Sleep/wake behaviour of endurance cyclists before and during competition

Journal of Sports Sciences, 2015 Vol. 33, No. 3, 293 299, http://dx.doi.org/10.1080/02640414.2014.942690 Sleep/wake behaviour of endurance cyclists before and during competition MICHELE LASTELLA 1, GREGORY DANIEL ROACH 1, SHONA LEIGH HALSON 2, DAVID THOMAS MARTIN 2, NICHOLAS PETER WEST 3 & CHARLI SARGENT 1 1 Appleton Institute for Behavioural Science, Central Queensland University, Adelaide, Australia, 2 Australian Institute of Sport, Physiology, Canberra, Australia and 3 Molecular Basis of Disease, Griffith Health Institute, Griffith University, Gold Coast, Australia (Accepted 27 June 2014) Abstract Good sleep is critical for optimising recovery and athletic performance. Yet, few studies have investigated how athletes sleep before and during competition. The aim of this study was to determine whether such sleep is poorer than that before a usual training day. Twenty-one male endurance cyclists (age: 19.9 ± 1.7 years) sleep/wake behaviour was assessed using wrist activity monitors for 11 nights, including a six-night baseline training phase, three nights before competition and two nights during competition. Cyclists had less sleep on the night before competition (6.5 ± 0.9 h) and during the first night of competition (6.8 ± 0.8 h) than at baseline (7.4 ± 0.6 h). Cyclists also went to bed and woke up earlier during competition than at baseline. Competition schedules and competition itself can disrupt the sleep/wake behaviour of athletes during competition. Future investigations should examine sleep during three stages of competition (i.e. before, during and after competition). This will help coaches develop a greater understanding of how sleep changes during different phases of competition and enable them to plan post-competition training programmes to ensure appropriate rest and recovery is obtained. Keywords: wrist activity monitors, sleep, cyclists, competition Introduction Athletes typically report sleeping poorly during the night(s) before competition (Erlacher, Ehrlenspiel, Adegbesan, & El-Din, 2011; Lastella, Lovell, & Sargent, 2014; Savis, 1994). Several factors disrupt the sleep of elite-standard athletes before competition including pre-competitive anxiety, excitement, unfamiliar sleep environments (e.g. hotels), changes in time zones (e.g. jet-lag) and early competition start times (Erlacher et al., 2011; Lastella et al., 2014; Waterhouse, Reilly, & Atkinson, 1997). Evidence that athletes experience poor sleep on the night(s) before competition has relied predominantly on retrospective techniques (Erlacher et al., 2011; Erlacher, Schredl, & Lakus, 2009; Lastella et al., 2014). Despite over 65% of athletes reporting sleep disturbances before competition, these data were limited to memory effects associated with retrospective techniques as athletes relied on their ability to recall how well they slept the night before competition and compare that sleep to a usual night s sleep (Erlacher et al., 2011). Polysomnography is the gold standard for objectively quantifying sleep and wakefulness, but its use to examine the sleep of elite-standard athletes is rare because equipment is expensive and not easily portable (Ancoli-Israel et al., 2003). One study conducted by Léger et al. (2008) used polysomnography to examine how sailors manage their sleep before and during a long-haul yacht race. Sleep declined throughout competition, and sleep management strategies were the underlying factors associated with overall performance. Given the limitations of polysomnography, Léger et al. (2008) were restricted to a small sample size. Wrist activity monitors provide a simple alternative to polysomnography as they are worn similar to a wristwatch, require minimal effort on behalf of the athlete and so enable researchers to investigate more participants (Ancoli-Israel et al., 2003). In healthy adults, wrist activity monitors have agreement for measures between 80 and Correspondence: Michele Lastella, Appleton Institute for Behavioural Science, Central Queensland University, 44 Greenhill Road, Adelaide 5034, Australia. E-mail: m.lastella@cqu.edu.au 2014 Taylor & Francis

294 M. Lastella et al. 91% with polysomnography for sleep/wake behaviour (Blood, Sack, Percy, & Pen, 1997; de Souza et al., 2003). Accordingly, we used wrist activity monitors as an objective form of sleep assessment to determine (1) whether athletes slept poorly before and during competition and (2) whether sleep on the nights before and during competition was related to overall performance ranking. Method Participants Twenty-one male endurance cyclists volunteered to participate in this study (mean ± s; age: 19.9 ± 1.7 years; stature: 179.6 ± 6.0 cm; body mass: 70.2 ± 7.2 kg). All participants completed the study. Participants were recruited by the coaching and/or the physiology staff at the Australian Institute of Sport. Before participation, cyclists received comprehensive verbal and written information about the purpose of the study. The study was approved by the human research ethics committees of the University of South Australia and the Australian Institute of Sport. Procedure Data were collected for 11 nights that comprised a six-night training phase (i.e. baseline), three nights before competition (i.e. pre-competitions 3, 2 and 1) and two nights during competition (i.e. competitions 1 and 2). During the baseline phase, participants were provided with a sleep-hygiene guide to assist their sleep/wake behaviours (e.g. appropriate bed and get-up times). In between baseline and pre-competition, participants completed a 3-week simulated grand tour followed by an 11-day recovery phase. Pre-competition data were obtained from the final 3 days of the recovery phase. The recovery phase served as preparation for the Tour of Canberra. During data collection, participants wore a wrist activity monitor and kept a self-report sleep diary. Participants recorded their bedtimes, get-up times and subjective sleep quality for all night-time sleep periods for the duration of the study. Participants were instructed not to remove the activity monitor except when showering or swimming. Sleep/wake assessments Sleep/wake behaviour was monitored using selfreport sleep diaries and wrist activity monitors (Philips Respironics, Bend, OR, USA). Data derived from the sleep diaries and wrist activity monitors were used to determine the amount and quality of sleep participants obtained. This was achieved using the Philips Respironics Actiwatch Algorithm where time was scored as wake unless (1) the sleep diary indicated the participant was lying down attempting to sleep and (2) the activity counts derived from the activity monitor were sufficiently low to indicate that the participant was immobile. Once these conditions were met simultaneously, time was scored as sleep. This scoring process was conducted with a sensitivity set at medium (Kushida et al., 2001; Tonetti, Pasquini, Fabbri, Belluzzi, & Natale, 2008). This algorithm has recently been used to quantify the sleep/wake behaviour in elite-standard athletes (Halson et al., 2014; Roach et al., 2013; Sargent, Halson, & Roach, 2014). The algorithm was also used to generate an activity plot (e.g. Figure 1) that Figure 1. Example of sleep/wake data and competition schedule for one participant.

provided a visual representation of participants sleep/wake behaviour. Visual inspection of the activity plot allowed users to assess the consistency of records and adjust or exclude data where appropriate. Based on the visual inspection of the activity plot, the following rules were applied: (1) if the activity monitor was not worn as indicated by no activity on the activity plot, data were excluded from all analyses and (2) if the discrepancy between the sleep diary and activity monitor data exceeded 30 min, the sleep dairy data were adjusted to match the activity monitor data. The following outcome measures were derived from the activity monitors and sleep diary data: Bedtime (hh:mm): self-reported clock time at which a participant went to bed to attempt to sleep. Get-up time (hh:mm): self-reported clock time at which a participant got out of bed and stopped attempting to sleep. Sleep offset time (hh:mm): clock time that a participant woke at the end of a sleep period. Sleep onset time (hh:mm): clock time that a participant fell asleep at the start of a sleep period. Sleep latency (min): time between bedtime and sleep onset time. Time in bed (h): time spent in bed attempting to sleep between bedtime and get-up time. Total sleep time (h): duration of sleep during a sleep period. Sleep efficiency (%): percentage of time in bed that was spent asleep. Mean activity score: sum of the activity counts between sleep onset and sleep offset divided by the number of epochs between sleep onset and sleep offset. Subjective sleep quality: participant s self-rating of sleep quality on a 5-point Likert scale from 1 (very good) to 5 (very poor). Training phase (baseline) The baseline training phase consisted of daily training rides of low-to-moderate intensity. Participants cycled 53 ± 22 km per day for a duration of 96 ± 102 min (speed 33.1 ± 2.7 km h 1 ). Sleep/wake behaviour and competition 295 Table I. Race and stage details. Stage 1 Stage 2 Stage 3 Stage 4 Type Criterium Road race Time trial Road race Start time (hh:mm) 16:00 09:30 15:00 09:00 Distance (km) 52 109 20 130 Duration range (min) 60 80 180 240 30 40 180 240 short-course criterium. A criterium is a road race (usually 1 3 km), where cyclists complete multiple laps (25 60 laps) in a closed-off city circuit (Jeukendrup, Craig, & Hawley, 2000). Day 2 consisted of Stages 2 and 3, a road race and a time trial and Day 3 consisted of Stage 4, a road race (Table I). As each race was conducted on public roads, road closures were in place and enforced by police. Performance times and overall ranking for each participant are presented in Table II. Statistical analysis Fully within-groups factorial analyses of variance (ANOVAs) were conducted to compare the sleep/ wake behaviour of endurance cyclists during a baseline training phase, before competition and during competition. Each ANOVA included phase as the within-groups factor (6 levels; baseline, pre-competitions 3, 2 and 1 and competitions 1 and 2). Effect sizes were calculated using Cohen s d, and the criteria to interpret the magnitude of the effect size were trivial (0 0.19), small ( 0.20 0.49), medium ( 0.50 0.79) and large ( 0.80) (Winter, Abt, & Nevill, 2014). An alpha of P < 0.05 was the criterion for statistical significance. Data are presented as means ± s. Pearson correlation coefficients (r) investigated relationships between the duration and quality of sleep elite athletes had before and during competition and overall performance ranking. Participants duration and efficiency of sleep during competition were used for correlation analyses. Performance was examined using the overall performance ranking of each participant from the Tour of Canberra. All data were analysed using SPSS (v17.0) statistical software. Competition The competition was a 3-day endurance road cycling race, the Tour of Canberra, which was governed by the rules and regulations of Cycling ACT and Cycling Australia. The Tour comprised four stages spread over 3 days. Day 1 consisted of Stage 1, a Results Participants took 28.7 ± 24.9 min to fall asleep and had 7.0 ± 0.8 h of sleep, sleep efficiency of 85.9 ± 5.3%, mean activity score of 15.7 ± 7.1 and rated their subjective sleep quality as good (2.3 ± 0.9). Descriptive statistics for baseline, each

296 M. Lastella et al. Table II. Results from the Tour of Canberra. Participant ranking (n = 21) Competition ranking (n = 116) Stage 1 01:07:19 Stage 2 03:01:10 Stage 3 00:25:42 Stage 4 03:11:51 Total time 07:45:59 1 8 ST 03:01:12 00:27:24 03:11:53 07:47:48 2 9 ST 03:01:24 00:27:08 03:12:03 07:47:54 3 19 ST 03:01:17 00:29:34 03:12:03 07:50:13 4 22 ST 03:01:12 00:30:14 03:11:53 07:50:38 5 24 ST 03:05:31 00:27:42 03:11:53 07:52:23 6 30 ST 03:05:31 00:27:26 03:17:44 07:56:45 7 36 ST 03:23:09 00:28:25 03:17:44 08:06:17 8 52 ST 03:23:09 00:29:43 03:12:03 08:12:14 9 54 ST 03:23:09 00:29:50 03:13:03 08:13:21 10 61 ST 03:23:09 00:29:40 03:15:02 08:15:30 11 64 ST 03:23:09 00:29:39 03:17:34 08:16:31 12 66 ST 03:23:09 00:30:21 03:19:21 08:20:10 13 67 ST 03:23:09 00:34:41 03:19:21 08:21:43 14 69 ST 03:21:09 00:28:25 03:27:03 08:25:58 15 76 ST 03:23:09 00:32:58 03:34:52 08:58:16 16 DNF ST 03:23:09 00:30:41 DNF 17 DNF ST 03:23:09 00:31:12 DNF 18 DNF ST 03:23:09 DNF 03:22:13 19 DNF ST 03:23:09 00:29:10 DNF 20 DNF ST 03:23:09 00:32:09 DNF 21 DNF ST 03:23:09 00:29:12 DNF Notes: ST = same time. Cyclists finished in a group for the short-course criterium. DNF = did not finish. night before and during competition are presented in Table III. Sleep onset time (F 5,86 = 2.7, P = 0.02), sleep offset time (F 5,104 = 3.2, P = 0.06), time in bed (F 5,87 = 2.6, P = 0.03) and total sleep time (F 5,85 = 2.9, P = 0.02) varied depending on phase. Sleep onset times were earlier on the third night before competition than the final night of competition (P =0.01, d=0.4; mean difference: 46 min; 95% likely range: 8 83 min). Sleep offset times were earlier at baseline and the third night before competition than the final day of competition (P =0.03, d = 0.8; mean difference: 57 min; 95% likely range: 59 119 min and P=0.02, d = 0.8; mean difference: 71 min; 95% likely range: 8 135 min), respectively. Participants spent less time in bed during the first night of competition than at baseline (P = 0.02, d = 0.6; mean difference: 46 min; 95% likely range: 36 88 min). Participants obtained less sleep the night before competition and during the first night of competition than at baseline (P =0.03, d = 0.4; mean difference: 37 min; 95% likely range: 20 73 min and P = 0.02, d = 0.5; mean difference: 41 min; 95% likely range: 56 75 min), respectively. Phase did not affect sleep efficiency (F 5,83 = 1.1, P = 0.37), sleep latency (F 5,85 = 1.8, P = 0.11), mean activity score (F 5,85 = 0.4, P = 0.84) or subjective sleep quality (F 5,64 = 1.8, P = 0.12). Sleep/ wake behaviour for all participants at baseline, before and during competition are presented in Figure 2. Table III. Sleep variables prior to and during competition. Baseline Pre-competition Competition Sleep variable BL (n = 21) Night 3 (n = 19) Night 2 (n = 18) Night 1 (n = 17) Night 1 (n = 17) Night 2 (n = 17) Bedtime (hh:mm) 22:20 ± 00:32 22:43 ± 01:01 22:20 ± 00:32 22:27 ± 00:55 22:22 ± 00:43 21:56 ± 00:41 Get-up time (hh:mm) 07:32 ± 00:28 07:43 ± 00:47 06:53 ± 00:20 07:12 ± 00:23 06:50 ± 00:19 06:35 ± 00:19 Sleep onset (hh:mm) # 22:21 ± 00:13 22:47 ± 01:00 a 22:22 ± 00:29 22:38 ± 00:32 22:23 ± 00:38 21:58 ± 00:37 a Sleep offset (hh:mm) # 07:33 ± 00:16 b 07:47 ± 00:47 c 07:20 ± 00:20 07:13 ± 00:23 06:51 ± 00:19 06:36 ± 00:19 b,c Sleep latency (min) 28.0 ± 13.7 17.7 ± 1.7 36.4 ± 2.7 36.4 ± 2.9 38.1 ± 4.6 29.2 ± 2.3 Time in bed (h) # 9.2 ± 0.3 d 9.0 ± 1.0 8.9 ± 0.6 8.7 ± 0.6 8.7 ± 0.8 d 9.1 ± 0.6 Total sleep time (h) # 7.4 ± 0.6 e,f 7.3 ± 0.8 6.9 ± 0.9 6.5 ± 0.9 e 6.7 ± 0.8 f 6.9 ± 0.8 Sleep efficiency (%) 86.4 ± 0.4 86.9 ± 5.6 85.3 ± 5.0 84.9 ± 5.0 85.8 ± 6.0 81.7 ± 7.5 Mean activity score 14.7 ± 5.3 14.3 ± 7.1 15.9 ± 6.1 17.4 ± 6.1 18.5 ± 10.3 20.7 ± 10.1 Subjective sleep quality 2.4 ± 0.6 2.0 ± 0.9 2.1 ± 1.0 2.6 ± 0.8 2.2 ± 0.9 2.6 ± 0.9 Notes: # Significant difference. Mean values with the same superscript are significantly different (P < 0.05).

Sleep/wake behaviour and competition 297 Figure 2. Sleep/wake behaviour of 21 endurance cyclists at baseline, before competition and during competition. Each line represents a single 24-h period from 20:00 to 20:00. The first six lines represent the sleep/wake behaviour during the baseline period. The remaining lines represent the three nights before and during competition. The black and white horizontal bars indicate the mean (±s) bedtime, sleep latency, sleep onset time, sleep offset time and get-up time for all night-time sleep periods. The light grey horizontal bar indicates the training schedule during the baseline period. The striped black and white horizontal bar indicates the competition registration. The dark grey horizontal bars indicate the competition schedule. There were no relationships in the amount r(13) = 0.20, P = 0.55 or quality of sleep r(13) = 0.19, P = 0.54 obtained during competition and overall performance ranking. Six cyclists were excluded from the correlation analyses as they did not complete all stage races. Discussion The main finding of this study was that the length and quality of sleep was poorest the night before competition. Cyclists took more than half an hour to fall asleep, had 6.5 h of sleep and reported the poorest sleep quality (i.e. sleep efficiency and selfreported sleep quality) on the night before competition. Sleep duration was almost an hour less on the night before competition than at baseline. These data are consistent with previous anecdotal reports that athletes sleep poorer than usual on the night before competition (Erlacher et al., 2011; Lastella et al., 2014). Studies have used retrospective techniques but these can be inaccurate as they require participants to recall how well they slept and/or how well they perceived they slept. Despite the limitations associated with these techniques, the findings from this study are consistent with previous reports that athletes sleep is poorer than usual on the night before competition (Erlacher et al., 2011; Lastella et al., 2014). Explanations for disturbances before competition include sleeping in an unfamiliar location (e.g. hotel), pre-competitive anxiety, and getting up early to consume and digest a high energy meal before competition (Erlacher et al., 2011; Lastella et al., 2014; Savis, 1994). Sleep onset and offset times were earliest during competition. Given that the first race started in the afternoon (i.e. 16:00 h) and the second and third races started in the morning (i.e. 09:00 and 09:30 h), sleep onset and sleep offset times could have been influenced by the competition schedule. This occurred because the earliest sleep offset times coincided with the second and third day of competition. Similar observations were made by Sargent et al. (2014) where the timing of training dictated the sleep/wake behaviour of elite-standard swimmers. On nights before training at 06:00 h, swimmers went to bed 2 h earlier and woke up 3 h earlier than the nights before a rest day (Sargent et al., 2014). In elite-standard sport, particularly during competition, adjusting sleep onset and offset times can encourage athletes to obtain more sleep and maximise their post-exercise recovery processes (Samuels, 2008). The duration and quality of sleep during competition had no influence on cyclists overall performance ranking. Effects of sleep deprivation on athletic performance are contradictory. Several studies have reported impairments in performance attributable to such deprivation (Oliver, Costa, Laing, Bilzon, & Walsh, 2009; Rodgers, Paterson, Cunningham, & Noble, 1995). For example, Oliver et al. (2009) examined the effect of one night of sleep deprivation on aerobic performance assessed by a 30-min treadmill run. Eleven males completed two randomised trials separated by 7 days: once after a baseline of 8.3 h of sleep, the other after 30 h of total sleep deprivation. Distance run after sleep deprivation (6037 ± 759 m) was less than at baseline (6224 ± 818 m) (Oliver et al., 2009). Conversely,

298 M. Lastella et al. other studies have reported no change in performance after sleep deprivation (Blumert et al., 2007; Meney, Waterhouse, Atkinson, Reilly, & Davenne, 1998). For example, Blumert et al. (2007) reported no changes in weightlifting performance after 24 h of sleep deprivation. These data indicate that high-intensity, short-duration exercise that is supported primarily by anaerobic metabolism tends to be unaffected by sleep deprivation, whereas endurance performance is adversely affected. In most cases, total sleep deprivation has been used to examine effects of sleep loss on athletic performance. Such deprivation is uncommon in elite-standard athletes, so conclusions from previous studies need to be interpreted with caution. Despite uncertainty about the impact of sleep deprivation on athletic performance, it is longer periods of sleep deprivation that have greater negative effects on performance (Van Dongen, Maislin, Mullington, & Dinges, 2003). There were no qualitative data collected concerning sleep before and during competition. This was to reduce additional burdens on cyclists during their competition. These data could have provided greater insight into the reasons why sleep was disrupted before and during competition. There were also no data collected on the night(s) after competition that could determine whether athletes sleep more or less after competition, and if so, how long it takes for sleep to return to baseline. At the end of competition, participants returned to their respective home states prohibiting the collection of these data. The results from this study confirm previous subjective data that athletes experience sleep disturbances before competition (Erlacher et al., 2011; Lastella et al., 2014). Given that sleep optimises recovery and athletic performance, coaches should use strategies to maximise the duration and quality of sleep their athletes obtain (Davenne, 2009; Mah, Mah, Kezirian, & Dement, 2011). While the timing of competition schedules are fixed, strategies such as establishing a regular bedtime, eliminating bedroom clocks, avoiding the consumption of alcohol and caffeine and napping can optimise the duration and quality of sleep athletes obtain (Halson, 2013; Waterhouse, Atkinson, Edwards, & Reilly, 2007). Reliable transport and accommodation close to the competition venue can reduce travel time and alleviate some of the anxiety associated with travelling to the competition venue (Davenne, 2009). Even so, such travel provides athletes with an opportunity to extend their sleep duration before competition. Based on the findings of the present study, the sleep/wake behaviour of elite-standard cyclists was affected by competition. However, although the duration and quality of sleep obtained before and during competition was less than the general target of 8 h of sleep per night (Belenky et al., 2003), the severity of sleep disturbance was not enough to identify any negative effects on performance. It is also difficult to determine accurate performance effects during stage races without the use of power meters (Balmer, Davison, & Bird, 2000). During longer competitive events, it is possible that this amount of sleep loss (i.e. 1.3 h per day or 9.1 h per week) can accumulate throughout the event and negatively impact performance towards the end of the competition. In conclusion, the cyclists in this sample confirmed anecdotes that athletes experience sleep disturbances before competition. This study suggests that competition schedules disrupt the sleep/wake behaviour of athletes during competition. There were no data collected on how athletes slept on the night(s) after competition. 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