Abstract
Background:
Student-athletes are subject to significant demands due to their concurrent sporting and academic commitments, which may affect their sleep. This study aimed to compare the self-reported sleep quality, quantity, and intraindividual variability (IIV) of students and student-athletes through an online survey.
Hypothesis:
Student-athletes will have a poorer sleep quality and quantity and experience more IIV.
Study Design:
Case-control study.
Level of Evidence:
Level 4.
Methods:
Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while sleep quantity and IIV were assessed using the Consensus Sleep Diary. Initially, the PSQI and additional questions regarding sport participation habits were completed by 138 participants (65 students, 73 student-athletes). From within this sample, 44 participants were recruited to complete the sleep diary for a period of 14 days.
Results:
The mean PSQI score was 6.89 ± 3.03, with 65% of the sample identified as poor sleepers, but no difference was observed between students and student-athletes. Analysis of sleep patterns showed only possibly to likely small differences in sleep schedule, sleep onset latency, and subjective sleep quality between groups. IIV analysis showed likely moderate to possibly small differences between groups, suggesting more variable sleep patterns among student-athletes.
Conclusion:
This study highlights that sleep issues are prevalent within the university student population and that student-athletes may be at greater risk due to more variable sleep patterns.
Clinical Relevance:
University coaches should consider these results to optimize sleep habits of their student-athletes.
Keywords: sport, training load, collegiate athlete, recovery
Student-athletes are subject to physical and psychological stresses from their concurrent sporting and academic commitments.2 Understanding and enhancing off-field practices and behaviors is an important consideration for performance, development, and well-being of student-athletes. Sleep plays an important role in different psychological and physiological functions such as memory consolidation,8 regulation of immune response,1 and glymphatic function.21 Such functions may support both academic and sporting performance. Despite this, little research exists on sleep among the student-athlete population.
University students experience changes in lifestyle and increased sleep problems,19 with findings demonstrating that students exhibit unhealthy sleep behaviors resulting in insufficient sleep and daytime sleepiness.17 Such findings are important because sleep is related closely to academic success and general health in students.9 It is unclear whether student-athletes experience poorer sleep than regular students due to the summative effects of sport and academic stressors. Consequently, a comparison of student-athletes with a “normal” student cohort is warranted. To date, no research has investigated the sleep habits of student-athletes in the United Kingdom.
The aim of this study was to describe sleep patterns among students and student-athletes. We hypothesized that student-athletes would have a poorer sleep quality and quantity when compared with students.
Methods
Participants
Participants from universities in the United Kingdom were invited to participate in the study via social media and the internet. Participants had to be enrolled in a British university to take part in the study. Participant demographics and enrollment allocation are presented in Table 1 and Figure 1. Participants provided informed consent prior to beginning the study. Ethics approval was granted by the university ethics board, and the recommendations of the Declaration of Helsinki were respected.
Table 1.
Participant characteristics a
| Age, y | Height, cm | Mass, kg | |
|---|---|---|---|
| PSQI | |||
| Total (n = 138) | 22.0 ± 3.1 | 175.3 ± 10.3 | 76.6 ± 22.8 |
| Student (n = 65) | 21.8 ± 2.9 | 177.2 ± 10.8 | 81.0 ± 24.5 |
| Student-athlete (n = 73) | 22.3 ± 3.4 | 173.3 ± 9.4 | 71.6 ± 19.7 |
| Sleep diary | |||
| Total (n = 44) | 22.8 ± 3.2 | 176.7 ± 10.6 | 76.5 ± 24.2 |
| Student (n = 19) | 23.5 ± 2.5 | 177.6 ± 10.4 | 75.6 ± 26.1 |
| Student-athlete (n = 25) | 22.2 ± 3.5 | 175.9 ± 10.9 | 77.2 ± 23.2 |
PSQI, Pittsburgh Sleep Quality Index.
Data are presented as mean ± SD.
Figure 1.
Flow diagram regarding participants’ inclusion and classification.
Procedures
Between March and April 2018, an electronic survey on sleep quality and quantity was conducted. Students and student-athletes at universities in the United Kingdom were invited to take part through social media via a link providing the electronic survey. This time corresponded to a nonexamination period for students and the in-season period for student-athletes. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Additionally, participants were asked to complete a sleep diary every morning for 2 weeks in order to describe their sleep patterns and intraindividual variability (IIV). Qualtrics software (Qualtrics) was used as the survey platform and to gather data.
Questionnaire
The questionnaire was initially used to characterize participants as students or student-athletes, using the following questions: (1) Do you participate in any sport? (2) What is your level of sport participation? and (3) What is your weekly training volume? After these questions, participants were classified as a student-athlete or student based on the following criteria: (1) compete at county level, as a minimum, and (2) train 6 or more hours per week. If participants did not meet 1 of these 2 criteria but were enrolled at a university, then they were considered a student. The survey used a validated instrument (PSQI)3,4 to then examine sleep; the 19 questions comprising the PSQI are combined into 7 clinically derived component scores, which are summed to obtain a global score ranging from 0 to 21 corresponding to the global PSQI score. Higher scores indicate worse sleep quality.
Sleep Diary
Sleep patterns were assessed using the Consensus Sleep Diary proposed by Carney et al.6 Participants were asked to complete this sleep diary every morning after waking up. A description of each variable used in the study is displayed in Table 2. The different nights were classified as either week (ie, from Sunday night through Thursday night) or weekend (from Friday night through Saturday night) due to potential effects on sleep.12 A subjective comments section was added to the diary to allow participants to explain any specific issues encountered during their night. Thematic analysis was conducted to group the different factors described by the participants that disturbed their sleep over the study period. After this process, the main elements were grouped into categories describing the root cause of the sleep disturbance.
Table 2.
Definitions of each sleep variable from the sleep diary
| Sleep Variable | Definition |
|---|---|
| Bedtime, hh:mm | Estimated clock time at which the participant attempts to sleep |
| Fall asleep time, hh:mm | Estimated clock time at which the participant fell asleep |
| Wake-up time, hh:mm | Estimated clock time at which the participant woke up |
| Time in bed, min | Time between bedtime and get-up time |
| Sleep onset latency, min | Time between bedtime and sleep onset |
| Total sleep time, min | Time spent asleep determined from sleep onset to wake-up time, minus any time spent awake |
| Sleep efficiency, % | Total sleep time divided by the time in bed |
| Subjective sleep quality | Self-reported sleep quality on a 4-point Likert-type scale, where 1 was deemed very good and 4 poor |
| Comments | Participants were free to write any comments about their night of sleep |
Statistical Analyses
After calculating PSQI variables (component scores and poor/good sleep status), means and standard deviations were calculated for total sleep hours, time in bed, sleep onset latency, sleep schedule (bedtime and get-up time) derived from the sleep diary with regard to status (student-athlete or student), and period (week or weekend). The magnitude-based inferential (MBI) approach to statistics was used to assess differences between status from PSQI.11 Effect sizes (ESs) and 90% confidence limits (CL) were quantified to indicate the practical meaningfulness of the differences in mean values. The smallest worthwhile change was set at 0.2. Sleep diary data were analyzed using linear mixed models with the period (week or weekend) or status (student or student-athlete) as fixed effects and the participant as the random effect. The least squares mean test provided pairwise comparisons between student and student-athlete for different periods (ie, overall, during the week, weekend). IIV for each sleep variable was calculated as the intraindividual standard deviation for each participant across the study period, and is reported as mean ± SD.5 The differences between students and student-athletes for IIV were further assessed using a similar MBI network as described.
Results
Participants
A flow diagram summarizing participant recruitment is presented in Figure 1. Regarding the PSQI analysis, 138 participants were included, with 65 students (25 females, 40 males) and 73 student-athletes (24 females, 49 males). Regarding student-athletes, 29% reported competing at the international level, while 48% and 23% competed at the national and county level, respectively. Student-athletes participated in a large range of different sports, such as rugby union (n = 29), soccer (n = 11), track and field (n = 8), tennis (n = 3), triathlon (n = 2), weight lifting (n = 2), cycling (n = 2), rowing (n = 2), judo (n = 2), and other sports (n = 12). Sleep pattern analysis was conducted on 44 participants, encompassing 19 students (6 females, 13 males) and 25 student-athletes (10 females, 15 males).
Sleep Quality
Sleep quality was assessed using the PSQI, with a mean score of 6.89 ± 3.03 and 65% falling above the threshold of 5, which identifies poor sleepers. An unclear difference was found regarding PSQI global score between students and student-athletes (ES ± 90% CL, −0.06 ± 0.28).
Sleep Patterns
Differences between students and student-athletes are presented as qualitative descriptor (ES ± 90% CL). Descriptive values from the sleep diary are summarized in Table 3. Comparisons between students and student-athletes for sleep duration and PSQI are shown in Figure 2. When considering only the status (student vs student-athlete) in the comparison, possibly earlier bedtime (small, 0.23 ± 0.36) and fall asleep time were found for student-athletes (small, 0.27 ± 0.46). Additionally, a possibly worse subjective sleep quality for student-athletes was found (small, 0.21 ± 0.33). During the week, possibly earlier bedtime (small, −0.23 ± 0.35) and fall asleep time (small, 0.23 ± 0.45) were found among student-athletes. Other results for this comparison were deemed unclear. Comparing the status only during the weekend, likely longer sleep onset latency (small, 0.38 ± 0.35) and worse sleep quality (small, 0.37 ± 0.36) were found among student-athletes. Additionally, possibly earlier fall asleep time (small, 0.37 ± 0.55) was found for student-athletes. Regarding the comments section, 83 comments were obtained from the sleep diary, and a sample is displayed in Table 4.
Table 3.
Descriptive data derived from the sleep diary during the week a
| Bedtime, hh:mm | Fall Asleep Time, hh:mm | Wake-Up Time, hh:mm | Time in Bed, hh:mm | Sleep Onset Latency, hh:mm | Total Sleep Time, hh:mm | Sleep Efficiency, % | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Week | Week-end | Week | Week-end | Week | Week-end | Week | Week-end | Week | Week-end | Week | Week-end | Week | Week-end | |
| Student | ||||||||||||||
| Mean | 22:53 | 23:07 | 23:13 | 23:41 | 07:39 | 8:02 | 09:09 | 09:29 | 00:18 | 00:13 | 07:52 | 08:09 | 86 | 86 |
| Minimum | 04:00 | 20:30 | 21:45 | 20:35 | 05:00 | 05:15 | 05:00 | 04:15 | 00:05 | 00:05 | 03:00 | 04:14 | 50 | 64 |
| Maximum | 21:00 | 04:00 | 04:30 | 04:00 | 11:00 | 12:00 | 13:30 | 14:30 | 00:60 | 00:60 | 11:05 | 12:21 | 100 | 100 |
| Student-athlete | ||||||||||||||
| Mean | 22:47 | 22:30 | 23:04 | 22:51 | 8:01 | 8:25 | 09:14 | 09:28 | 00:18 | 00:18 | 08:01 | 08:07 | 87 | 86 |
| Minimum | 20:00 | 20:00 | 20:30 | 20:45 | 05:00 | 05:00 | 00:45 | 02:30 | 00:05 | 00:05 | 00:33 | 01:40 | 43 | 46 |
| Maximum | 04:50 | 04:55 | 04:55 | 04:55 | 12:00 | 12:00 | 14:15 | 16:00 | 00:60 | 00:60 | 12:28 | 11:58 | 100 | 104 |
Data are presented as mean and range.
Figure 2.
Difference in sleep parameters between student-athletes and students. *Possibly and **likely change/difference between level. Gray zone, trivial difference. PSQI, Pittsburgh Sleep Quality Index.
Table 4.
Quotes from the comments section of the diary
| Sleep Stressors Categories | Representative Comments from the Sample |
|---|---|
| Social | Went out |
| I worked late | |
| Went on a night out and had a couple of drinks so I had to wake up quite often to go to the toilet and my head did not feel great | |
| Sport-related | Very anxious about recent breakdown in relationship with coaches. Was up late trying to find a solution, and consequently couldn’t get to sleep |
| Played a rugby game in the evening and had a red bull, caffeine pill, and energy gel. Kick off was 7:45 and did not get home until midnight | |
| Had to get up early for training | |
| Academic | Stayed up later than usual working on my laptop (university work) and could not sleep after that |
| Had an important deadline at 12 so I stayed up almost all night to finish my work | |
| Deadline Thursday | |
| Environmental | Housemate slammed the door and woke me up, affected my sleep |
| Regular sized coffee at 7 pm | |
| Bedroom temperature was too hot |
Intraindividual Variability
Results regarding the IIV are displayed in Figure 3, and the descriptive data are provided in Table 5. A very likely greater variability was found for bedtime (moderate, 0.88 ± 0.50) and time in bed (moderate, 0.73 ± 0.52) with student-athletes. Moreover, student-athletes presented likely greater variability for wake-up time (moderate, 0.66 ± 0.52) and fall asleep time (small, 0.56 ± 0.50). Similarly, possibly more important IIV was found for sleep onset latency (small, 0.39 ± 0.52) and total sleep time (small, 0.40 ± 0.50). Other results were deemed unclear.
Figure 3.
Difference in intraindividual variability (IIV) between student-athletes and students. *Possibly, **likely, and ***very likely change/difference between level. Gray zone, trivial difference.
Table 5.
Intraindividual variability of sleep parameters derived from the Consensus Sleep Diary for students and student-athletes a
| Student | Student-Athlete | ES ± 90% CI | |
|---|---|---|---|
| Bedtime, hh:mm | 0:51 ± 0:32 | 2:01 ± 2:06 | 0.88 ± 0.50*** |
| Fall asleep time, hh:mm | 1:09 ± 1:29 | 2:11 ± 2:43 | 0.56 ± 0.50** |
| Wake-up time, hh:mm | 0:53 ± 0:27 | 1:04 ± 0:24 | 0.66 ± 0.52** |
| Time in bed, hh:mm | 01:07 ± 00:33 | 01:31 ± 00:35 | 0.73 ± 0.52*** |
| Sleep onset latency, hh:mm | 00:10 ± 00:06 | 00:12 ± 00:06 | 0.39 ± 0.52* |
| Total sleep time, hh:mm | 00:57 ± 00:30 | 01:13 ± 00:34 | 0.40 ± 0.50* |
| Sleep efficiency, % | 7 ± 3 | 6 ± 3 | −0.30 ± 0.51 |
| Sleep quality, AU | 1.0 ± 0.3 | 1.0 ± 0.3 | −0.01 ± 0.52 |
AU, arbitrary units; ES, effect size.
Results are presented as mean ± SD. *Possibly, **likely, and ***very likely change/difference between levels.
Discussion
Findings from this study strengthen the notion that students in general have poor sleep quality and that student-athletes experience more IIV. These findings are relevant due to the importance of sleep for general well-being, academics, and sport performance.9,10
Comparisons between student-athletes and students failed to find meaningful differences in sleep quality via the PSQI. Results from the sleep diary also did not show important differences between the populations, with only possibly to likely small differences reported over the study periods (week, weekend, and overall). The lack of important differences found in our study may be due to the fact that both populations share similar academic stressors, which affect sleep negatively.16,19 In support, previous research found that environmental factors such as altered lifestyle, a noisy sleep environment, and use of electronic devices can all negatively affect sleep in students. These stressors are more than likely shared by both student-athletes and nonathlete students, which could explain the lack of differences. However, such a conclusion cannot be drawn in this study as environmental stressors were not investigated. The lack of differences between groups could also be explained by the methods employed to quantify sleep patterns (sleep dairy and nonspecific sleep questionnaire to athlete), which may not be sensitive enough to observe differences between populations.
When considering only student-athletes, 67% (n = 49) of the population were identified as “poor sleepers.” This figure is greater than the previously reported prevalence in an American student-athlete population (42%), indicating that the scale of sleep disturbance in this population may be greater than previously thought. Analysis of the sleep diaries indicates that, on average, this student population met the minimum recommended standard of 7 hours sleep duration per night recommended by the American Society of Sleep.20 However, 1 in 5 participants experienced a sleep quantity below this recommended value. In comparison, 39% of American collegiate athletes fail to reach the recommended amount of sleep.16 Based on the present data, it appears important to improve sleep quality among this population. Optimizing the conditions and practices that promote continuous and effective sleep may help students succeed in both sport and academics.14
Comments gathered in the sleep diary showed that participants experienced different situations that affected their sleep. When such comments were compared with participant sleep patterns in a qualitative way, it became apparent that in some cases, contextual factors resulted in several cases of sleep restriction. Sleep restriction is known to have negative implications on health, such as increased risk of inflammation13 and obesity.7 As a result of the consequences of poor sleep on health and academic performance, universities should use these results to provide advice regarding good sleep hygiene strategies to students. Additionally, the use of an open section may help practitioners individualize sleep hygiene strategies with their athletes.
Differences were found in terms of IIV, whereby student-athletes had greater variability. We observed likely moderate to possibly small differences in sleep schedules, with student-athletes having greater inconsistency. This may be explained by time constraints induced by a combined sport and academics schedule. For example, in order to fit training into the schedule of a student-athlete, training has to be conducted either before or after school, which can lead to an adjustment to bedtime and wake time. Such compensatory behaviors have been observed previously among rugby league and swimming populations.5,15,18 However, such an explanation remains speculative. Future studies should collect academic and training schedules in order to investigate possible interactions with sleep parameters and IIV. As a consequence of inconsistent sleep schedules, time in bed and total sleep time were more variable among student-athletes. These results have a number of practical implications that relate to universities as they suggest that student-athletes should be permitted to have an adjustable timetable in order to train at a consistent time, which can enable proper sleep habits. Despite potential applications, these results should be considered with caution, as the effects ranged only from small to moderate and the sample size was small.
Limitations
The sample size in this study was small compared with other studies and affects its generalizability. Therefore, the questionnaire used in this study (the PSQI) may not be sensitive enough in athletic populations, which may explain the lack of difference observed. As such, more specific questionnaires or objective measures of sleep could be used to further assess sleep patterns among athletes and understand which stressors may affect their sleep. For example, given the large number of rugby players involved in this study (n = 29), concussion may be one factor that explains the present results. However, this aspect has not been investigated and requires further study. Finally, the lack of difference may be due to the sporting level of the student-athlete (eg, nonelite).
Conclusion
No differences were found between students and student-athletes, suggesting that both populations experienced poor sleep. As a consequence, to reduce sleep disturbances and potentially improve sporting and academic performance, universities should ensure that the academic, training, and sleep requirements of students-athletes align. Considering IIV, student-athletes experienced more variability in their sleep. However, future studies in the area of sport science should assess IIV with a bigger sample size.
Acknowledgments
The authors would like to thank all the participants for taking part in this study.
Footnotes
The authors report no potential conflicts of interest in the development and publication of this article.
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