Sleep or swim? Early-morning training severely

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Sleep or swim? Early-morning training severely restricts the amount of sleep obtained by elite swimmers a

b

Charli Sargent , Shona Halson & Gregory Daniel Roach a

a

Appleton Institute , Central Queensland University , Adelaide , SA , Australia

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Australian Institute of Sport , Canberra , ACT , Australia Published online: 06 Jul 2012.

To cite this article: Charli Sargent , Shona Halson & Gregory Daniel Roach (2014) Sleep or swim? Early-morning training severely restricts the amount of sleep obtained by elite swimmers, European Journal of Sport Science, 14:sup1, S310-S315, DOI: 10.1080/17461391.2012.696711 To link to this article: http://dx.doi.org/10.1080/17461391.2012.696711

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European Journal of Sport Science, 2014 Vol. 14, No. S1, S310
S315, http://dx.doi.org/10.1080/17461391.2012.696711

ORIGINAL ARTICLE

Sleep or swim? Early-morning training severely restricts the amount of sleep obtained by elite swimmers

CHARLI SARGENT1, SHONA HALSON2, & GREGORY DANIEL ROACH1 Appleton Institute, Central Queensland University, Adelaide, SA, Australia, and 2Australian Institute of Sport, Canberra, ACT, Australia

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1

Abstract Good sleep is essential for optimal performance, yet few studies have examined the sleep/wake behaviour of elite athletes. The aim of this study was to assess the impact of early-morning training on the amount of sleep obtained by world-class swimmers. A squad of seven swimmers from the Australian Institute of Sport participated in this study during 14 days of high-intensity training in preparation for the 2008 Olympic Games. During these 14 days, participants had 12 training days, each starting with a session at 06:00 h, and 2 rest days. For each day, the amount of sleep obtained by participants was determined using self-report sleep diaries and wrist-worn activity monitors. On nights that preceded training days, participants went to bed at 22:05 h (s00:52), arose at 05:48 h (s00:24) and obtained 5.4 h (s1.3) of sleep. On nights that preceded rest days, participants went to bed at 00:32 h (s 01:29), arose at 09:47 h (s01:47) and obtained 7.1 h (s1.2) of sleep. Mixed model analyses revealed that on nights prior to training days, bedtimes and get-up times were significantly earlier (pB0.001), time spent in bed was significantly shorter (pB0.001) and the amount of sleep obtained was significantly less (pB0.001), than on nights prior to rest days. These results indicate that early-morning training sessions severely restrict the amount of sleep obtained by elite athletes. Given that chronic sleep restriction of B6 h per night can impair psychological and physiological functioning, it is possible that early-morning schedules actually limit the effectiveness of training.

Keywords: Swimming, sleep restriction, elite athletes, wrist activity monitor, training schedules

Introduction In order to function effectively, it is essential that humans obtain a sufficient amount of sleep. When sleep is restricted to B6 h per day, there is substantial disturbance in cognitive capacity (Axelsson et al., 2008; Belenky et al., 2003), glucose metabolism (Spiegel, Leproult, & van Cauter, 1999), appetite regulation (Spiegel, Tasali, Penev, & van Cauter, 2004) and immune function (Vgontzas et al., 2004). While there are considerable data available related to the amount of sleep obtained by adults in the general population, there are few published data related to the amount of sleep obtained by elite athletes. This appears to be a considerable oversight given that sleep has been recognised as an essential component of recovery from, and preparation for, high-intensity training (Mah, Mah, Kezirian, & Dement, 2011; Reilly &

Edwards, 2007; Robson-Ansley, Gleeson, & Ansley, 2009; Samuels, 2008). The few studies that have examined the relationships between training schedules and sleep patterns in elite athletes have focused on the effects of a bout of exercise on the quantity and quality of sleep obtained on a single night (e.g. Driver et al., 1994; Netzer, Kristo, Steinle, Lehmann, & Strohl, 2001; Taylor, Rogers, & Driver, 1997). In contrast, the aim of this study was to examine the amount of sleep obtained by elite swimmers, on training days and rest days, during a 14-day period of high-intensity training. Methods Participants Seven nationally competitive swimmers (one female and six male) from the same training squad gave

Correspondence: C. Sargent, Appleton Institute, Central Queensland University, PO Box 42 Goodwood SA 5034, Australia. E-mail: [email protected] # 2013 European College of Sport Science

Sleep or swim? Table I. Participant characteristics

Age (years) Height (cm) Mass (kg) BMI (kg ×m2) Sum of 7 skinfolds (mm)

Mean9s

Range

22.591.7 184.0912.4 77.7913.9 22.791.6 44.999.8

20.0
24.4 162.4
195.4 52.7
91.0 20.0
24.9 28.1
57.3

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Note: BMI, body mass index.

written informed consent to participate in this study as volunteers (see Table I). The participants were recruited with the assistance of a senior physiologist (S. Halson) at the Australian Institute of Sport. At the time of the study, the participants were in good health and were free of any medical or psychological disorders. None of the participants had previously identified sleep as a concern or had been previously diagnosed with a sleep disorder. The participants were not taking any medications or training supplements during the course of the study, but were permitted to consume caffeine or alcohol. The study was approved by the Australian Institute of Sport Research Ethics Committee. Training schedule The study was conducted in November 2007, midway through the participants’ yearly training cycle,

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and approximately four months prior to the selection trials for the 2008 Australian Olympic Swim Team. Ultimately, six of the seven swimmers qualified to compete in the 2008 Olympic Games in Beijing, China. As part of their preparation for the selection trials, the participants completed a 14-day programme of high-intensity swimming training developed by the squad’s coach (Figure 1). The programme consisted of 12 training days (Days 1
3, 5
10, 12
14) and two rest days (Days 4, 11). On eight of the training days, 2-h training sessions were scheduled for the morning (06:00
08:00 h) and afternoon (16:00
18:00 h). On the other four training days, a 2-h training session was scheduled for the morning only (06:00
08:00 h). Participants swam 6.690.7 km (mean9SD) in morning training sessions and 6.290.8 km in afternoon training sessions. In total, participants swam 128.8 km during the 14-day training programme.

Sleep/wake assessments During the study, all participants lived in residential accommodation on-site at the Australian Institute of Sport. Each participant slept in a private bedroom, but shared bathroom and living room facilities with other participants. The residential accommodation was situated approximately 200 m from an indoor

Figure 1. Sleep/wake patterns of seven elite swimmers during a 14-day high-intensity training programme. Each line represents a 24-h study day from 20:00 to 20:00 h. Black bars indicate the scheduled timing of training sessions. White bars indicate the mean (9s) start and end times of night-time sleep periods. Grey bars indicate the mean start and end times of daytime naps; the numbers in the grey bars represent the number of participants that napped on that day. On two occasions during the study, participants overslept and missed the scheduled start of training. This occurred on Day 9 for four participants and on Day 12 for two participants.

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swimming facility where all of the training sessions were conducted. Participants’ sleep/wake behaviour was monitored using self-report sleep diaries and wrist activity monitors (Philips Respironics, Bend, Oregon). In field-based studies that involve data collection from multiple participants simultaneously over consecutive nights, activity monitors are typically preferred over polysomnography (PSG)
the gold standard for monitoring sleep
because they are portable, non-invasive and operate remotely without an attendant technician. Validation studies comparing wrist activity monitors with PSG report high correlations for sleep duration (i.e. 0.84
0.89) and moderate to high correlations for wake time within sleep (i.e. 0.65
0.76) (Jean-Louis, Zizi, von Gizycki, & Hauri, 1999; Weiss, Johnson, Berger, & Redline, 2010). In the sleep diaries, participants recorded the start and end date/time for all sleep periods (i.e. nighttime sleeps and daytime naps). The activity monitors are devices worn like a wristwatch that continuously record body movement (stored in 1-min epochs for this study). Participants wore an activity monitor on the same wrist for the duration of the study, except when swimming or showering. Data from the sleep diaries and activity monitors were used to determine when participants were awake and when they were asleep. Essentially, all time was scored as wake unless (1) the sleep dairy indicated that the participant was lying down attempting to sleep and (2) the activity counts from the monitor were sufficiently low to indicate that the participant was immobile. When these two conditions were satisfied simultaneously, time was scored as sleep. This scoring process was conducted using Philips Respironics’ Actiwatch Algorithm with sensitivity set at ‘medium’ (Kushida et al., 2001; Tonetti, Pasquini, & Fabbri 2008). This algorithm has been used to quantify sleep/wake patterns in airline pilots (Roach, Sargent, Darwent, & Dawson, 2012), long-haul truck drivers (Darwent, Roach, & Dawson, 2012) and train drivers (Lamond, Darwent, & Dawson, 2005). The following dependent variables were derived from the sleep diary and activity monitor data: . Bedtime (hh:mm): the self-reported clock time at which a participant went to bed to attempt to sleep. . Get-up time (hh:mm): the self-reported clock time at which a participant got out of bed and stopped attempting to sleep. . Time in bed (h): the amount of time spent in bed attempting to sleep between bedtime and get-up time. . Sleep onset latency (min): the period of time between bedtime and sleep start.

. Sleep duration (h): the amount of time spent in bed asleep. . Sleep efficiency (%): sleep duration expressed as a percentage of time in bed. . Wake after sleep onset (%): the amount of time spent awake expressed as a percentage of assumed sleep (i.e. the difference in time between sleep start and sleep end). . Daytime nap duration (h): the amount of time spent in bed asleep during a daytime nap. . Total sleep time (h): the sum of the sleep obtained at night and any sleep obtained the following day during a daytime nap(s). For statistical analyses, night-time sleep periods were characterised as one of two types: a sleep period that preceded a training day or a sleep period that preceded a rest day. The effect of day type (i.e. training day or rest day) on each dependent variable was assessed using linear mixed effects models. In each model, day type (i.e. training day or rest day) was specified as a fixed effect and entered as a repeated variable using a ‘diagonal’ covariance type. To account for interindividual differences in each model, participants were specified as a random effect. All results are reported as mean9s.

Results There was a marked difference in the participants’ sleep/wake behaviour on training days and rest days (Figure 1). For sleep periods that preceded a training day, participants went to bed at 22:05 h, arose at 05:48 h, spent 7.7 h in bed and obtained 5.4 h of sleep (i.e. 71% of time in bed). For sleep periods that preceded a rest day, participants went to bed at 00:32 h, arose at 09:47 h, spent 9.3 h in bed and obtained 7.1 h of sleep (i.e. 77% of time in bed). Mixed model analyses revealed that on nights prior to training days, bedtimes and get-up times were significantly earlier, time spent in bed was significantly shorter, and the amount of sleep obtained was significantly less, than on nights prior to rest days (Table II). There was no difference in sleep onset latency or wake time after sleep onset on training days and rest days. Participants napped on some of the training days, but no participants napped on either of the rest days. Even though some participants supplemented night-time sleeps with daytime naps on training days, they still obtained significantly less sleep on training days than on rest days (Table II). On two occasions during the study, participants overslept and missed the scheduled start of training. This occurred on Day 9 for four participants and on Day 12 for two participants.

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Table II. Sleep/wake variables on training days and rest days (mean9s) Measure

Training days

Rest days

p-Value

Bedtime (hh:mm) Get-up time (hh:mm) Time in bed (h) Sleep onset latency (min) Sleep duration (h) Sleep efficiency (%) Wake after sleep onset (%) Daytime nap duration (h) Total sleep time (h)

22:05900:52 05:48900:24 7.790.9 40.8943.2 5.491.3 70.7915.1 17.698.8 0.290.5 5.691.4

00:32901:29 09:47901:47 9.391.7 31.8921.6 7.191.2 77.297.5 16.297.7 0.090.0a 7.191.2

B0.001 B0.001 B0.001 0.543 B0.001 0.220 0.629 0.108 0.006

Note: aParticipants did not nap on rest days.

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Discussion The aim of this study was to examine the amount of sleep obtained by elite swimmers during a 14-day period of high-intensity training. The three major findings of this study are that the average amount of sleep obtained each day was low, the percentage of time in bed that was converted into sleep (i.e. sleep efficiency) was poor and substantially less sleep was obtained on training days than on rest days. The amount of sleep obtained by swimmers on nights prior to training days, i.e. 5.4 h, was well below the generally accepted target of 7
8 h/day. This level of sleep restriction could have acute and chronic effects on training performance. First, athletes who obtain an insufficient amount of sleep on the night immediately prior to a training day selfreport poorer mood, and higher exertion, during training than normal (Reilly & Piercy, 1994; Sinnerton & Reilly, 1992). It is likely that these effects on perceived mood and exertion could impair an athlete’s motivation, and thus their ability to train effectively, particularly during sessions that require high levels of intensity (Reilly & Edwards, 2007). Second, people who obtain an insufficient amount of sleep over several consecutive days have impaired immune function (Vgontzas et al., 2004), which in the case of elite athletes, places them at greater risk of developing upper respiratory tract infections and other health problems (Cohen, Doyle, Alper, Janicki-Deverts, & Turner, 2009). Furthermore, people who obtain an insufficient amount of sleep over several consecutive days have impaired cognitive capacity (Belenky et al., 2003), glucose metabolism (Spiegel et al., 1999) and appetite regulation (Spiegel et al., 2004), each of which could impair the training performance of elite athletes. For night-time sleep periods that preceded training days and rest days, the percentage of time in bed that swimmers converted into sleep, i.e. sleep efficiency, was 71 and 77%, respectively. These values are substantially lower than the typical sleep efficiency of 90% for healthy young adults (Carskadon & Dement, 2005; O’Connor &

Youngstedt, 1995). The swimmers had relatively low sleep efficiency because they took longer than normal (i.e. 20 min) to fall asleep on nights prior to training days (i.e. 41 min) and rest days (i.e. 32 min) (Carskadon & Dement, 2005; O’Connor & Youngstedt, 1995), and the percentage of time that they spent awake during sleep periods on nights prior to training days (i.e. 18%) and rest days (i.e. 16%) was higher than normal (i.e. 5%; Carskadon & Dement, 2005; O’Connor & Youngstedt, 1995). The swimmers may have taken longer than normal to fall asleep because some elite athletes experience elevated levels of anxiety during intensive training (Fry et al., 1994; Millet, Groslambert, Barbier, Rouillon, & Candau, 2005), and anxiety can interfere with a person’s ability to fall asleep (Lindberg et al., 1997). The swimmers may have been awake more than normal during sleep periods because elite athletes are often on hydration programmes that increase the frequency of toilet visits (Robson-Ansley et al., 2009), and the muscle soreness and physical discomfort associated with high-intensity training can disturb sleep (Driver et al., 1994). In addition, caffeine and alcohol ingestion close to bedtime are known to affect sleep latency and sleep efficiency (Arnedt et al., 2011; Drapeau et al., 2006) and the swimmers in this study were free to consume both of these substances during the two-week data collection period. The amount of sleep obtained by swimmers was greatly influenced by their training schedule. On nights prior to training days, the swimmers spent less time in bed and obtained less sleep compared with nights prior to rest days. The primary cause of this difference in sleep appears to be the fact that the first session on training days was scheduled to start very early in the morning (i.e. 06:00 h). Indeed, the swimmers got out of bed 4 h earlier on training days than on rest days (i.e. 05:48 vs. 09:47 h). The swimmers attempted to compensate for having to wake earlier on training days by going to bed earlier, but the difference in bedtimes on nights prior to training days and rest days was only 2.5 h (i.e. 22:05

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vs. 00:32 h). Although some of the swimmers supplemented night-time sleeps with daytime naps on training days, they still obtained significantly less sleep in total on training days than on rest days. The results obtained from this observational study indicate that a certain type of training schedule (i.e. early-morning starts) has a negative impact on sleep. In future, it will be important to systematically evaluate the impact of different types of training schedules on sleep in randomised controlled trials. The impact of early start times on the amount of sleep obtained by elite athletes has not been shown previously, but similar results have been found with ˚ kerstedt, other populations (see Ingre, Kecklund, A So¨derstro¨m, & Kecklund, 2008; Kecklund & ˚ kerstedt, 1995; Roach et al., 2012). For example, A Spencer and Montgomery (1997) examined the impact of various work schedules on the sleep patterns of 175 short-haul airline pilots and found that approximately 30 min of sleep was lost for every hour that the start of work was advanced prior to 09:00 h. In this study, the swimmers’ first session on training days occurred 3 h before 09:00 h, and they obtained 1.7 h less sleep on training days than on rest days. These results are consistent with those that would be expected based on Spencer and Montgomery’s data. At a basic level, it seems reasonable to expect that people required to get up early should be able to obtain a reasonable amount of sleep by going to bed earlier. However, the swimmers in this study, and the pilots in the Spencer and Montgomery study, were not able to do so. There are two reasons why it is difficult, in practice, to substantially advance one’s bedtime. First, from a lifestyle perspective, many people have social and/or family commitments in the evening that limit the extent to which they can advance their bedtime (Folkard & Barton, 1993; Tucker, Smith, Macdonald, & Folkard, 1998). Second, from a physiological perspective, there is a ‘forbidden zone’ for sleep in the early evening, such that even if one is in bed, it is difficult to initiate or maintain sleep (Lavie, 1986). There are some limitations of this study that should be considered when interpreting its results. First, the number of participants was limited by the size of the training squad. In this study, we specifically recruited participants from the same training squad. However, these squads typically consist of only 7
10 athletes. It is possible that the small sample size may have resulted in the study being under-powered, but the fact that five out of the nine mixed model analyses showed a significant difference between training days and rest days indicates that the study had sufficient power. Second, given that this study was conducted in a field setting, there were many uncontrolled factors that may have affected

participants’ sleep, other than the timing of training sessions (e.g. use of caffeine and alcohol). Third, the amount and quality of sleep obtained by the participants in this study was assessed using wrist activity monitors rather than PSG. PSG is considered to be the gold-standard for monitoring sleep in laboratorybased studies because it measures the depth of an individual’s sleep. However, in field-based studies such as this one, where the primary measure is sleep duration rather than sleep depth, activity monitors are a reasonable alternative. Although early-morning starts are a common practice amongst coaches and elite athletes in many sports (e.g. swimming, rowing, triathlon), there are actually no published data to indicate that there is a sound physiological rationale to train in the early morning. Rather, it is likely that early-morning starts are a legacy from a time when non-professional athletes had to train before work or school. The results of this study clearly indicate that training schedules that require early-morning starts restrict sleep to a level below the recommended daily target for healthy adults. While an obvious strategy to compensate for early-morning starts is an advance of bedtime, the presence of a ‘forbidden zone’ for sleep may discourage individuals from going to bed much earlier than normal. In the case of the present group of swimmers, delaying the start of morning training by 2 or 3 h should enable them to obtain more sleep than they currently do. Acknowledgements This study was financially supported by the Australian Research Council. The authors are grateful to the athletes and coaching staff for their time and commitment during this study. References Arnedt, J. T., Rohsenow, D. J., Almeida, A. B., Hunt, S. K., Gokhale, M., Gottlieb, D. J., et al. (2011). Sleep following alcohol intoxication in healthy, young adults: Effects of sex and family history of alcoholism. Alcoholism: Clinical and Experimental Research, 35, 870
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