The Correlation Of Sleep Extension And Athletic Performance

Literature driven synthesis

In everyone’s body there is an internal clock that releases chemicals in a daily rhythm. This is otherwise known as a circadian rhythm. The suprachiasmatic nucleus in the hypothalamus of the brain is suggested as where this circadian rhythm is controlled (Beersma & Gordijn, 2007). The circadian cycle also controls basic components of exercise. These components include muscular strength, flexibility, body temperature, and vigilance and may be altered by the disturbance of sleep (Famodu, 2014). It is highly temperamental though and can be altered by zeitgebers like the light/dark cycle, amounts of prior sleep, physical activity, neurotransmitters and even diet (Fullagar, et al., 2015).

There are many factors that can influence an athlete’s ability to have high success in performance. Some well-known factors are physical conditioning (Harries, Lubans, & Callister, 2015) and diet (Spriet, 2015), but even sleep plays a major role in athletic performance. Sleep has been shown to be essential for better recovery and prime performance and in turn exercise can promote good sleep developing a reciprocal arrangement (Brand, Beck, Gerber, Hatzinger, & Holsboer-Trachsler, 2009).

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Although it is well sustained that sleep is essential, many studies show that the average adult sleeps less than the 7-9 hours recommended by the National Sleep Foundation (Luckhaupt, Tak, & Calvert, 2010). For athletes, some scientist recommend they achieve 9-10 hours of sleep per night (Venter, 2012), but recent evidence shows that this is not the case (Sargent, Halson, & Roach, 2014). An athlete’s sleep can be affected for many reasons including the times of practices and games, jetlag from travel, and anxiety about competition. With sleep deprivation in athletes there is a plethora of evidence to suggest that there are many detrimental effects on performance (Fullagar, et al., 2015).

Due to much new research emerging on the vital importance of sleep a few important sleep interventions have been developed to try and optimize sleep for peak recovery and performance. Such interventions include sleep extension and napping, sleep hygiene, and post-exercise recovery strategies (Fullagar, et al., 2015). The focus of this essay is to go into more depth in sleep extension and napping. The intervention is focused on extending the amount of time sleeping either through increasing total sleep duration or through short naps (Bonnar, Bartel, Kakoschke, & Lang, 2018).

Three particular studies were reviewed on sleep extension with three different athletic populations. In the first study by C.D. Mah, K.E. Mah, Kezirian, and Dement (2011) they did a pretest-posttest study of 2-4 week normal sleep schedule, followed by a 5-7 week extended sleep schedule in a sample of university basketball players. During the extended sleep schedule the players were asked to spend a minimum of 10 hours in bed each night. The results showed significant improvements in half-court and full court sprints, shooting accuracy, vigor and mood, as well as significant decreases in sleepiness and fatigue.

The next study by Schwartz and Simon (2015) examined whether sleep extension had an effect on tennis serve accuracy in university varsity players. Their study was one-week habitual routine followed by one-week sleep extended routine with pretest/posttest. With this study they asked participants to sleep for nine hours per night. They found that after just one-week tennis serve accuracy significantly improved, and sleepiness levels significantly decreased.

The last study looked at was by Famodu (2014), and they examined the effects of sleep extension on university female track and field members. Similar to the last study there was one-week habitual routine followed by one-week sleep extended routine with pretest/posttest. The sleep extension in this study was one hour. Contrary to the previous studies, this study found trends towards but no significant improvements in power, fatigue or reaction times. Only mood was a statistically significant difference. However, looking at the research there are many limitations in this current topic.

Literature driven critique

There were many limitations to these particular studies that were examined and also to other related research. The first limitation is that while there is much research on sleep extension and cognitive performance, the research on physical performance is somewhat lacking and shows some inconsistent findings. Since this topic seems to be gaining in momentum hopefully in the future there will be much more research in this particular area.

Another big limitation of this research is with the samples. Of the studies discussed above all had quite small samples; 11, 12, and 21 respectively. The problem with small sample sizes is that they have low statistical power and can increase the rate of a false-positive result (Forstmeier, Wagenmakers, & Parker, 2016). Also, all of these samples obtained were through convenience sampling. Mainly, the problems with convenience sampling are that there is sampling bias and that the sample being looked at is not representative of the population. This causes many problems with low generalizability and results in low external validity (Etikan, Musa, & Alkassim, 2016). Having said that, it seems quite difficult to find participants to partake in a sleep study because of the complexity of sleep.

One final problem with the sample was the differentiation in genders. The basketball study was all males, the tennis study was seven females and 5 males, and the track study was all females. There is research to show that the circadian rhythms are different between males and females, with males being truer to the full 24-hour clock and females being more likely to have a short clock. This can make it more likely they wake at an earlier time, which may lead to increased susceptibility for sleep disorders like insomnia (Krishnan & Collop, 2006). In addition, sleep has been shown to be partially affected by hormonal factors. In women, the menstrual cycle, pregnancy and menopause have been shown to be related to sleep disturbances (Mehta, Shafi, & Bhat, 2015).

To continue, another major limitation to this current topic is varying study designs between experiments. While the basketball study had a pretest after 2-4 weeks habitual routine and a posttest after 5-7 weeks sleep extension the other two studies only had one-week habitual and one-week sleep extension. This could have caused some inconsistencies in the findings because the length of the sleep extension may have been inadequate to account for the amount of accumulated sleep debt to show differences in athletic performance (National Library of Medicine, 2007).

Furthermore, the amount of sleep extension varied from one study to the other, with the basketball players increasing 110.9 ± 79.7 minutes, tennis players increasing 103.6 minutes, and track runners only increasing by 22.1 minutes. Only the track study found insignificant finding for athletic performance, and seems flawed due to that even with sleep extension the average amount of sleep for a participant was only 7.5 hours. That is the very low side for recommended amount of sleep by the National Sleep Foundation.

Lastly, some of the protocols used to obtain physical performance seemed not well suited. In the track study by Famodu (2014) most of the participants obtained were long distance runners and physical performance was acquired through the Wingate test. It could be argued that this test is more suited towards anaerobically trained athletes, such as sprinters, rather than aerobically trained athletes.

Future directions for research in this topic

Since the limitations have been looked at, now improvements and future directions will be discussed. There is a scarcity of recent literature reviews on the topic of sleep extension and physical performance, so recent research should re-evaluate the link between the two. It is also important to note that none of the studies that were assessed had a control group. This would be beneficial because there would be a group of the population wanted to be looked at that is controlled for certain variables to have a comparison to (Bate & Karp, 2014). Although there might be some difficulties with this for specific team sports due to small team sizes. So perhaps, it would be more practical to look outside the population of just one team.

An increased sample size, trying to avoid convenience sampling with an even amount of males and females would benefit future studies as well. This would increase statistical power and would decrease the likelihood of a type II error (Forstmeier, Wagenmakers, & Parker, 2016). Sample size estimation would be a good statistical measure that future studies should perform before gathering participants. If future researcher obtain the correct amount of participants based upon their sample size estimation the power of the study to draw conclusions increases, as well as the generalizability to the population that the study is examining (Eng, 2003).

The next step for future research would be to start testing different lengths of sleep extension to see which best increases physical performance. With the study by Famodu (2014), 22 minutes increase showed trends towards but no significant improvements in physical performance, but was only one week for sleep extension. Perhaps if future studies increased the amount of sleep extension or increased the duration of the study itself, like the study by Mah et al. (2011), they would find significant results.

In addition, there should be more focus on team vs. individual sports. For instance, sports such as football and basketball heavily rely on teamwork, and it is difficult to infer one player’s individual performance on a positive game outcome. While sports like tennis and track and field are easier to compare to competition outcomes. Moreover, future research should focus more on performance tests that measure to sport-specific athletic ability. The amount of these tests in new research should be increased, as well as research on measuring their ability to accurately predict performance in competition. Future studies should tailor their measurements to the particular sport with emphasis on evaluating athletic performance for competition rather than practice.

An experimental design to test one of these future directions

If I were to design a new experiment I would test the effect of different lengths of sleep extension in football players. It would be important to know at what amount of sleep would peak performance happen. I chose this particular population because of the amount of participants would be quite high, especially in the United Kingdom. Theoretically speaking, if I had enough participants I would conduct this experiment with 3 experimental groups and a control with matched allocation for gender. The control would just follow their normal habitual sleep routine, group 1 would be asked to sleep eight hours, group 2 nine hours, and group 3 ten hours. All participants would have two weeks sleeping at their normal sleep routine with baseline testing, followed by six weeks of either sleep extension or habitual for the control with testing every week. A schematic is given in Appendix 1.

In total, participants would be required for eight visits. The first visit is preliminary with explanation of the study, informed consent, and the use of the actigraphy watch. The second visit would be after the two weeks habitual sleep routine for baseline testing. Afterwards testing would be every week for six weeks. As for measurements, the sleep/wake cycle will be measured using an actigraphy watch. While this method is not the strictest measurement, it is the most cost effective and has been shown in previous research to be highly correlated with polysomnography (Quante et al., 2018).

In addition, the Epworth Sleepiness Scale (ESS), as well as the Profile of Mood States (POMS) tests would be administered to record daytime sleepiness and mood changes in participants. The ESS is 8 standardized daily situations that the participants rate on a scale of 0-3. The possible score ranges for this test are between 0-24 with 24 being the greatest sleepiness. As for POMS, this is a questionnaire having participants report on 67 distinct mood states with 6 subcategories. Both of these tests would be reported at baseline, and weekly through stage 2.

The Psychomotor Vigilance Task (PVT) would also be given to participants at baseline and twice daily through stage 2 at two specific times of the day. This test is used to measure reaction time performance. As for sport-specific tests I would run the wall-volley test, slalom dribble, and straight dribble set out by Reilly and Holmes (1983), which are an accuracy test, and two dribbling tests. I would also use the Loughborough soccer-passing test, and the Loughborough soccer-shooting test as set out by Ali et al. (2007).

Furthermore, I would use the 40m linear sprint test as used by Haugen, Tønnessen, & Seiler (2012). I choose these specific tests because of their high validity and reliability (Ali, 2011; Altmann, Ringhof, Neumann, Woll, & Rumpf, 2019). All of these sport-specific test would be given at baseline and every week during phase 2. With these tests in place I believe this would bring more knowledge to the research on how sleep extension affects the physical performance of football players.

References

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