, the need for recovery is essential. Inadequate recovery can detrimentally influence the quality of an athlete’s training, and reduce the stimulus for adaptation (Shearer et al., 2015). Therefore, an athlete’s ability to balance training and competition stressors with appropriate and adequate recovery is vital to achieve optimal athletic performance as well as reducing the risk of injury (Fullagar et al., 2015; Sargent et al., 2014).
In a typical National Rugby League (NRL) season, teams play a minimum of 24 matches (26 rounds with two bye rounds) (Shearer et al., 2015). This accumulation of training and competition causes disruptions to physiological and psychological functions (Twist & Worsfold, 2014; West et al., 2014). The frequent accelerations and decelerations during the game cause eccentric muscle damage (Waldron et al., 2011; Gabbett, 2012). This muscle damage and inflammation can also contribute to fatigue (Twist et al., 2013). Muscle damage is also caused by trauma from collisions, which is supported by research showing a positive relationship between the amount of collisions and blood markers of tissue damage (creatine kinase) (Twist et al., 2012; McLellan et al., 2011b). Additionally, muscle damage is caused from metabolic stress that is associated with prolonged high intensity exercise (Tee et al, 2007), thus highlighting the necessity of adequate recovery. However, ensuing recovery is compromised by turnarounds (days between games) and, ultimately, the imposed training loads (McLean et al., 2010). Moreover, insufficient recovery can negatively impact on subsequent training and reduce the stimulus for adaptation (Shearer et al., 2015). Therefore, adequate recovery from training and competition stressors is paramount for optimal performance (Fullagar et al., 2015; Sargent et al., 2014). As part of the recovery process, it has been suggested that a fundamental component of managing the balance between stress and recovery in team sport athletes is adequate quantity and quality of sleep (Fullagar et al., 2015).
Sleep is defined as a reversible condition of reduced responsiveness, which is associated with immobility (Cirelli et al., 2008). The lack of one’s ability to react to a stimulus differentiates sleep from quiet wakefulness, while its controlled reversibility differentiates sleep from a comatose state (Cirelli et al., 2008). Sleep plays an important role in biological functions including physiological processes, memory, learning and cognition (Cirelli et al., 2008; Dattilo et al., 2011). As an example of its fundamental importance, various studies have reported that an inadequate amount of sleep can negatively affect physiological functioning, including the autonomic nervous system, endocrine system and biochemical function (Spiegel et al., 1999), cognitive function (Belenky et al., 2003), mood states (Sinnerton et al., 1992), neuromuscular function (HajSalem et al., 2013), gene expression regulation (Möller-Levetetal.,2013), glucose metabolism (Spiegel et al., 1999) and immune function (Krueger et al., 2011). A wider collection of evidence recommends 8 hours of night-sleep per 24-hour day for adults to prevent neuro-behavioural deficits (Van Dongen et al., 2003). Consequently, adult guidelines purported by the National Sleep Foundation echo these research findings (as shown in in Figure 2), and recommend sleep of between 7 and 9 hours per night (Hirshkowitz, et al., 2015).
Figure 2: Sleep duration recommendations by the National Sleep Foundation (Hirshkowitz, et al., 2015).
Normal human sleep involves alternating regularly between two phases: rapid eye movement (REM) and Non-REM (NREM). As presented in Figure 3, NREM sleep is divided further into four stages (stages 1,2,3,4) related to the depth of sleep. A continuum of sleep depth appears to exist, with arousal thresholds lowest during stage 1, and highest during stage 4, with stages 3 and 4 often referred to as slow wave sleep (Akerstedt & Nilsson, 2003). NREM involves minimal psychological activity and is associated with low muscle tonus. In contrast, REM sleep involves dreaming and muscles paralysis (Carskadon et al., 2011). “Normal” sleep begins in the NREM phase 1, and progresses through deeper NREM stages (2,3,4), before entering REM sleep, which typically occurs about 80-100 minutes after sleep commencement (Carskadon et al., 2011). NREM and REM sleep alternate in periods of approximately 90 minutes, with REM sleep periods increasing as the night progresses (Carskadon et al., 2011). This alternation between REM and NREM sleep allows for various sleep functions to occur and ensures appropriate physiological and neural functioning for waking the following day (Davenne, 2009).
Figure 3. The behavioural states during the sleep-wake cycle, including when an individual is awake, during REM sleep, and during NREM (Hobson, 2005).
During sleep, metabolic activity is at its lowest, whilst the endocrine system increases growth hormone secretion, encouraging physiological restoration (Halson, 2014). REM sleep is associated with dreaming and muscle paralysis (Halson, 2014) and the brain is highly active during this phase, which allows the individual to restore mentally and aids in memory consolidation (Davenne, 2009; Halson, 2014). In contrast to the restorative nature of REM sleep, memory and motor skills can be negatively influenced after a loss of REM sleep (Stickgold & Walker, 2007). Thus, REM sleep is fundamental following the learning of complex techniques or strategies (Venter, 2014) and after learning new motor skills (Leeder et al., 2012). Conversely, during NREM sleep, the brain reduces its activity which is proposed to assist with energy conservation and nervous system recuperation (Nédélec et al., 2015). Accordingly, as an overall synopsis, it has been suggested that REM sleep assists in restoring the brain, while NREM helps restore the body (Nédélec et al., 2015).
Research has suggested that slow wave sleep (SWS) (stage 3 & 4 of NREM) is particularly important for athlete recovery (Halson, 2014). This is supported by observed increases in SWS following an increase in metabolic stress following marathon running (Shapiro et al., 1981). SWS may also provide an ideal condition for anabolic functions to occur, as the release of growth hormone is associated with SWS (Weitzman, 1976). During the deepest periods of SWS, growth hormone secretion is at its highest. This release of growth hormone has a significant effect on the growth and repair of muscle, bone building and fat metabolism (Weitzman, 1976), which is imperative in the recovery from strenuous training and competition. Concurrently, the release of cortisol is suppressed during SWS, creating the ideal environment for anabolism (Sassin et al., 1969).
In contrast, when SWS is reduced, there is a reduction in performance and an increase in daytime sleepiness (Mah et al., 2011; Halson, 2014). Research has demonstrated that when energy expenditure increases during the day, there is an increase in growth hormone release the following night (Kanaley et al. 1997). However, when an athlete has limited NREM sleep, circulating growth hormone levels decrease dramatically (Kato et al. 2002), highlighting the importance of achieving adequate amounts of sleep.
From an athletic perspective, disruptions to the timing of sleep phases, or the quality and duration of sleep within each phase, can result in inhibition of psychological and physiological recovery from exercise (Samuels, 2008). More specifically, team sport athletes are commonly exposed to prolonged intermittent-sprint bouts through both high intensity training and competition. Thus, this exposure will likely increase the need for recovery and, therefore, the necessity for sleep to aid cognitive and physiological restoration (Lastella et al., 2014; Fullagar et al., 2015).
Measures of Sleep
There are a variety of methods available to assess an individual’s sleeping patterns (Leeder et al., 2012). One of the most cost-effective ways is through subjective sleep questionnaires. However, research has shown a poor relationship between subjective sleep questionnaire measures and objective measures of sleep (Leeder et al., 2012). The two most common objective methods for sleep assessment are polysomnography and wrist-watch actigraphy (Halson, 2014).
Polysomnography (PSG) is considered the ‘gold standard’ for assessing sleep quality and quantity (Kushida et al., 2001). Polysomnography assesses body functions such as brain activity, muscle activity, eye movements and cardiac activity, which provide insight into sleep stages. In order to determine and classify sleep stages, PSG involves various parameters such as electroencephalography (EEG), electrooculography (EOG) and electromyography of the submentalis (EMG). Data obtained from PSG tests include: total sleep time, sleep-onset latency, wake after sleep onset, sleep efficiency, sleep fragmentation index, number of awakenings, time in each stage of sleep and sleep stage percentages (Halson, 2014). Despite the accuracy of PSG, it is expensive and invasive for participants (e.g. wiring). Another disadvantage of PSG is that it requires professionally trained staff, often analysing sleep in an instrumented ‘sleep lab’. Sporting clubs are unlikely to invest in equipment that requires additional staff to operate, and players may not be comfortable with these techniques on a regular basis. Moreover, sporting teams are often required to travel, which makes PSG unfeasible. These disadvantages limit its use in an applied (i.e. non-research or clinical) setting, particularly in an athlete setting (Halson, 2014; Leeder et al., 2012).
Wrist-watch actigraphy involves a device typically worn on the wrist that continuously records body movement (Halson, 2014). Actigraphy measures accelerations and movement and estimates sleep and waking states based on algorithms, but does not measure stages of sleep. Actigraphy is a non-invasive method to estimate an individual’s sleep quality and quantity, and is more feasible and cost-effective than PSG (Leeder et al., 2012; Shearer et al., 2015). Actigraphy data obtained includes: total sleep time, sleep-onset latency, wake after sleep onset and sleep efficiency (Halson, 2014). Research into actigraphy has shown an accuracy of 80% when compared to PSG for total sleep time and efficiency (Kushida et al., 2001; Babin et al., 1997; Leeder et al., 2012). Whilst actigraphy has less accuracy than the more comprehensive PSG analysis, it is more sensitive to awakenings than subjective reporting (Kushida et al., 2001; Shearer et al., 2015). Advantages of actigraphy compared to PSG are the size, transportability and ease of wear, making it a more convenient way to collect data on an individual’s sleeping patterns, whether it be at home or whilst travelling (Nédélec et al., 2015), whilst providing more objective, accurate and detailed information than subjective reporting.
Despite the ease of actigraphy, there are still costs and comfort issues to consider. Players are still required to wear an object on their wrist at all times, which can cause inconvenience. The most commonly used and simplest method to assess sleep is through subjective sleep questionnaires. Sleep diaries require participants to record the time that they attempted to fall asleep and their wake time. Although feasible and easy to use, sleep questionnaires are poorly correlated with objective measures of sleep (Leeder et al., 2009; Richmond et al., 2004), and are not as accurate in assessing sleep patterns (Richmond et al., 2004). Sleep diaries ideally should be used in conjunction with actigraphy to better determine when participants are awake and asleep and, thus, refine the accuracy of actigraphy measures of sleep (Halson, 2014).
Factors Affecting Sleep
Homeostatic sleep drive and circadian rhythms drive the body’s sleep-wake cycle. Homeostatic processes involve an increasing sleep drive that builds during wake and is dissipated in sleep (Borbely, 1982; Akacem, 2005). On the other hand, the circadian process involves natural 24-hour rhythms of physiological activity (Borbely, 1982; Akacem, 2005). The ability to fall asleep relies on the circadian system, with sleep propensity being highest approximately two hours after the onset of the secretion of melatonin (Lavie, 2001; Liu et al., 2000; Shochat et al., 1997), and when core temperature is declining (Dijk et al.., 2001). Humans are highly sensitive to the light-dark cycle (Czeisler et al., 1986), and when exposed to light, melatonin is suppressed, which affects circadian rhythms; thus, sleep propensity is shifted. Additionally, disruptions to the environment (e.g., athletic competition at night and/or training later into the night) can alter circadian rhythms, with normal sleep-wake cycles becoming desynchronized (Beersma et al., 2007; Reilly & Edwards, 2007). This delay in sleep propensity can result in neurocognitive and physiological changes which ultimately compromise performance (Fullagar et al., 2015). From the perspective of a footballer, the quality and quantity of sleep can be affected by many factors beyond the natural variation in physiological and behavioural processes over a 24-hour period (e.g. circadian rhythms, thermoregulation, hormone regulation), including playing experience, fitness, sleep environments, scheduling and motivation (Drust et al., 2005).
Sleep, Performance & Recovery
Sleep is a necessity for human health and is essential for athletic recovery due to the physiological and psychological restorative effects (Shearer et al., 2015). It has been suggested that sleep is the most effective and most accessible recovery strategy available to athletes (Halson, 2008). Sleeping is a fundamental part of athletic recovery from training and competition, as well as for the preparation of training and competition (Fullagar et al., 2015). A lack of sleep results in a fatigued state, consequently reducing physiological function and performance (Fullagar et al., 2015).
The importance of sleep as a recovery method is widely acknowledged within team sport; however little objective data exist demonstrating the role that sleep plays in post-exercise recovery (i.e. returning the athlete to baseline psycho-physiological state) (Fullagar et al., 2015). Various studies have examined the effects of sleep restriction or deprivation on recovery, with results showing sleep deprivation can lead to dysfunction of the immune and endocrine systems, which may result in an impairment of an athlete’s adaptation to training (Reilly and Edwards, 2007).
There are three key concepts which determine the restorative outcome of sleep: the duration (total sleep time), quality (e.g. efficiency, awakenings, etc), and timing of sleep (circadian phase) (Fullagar et al., 2015; Samuels, 2008). Research in athletic populations has shown that training volume (Jürimäe, et al., 2004), training times (Sargent et al., 2014) and competition itself (Lastella et al., 2014; Richmond et al., 2004) can all influence an athlete’s sleep quality (Shearer et al., 2015). It is proposed that disturbances to the duration, quality, or timing of sleep may negatively affect the post-exercise recovery process, and ultimately the stress-recovery balance (Fullagar et al., 2015). Thus, it is essential that athletes understand when sleep duration and/or quality may affect subsequent recovery (Fullagar et al., 2015).
Duration and Quality
There is considerable debate regarding the amount of sleep that an athlete requires for adequate recovery and functioning, and for the maintenance of performance (Samuels, 2008; Sargent et al., 2014). While the National Sleep Foundation recommends sleep of between 7 and 9 hours per night (Hirshkowitz, et al., 2015), a large amount of literature suggests that athletes sleep less than these minimum recommendations (Sargent et al., 2014). For example, one study reported that Olympic athletes attain an average of 6 h 55 min of sleep per night (Leeder et al., 2012). Leeder et al. (2012) also reported athletes spent more time awake in bed and had lower sleep efficiency when compared to healthy controls. Similarly, Mah et al. (2011) assessed sleep using wrist-watch actigraphy in collegiate basketball players during a competitive season, showing athletes were well below the recommended adult sleep duration (6 h 40 min). Sargent et al. (2014) also found that Olympic swimmers had a sleep duration lower than recommended, with just 6 h 12 min of sleep per night during a period of intense training. In contrast, athletes from Australian Rules football achieve between 8.1 and 8.7 h of sleep per night (Richmond et al., 2007). Moreover, it is well documented that team-sport athletes achieve more sleep than individual athletes (Leeder et al., 2012; Richmond et al., 2007; Sargent et al., 2014). Literature suggests that individual athletes achieve less sleep due to high training demands (Taylor et al., 1997).
Research has found that athletes who sleep for an insufficient amount of time on the night prior to training had poorer self-reported moods, and higher exertion during training than normal (Reilly and Piercy, 1994; Sinnerton & Reilly, 1992). These effects on mood and exertion can negatively influence an athlete’s motivation and, therefore, their ability to perform, especially during higher intensity sessions (Reilly and Edwards, 2007). Additionally, consecutive nights of insufficient sleep can impair the immune system (Vgontzas et al., 2004), cognitive function (Belenky et al., 2003), glucose metabolism (Spiegel et al., 1999) and appetite regulation (Spiegel et al., 2004), all of which could impair the training performance of an athlete.
Sleep quality is fundamental for optimal functioning and restoration (Krystal et al., 2008). Athletes are commonly exposed to jet lag, altering training and competition schedules (Eagles et al., 2014; Fullagar et al., 2016; Taylor et al., 2016), fatigue, and unfamiliar sleeping environments, all of which can affect sleep quality (Robey et al., 2014). Measures of sleep quality often include efficiency, number of awakening, awakening length and perceived sleep quality. It has been reported that athletes take longer to fall asleep, spend more time awake in bed, and have lower sleep efficiency when compared to healthy controls (Sargent et al., 2014). Research into individual and team sport athletes show individual athletes having poorer sleep efficiency (Lastella et al., 2015), with differences in training demands likely responsible for the quality of sleep athletes achieve. This is contrary to previous research that found healthy, fit individuals have higher sleep quality when compared to sedentary individuals (Shapiro et al., 1981).
Another study examined the sleep quality of swimmers during 14 days of intense training (Sargent et al., 2014). Findings showed the sleep efficiency preceding training and rest days to be 71% and 77%, respectively. This is considerably lower than typical sleep efficiency of healthy young adults (90%) (Spriggs, 2014). Additionally, these swimmers took longer to fall asleep on nights prior to training days (41 minutes) when compared to rest days (32 minutes), with the time spent awake also being larger prior to training days (18%) compared to rest days (16%) (Sargent et al., 2014). It was suggested that the athletes took longer to fall asleep due to elevated levels of anxiety during intensive training (Sargent et al., 2014; Fry et al., 1994); however, this could also be from greater fatigue prior to rest days. These swimmers were also on a hydration program and therefore may have been awake for longer periods due to the frequency of toilet visits (Sargent et al., 2014; Robson-Ansley et al., 2009). Additionally, swimmers were free to consume caffeine and alcohol for the duration of the study which could have largely influenced sleep quality (Sargent et al., 2014; Arnedt et al., 2011). Overall, sleep duration and quality can be largely influenced by the demands of training and competition, suggesting a relationship between training demands and sleep quality.
Timing of Sleep
It has been established that the timing of sleep (i.e. time of day at sleep onset) can influence the efficiency of a given sleeping bout. For example, an individual’s bedtime affects their circadian rhythms, which can affect both the quality and duration of sleep (Fullagar et al., 2015b). A disruption to sleep quality and/or duration will impact an athlete’s post-exercise recovery process (Fullagar et al., 2015; Samuels, 2008). It would seem logical to suggest that athletes go to bed earlier to increase the amount of sleep they obtain; however, there are two main reasons why this may not be true. Firstly, an individual’s lifestyle and social commitments can limit how much they can advance their bedtime (Tucker et al., 1998). Secondly, it may be physiologically difficult to initiate and maintain sleep earlier than usual, due to ‘sleep propensity’ (Lavie, 1986; Sargent et al., 2012). Throughout a 24-hour period, sleep propensity varies, which is regulated by the circadian process. Sleep propensity is highest from midnight through to 7am, with a secondary peak in the mid-afternoon (Sargent et al., 2014). This is followed by low propensity in the early evening when individuals get little to no sleep (Lack and Lushington, 1996) and is known as “the forbidden zone for sleep” (Lavie, 1986). Therefore, advancing bedtimes on a night prior to an early morning training session does not guarantee longer sleep durations. In fact, some individuals may not even be able to initiate sleep due to this “forbidden zone for sleep” (Folkard and Barton, 1993). However, several early morning training starts in succession may affect the circadian system causing the optimum sleep time to shift earlier in the evening, allowing athletes to initiate sleep earlier (Sargent et al., 2014). Moreover, one study found when athletes were required to get up early on training days, sleep onset still occurred at relatively the same time (Sargent et al., 2014), and the onset of sleep was similar to that recorded on nights leading to rest days.
Sleep Restriction and Deprivation
Sleep restriction following team sport competition can affect the time course of recovery (Fullagar et al., 2015). Indeed, sleep loss has been associated with cognitive impairment and mood disturbances, which can negatively impact performance (Lastella et al., 2014). The ability to make accurate decisions quickly is just as important as the execution of superior skills (McMorris et al., 1997; Royal et al., 2006; Lastella et al., 2014). Additionally, psychological factors (e.g. mood) can have a large impact on competition performance (Birrer et al., 2010). One study investigated the effect of complete sleep deprivation (0 hours) compared with ‘normal’ sleep (8 hours) on the physiological and perceptual recovery of rugby league players (n=11) following match play (Skein et al., 2013). Findings showed that sleep deprivation negatively affects recovery with significantly lower mean and peak countermovement jump height, as well as decrements in cognitive reaction time (Skein et al., 2013). Research has also shown impairments to the stress-recovery balance following night matches (Fowler et al., 2014). Fowler et al. (2014) found significant reductions in sleep duration and quality on match night, when compared to the night prior to a match (for away matches). Although detailed information in team sports is lacking, data collected from 10 elite synchronised swimmers found significant reductions in sleep quality and efficiency was linked with increased fatigue and impaired exercise capacity (Schaal et al., 2014). Moreover, investigation into the cardiovascular and metabolic responses of submaximal exercise following 24 hours of sleep deprivation showed increased minute ventilation and oxygen consumption during the recovery period, highlighting the negative effects of sleep loss on physiological recovery (McMurray et al., 1984).
In contrast, swimmers limited to 2.5 hours of sleep for three consecutive nights exhibited no performance impairment in 50 m or 400 m swim time (Sinnerton & Reilly, 1992). However, other sports that require higher cognitive demands (such as team-based ball sports) may be more susceptible to a reduction in performance (Edwards & Waterhouse, 2009; Reyner & Horne, 2013). Research into dart throwing performance following 3-4 hours of sleep restriction found accuracy declined (Edwards and Waterhouse, 2009). Correspondingly, tennis serve accuracy reduced following sleep restriction in collegiate tennis players (Reyner and Horne, 2013). Thus, it is apparent that sleep restriction may be detrimental to performance, particularly in sporting tasks that require high cognitive demands.
Research has also shown that several nights of inadequate sleep in succession can cause deficits to neurobehavioral performance and a rise in the feelings of fatigue and sleepiness (Belenky et al., 2003); Van Dongen et al., 2003), as well as impaired immune function (Vgontzas et al., 2004). Consecutive nights of poor sleep quantity can also impair an individual’s cognitive capacity (Belenky et al., 2003), glucose metabolism (Spiegel et al., 1999) and appetite regulation (Spiegel et al., 2004), contributing to poor training performance. Sleep deprivation may impair the immune and endocrine systems which results in inadequate recovery and, thus, an impairment to the athlete’s adaptation to training (Reilly and Edwards, 2007). In contrast, when sleep duration is increased, reaction times, sleepiness and fatigue all improve (Kamdar et al., 2004). In summary, sleep restriction and sleep deprivation has been shown to negatively influence recovery, which influence subsequent training performance.
Influence of Scheduling
An important aspect in obtaining sufficient sleep for athletes is the time into bed and rise times, thus determining the actual time available to attempt sleep. Such timelines are behavioural in nature and often influenced by training and competition schedules (Sargent et al., 2014; Sinnerton et al., 1992). For example, various studies have shown that the sleep/wake behaviour of athletes is directly affected by the time of day that they are required to train (Sargent et al., 2014). In elite Australian swimmers in the lead up to the 2008 Olympic Games, differences were found for time spent in bed and sleep duration when athletes were required to train between 6:00 am and 8:00 am compared to rest days (Sargent et al., 2014). On nights prior to training days, athletes’ bed times and rise times were significantly earlier, and time spent in bed was shorter, when compared to rest days (Sargent et al., 2014). Another study assessed the effects of high intensity night training on sleep in elite male youth soccer players (Robey et al., 2014). The findings demonstrated that athletes were going to bed later and waking up later on nights where they were required to train in the early evening, compared to nights where they were not required to train or when they participated in light training during the day (Robey et al., 2014; Sargent et al., 2014). However, this study found no differences between training days and off days for sleep duration and efficiency (Robey et al., 2014). In agreement with Sargent et al. (2014), no difference in latency or wake after sleep onset were evident on training days compared to non-training days (Robey et al., 2014). Although some athletes attempt to compensate for early training sessions by going to bed earlier, they still slept significantly less than on nights prior to a non-training day (Robey et al., 2014; Sargent et al., 2014). Thus, the authors concluded that the amount of sleep obtained was greatly influenced by training schedule (Robey et al., 2014; Sargent et al., 2014; Lastella et al., 2014).
When training start time was between 7:00 am and 10:00 am, athletes were achieving six to seven hours of sleep, and when training time was between 10:00 am and 11:00 am, athletes achieved greater than seven hours (Sargent et al., 2014). Additionally, sleep duration had a significant effect on pre-training fatigue with shorter sleep durations related to higher levels of pre-training fatigue. Repeated early morning training sessions can have a cumulative effect, which can result in sleepiness, fatigue and maladaptation (Fry et al., 1994; Halson et al., 2004). This research demonstrates that training schedule may influence an athletes’ sleep/wake behaviour, and the necessity for coaches to re-evaluate the scheduling of training to maximise sleep and ultimately athletic performance (Mah et al., 2011).
More relevant to professional team sports, the scheduling of competition likely plays a large role in sleep parameters (Fullagar et al., 2015). The demands of the competition as well as post-match duties are multifaceted and influence an athlete’s ability to sleep (Fullagar et al., 2015). Following night matches, athletes are commonly exposed to post-match commitments, such as recovery, medical treatment and media obligations, all of which can impede an athlete’s ability to get home early enough for an adequate amount of sleep (Nédélec et al., 2013). The quality of an athlete’s sleep may also be influenced by the elevated arousal from games, possibly negatively impacting an athlete’s ability to recover (Nédélec et al., 2013; Fullagar et al., 2015). All these factors may influence the athlete’s preparation for optimal training and successful competition. This notion is supported by literature showing that players were delaying bedtime following a 6:00 pm match (Fullagar et al., 2015). Because in bed time was delayed, sleep duration was compromised, with sleep latency also being prolonged by ten minutes when compared to a training day (Fullagar et al., 2015). Additionally, research into elite rugby league players has shown that following a night match, players’ bed time was much later, and therefore total sleep time was reduced (Eagles et al., 2016). Furthermore, results from a sample of 283 elite individual and team sport athletes showed that they suffered from sleep disturbances following a late-night training session or match (Juliff et al., 2014), demonstrating that team sport athletes can frequently be exposed to reduced sleep durations and quality (Fullagar et al., 2015). Moreover, scheduling of training and competition has shown to be a major contributor to sleeping poorly, as well as limiting recovery from and for the preparation of training and competition (Sargent et al., 2014; Fullagar et al., 2015).
Napping and Sleep Extension
One method rarely documented to recover from sleep debt is napping (Daniel et al., 2010). Research has shown that napping improves alertness, feelings of sleepiness, short term memory and reaction time (Waterhouse et al., 2007; Fullagar et al., 2015). Additionally, a 30-minute afternoon nap has been found to improve mean sprint performance following 4-5 h sleep restriction (Waterhouse et al., 2007). These benefits suggest athletes should, when necessary, utilise napping; for example, a post-lunch nap prior to a night match (Fullagar et al., 2015). Although napping may assist in optimising performance, it is important to not disrupt succeeding sleep periods (e.g. by having a late afternoon nap and/or a nap greater than 30 minutes), as this could negatively affect subsequent recovery (Fullagar et al., 2015). Even though there is research to suggest napping is beneficial to performance, there is limited information surrounding the effectiveness of napping for recovery (Fullagar et al., 2015).
Extending the duration of an individual’s sleep can also improve performance measures (Fullagar et al., 2015). Extending sleep following a period of sleep loss has shown to improve physiological and cognitive performance that was previously disrupted (Mah et al., 2011). One study following a 3-week period of extending sleep found faster sprint and reaction times, as well as improved shooting accuracy, energy and mood (Mah et al., 2011). This study not only suggests sleep extension will enhance performance, it also suggests improvements in wellbeing, thus optimising athletes’ mental preparedness (Mah et al., 2011). However, obtaining this extra sleep may be difficult due to natural circadian rhythms and the ‘forbidden zone for sleep’.
Training Load and Sleep
Despite the importance of sleep on recovery and adaptation to training, and the aforementioned challenges to sleep quality often faced by elite athletes, little information exists describing how athletes cope with different training and sleeping environments and competition schedules. For example, it is frequently reported that sleep is critical for recovery from both training and competition stressors; however, the effect of sleep on recovery, particularly in team sport athletes, remains unclear (Fullagar et al., 2015).
Sleep loss has been associated with overtraining and/or overreaching, with a negative relationship observed between sleep quality and training load (Jürimäe et al., 2004). Excessive exercise loads may disturb the stress-recovery balance, which could lead to performance decrements and injury (Kellman, 2010; Fullagar et al., 2015). Periods of high physical loads can potentially cause sleep impairment. One study demonstrated the effects of increased match exposure in baseball (Kutscher et al., 2013), with players’ strike zone judgement impaired. The authors proposed that this was a fatigue-induced decline in performance, with reduced sleep being one of the reasons responsible (Kutscher et al., 2013). Research has also suggested that high training volumes are associated with greater sleep disruptions (Taylor et al., 1997). Possible reasoning for this include exercise-induced muscle damage, which can increase perceptual fatigue and perceived soreness, which can disrupt sleep (Neddelec et al., 2012). Although research has shown that high training loads may negatively affect sleep, there are currently limited data in team sports.
Research has shown that elite athletes attain less sleep before and during competition periods, with team sport athletes being more susceptible to a decrease in sleep duration and quality following a night of competition (Fullagar et al., 2015). Moreover, there is limited research into sleeping behaviours in response to highly demanding training (Le Meur et al., 2013; Taylor et al., 1997). One study assessing female swimmers measured sleep with PSG during the onset of training, during heavy training, and during a pre-competition taper (Taylor et al., 1997). Sleep onset latency, time awake after sleep onset, total sleep time, rapid eye movement, and sleep times were similar at all three training phases (Taylor et al., 1997). However, the number of body movements was significantly higher during larger training volumes, suggesting sleep disruption (Taylor et al., 1997). Another study explored sleep behaviours during periods of high physical and mental stress in ballet dancers (Fietze et al., 2009). This study found significant reductions in sleep duration, sleep efficiency, time in bed and an increase in wakefulness after sleep onset (Fietze et al., 2009). Moreover, research into functional overreached athletes showed decreases in sleep quality and quantity during an overload period (Meeusen et al., 2013). Similarly, research into overreached and over-trained athletes has reported decreased levels of perceived sleep quality (Jurimae et al 2004; Matos et al., 2011). Although research into overreached athletes is an extreme case, these studies suggest that heavy training loads may be detrimental to sleep quality. In summary, there is limited objective data on the effects of various training loads in team sport athletes.
It is widely acknowledged that sleep is fundamental for athletic recovery and performance. A lack of sleep undoubtedly results in a fatigued state, impairing physiological function and performance. Evidence has shown that training volume and the scheduling of competition and training can largely influence an athlete’s sleep quantity and quality, however data in team sports is limited. There is little information investigating intensified training blocks (e.g. heavy, pre-season training week) and its impact on sleeping patterns. Further information regarding sleeping habits and interventions to optimise sleep quality may assist team sport athletes in achieving optimal sleep and recovery and, ultimately, higher quality performances. Therefore, this study will explore the effect of training loads and scheduling on the sleep patterns in professional rugby league players.
Cite This Work
To export a reference to this article please select a referencing stye below:
Related ServicesView all
Related ContentAll Tags
Content relating to: "Sports"
Sports are a combination of skill and physical activity, and can be done as either an individual or as part of a team. Sports can help you to keep fit and provide you or others with entertainment.
Sociological Reasons for Sports Participation: A Bourdieusian Approach
This study will draw on the work of Pierre Bourdieu and will explore the sociological factors that influence young people’s participation in physical activity....
Analysis of NBA Player Performance, Popularity and Salary
This paper aims at finding relations to NBA player’s salary with their performance along with their twitter follower by analysing the linear regression, PCA, SVM and Decision Tree technique....
DMCA / Removal Request
If you are the original writer of this literature review and no longer wish to have your work published on the UKDiss.com website then please: