Introduction
Elite athletes have busy schedules with commitments to training, competition, and media. Athletes are increasingly using electronic devices (32) to stay connected with family, friends, and coaches. Although beneficial for communication, excessive late night use of electronic devices can result in decreased sleep duration (SD) and, potentially, athletic performance (36), as sleep is crucial for psychological functioning and daily performance of athletes (5). Indeed, the high usage of electronic devices and potential negative effects on overnight sleep and subsequent next-day performance were key factors in the imposition of a “tweeting ban” on Australian athletes during the 2014 Sochi Winter Olympics (31).
A growing body of evidence indicates that the use of electronic devices in the evening before sleep negatively affects the quality of sleep obtained in young adults (19,24,36). Specifically, electronic device usage before sleep has been shown to increase the severity of insomnia symptoms (19), and regular evening use of electronic devices can lead to an acute and/or a chronic sleep debt (36). These negative effects seem to be particularly common in adolescents, with one in 5 reporting nightly bedtime delay and increased time taken to fall asleep as a consequence of electronic media use (24).
The main mechanism underlying the disruptive effects of electronic devices (smartphones, tablets, computers, and televisions) on sleep is thought to be light emission from the device in the evening immediately before sleep. Early studies suggested that light intensity and/or brightness were responsible for these negative influences on sleep (7,13); however, more recent work indicates that the spectral distribution or wavelength of the light may be of more importance than brightness (41). Today's electronic devices contain light emitting diodes that emit a shorter wavelength light, making it more “blue” in appearance. Such light emissions can affect sleep by disrupting normal circadian rhythms, particularly by suppressing the secretion of melatonin (16,41), with resultant increased alertness and delayed time of sleep onset (SO) (16,27). These changes in alertness and time of SO can lead to sleep loss and reduced sleep efficiency (SE).
Sleep loss can have a negative impact on aspects of physical function known to be important for recovery and performance in competitive sports (14,21). For example, reduced muscle glycogen storage, increased perceived stress, and reduced sprint performance have all been found to occur as a consequence of sleep loss (38). Measurements of anaerobic power have been shown to be impaired after 36 hours without sleep (39), and both decreased SD and increased sleep latency (SL) have been shown to negatively affect testosterone levels, hand grip strength, and walking speed, all of which can impact physical performance (2).
Sleep loss also negatively affects cognitive performance and reaction time, both of which are important components of athletic performance. Restricting sleep to 4–5 hours per night increases mood disturbances and the frequency and duration of psychomotor vigilance test lapses (17), whereas after 77 hours of wakefulness lapses significantly increase (23).
Therefore, the aim of this study was to determine the impact of the removal of electronic devices for a period of 48 hours on judo athlete's (judokas) overnight sleep quantity and sleep quality, and the effect of any changes in sleep on subsequent physical and cognitive performance.
Methods
Experimental Approach to the Problem
The study was undertaken at the Australian Institute of Sport (AIS) Combat Center. This study was conducted at an international judo camp held at this facility over 6 consecutive days and nights in September 2014. All athletes traveled to Canberra from the eastern seaboard of Australia; therefore, jetlag or circadian misalignment was not present.
The training camp required 3 training sessions per day. These occurred between the hours of 06 30–08 00, 10 00–12 00, and 16 00–18 00. On day 7, the first training session was at 09 30 hours, i.e., the athletes were provided an extended sleep opportunity on the previous night (night 6).
Meals and accommodation were provided at the AIS village. All athletes shared apartment style accommodation for the duration of the camp with 2 athletes per room. Shared sleeping environment was based on sex.
Subjects
Twenty-three athletes (12 males and 11 females) participated in this study. Their mean age was 18 ± 2 years (range 16–24 years), and mean body mass was 66 ± 10 kg (48–81 kg). Five participants in the device-restricted group (2 males, 3 females) were excluded from the final analyses because of their unanticipated self-reported use of electronic devices during the 48-hour restriction period. Specifically, these athletes watched television, accessed Facebook, and sent text messages by obtaining electronic devices from the participants in the control group or from other persons at the AIS. Thus, final analyses were based on a total of 18 athletes, 9 in the device-restricted group (all males), and 9 in the control group (8 females, 1 male).
Information regarding the testing protocols and expected commitment to the study was provided to participants before obtaining their written consent, and parental or guardian written consent was obtained from any participants younger than 18 years of age. Ethical approval for the study was obtained from the Human Research Ethics Committees of the AIS and The University of Western Australia.
Procedures
This study was an observational/intervention design. On the evening of day 1, participants were asked to self-allocate to a control group or a device-restricted group. The device-restricted group (n = 9) had all electronic devices (e.g., laptops, phones, and tablets) removed for a period of 48 hours on days 3 and 4 (i.e., including nights 3 and 4). On the morning of day 5, electronic devices were returned to the device-restricted group. The control group (n = 9) was permitted to use electronic devices ad libitum over the study period.
All athletes wore a wrist-activity monitor (Readiband; Fatigue Science, Vancouver, BC Canada) continuously over the monitoring period to provide measures of sleep. Athletes also completed a diary each morning that included self-reported estimates of time in bed. Additional diary questions included daily electronic device use, daily caffeine use, and the rating of perceived exertion (RPE) during all training periods.
Cognitive performance was measured on days 2 and 4, and physical performance was assessed on days 2, 3, 4, 5, and 6. At the commencement of the study, (evening of day 1), questionnaires were given to collect general information including demographic and anthropometric data, sleep history, risk of insomnia, sleepiness, and obstructive sleep apnea (OSA).
Specific Measurements
Actigraphy
An activity monitor and a sleep/training diary was issued to each athlete at 20:00 on the evening of day 1 (night 1) and retrieved on the morning of day 7 (after night 6). Activity monitors were worn on the nondominant wrist throughout the monitoring period, including during training and sparring. These devices have been shown to accurately detect sleep/wake episodes (overall accuracy of 93%) in comparison with the gold standard of polysomnography (15,33).
Sleep-related measures were derived from each device using Readiband Sync software. These included SD, SL, time of SO, wake after SO, SE, and time at wake (WT).
Diary
Athletes were provided with a sleep/training diary, which they carried with them throughout the study period. The diary contained questions relating to their sleep, electronic device use, caffeine use, and training effort.
Electronic Device Use
Electronic device usage information included the duration of use in hours and minutes, the type of device/s used, and the type of activity undertaken in the 2 hours before bed.
Caffeine Consumption
Quantity and type of caffeine consumption were recorded at the end of each day before sleep.
Training Effort
Rating of perceived exertion was recorded after each training session. The RPE scale is used to record the self-perceived exercise intensity and is strongly correlated with several other physiological measures of exertion (4).
Cognitive Performance
The Cogstate research tool (Cogstate Ltd., Melbourne, Australia) and Cogstate Research software were used to assess cognitive function. This computerized testing system has been shown to provide repeatable and sensitive measures of cognitive status (11) and has been used extensively in a range of applications including (but not limited to) sleep, cognitive performance, and exercise (26). Testing occurred in all participants between 13 00 and 16 00 hours on days 2 and 4. The testing environment and testing stations were standardized for each session. The primary measure of cognitive performance was an identification task “speed of performance,” which measured reaction times for correct responses. A secondary measure was the total number of errors made in attempting to learn the same hidden pathway on 5 consecutive trials at a single session. For both tests, a lower score indicated a better performance. Athletes conducted a familiarization test before each testing period on each day.
Physical Performance
A single leg three hop test (SL-THT) was used to assess physical performance, mainly because of its simplicity and relative ease of use for athletes. Although jump tests and a “judo-specific test” have been used in other judo studies (1,25), these were deemed less relevant to the athletes in this study given their different skill level (judo ranks) and demographic and anthropometric factors. The SL-THT was conducted on judo training mats each afternoon (days 2–6) between 16 00 and 16 30 hours. The time of day was kept constant to control for any circadian variation in performance (12). A 10-minute warm-up was conducted before each testing session. This was facilitated and led by the Judo Australia training staff. The warm-up was conducted on the tatami mats (approx. 25 × 25 m) and consisted of jogging around the perimeter, 20 m sprints, forward rolls, backward rolls over 20 m, break falls, and general mobility.
After a warm-up, each athlete attempted to jump as far as possible on one leg, starting the test on one leg and finishing the test by landing on 2 feet with knees slightly flexed. A total of 4 tests were conducted on each occasion, with 2 attempts starting on each leg. The primary outcome measure was the sum of the greatest distances achieved when starting on the left and right leg. Athletes conducted a familiarization test before each testing period on each day.
Anthropometric and Demographic Measurements
Anthropometric measurements were collected on day 2 of the training camp between 13:00 and 16:00, immediately before the cognitive performance tests. Measurements included height (cm), mass (kg), and neck circumference (cm). Body mass index was calculated from weight/height2 (kg·m−2).
Sleep-Related Questionnaires
Insomnia was assessed using the validated Insomnia Severity Index (ISI). The ISI consists of 5 separate questions that ask the participant to self-rate their own experience with insomnia, each with a scale of 0–4. The questions relate to severity, satisfaction, noticeability and worry or distress associated with their insomnia. Scores were aggregated and assessed against a criterion. A score greater than 15 indicates clinical insomnia (3).
Daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS). The ESS is a self-reported scale that asks how likely an individual is to doze off or fall asleep in common daytime situations. Scores in excess of 9 indicate excessive daytime sleepiness (10).
Obstructive sleep apnea risk was assessed using the Berlin Questionnaire (18), which assigns risk of OSA based on the presence and frequency of snoring behavior, wake time sleepiness or fatigue, and a history of obesity and/or hypertension. A positive response to 2 or more of these categories indicates risk for OSA.
Statistical Analyses
Night 1 was considered a familiarization night and night 2 as the baseline night for all sleep-related measures. Nights 3 and 4 were considered the experimental nights for the device-restricted group, whereas night 5 was considered as a return to baseline conditions. Night 6 was considered to be a recovery night, as athletes did not have a scheduled training session until 09 30 hours the next morning.
A 2-way repeated measures analysis of variance was conducted on the raw data for all variables (device-restricted and control) over the 6 nights and 5 days (no data were collected on day 1) for sleep, electronic device use, and cognitive and physical performance data. An unpaired t test was used to compare anthropometric data between groups, and between self-reported and actigraph measures of SD, WT, and SO.
Analyses were undertaken using SigmaStat version 13. Normality and equal variance were assessed using the Shapiro–Wilk and Brown–Forsythe tests, respectively. Nonparametric data were compared using a Mann–Whitney rank sum test and Fisher's least significant difference test for post hoc testing. Data are presented as mean ± SD for each group, and p ≤ 0.05 was considered as statistically significant for all tests.
Results
Demographic, anthropometric, and sleep history data of participants are summarized in Table 1. Two athletes in the device-restricted group and one athlete in the control group reported subthreshold insomnia (ISI score 8–14). Four athletes in the device-restricted group and one athlete in the control group reported having excessive daytime sleepiness (ESS score >10), and 4 athletes within the device-restricted group and 3 athletes within the control group were characterized as being at risk for OSA (Berlin Questionnaire scores >2). All these athletes were included in subsequent analyses.
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Table 1.: Descriptive characteristics of the sample population.*
Training and Electronic Device Usage
All athletes attended (Figure 1) the same training sessions each day and trained for an average of 255 minutes per day. Athletes from both groups reported an RPE of 7 ± 1 after all training sessions (see Table, Supplemental Digital Content 1, https://links.lww.com/JSCR/A28).
Figure 1.: Electronic device usage between groups. Data presented as mean minutes of use in the 2.0 hours before sleep. n = 9 for the device-restricted group and n = 9 for control group. DRG = device-restricted group.
Compared with day 2 (baseline), electronic device decreased by 11 minutes on night 3 (p ≤ 0.05) and increased by 18 minutes on night 4 (p ≤ 0.05). As per the protocol, the device-restricted group did not use electronic devices on days 3 and 4. On each of the 6 nights, device use was greater in the control than in the device-restricted group (p ≤ 0.05).
Actigraphic Sleep Measures on Night 2 vs. Nights 3 and 4
When considering night 2 data as a “baseline” for each group, removal of electronic devices on nights 3 and 4 (device-restricted group) resulted in no significant within-group or between-group differences in any sleep measure (Table 2).
Table 2.: Actigraphy data.*†‡
Actigraphic Sleep Measures on Night 6
Athletes were provided an extended sleep opportunity on night 6, by virtue of a later next-day training time (commencing at 09 30 hours). Compared with night 2, SD on night 6 tended to increase in the device-restricted group by 30 ± 51 minutes (p = 0.32) and significantly increased in the control group by 46 ± 35 minutes (p = 0.03). Sleep duration on night 6 was not significantly different between the groups (p = 0.08).
Sleep Diary Measures
Data from all nights of self-reported sleep data were pooled from the device-restricted and control groups (n = 18) and compared with actigraphy-derived measures.
Compared with actigraphy-based measures, subjective measures of SD were significantly overestimated by 58 ± 85 minutes (490 ± 66 minutes vs. 432 ± 63 minutes, respectively, p = 0.001); subjective measures of WT were significantly greater by 4 ± 57 minutes (06:12 ± 61 minutes vs. 06:08 ± 42 minutes, respectively, p = 0.001); and subjective measures of SO were significantly underestimated by 37 ± 72 minutes per night (21:57 ± 45 minutes vs. 22:24 ± 51 minutes, respectively, p = 0.001) (see Table, Supplemental Digital Content 2, https://links.lww.com/JSCR/A29).
Cognitive and Physical Performance
Compared with day 2 (baseline), speed of performance and errors were unchanged on days 3 and 4 in the device-restricted and control groups. There were no differences between groups in either speed of performance (p = 0.47) or errors (p = 0.13).
Similarly, compared with day 2 (baseline), the distance jumped was unchanged on days 3 and 4 in the device-restricted and control groups. There were no differences between groups for distance jumped (p = 0.07) (see Tables, Supplemental Digital Content 3, https://links.lww.com/JSCR/A30 and 4, https://links.lww.com/JSCR/A31).
Discussion
The major hypothesis of this study was that removal of all electronic devices from elite athletes midway through a training camp would provide an increased sleep opportunity and result in earlier time to sleep and increased SD. We also hypothesized that these changes would result in improved cognitive and physical performance on the days following any nights with increased SD.
Although removal of electronic devices resulted in a tendency toward an earlier time of SO, by 37 and 36 minutes on the 2 device-restricted nights, these changes were not significantly different from those in the control group, who also went to bed earlier on these same 2 nights by 7 and 22 minutes, respectively. Such findings highlight the importance of a control group when assessing the effects of any such interventions. It is highly likely that the lack of statistical significance in SD between the groups in the present study is as a result of the athletes being required to awaken at a set time every morning (which prevented sleep extension) so that training could commence at 06:30 each day. In this regard, it was notable that on night 6 when the athletes were provided with an extended sleep opportunity, as training start time on day 7 was delayed until 09 30 hours, the waking time was later, thus increasing SD by 30 minutes in the device-restricted group and 46 minutes in the control group.
The lack of effect of electronic device removal on SD in this study contrasts with other studies in young children, preadolescent, and adolescent cohorts that have reported an increase in SD with device removal (6,9,22). However, it is likely that such studies have limited relevance to the young elite athletes who participated in this study, who will tend to have an “owl” chronotype, characterized by a delayed time of SO and later evening use of electronic devices (i.e., after 21 00 hours) with high social media use (20). These biological and behavioral factors make it difficult to encourage athletes to go to bed earlier and increase their SD in this way, whereas allowing athletes to sleep later the next morning might be a more productive strategy to increase SD (29,35).
This study found no relationships between removal of electronic devices and changes in sleep or cognitive or physical performance. This is most likely due to the lack of effect of electronic device removal on SD in these athletes and due to the relatively low numbers of participants available. A selection bias was apparent in this study with males tending to self-select to the device-restricted group and females self-selecting to the control group. The tendency (p = 0.07) for an increased distance to be jumped in the single hop jump test in the device-restricted group could be a consequence of the greater number of males in this group.
It is possible that alternative methods of extending sleep in athletes or longer periods of sleep extension are required to elicit cognitive or physical performance improvements. For example, studies of short-term sleep extension ranging from 2 to 4 days have shown no effect on athletic performance (8,40); a study conducted in college basketball athletes found that 5–7 weeks of sleep extension (athletes were instructed to try to achieve 10 hours sleep each night) was associated with improvements in anaerobic, skill-based, and cognitive performance (28). Although small in number, these studies to date suggest that promotion of sleep extension in athletes requires an approach that is relevant to the athletic group based on time of day for competition, training schedules, and age (8,28,40). Data from this study support the notion that athletes can increase SD if allowed a later waking time. Such a concept is consistent with the findings from previous studies in athletes and in the mining industry showing that early morning starts truncate sleep opportunity and lead to sleep loss (30,34). However, further studies are required to determine whether performance benefits can be elicited from shorter duration sleep extension opportunities, especially during training camps, as was the case in this study.
This study used both actigraphy devices and sleep diaries to collect objective and subjective measures of sleep, respectively. It was notable that subjective estimates of sleep from the diary overestimated objective SD (actigraph) by 65 minutes per night. Such findings are consistent with those reported in earlier studies in adolescent populations (37) and highlight the importance of using objective measures of sleep (wherever possible) in any study of elite athletes.
A selection bias was apparent in this study with males tending to self-select to the device-restricted group and females self-selecting to the control group. Athletes were accommodated at the AIS athlete village. Males and females were allocated to apartment style accommodation and athletes shared a room with one other athlete. It is possible that this sleeping environment impacted the quality of sleep due to a change from home sleeping environment; however, these conditions are representative of the usual sleeping environment during training camps and international competition.
It was also noticeable that athletes who self-allocated to the control group were strongly opposed to surrendering their electronic devices at any stage of the camp, perhaps indicating an addictive relationship to electronic devices in those who self-allocated to the control group. Further support for the difficulty that elite athletes have in abstaining from the use of electronic devices during a training camp was seen in the 5 of the 12 athletes who self-allocated to the device-restricted group but had to be excluded from post hoc analyses as they either watched television, or accessed Facebook or sent text messages by obtaining electronic devices from others. It was also notable that on the night immediately after return of the devices, the device-restricted group had reduced SD (by 56 minutes) and SE (by 12%) compared with the control group, perhaps reflecting a compensatory increased use in response to the loss of devices in the preceding 48 hours.
Practical Applications
The data from this study indicate that removal of electronic devices for a period of 48 hours has little effect on time of SO and SD of elite athletes, at least under circumstances where training schedules precluded the athletes from delaying their next-morning wakeup. Furthermore, the study demonstrates the difficulty in increasing SD by providing an environment more conducive to sleep in the evening. In contrast, young adult athletes seem to be able to increase SD by delaying wake time, but only when such an opportunity is presented, as was the case in this study on the final day of the training camp. A practical recommendation of this study could be that daily training start times should be delayed until after 08 00 hours in young athletes (<21 years old). In more general terms, the scheduling of training camps should be designed to consider sleep and recovery to support sleep-related optimization and efficacy of the training and the consolidation of skills.
Conclusion
This research study has shown that the removal of electronic devices for a period of 48 hours during a judo camp does not affect sleep, cognition, or physical performance. This study also suggests that an extended morning, rather than evening, sleep opportunity may be necessary to increase SD in young elite athletes.
Acknowledgments
Many thanks to Fatigue Science, Vancouver, British Columbia for the supply of Readibands and Cogstate Ltd Research, Melbourne, Victoria. Many thanks also to the PhD scholars, staff at the AIS, and Judo Australia.
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