Abstract
Study Objectives:
Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between US high school student-athletes and nonathletes during a semester of school and competition.
Methods:
Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years, and 38 nonathletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8–14 consecutive days and nights, measuring total sleep time, sleep efficiency, bedtimes, wake times, and steps counted.
Results:
Student-athletes and nonathletes did not differ in total sleep time (440.4 ± 46.4 vs 438.1 ± 41.7 min, P = .82) and sleep efficiency (93.6 ± 2.3 vs 92.9 ± 2.3%, P = .20). Fitbit data revealed that 79% of student-athletes and 87% of nonathletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs 8,418 ± 2,489, P < .01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased total sleep time (P < .01). Earlier wake times were significantly correlated to increased steps per day (P < .01).
Conclusions:
Participation in high school sports may not have a detrimental effect on a student’s sleep habits. High school students are not meeting the recommended 8–10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health.
Citation:
Ungaro CT, De Chavez PJD. Sleep habits of high school student-athletes and nonathletes during a semester. J Clin Sleep Med. 2022;18(9):2189–2196.
Keywords: sleep, student-athlete, activity tracking, sports, students
BRIEF SUMMARY
Current Knowledge/Study Rationale: Previous studies have shown that poor sleep practices have been linked to adverse outcomes. High school student-athletes are particularly vulnerable to the decrements connected to poor sleep due to the balance of their academic and athletic workloads during a highly stressful period of their adolescent lives. Additionally, studies in collegiate student-athletes have demonstrated that they are not getting the recommended amount of sleep.
Study Impact: No study has investigated high school student-athletes and nonathletes objective sleep during a semester. Seventy-nine percent of student-athletes and 87% of nonathletes failed to meet the minimally recommended 8 hours of total sleep time per night. High school student-athletes had earlier bedtimes and wake times. Earlier bedtimes and wake times were observed to have a positive effect on sleep duration and efficiency for all students in the study. Encouraging earlier bedtimes and wake times as well as consistent sleep practices may provide individuals’ the proper recommendations for better sleep health regardless of participating in sports or not.
INTRODUCTION
Good sleep is a pillar of health alongside nutrition, physical activity, and mental well-being. Lack of sleep has serious physiological consequences and has been linked with chronic disease conditions, yet many people have, or report having, issues with poor sleep quality and quantity. Previous studies1–5 have shown that poor sleep practices have been linked to adverse outcomes such as obesity, injury, depression, poor academic performance, increased risk-taking behavior, and lower quality of life across all ages and sexes. Furthermore, inadequate sleep has been shown to have a negative effect on the development of the adolescent’s prefrontal cortex.6
High school student-athletes are particularly vulnerable to the decrements connected to poor sleep due to the balance of their academic and athletic workloads during a highly stressful period of their adolescent lives.6 Additionally, studies in adult athletes and collegiate student-athletes have demonstrated that these cohorts are not getting the recommended amount of sleep.7–9 Insufficient sleep in these groups resulted in significantly worse moods and poorer physical and cognitive performance than those who averaged over 8 hours of total sleep time (TST) per night.8–11 Mah et al8 conducted a sleep extension study on collegiate student-athletes to see whether increasing sleep duration could improve athletic performance from baseline. Participants were able to extend their TST by ∼111 minutes per night, which improved sprint times (16.2 ± 0.6 s at baseline vs 15.5 ± 0.5 s, P < .01) and free-throw and 3-point field goal percentages by > 9%. Schwartz and Simon12 showed that 7 nights of self-reported sleep extension of ∼100 min/night resulted in improved tennis serve accuracy from baseline (35.7 vs 41.8%, P < .05).
Moreover, international studies and sleep lab research on high school students2,13–15 have demonstrated that these populations are not getting the recommended 8–10 hours of sleep and > 85% sleep efficiency (SE) per night according to the National Sleep Foundation,13 with a few studies measuring sleep duration ranging from 7.3 to 7.5 hours on average and SE from 76.5 to 92.3%.14,16 These outcomes support the theory that high school student-athletes may not be getting adequate sleep quantity or quality required to perform at an optimal level during a critical moment in their lives. Consequently, a comparative analysis of high school student-athletes and their nonathlete peers during a semester of school was warranted to determine if insufficient sleep quantity and quality are shared among all high school students.
To date, no studies have examined objective sleep outcomes of US high school student-athletes and nonathletes while enrolled in a semester of school in their free-living environments. As mentioned earlier, previous work6,9,17 on collegiate student-athletes suggests that US high school student-athletes may be vulnerable to insufficient sleep during a critical moment in their development and their adolescent lives.
The present study aimed to investigate the differences in objective measures of TST in minutes, SE, bedtimes and wake times, and steps per day between US high school student-athletes and their nonathlete peers during a semester of high school. The hypothesis was that student-athletes would not get the recommended 8–10 hours of TST and > 85% SE and nonathlete students would get significantly more TST and SE due to their lack of athletic commitments, athletic responsibilities, stress, and injury that may occur from sports participation, despite the benefits that physical activity have shown on adolescent sleep.18 A secondary aim of this study was an exploratory analysis into any correlations that may have existed between the objective measures for all participants and within groups. The hypothesis was that later bedtimes would be correlated with less TST and SE, with student-athletes going to bed later. Both groups would display inconsistent sleep schedules from weekdays to weekends. Earlier wake times would correlate with more steps per day with student-athletes getting up earlier and recording more steps.
METHODS
Participants
During the COVID-19 pandemic, the principal investigator recruited 72 students from 2 southwestern US high schools (1,040 and 404 enrolled students) located in the same state between October and December of 2020 through convenience sampling. Eighteen males (age 15.6 ± 0.8 years; body mass index 26 ± 6 kg/m2) and 16 females (age 15.9 ± 0.9 years; body mass index 24 ± 5 kg/m2) comprised the student-athlete group. Ten males (age 16.3 ± 0.8 years; body mass index 29 ± 8 kg/m2) and 28 females (age 16.3 ± 0.7 years; body mass index 24 ± 6 kg/m2) were part of the nonathlete group, see Table 1 for descriptive statistics. Student-athletes were not considered elite. All student-athletes participated in organized athletics associated with their high school and governed by the state’s University Interscholastic League and maintained school enrollment and sports participation throughout the study. There was no threshold on the level (junior varsity or varsity), starting status, or amount of playing time to be categorized as a student-athlete. Student-athletes participated in their sport 5 days a week, with the average self-reported duration being 107 ± 37 min. All nonathlete students were not involved in any sport during the study period and maintained enrollment in the school throughout the study. All participants were attending in-person classes at the start of their study period, with 8 student-athletes and 9 nonathletes having to go to remote learning, temporarily, during some portion of the study.
Table 1.
Descriptive statistics nonathletes and student-athletes.
| Males | Females | Total | |
|---|---|---|---|
| Nonathletes | |||
| n | 10 | 28 | 38 |
| Age (years) | 16.3 (0.8) | 16.3 (0.7) | 16.3 (0.7) |
| Height (cm) | 168.9 (7.9) | 160.2 (6.2) | 162.5 (7.6) |
| Weight (kg) | 82.4 (20.3) | 62.9 (15.7) | 68.0 (18.8) |
| Body mass index | 29.0 (7.6) | 24.4 (5.6) | 25.6 (6.4) |
| Race, n (%) | |||
| White | 2 (5.3) | 13 (34.2) | 15 (39.5) |
| Black or African American | 0 (0.0) | 2 (5.3) | 2 (5.3) |
| American Indian/Alaskan Native | 1 (2.6) | 3 (7.9) | 4 (10.5) |
| Other | 7 (18.4) | 10 (26.3) | 17 (44.7) |
| Ethnicity, n (%) | |||
| Non-Hispanic | 1 (2.6) | 12 (31.6) | 13 (34.2) |
| Hispanic | 9 (23.7) | 16 (42.1) | 25 (65.8) |
| Student-Athletes | |||
| n | 18 | 16 | 34 |
| Age (years) | 15.6 (0.9) | 15.9 (0.9) | 15.8 (0.8) |
| Height (cm) | 178.3 (7.0) | 162.6 (6.7) | 170.9 (10.5) |
| Weight (kg) | 82.0 (20.6) | 64.3 (14.4) | 73.7 (19.8) |
| Body mass index | 25.8 (6.4) | 24.3 (5.3) | 25.1 (5.9) |
| Race, n (%) | |||
| White | 13 (38.2) | 10 (29.4) | 23 (67.6) |
| Black or African American | 2 (5.9) | 3 (8.8) | 5 (14.7) |
| American Indian/Alaskan Native | 1 (2.9) | 0 (0.0) | 1 (2.9) |
| Other | 2 (5.8) | 3 (8.8) | 5 (14.7) |
| Ethnicity, n (%) | |||
| Non-Hispanic | 13 (38.2) | 11 (32.4) | 24 (70.6) |
| Hispanic | 5 (14.7) | 5 (14.7) | 10 (29.4) |
| Sport, n (%) | |||
| Football | 10 (29.4) | 0 (0.0) | 10 (29.4) |
| Soccer | 0 (0.0) | 2 (5.9) | 2 (5.9) |
| Baseball | 3 (8.8) | 0 (0.0) | 3 (8.8) |
| Cross Country | 2 (5.9) | 4 (11.8) | 6 (17.6) |
| Track | 0 (0.0) | 1 (2.9) | 1 (2.9) |
| Volleyball | 0 (0.0) | 5 (14.7) | 5 (14.7) |
| Marching Band | 0 (0.0) | 1 (2.9) | 1 (2.9) |
| Tennis | 2 (5.9) | 0 (0.0) | 2 (5.9) |
| Basketball | 1 (2.9) | 3 (8.8) | 4 (11.8) |
Values are presented as mean (standard deviation) or n (%) where indicated.
Student-athletes were comprised of the following sports: boys’ football (n = 10), boys’ baseball (n = 3), boys’ cross country (n = 2), boys’ tennis (n = 2), boys’ basketball (n = 1), girls’ soccer (n = 2), girls’ cross country (n = 4), girls’ track and field (n = 1), girls’ volleyball (n = 5), marching band (n = 1), and girls’ basketball (n = 3). See Table 1 for displays descriptive statistics for each group. This study was approved by the Sterling Institutional Review Board (Atlanta, GA) and funded by PepsiCo. All participants provided written informed consent and assent for minors via DocuSign prior to starting the study.
Cross-sectional design
After giving informed consent, participants were instructed to complete a general health and demographics questionnaire via DocuSign. Following the completion of all questionnaires, participants were provided with a Fitbit Charge 4 (Fitbit Inc., San Francisco, CA) wrist-worn activity tracker and instructions on how to set up, charge, and sync their Fitbits. Fitbit was chosen due to their affordability, familiarity, and validity for study outcomes of interest.19–22 Participants were told to wear their Fitbits on their nondominant hand continuously for 2 weeks, removing only when necessary (ie, charging, swimming, sport requirement). Subject inclusion required a minimum of 8 and a maximum of 14 evenings of sleep monitoring. Thirteen participants (8 student-athletes and 5 nonathletes) did not meet the 8-day requirement and were removed from the data set.
Measurements
Fitbit Charge 4 is a validated wrist-worn 3-axis accelerometer with an optical sensor that tracks changes in motion patterns, altitude, location, and heart rate.19–22 The default sleep sensitivity was set at normal for all users. Fitbit sleep data was calculated using Fitbit’s Classic sleep calculation taking measurement every 60 seconds. Overnight objective sleep variables included TST (minutes asleep), SE % (ratio of TST and time in bed × 100), weekday bedtimes and wake times, weekend bedtimes and wake times, and resting heart rate (RHR). Steps counted per day were included in the data set as a measure of physical activity. TST, SE, RHR, and steps counted are reported as daily averages over the study period. Weekday and weekend data were the average for each day during those periods.
Data analysis
All Fitbit data were stored and accessed using Fitabase software (Small Steps Labs, San Diego, CA). All data were analyzed using commercial software (SPSS 26.0, IBM SPSS, Armonk, NY). Raw activity tracking data were uploaded and stored on a device-specific application group platform (Fitabase) that was web-based and could only be accessed with permission and login credentials. Deidentifiable identifiers were used to determine and analyze the participants’ raw data files. Informed consent and surveys were encrypted at transit and at rest and administered through DocuSign via email. The assumption of normality and equal variance were carried out using Shapiro-Wilk’s and Levene’s tests. Objective measures of sleep variables were compared between student-athletes and nonathletes using independent t-tests when assumptions of normality and equal variances were met. Pearson correlation coefficient, and Cohn’s d for effect size are reported where appropriate. All tests that showed significance met the assumptions of normality and equal variances. Paired samples t-tests were used to compare particpants’ weekday and weekend variables. Analysis of variance were performed on steps counted and RHR to control for Type I errors with the significance level set at a P < .05. Data are presented as mean ± standard deviation 95% confidence interval.
RESULTS
Jointly, 79% of student-athletes and 87% of nonathletes failed to get greater than the minimally recommended 8 hours (480 min) of TST per night, with a range of 341 to 545 minutes. The National Sleep Foundation13 indicates that 7–8 hours of sleep per night may be appropriate for teenagers 14–17 years of age. In this study, 55.9% of student-athletes and 52.6% of nonathletes got 7–8 hours of TST per night, see Figure 1 for TST prevalence. Two-tailed independent t-tests were run on group means for TST, SE, bedtimes, wake times, RHR, and steps per day, see Table 2 for results. Student-athletes TST was similar to nonathletes (440.4 ± 46.4 vs 438.1 ± 41.7 min, t(70) = −0.23, P = .82). Student-athletes had a higher SE than nonathletes (93.6 ± 2.3 vs 92.9 ± 2.3%) but did not reach significance, t(70) = −1.31, P = .20.
Figure 1. Prevalence of student-athlete and nonathlete Fitbit total sleep time.
Data were binned into < 6 hours, 6–7 hours, 7–8 hours, > 8 hours of total sleep time/night.
Table 2.
Student-athlete and nonathlete Fitbit sleep and step outcomes.
| Total (n = 72) | Nonathletes (n = 38) | Student-Athletes (n = 34) | Δ | 95% CI | |
|---|---|---|---|---|---|
| Total sleep time (min) | 439.1 (43.6) | 438.1 (41.7) | 440.4 (46.4) | −2.3 | −23.0, 18.4 |
| Sleep efficiency (%) | 93.2 (2.3) | 92.9 (2.3) | 93.6 (2.3) | −0.7 | −1.8, 0.4 |
| Weekday bedtimes | 23:09 (00:58) | 23:23 (00:57)* | 22:53 (00:56) | 00:30 | 00:04, 00:57 |
| Weekday wake times | 6:53 (00:55) | 7:06 (01:04) | 6:39 (00:40) | 00:27 | 00:01, 00:52 |
| Weekend bedtimes | 24:27 (1:20) | 24:35 (1:19) | 24:19 (1:22) | 00:16 | −00:22, 00:54 |
| Weekend wake times | 8:40 (1:15) | 8:54 (1:25) | 8:23 (1:00) | 00:31 | −00:04, 01:06 |
| Steps per day | 9,250 (2,432) | 8,418 (2,489)* | 10,164 (2035) | −1,745 | −2,822, −669 |
| Resting heart rate (beats/min) | 65.6 (8.1) | 68.1 (7.9)* | 62.8 (7.5) | 5.4 | 1.7, 9.0 |
*Significant at P < .05, independent 2-tailed t-test. Values are presented as mean (standard deviation) or difference and 95% confidence intervals (CI) where indicated.
Weekday and weekend bedtimes and wake times were significantly different for both groups, showing inconsistency in their sleep schedules. Student-athletes went to bed and woke up earlier during the week than nonathletes, with bedtimes being significant (22:53 ± 0:56 vs 23:23 ± 00:57), t(70) = 2.29, P = .03, but wake times did not show any significance differences (06:39 ± 00:40 vs 07:06 ± 1:04), t(70) = 2.10, P = .14.
On the weekends, student-athletes went to bed and woke up earlier but failed to reach significance (24:19 ± 1:22 vs 24:35 ± 1:19, t(70) = 0.86, P = .18; 8:23 ± 1:00 vs 8:54 ± 1:25, t(70) = 1.78, P = .09) compared to nonathletes. Student-athletes had significantly lower RHR (62.8 ± 7.5 vs 68.1 ± 7.9 beats/min) compared to nonathletes. Not surprisingly, student-athletes got more steps per day than nonathletes (10,164 ± 2,035 vs 8,418 ± 2,489 steps per day), F(1,69) = 6.56, P < .02, while controlling for wake times. Across both groups, significant sex differences only occurred for steps per day, with males getting more (10,252 ± 2,050 vs 8,600 ± 2,461), t(70) = −2.96, P < .01 and RHR, with males having lower RHR than females (62.7 ± 7.8 vs 67.5 ± 7.8 beats/min), t(70) = 2.52, P < .05.
For all participants, a small but significant correlation was displayed between increased steps per day and a decrease in TST, r(72) = −0.24, P < .05. However, significance was lost when analyzing just the student-athlete cohort, r(34) = −0.21, P = .22. Moderate and significant correlations were observed for increased TST with earlier weekday bedtimes, r(72) = −0.42, P < .01 and weekend bedtimes, r(72) = −0.41, P < .01. When analyzing all students, later wake times significantly correlated with more TST, r(72) = 0.39, P < .01; however, this did not reach significance for the student-athlete group, r(34) = 0.18, P = .32. Across both groups, more steps per day had a significant link with lower RHR, r(72) = −0.25, P < .05, earlier weekday wake times, r(72) = −0.39, P < .01, and earlier weekend wake times, r(72) = −0.24, P < .05. A strong correlation was observed for weekday bedtimes and wake times, r(72) = 0.52, P < .01. Table 3 shows the combined student-athlete and nonathlete Pearson correlation coefficients due to the limited amount of group differences.
Table 3.
Student-athlete and nonathlete outcome correlations.
| TST | SE | Steps/Day | Weekday | Weekend | RHR | BMI | |||
|---|---|---|---|---|---|---|---|---|---|
| Bedtime | Wake Time | Bedtime | Wake Time | ||||||
| TST | |||||||||
| Pearson correlation | 1.00 | −.03 | −.24* | −.42** | .39** | −.41** | .19 | −.07 | −.09 |
| Sig. (2-tailed) | .78 | .05 | .00 | .00 | .00 | .12 | .58 | .44 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| SE | |||||||||
| Pearson correlation | −.03 | 1.00 | .20 | .05 | −.21 | −.20 | −.19 | −.08 | −.16 |
| Sig. (2-tailed) | .78 | .10 | .70 | .07 | .10 | .11 | .51 | .18 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| Steps/Day | |||||||||
| Pearson correlation | −.24* | .20 | 1.00 | −.09 | −.39** | −.16 | −.24* | −.25* | .09 |
| Sig. (2-tailed) | .05 | .10 | .43 | .00 | .19 | .05 | .04 | .45 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| Weekday Bedtime | |||||||||
| Pearson correlation | −.42** | .05 | −.09 | 1.00 | .52** | .37** | .25* | −.07 | −.15 |
| Sig. (2-tailed) | .00 | .70 | .43 | .00 | .00 | .04 | .54 | .21 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| Weekday Wake Time | |||||||||
| Pearson correlation | .39** | −.21 | −.39** | .52** | 1.00 | .20 | .35** | .05 | −.09 |
| Sig. (2-tailed) | .00 | .07 | .00 | .00 | .09 | .00 | .67 | .44 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| Weekend Bedtime | |||||||||
| Pearson correlation | −.41** | −.20 | −.16 | .37** | .20 | 1.00 | .45** | −.06 | .10 |
| Sig. (2-tailed) | .00 | .10 | .19 | .00 | .09 | .00 | .62 | .40 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| Weekend Wake Time | |||||||||
| Pearson correlation | .19 | −.19 | −.24* | .25* | .35** | .45** | 1.00 | .00 | .05 |
| Sig. (2-tailed) | .12 | .11 | .05 | .04 | .00 | .00 | 1.00 | .70 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| RHR | |||||||||
| Pearson correlation | −.07 | −.08 | −.25* | −.07 | .05 | −.06 | .00 | 1.00 | .16 |
| Sig. (2-tailed) | .58 | .51 | .04 | .54 | .67 | .62 | 1.00 | .19 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
| BMI | |||||||||
| Pearson correlation | −.09 | −.16 | .09 | −.15 | −.09 | .10 | .05 | .16 | 1.00 |
| Sig. (2-tailed) | .44 | .18 | .45 | .21 | .44 | .40 | .70 | .19 | |
| n | 72 | 72 | 72 | 72 | 72 | 72 | 72 | 72 | |
*Correlation is significant at the .05 level (2-tailed). **Correlation is significant at the .01 level (2-tailed). BMI = body mass index, RHR = resting heart rate, SE = sleep efficiency, TST = total sleep time.
DISCUSSION
Sleep is fundamental to our health and well-being, and the evidence has demonstrated that sleep can significantly impact cognitive, academic, and athletic performance. Sleep is an emerging topic in the health and wellness space, including sports performance, recovery, and injury prevention. Little is known about the sleep habits of high school student-athletes. Moreover, no research has compared the sleep habits of high school student-athletes to their nonathlete peers while attending school. This exploratory analysis was the first to investigate these sleep differences.
The data revealed that both high school student-athletes and nonathletes did not meet the recommended 8–10 hours of TST by the National Sleep Foundation, with 79% of student-athletes and 87% of nonathletes failing to get greater than 8 hours of sleep per night during the semester. However, 56% of student-athletes and 53% of nonathletes did get 7–8 hours of TST per night, which the NSF states, may be appropriate for teenagers. Although TST was similar between groups (P = 0.82), it was surprising to see that the nonathletes had less sleep (438 min) per night than their student-athlete classmates (440 min), upholding the null hypothesis that no differences existed for TST between these groups.
The secondary aim of the study was to investigate whether differences between groups existed for SE. No participants recorded a SE below 85%, which is recognized as poor sleep quality for adolescents23; this was an unanticipated observation, considering previous research7,9,24,25 has shown poor SE in collegiate student-athletes. Like TST, student-athletes had better SE (93.6%) than nonathletes (92.9%) but failed to reach significance (P > .05).
The novelty of this study is that it is the first investigation into the objective sleep habits of high school student-athletes and nonathletes during a semester in their free-living environments. Additionally, no evidence exists comparing the sleep habits of collegiate student-athletes and their nonathlete peers. A handful of studies have been conducted comparing adult athletes to nonathletes with mixed results. Demirel26 investigated the self-reported sleep outcomes of adult athletes and nonathletes after cupping therapy. The authors concluded that the adult athletes had significantly better self-reported Pittsburgh Sleep Quality Index scores (1.45 ± 0.32 vs 1.57 ± 0.31) and self-reported sleep duration (7.81 ± 0.92 vs 7.49 ± 1.14 hours) than the nonathlete adults.26 Another analysis used objective sleep measures and demonstrated that elite Olympic athletes got less TST than a nonathletic control group after 4 days of monitoring (6.92 ± 0.72 vs 7.18 ± 0.42 hours/night) and worse SE (80.6 ± 6.4 vs 88.7 ± 3.6%).27
During this study, the average school start and end times for all participants were 08:07 ± 00:31 am and 03:41 ± 00:59 pm. Sleep duration was higher in students with earlier bedtimes, and SE was higher in students with earlier wake times. High school student-athletes had earlier bedtimes and wake times, which was counter to the initial hypothesis that student-athletes would have less TST and later bedtimes due to the competitive anxiety, injury, and stress of being a student-athlete making it difficult to sleep. The pandemic may have lessened the burden on these student-athletes since the competitive atmosphere surrounding them was much different than prepandemic days with less travel and lower attendance at events due to COVID restrictions.
Moreover, for all students, physical activity had a significant relationship with lower RHR and earlier wake times. When combining all students, there was a small negative correlation for increased TST and less physical activity, which is contrary to previous research18 associating a positive relationship between steps per day and TST. However, the authors find it important to note that this correlation was not significant when analyzing just the student-athletes, indicating that there was not a consistent relationship between physical activity and TST across both groups. This inconsistent relationship may have been due to some students spending more time in bed and less time being active during the day. This concept is supported by this study’s findings that earlier wake times had a strong relationship with increased physical activity.
Participating in athletics may have provided these student-athletes with the foundation and discipline necessary for healthy and consistent habits, which was demonstrated by their earlier bedtimes and wake times, lower resting heart rates, and increased physical activity compared to their nonathlete peers. This is purely speculative, and more research is needed to determine whether participating in athletics is a burden, by balancing responsibilities and commitments, or provides the structure and discipline needed for healthy sleep habits.
Based on the observations made in this study and previous research,5,28,29 encouraging earlier bedtimes and wake times and targeting at least 8 hours of TST, as well as maintaining consistent sleep practices may provide individuals’ the proper recommendations for better sleep and physical health regardless of participating in sports or not as these variables related to the healthiest sleep, physical activity, and cardiovascular outcomes measured in this study. Additional research is necessary to decipher if any causation exists between these observations and whether sleep stages differ between groups.
Conducting this study during a global pandemic was a major limitation due to unknown effects the pandemic may have had on the students’ daily lives. As mentioned previously, some participants from both groups had to move to remote learning and athletic events were not conducted in the same manner as before the pandemic. The pandemic most likely had an influence on the study outcomes making comparisons across prepandemic sleep research difficult. This study was designed and approved prior to the pandemic; no pandemic-related questions were asked on the surveys. The students self-reported if any major deviations occurred in their daily schedules, which is how quarantined participants were collated.
Additional limitations include no causation or interventions were tested for any outcomes, the sample size was small, sex was unbalanced between groups. No external factors were collected that may have influenced participant sleep (ie, perceptions, socioeconomic status, stress), and naps were not included in the analysis as the focus was on overnight sleep habits. There were no sleep stages or levels of physical activity analyzed due to lack of validity for Fitbit to measure these outcomes, as previous research has shown an effect on the level of daily activity on nightly sleep.30
No academic or athletic performance measures were collected. Understanding these performance and health outcomes (ie, grade point average, games started, injuries) would have provided further support that good sleep increases performance and health at the high-school level. Furthermore, extending the observational period throughout the whole semester would provide a more accurate depiction of the students’ lives. The use of activity trackers makes it possible to observe sleep data over long spans of time.
In conclusion, earlier bedtimes and wake times should be promoted by faculty, parents, and coaches, as these variables were related to healthier outcomes for TST, SE, steps per day, and RHR. Using activity trackers can provide these stakeholders the awareness, education, and strategies to optimize student’s health, performance, and well-being through healthy sleep habits. Finally, these results should add to and encourage future research into student’s sleep, expanding upon this work with broader populations, balanced sexes, and across longer durations.
ABBREVIATIONS
- RHR
resting heart rate
- SE
sleep efficiency
- TST
total sleep time
DISCLOSURE STATEMENT
All authors have seen and approved this manuscript. This study was funded by the Gatorade Sports Science Institute, a division of PepsiCo, Inc. All authors are employed by PepsiCo R&D. The views expressed in this manuscript are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc. The authors report no conflicts of interest.
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