Abstract
Objective:
Examine associations between a range of sleep problems and academic performance in a national sample of collegiate athletes.
Participants:
Data were obtained from the National College Health Assessment of US college/university students from 2011-2014 (N=8,312 collegiate athletes).
Methods:
Univariate comparisons for all sleep variables and demographics were stratified across GPA, using one-way ANOVA for continuous variables and chi-square for categorical variables. Multinomial logistic regression models, with GPA as outcome (reference = A) and sleep variable as predictor, were examined and adjusted for age, sex, and survey year. Ordinal regression examined a 1-level change in GPA associated with each sleep variable, adjusted for covariates.
Results:
Sleep-difficulty was associated with increased likelihood of B/C averages. Initial-insomnia was associated with increased likelihood of B/C, and D/F averages. tiredness was associated with increased likelihood of B/C, and D/F averages.
Conclusions:
Sleep problems are highly prevalent and associated with poorer academic performance in collegiate athletes.
Keywords: collegiate athletes, athletics, academic performance, sleep, sleep problems, insomnia
INTRODUCTION
College is a transformative time when many students first gain the autonomy to make decisions that can adversely impact their health and academic performance. The lack of exercise, binge drinking, poor time management, and unbalanced diets are behaviors that have been shown to negatively affect the well-being of college students.1–3 In college, students are often challenged to adjust to new peer groups, social settings, greater access to drugs and alcohol, and increased independence.4 In addition to these concerns, poor sleep is a persistent problem experienced by many US college students. For example, a study based on the Pittsburgh Sleep Quality Index (PSQI) reported that up to 60% of college students suffered from poor sleep quality.3 Sleep problems, which include sleep difficulties, initial insomnia, insufficient sleep, daytime tiredness, and sleepiness can worsen the academic performance of college students.5,6
Although sleep problems in college students have been well documented,7–9 literature that has interrogated the relationship between sleep problems and academic performance in collegiate athletes is limited. Recently, both the NCAA Inter-association Task Force on Sleep and Wellness and the International Olympic Committee have released consensus statements urging universities and colleges to prioritize sleep health among their athlete populations, as sleep has significant implications for overall health and athletic and academic performance.10,11 The research that presently exists with this population has primarily focused on the effect of athletic performance on sleep quality. For example, previous studies indicate that early morning training sessions and late evening games can offset the intrinsic circadian rhythm and impact the amount of sleep collegiate athletes obtain, further exacerbating sleep problems.12–14 Several studies indicate that sleep is critical for cognitive function, proper physiological functioning, mental health, and is vital for peak performance in collegiate athletes.5,15–21 Practice time, travel, competition, balancing athletics and academics, and student life are factors that may adversely affect sleep in collegiate athletes.5,15,22–24 Despite athletic performance problems that result from inadequate sleep, the void in research specifically focused on sleep problems and academic performance hinders athletic departments, coaches, and the NCAA, from collaborating with sleep professional to develop targeted intervention solutions for college athletes.
Understanding the impact of sleep problems on academic performance among collegiate athletes is critical for a number of reasons. Poor academic performance during college can jeopardize athletic participation and academic eligibility, as well as limit employment opportunities and career aspirations after graduation, and lead to lower lifetime wages.13,25 Future educational opportunities may suffer if academic struggles lead to suspension or expulsion from college.26,27 Additionally, the economic model of human capital suggests academic struggles in college could jeopardize an athlete’s long-term benefits (e.g., more fulfilling work environment, increased health, extended life, more informed purchases, and lower rates of unemployment).28,29 It is clear that the implications for poor academic performance can last a lifetime.
In the present study, we analyzed data from 8,312 US college/university athletes to understand the effects of sleep problems on academic performance. We hypothesized that sleep problems among collegiate athletes are associated with poorer academic performance, and that this relationship exists across multiple domains of sleep problems. Findings from this study will contribute to our understanding of the relationship between various types of sleep problems and academic performance among collegiate athletes. Additionally, these results serve as a call to improve and adopt specific interventions that holistically addresses the type of sleep problems that can lead to poor academic performance in collegiate athletes. To our knowledge, this is the first study to examine this relationship in a nationally representative sample of collegiate athletes.
Previous Studies
Academic Performance and Sleep
The effects of sleep on the academic performance in the general student body population have been well documented.6 In a study at a large private university involving a random sample of 200 students living in on-campus residence, Trockel et al30 discovered that wake up times and other poor sleep habits were associated with lower grades. Delayed sleep phase syndrome (DSPS), extending the weekend sleep schedule to the weekday, has been shown to induce impaired academic performance in college students. Among 211 first year psychology college students, academic performance was consistently lower for those suffering from DSPS, compared to non-DSPS students.31 A study that used official grades from the university register showed, students with later wake-up and bedtimes on weekend and weekdays performed poorer academically.30 Various performance parameters (e.g., GPA, teacher comments, self-rated average grades) have been used to measure academic performance in college students. Yet, these, and any other measures are rarely employed to examine the sleep-academic performance relationship in collegiate athletes.
Athletic Performance and Sleep
The NCAA annual academic progress report claims that collegiate athletes perform better the general student body.32 However, a report from the Pacific Athletic Conference indicates that 71% of student athletes mention athletic commitments as the top activity that prevents them from receiving sufficient sleep; and 77% believe they receive less sleep than their non-student athlete peers.24 In a qualitative study of a major college basketball programs, athletes expressed that physical exhaustion and fatigue from the lack of sleep was a catalyst for the loss motivation to perform academically.13 Sleep can easily become compromised as student-athletes try to balance the multiple demands on their time. Collegiate athletes with sleep deficiency are at increased risk for acute illnesses, traumatic sport injuries, and the development of chronic diseases.12 The conclusions of these seemly contradictory outcomes raise the possibility that collegiate sport participation may actually hinder athletes’ ability to get adequate sleep, thus negatively impacting their ability to perform up to their academic potential.
Athletic Sleep Interventions
The sleep intervention literature for athletes has focused almost exclusively on athletic performance and recovery. This thread of research concludes that established sleep assessment and intervention strategies are rendered ineffective in athletes due to the uniqueness of their lifestyles and travel schedules.33 Given the implications for performance and recovery outcomes, there are many explanations regarding why sub-optimal sleep is a cause for concern in athletes. For instance, subjective feelings of hunger and carbohydrate metabolism are negatively affected by poor sleep.34 Another important factor is that sleep restores cerebral glycogen depleted during waking hours.35 Both cerebral glycogen depletion and feelings of hunger can be detrimental to athletic performance in collegiate athletes. The cognitive consequences of sleep deprivation include heightened fluctuation in mood stability, deficits in fine motor movement, memory (e.g., consolidation of motor tasks), and decision-making.36 Overall, current data indicate that sleep loss adversely impacts the physical and cognitive performance of athletes.33 A systematic review of sleep interventions consisting of 10 studies and a total of 218 athletes ranging between 18-24 years old from various sports (e.g., swimming, soccer, basketball, tennis), concluded that sleep extension and napping, sleep hygiene, and post-exercise recovery strategies were the most effective methods to improve performance and/or recovery outcomes in athletes. While these intervention strategies have produced clear benefits in athletic performance and recovery,36,37 the lack of empirical research focused on sleep and academic performance hinders our ability to develop effective intervention methods in this area.
METHODS
Data Source
Data were obtained from the National College Health Assessment II, an annual survey of US college/university students conducted by the American College Health Association. This health assessment is the largest known data set examining the health of college students in the US. Complete information about this dataset is available online.15,24,25 Data from 2011-2014 were used, since these years included identical questions. Surveys are administered on paper, in person, and online across a number of college campuses each year. Different campuses are included each year and thus years can be combined (as there is no duplication). Institutions are kept anonymous in the survey, as are individuals, in order to promote honest reporting. Previous information about the generalizability, reliability, and validity of the dataset is available, reporting a median response rate of 19%.17 A total of N=112,849 students participated during these years. Of these, N=8,683 identified themselves as varsity athletes. Approval from a university IRB was received prior to analyzing and reporting the data. For a thorough description of the data source, please refer to the method’s section of a manuscript published by Hosik Min.38
Measures
Sleep difficulties.
Participants were asked, “Within the past 12 months, have any of the following been traumatic or very difficult for you to handle?” This was followed by a list of conditions, including “sleep difficulties.” Those who answered “yes” to this question were coded to have sleep difficulties.
Initial insomnia.
Participants were asked, “In the past 7 days, how often have you had an extremely hard time falling asleep?” Responses were recorded as 0-7. Those who reported this problem at least 3 days per week were coded “yes” for initial insomnia.
Insufficient sleep.
Individuals were asked, “On how many of the past 7 days did you get enough sleep so that you felt rested when you woke up in the morning?” Responses were coded as 0-7 and recoded to reflect nights per week of insufficient sleep (e.g., 0=7, 1=6, etc.).
Daytime tiredness.
Participants were asked, “In the past 7 days, how often have you felt tired, dragged out, or sleepy during the day?” Responses were recorded as 0-7. Those who reported at least 3 days per week were coded “yes” for daytime tiredness.
Sleepiness.
Daytime sleepiness was assessed with the following item, “People sometimes feel sleepy during the daytime. In the past 7 days, how much of a problem have you had with sleepiness (feeling sleepy, struggling to stay awake) during your daytime activities?” Response choices were “No problem at all,” “little problem,” “More than a little problem,” “A big problem,” and “A very big problem.” Those who reported sleepiness as being at least “a big problem” were coded “yes” for sleepiness.
Sleep difficulties interfering with academics.
Participants were asked, “Within the past 12 months, have any of the following affected your academic performance?” One of the options was “sleep difficulties.” Response options were, “This did not happen to me” (reference group), “I have experienced this issue, but my academics have not been affected,” or one of 4 potential ways this could have interfered with academics, including a lower grade on an exam or project, a lower course grade, an incomplete or dropped course, and/or significant disruption in thesis, dissertation, research, or practicum work. Those who endorsed any of these 4 outcomes were coded as “Yes” for sleep difficulties interfering with academics.
Self-Reported Grade Point Average (GPA).
Participants were asked, “What is your approximate cumulative grade average?” Response options included A, B, C, or D/F.
Covariates.
All analyses were adjusted for age, sex, and survey year.
Statistical Analyses
All variables were examined visually for outliers and implausible values. Descriptive statistics were computed for all variables. Univariate comparisons for all sleep variables and demographics examined were stratified across GPA, using one-way ANOVA for continuous and chi-square for categorical variables. To evaluate whether sleep problems were associated with decreased academic performance, multinomial logistic regression models, with GPA as outcome (reference = A) and sleep variable as predictor were examined, adjusted for age, sex, and survey year. Also, ordinal regression examined a 1-level change in GPA associated with each sleep variable, adjusted for covariates. Since insufficient sleep was assessed as number of days per week, a post-hoc sensitivity analysis repeated the multinomial logistic regression analyses using each available cut point (e.g., at least 1 day / week, at least 2 days / week, etc.). All analyses were performed with STATA 14.0,39 with p<0.05 indicating statistical significance.
RESULTS
Characteristics of the Sample
Data from N= 8,683 student athletes between 2011-2014 were aggregated. Characteristics of the sample are reported in Table 1. Overall, about 20% of the sample reported sleep difficulty, about 22% reported initial insomnia, about 61% reported daytime tiredness, about 16% reported daytime sleepiness, 28% reported that they experienced sleep problems but this did not interfere with academics and 18% reported sleep problems that interfered with academics. About 57% of the sample reported insufficient sleep on 4 nights per week or more.
Table 1.
Characteristics of the Sample and Stratification by Academic Performance
Variable | Characteristics | Complete Sample | A | B | C | D/F | P-value |
---|---|---|---|---|---|---|---|
N | 8,312 | 3,192 | 4,381 | 706 | 33 | *** | |
Age | Years | 19.7 ± 1.8 | 19.6 ± 1.9 | 19.6 ± 1.7 | 19.9 ± 1.9 | 21.5 ± 4.1 | *** |
Sex | Male (%) | 38.52 | 34.20 | 40.01 | 48.20 | 55.56 | *** |
Female (%) | 61.48 | 65.80 | 59.99 | 51.80 | 44.44 | *** | |
Race/Ethnicity | Non-Hispanic White (%) | 75.00 | 79.51 | 74.50 | 58.78 | 51.52 | *** |
Black/African-American (%) | 4.74 | 2.26 | 5.27 | 12.46 | 9.09 | *** | |
Hispanic/Latino (%) | 6.54 | 4.61 | 7.24 | 11.05 | 6.06 | *** | |
Asian (%) | 4.81 | 6.20 | 4.04 | 3.54 | *** | ||
American Indian/Alaskan Native (%) | 1.42 | 1.25 | 1.53 | 1.56 | *** | ||
Other | 6.00 | 4.54 | 6.09 | 10.91 | 30.30 | *** | |
Year | 2011 (%) | 25.83 | 25.47 | 26.11 | 25.35 | 33.33 | |
2012 (%) | 26.79 | 24.91 | 28.21 | 26.77 | 21.21 | ||
2013 (%) | 28.89 | 30.76 | 27.64 | 28.33 | 24.24 | ||
2014 (%) | 18.49 | 18.86 | 18.03 | 19.55 | 21.21 | ||
Institution Type | Public (%) | 31.60 | 27.91 | 31.68 | 46.88 | 51.52 | *** |
Private (%) | 68.40 | 72.09 | 68.32 | 53.12 | 48.48 | *** | |
Sleep-difficulty | Yes (%) | 19.80 | 15.85 | 21.25 | 28.04 | 33.33 | *** |
Initial-insomnia | Yes (%) | 21.78 | 18.24 | 22.77 | 30.53 | 45.45 | *** |
Tiredness | Yes (%) | 60.90 | 57.59 | 62.25 | 67.05 | 69.70 | *** |
Insufficient-sleep | Days /Week | 3.83 ± 1.85 | 3.65 ± 1.85 | 3.89 ± 1.82 | 4.24 ± 1.90 | 4.36 ± 2.51 | *** |
Sleepiness | Yes (%) | 32.75 | 29.53 | 34.01 | 39.29 | 36.36 | *** |
Interfere | Did not experience (%) | 54.64 | 57.17 | 53.51 | 51.01 | 36.36 | *** |
Did not affect (%) | 27.65 | 31.07 | 26.80 | 17.67 | 21.21 | *** | |
Affected (%) | 17.71 | 11.76 | 19.68 | 31.32 | 42.42 | *** |
p < .05,
p < .01,
p < .001.
When results were stratified across GPA groups (reported in Table 1), significant differences were found for age (higher age in D/F students), sex (greater percentage of females with higher grades), and all sleep disturbances.
Table 2 reports correlations between the sleep variables. Although these variables represent overlapping constructs, they correlate with each other only moderately.
Table 2.
Spearman Correlations Among Sleep Variables
Sleep-Difficulty | Initial-Insomnia | Tiredness | Insufficient Sleep | Sleepiness | |
---|---|---|---|---|---|
Initial-Insomnia | 0.3731 | ||||
Tiredness | 0.2176 | 0.1981 | |||
Insufficient Sleep | 0.2538 | 0.1982 | 0.4636 | ||
Sleepiness | 0.2652 | 0.1626 | 0.313 | 0.3618 | |
Interfere | 0.4254 | 0.3238 | 0.2515 | 0.2492 | 0.2448 |
Sleep Disturbances Associated with Academic Performance
Results of adjusted regression analyses examining relationships between sleep disturbances and academic performance are reported in Table 3. The presence of sleep difficulty was associated with a 47% increased likelihood of a B average, compared to an A. Also, sleep difficulty was associated with a 118% increased likelihood of a C average and a 111% increased likelihood of a D/F average. Initial insomnia was associated with a 35% increased likelihood of a B average, a 108% increased likelihood of a C average, and 271% increased likelihood of a D/F average. Daytime tiredness was associated with a 25% increased likelihood of a B average, a 62% increased likelihood of a C average, and a 190% increased likelihood of a D/F average. Daytime sleepiness was associated with a 22% increased likelihood of a B average, a 103% increased likelihood of a C average, and a 242% increased likelihood of a D/F average.
Table 3.
Associations between Sleep Disturbances and GPA, Relative to Likelihood of being an “A” Student
B Grade | C Grade | D/F Grade | |||||
---|---|---|---|---|---|---|---|
Variable | RRR | 95% CI | RRR | 95% CI | RRR | 95% CI | |
Sleep-difficulty | Yes | 1.46*** | (1.29, 1.65) | 2.11*** | (1.73, 2.57) | 1.96 | (0.81, 4.74) |
Initial-insomnia | Yes | 1.35*** | (1.20, 1.51) | 2.04*** | (1.68, 2.47) | 3.53** | (1.59, 7.80) |
Tiredness | Yes | 1.27*** | (1.15, 1.40) | 1.73*** | (1.45, 2.07) | 3.11* | (1.23, 7.89) |
Sleepiness | Yes | 1.23** | (1.08, 1.40) | 2.03*** | (1.64, 2.50) | 3.43** | (1.45, 8.12) |
Insufficient-sleep | Per Day | 1.08*** | (1.05, 1.10) | 1.21*** | (1.15, 1.26) | 1.21 | (0.98, 1.49) |
Not experienced | Reference | Reference | Reference | ||||
Interfere | Not affected | 0.96 | (0.86, 1.07) | 0.71** | (0.57, 0.90) | 1.25 | (0.45, 3.51) |
Affected | 1.90*** | (1.66, 2.19) | 3.36*** | (2.72, 4.15) | 5.37*** | (2.18, 13.19) |
Note. RRR= relative risk reduction; CI = confidence interval.
p < .05,
p < .01,
p < .001.
Results of analyses examining insufficient sleep are reported in Table 4. Insufficient sleep was associated with an 8% increased likelihood of a B average for each day of insufficient sleep. Similarly, insufficient sleep was associated with a 21% increased likelihood of both a C and D/F average for each day reported. Results of a sensitivity analysis, testing each cutoff, are reported in Table 3. Irrespective of cutoff used, increased likelihood of a B average (vs A) was 24%-40%, depending on the cutoff used. For increased likelihood of a C average, a cutoff of 1 or more days per week was not associated with increased risk, but all other cutoffs were, with an increased likelihood between 64%-100%, depending on the cutoff used. For increased likelihood of a D/F average, a significant relationship was only seen for a cutoff of 6 or more days per week (132% increased likelihood) and 7 days per week (328% increased likelihood).
Table 4.
Associations between Days of Insufficient Sleep and Academic Performance
B | C | D/F | ||||
---|---|---|---|---|---|---|
Insufficient-sleep | RRR | 95% CI | RRR | 95% CI | RRR | 95% CI |
0 Days | 1.000 | Reference | 1.000 | Reference | 1.000 | Reference |
1 or more days | 1.34* | (1.06, 1.70) | 1.25 | (0.83, 1.89) | 0.49 | (0.14, 1.71) |
2 or more days | 1.41*** | (1.22, 1.64) | 1.70*** | (1.27, 2.28) | 1.30 | (0.38, 4.43) |
3 or more days | 1.27*** | (1.15, 1.41) | 1.74*** | (1.42, 2.14) | 1.55 | (0.61, 3.90) |
4 or more days | 1.25*** | (1.14, 1.37) | 1.82*** | (1.53, 2.18) | 1.84 | (0.81, 4.18) |
5 or more days | 1.24*** | (1.13, 1.37) | 1.96*** | (1.65, 2.33) | 1.79 | (0.82, 3.91) |
6 or more days | 1.24*** | (1.10, 1.40) | 1.99*** | (1.64, 2.42) | 2.29 | (0.98, 5.35) |
7 days | 1.25* | (1.05, 1.48) | 1.82*** | (1.39, 2.39) | 3.97** | (1.56, 10.12) |
Note. RRR= relative risk reduction; CI = confidence interval.
p < .05,
p < .01,
p < .001.
The perception of there being a sleep problem that interfered with academics was associated with actual reported lower GPA, as shown in Table 3. Compared to those who reported that they did not have a sleep problem, those who reported that they experienced sleep difficulties that did not interfere with academic performance were at no increased likelihood for lower GPA (and showed decreased likelihood for a C average). On the other hand, when students reported that sleep problems interfered with academics, they were 87% more likely to have a B, 216% more likely to have a C, and 428% more likely to have a D/F.
A post-hoc analysis used ordinal regression to examine whether the presence of sleep disturbance was associated with a change in GPA level, irrespective of actual value. Results of this analysis are reported in Table 5. Overall, sleep difficulty was associated with a 60% increased likelihood of reduced GPA by one grade level, initial insomnia was associated with a 52% increased likelihood of a lower grade level, daytime tiredness was associated with a 32% increased likelihood of a lower grade, and daytime sleepiness was associated with a 44% increased likelihood of a lower grade. Each day of insufficient sleep was associated with an 11% increased likelihood of a lower grade. Having a sleep problem that did not appear to interfere with academics was associated with an 11% likelihood of increasing a grade level compared to those who did not have a sleep problem. However, those with a sleep problem that seemed to interfere with academics, had a 111% increased likelihood of having a reduced grade, compared to those with no sleep problem to report.
Table 5.
Associations between Sleep Disturbances and Likelihood of a Lower Grade
Variable | OR | 95% CI | |
---|---|---|---|
Sleep-difficulty | 1.56*** | (1.40, 1.74) | |
Initial-insomnia | 1.50*** | (1.35, 1.67) | |
Tiredness | 1.36*** | (1.25, 1.49) | |
Sleepiness | 1.44*** | (1.28, 1.62) | |
Insufficient-sleep | 1.11*** | (1.08, 1.13) | |
Not experienced | 1.00 | Reference | |
Interfere | Not affected | 0.90* | (0.82, 1.00) |
Affected | 2.15*** | (1.91, 2.43 |
Note. OR= odds ratio; CI = confidence interval.
p < .05,
p < .01,
p < .001.
DISCUSSION
The present study assessed if sleep problems of collegiate athletes were associated with academic performance. It was hypothesized that sleep problems would indicate an association with lower GPA. The primary findings of this study determined there was a likelihood of collegiate athletes with sleep problems to earn a lower GPA compared to earning an A average or the highest GPA. Reported sleep problems across multiple domains, including sleep difficulties, insufficient sleep, insomnia, and daytime tiredness, were highly predominant and associated with poorer self-reported GPA in collegiate athletes.
When asked if a sleep problem interfered with academics, of those students who responded positively, 87% were more likely to report a B average, 216% more likely to report a C average, and 428% more likely to report a D/F average. To the best of our knowledge, these associations have not been previous examined in collegiate athletic population data. While previous studies have examined the relationship between quality of sleep and athletic performance, few relate sleep to academics, and even fewer have associated sleep problems with self-reported GPA among collegiate athletes.
A noteworthy finding in the present study is that collegiate athletes who reported having a sleep problem that did not appear to interfere with academics was associated with an 11% likelihood of increasing a grade level compared to those who did not report having a sleep problem. Although it is difficult to identify the specific reason(s) for this outcome, perhaps the perception of a sleep problem causes certain collegiate athletes to adopt effective sleep strategies or study habits that increase academic performance. Conversely, those who reported a sleep problem that appeared to interfere with academics had a 111% increased likelihood of having a reduced grade, compared to those with no sleep problem to report. These findings are consistent with a study of undergraduate college students that concluded the quality, irregularity, and deprivation of sleep were significantly associated with two measures of academic performance.40 Given the unique vulnerability of athletes, this outcome suggests that interventions specifically designed for athletes are required. The present results suggest the relationship between sleep problems and academics in collegiate athletes is strong and significant.
Competing athletic and academic demands and rigid scheduling are factors of the collegiate athlete experience that differ most in comparison to non-athlete college students. Traditional college students are generally able to determine and manage their own academic and work schedules, whereas athletes have coaches and advisors who predetermine and monitor their daily routines. In addition to course work and athletic practices, collegiate athletes have mandatory academic monitoring appointments and tutoring sessions.41 Additionally, collegiate athletes are restricted from being part-time students. According to NCAA regulations, athletes must be enrolled in at least 12 credit hours per semester to maintain NCAA eligibility.42 Finally, collegiate athletes regularly travel for competitions. Extensive travel means athletes often miss classes and exams, which can lead to tensions between students and faculty. Collegiate athletes report being negatively perceived by professors, failing to receive accommodations for athletic competitions, and being openly criticized in the classroom.43–45
A 2003 survey of NCAA institutions revealed that 69.2% employed a maximum of three academic advisors who have been previously trained to understand NCAA rules.46 Often, employees designated to support collegiate athletes rely on general campus support systems that have limited knowledge of the challenges faced by this unique population. When collegiate athletic department employees serve as the primary resource responsible for academic progress, athletes may not receive the proper guidance and support needed to address sleep problems.
Solutions
One strategy that may help to enhance collegiate athlete’s well-being is to ensure that coaches and athletic department administrators are aware of the empirical evidence that highlights the relationship between sport participation, sleep problems, and academic performance. A study by Watson reveals that collegiate athletes may feel uncomfortable seeking help outside of the athletic department because other members of the university do not understand the needs associated with being an athlete.47 Academic support centers for collegiate athletes have been found to primarily prioritize keeping athletes eligible, rather than ensuring academic success and competitive post-graduate opportunities.48 Because sleep problems are pervasive among collegiate athletes, university athletic departments should consider employing full-time practitioners to educate coaches and develop research-based interventions that can help guard against declines in academic performance. Having full-time sleep professionals on staff can signal to athletes and coaches alike that sleep health and academic performance are integral components of an athlete’s overall well-being.
University mental health counselors may serve as a second layer of support that may advocate for the sleep needs of collegiate athletes. Collegiate athletes underutilize university counseling services,49 due to fear of stigmatization by coaches, teammates, peers, and fans,50 and because counselors do not understand their unique experiences, pressures, and needs.51 Broughton and Neyer52 reveal that counselors can improve relationships with their collegiate athletes by increasing their knowledge of sports. Advanced knowledge and understanding of athletic life may allow a counselor to more comprehensively address sleep and other issues faced by this population.53
Limitations
There are several limitations in this study that should be considered when interpreting the results. First, this study utilized a cross sectional design which precludes an inference of causality. The relationships drawn are not causal, rather they are associative. Second, this study used self-reported GPA data, which are at risk of manipulation by the athlete. Athletes may not be accounting for their current semester of work. Measures of sleep were self-reported, rather than collected by a validated survey instrument. Participants could have attributed or misattributed a health issue to a sleep problem. Participants did not receive consultation with a sleep expert before reporting their complaints. Additionally, the sample of study participants was not random. This was a voluntary survey taken by students from various colleges and universities. Another limitation of the data is the inability to determine the Division level (I, II, III) of the athletes. The time constraints placed on athletes may vary by NCAA Division and type of sport (e.g., football, basketball, field hockey), which, may cause variation in reported sleep problems. Lastly, there is no objective measure of cognitive performance that could mediate the relationship of variation between collegiate athletes. Despite these limitations, this study is a significant contribution to our understanding of various types of sleep problems experienced by collegiate athletes and the relationship to academic performance.
Conclusions
At this time, there is little known about the association between sleep problems and academic performance among athletes. The results of our study reveal that athletes who report sleep problems are more likely to be academically disadvantaged. This finding has implications for college and sport administrators, coaches, and individual and team performance. Collegiate athletes require quality sleep in order to perform at a high level athletically and academically.15,24,54–57 While adjusting practice times, or limiting travel time to sporting events may be impractical, college coaches and administrators should work with sleep professionals that understand collegiate athletics to actively pursue strategies for athletes to attain the highest quality sleep possible.
As reported elsewhere,58 the lack of awareness among key stakeholders (e.g., collegiate athletes, coaches, parents, administrators) regarding the importance of sleep and the scheduling constraints could lead to insufficient and poor reported sleep in athletes. Implementing techniques to educate collegiate athletes and other key stakeholders regarding the quality, quantity, and environment of sleep could benefit academic performance. Computing technologies (e.g., smartphone applications, web-based video instruction, and online podcast) should also be explored to support healthy sleep behaviors and improve academic performance in current and future generations of collegiate athletes.59 Further research is required to understand the intricacies and impact of sleep on the academic performance of collegiate athletes. Future studies should examine the relation between sleep and other factors such as drug and alcohol use, social jet lag, and rigor of course work, and how they independently and collectively impact the academic achievement of collegiate athletes.
Coaches, teammates, friends, parents, and other university personnel need to be educated in order to maximize the support they can provide to collegiate athletes. Future research also needs to consider how to train collegiate athletes to advocate for the support needed to be able to succeed personally, professionally, and academically.
ACKNOWLEDGEMENTS
This work was supported by K23HL110216, R01MD011600, and an Innovations grant from the National Collegiate Athletic Association.
This study was supported by a grant from the National Institute of Aging, 1K01AG05462-01A1.
This study was supported by a grant from the National Heart Lung Blood Institute, R25HL10544-08, and the NYU Behavioral Sleep Medicine PRIDE Program.
REFERENCES
- 1.Ruthig JC, Haynes TL, Stupnisky RH, Perry RP. Perceived academic control: mediating the effects of optimism and social support on college students’ psychological health [published online ahead of print November 13, 2008]. Soc Psychol Educ. doi: 10.1007/s11218-008-9079-6. [DOI] [Google Scholar]
- 2.Nelson MC, Story M, Larson NI, Neumark-Sztainer D, Lytle LA. Emerging adulthood and college-aged youth: an overlooked age for weight-related behavior change. Obesity. 2008; 16(10): 2205–2211. doi: 10.1038/oby.2008.365. [DOI] [PubMed] [Google Scholar]
- 3.Arnett JJ. Emerging adulthood: understanding the new way of coming of age. In: Arnett JJ, Tanner JL, eds. Emerging adults in America: coming of age in the 21st century. Washington, DC: American Psychological Asssociation; 2006: 3–19. [Google Scholar]
- 4.Taylor DJ, Bramoweth AD, Grieser EA, Tatum JI, Roane BM. Epidemiology of insomnia in college students: relationship with mental health, quality of life, and substance use difficulties. Behav Ther. 2013; 44(3): 339–348. doi: 10.1016/j.beth.2012.12.001. [DOI] [PubMed] [Google Scholar]
- 5.Grandner MA. Sleeping Disorders. In: Brown GT, ed. Mind, Body, and Sport: Understanding and Supporting Student-Athlete Mental Wellness. Indianapolis, IN: National Collegiate Athletic Association; 2014: 51–53. [Google Scholar]
- 6.Curcio G, Ferrara M, De Gennaro L. Sleep loss, learning capacity and academic performance [published online ahead of print March 24, 2006]. Sleep Med Rev. doi: 10.1016/j.smrv.2005.11.001. [DOI] [PubMed] [Google Scholar]
- 7.Buboltz WC Jr, Brown F, Soper B. Sleep habits and patterns of college students: a preliminary study. J Am Coll Health. 2001; 50(3): 131–135. doi: 10.1080/07448480109596017. [DOI] [PubMed] [Google Scholar]
- 8.Lund HG, Reider BD, Whiting AB, Prichard JR. Sleep patterns and predictors of disturbed sleep in a large population of college students. J Adolesc Health. 2010; 46(2): 124–132. doi: 10.1016/j.jadohealth.2009.06.016. [DOI] [PubMed] [Google Scholar]
- 9.Forquer LM, Camden AE, Gabriau KM, Johnson CM. Sleep patterns of college students at a public university. J Am Coll Health. 2008; 56(5): 563–565. doi: 10.3200/JACH.56.5. [DOI] [PubMed] [Google Scholar]
- 10.Kroshus E, Wagner J, Wyrick D, Athey A, Bell L, Benjamin HJ, et al. Wake up call for collegiate athlete sleep: narrative review and consensus recommendations from the NCAA Interassociation Task Force on Sleep and Wellness. Br J Sports Med. 2019; 0: 1–8. doi: 10.1136/bjsports-2019-100590. [DOI] [PubMed] [Google Scholar]
- 11.Reardon CL, Hainline B, Aron CM, Baron D, Baum AL, Bindra A, et al. Mental health in elite athletes: International Olympic Committee consensus statement. Br J Sports Med. 2019; 53: 667–699. doi: 10.1136/bjsports-2019-100715. [DOI] [PubMed] [Google Scholar]
- 12.Copenhaver EA, Diamond AB. The value of sleep on athletic performance, injury, and recovery in the young athlete. Pediatr Ann. 2017; 46(3): 106–111. doi: 10.3928/19382359-20170221-01. [DOI] [PubMed] [Google Scholar]
- 13.Adler P, Adler PA. From idealism to pragmatic detachment: The academic performance of college athletes. Sociol Educ. 1985: 241–250. doi: 10.2307/2112226. [DOI] [Google Scholar]
- 14.Sargent C, Halson S, Roach GD. Sleep or swim? Early-morning training severely restricts the amount of sleep obtained by elite swimmers. Eur J Sport Sci. 2014; 14: 310–315. doi: 10.1080/17461391.2012.696711. [DOI] [PubMed] [Google Scholar]
- 15.Davenne D Sleep of athletes – problems and possible solutions. Biol Rhythm Res. 2008; 40(1): 45–52. doi: 10.1080/09291010802067023. [DOI] [Google Scholar]
- 16.Killgore WD. Effects of sleep deprivation on cognition. Prog Brain Res. 2010; 185: 105–129. doi: 10.1016/B978-0-444-53702-7.00007-5. [DOI] [PubMed] [Google Scholar]
- 17.American college health association-national college health assessment II: reference group executive summary spring 2014. https://www.acha.org/documents/ncha/ACHA-NCHA-II_ReferenceGroup_ExecutiveSummary_Spring2014.pdf. ACHA technical report. Published 2014. Accessed May 29, 2018.
- 18.Buxton OM, Marcelli E. Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Soc Sci Med. 2010; 71(5): 1027–1036. doi: 10.1016/j.socscimed.2010.05.041. [DOI] [PubMed] [Google Scholar]
- 19.Mukherjee S, Patel SR, Kales SN, Ayas NT, Strohl KP, Gozal D, et al. An official American Thoracic Society statement: the importance of healthy sleep. Recommendations and future priorities. Am J Respir Crit Care Med. 2015; 191(12): 1450–1458. doi: 10.1164/rccm.201504-0767ST. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kenney SR, Lac A, LaBrie JW, Hummer JF, Pham A. Mental health, sleep quality, drinking motives, and alcohol-related consequences: a path-analytic model. J Stud Alcohol Drugs. 2013; 74(6): 841–851. doi: 10.1080/07448481.2014.897953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gupta L, Morgan K, Gilchrist S. Does elite sport degrade sleep quality? A systematic review. Sports Med. 2017; 47(7): 1317–1333. doi: 10.1007/s40279-016-0650-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Robbins JE, Madrigal L, Stanley CT. Retrospective Remorse: College Athletes’ Reported Regrets from a Single Season. J Sport Behav. 2015; 38(2). [Google Scholar]
- 23.Walters PH, College W. Sleep, the athlete, and performance. Strength Cond J. 2002; 24(2): 17–24. doi: 10.1519/00126548-200204000-00005. [DOI] [Google Scholar]
- 24.Penn Schoen Berland. Student-athlete time demands. http://sports.cbsimg.net/images/Pac-12-Student-Athlete-Time-Demands-Obtained-by-CBS-Sports.pdf. Penn Schoen Berland technical report. Published April 2015. Accessed May 30, 2019.
- 25.Simons HD, Rheenen DV, Covington MV. Academic motivation and the student athlete. J Coll Stud Dev Baltim. 1999; 40(2): 151. [Google Scholar]
- 26.Crosnoe R. Academic and health-related trajectories in adolescence: the intersection of gender and athletics. J Health Soc Behav. 2002: 317–335. doi: 10.2307/3090207. [DOI] [PubMed] [Google Scholar]
- 27.Rosenbaum JE, DeLuca S, Miller SR, Roy K. Pathways into work: short-and long-term effects of personal and institutional ties. Sociol Educ. 1999: 72(3): 179–196. [Google Scholar]
- 28.Baum S, Payea K. Education pays 2004: the benefits of higher education for individual and society. https://trends.collegeboard.org/sites/default/files/education-pays-2004-full-report.pdf. College Board technical report Published 2004. Accessed May 30, 2019.
- 29.Leslie LL, Brinkman PT. The Economic Value of Higher Education. New York, NY: Macmillian Publishing; 1988. [Google Scholar]
- 30.Trockel MT, Barnes MD, Egget DL. Health-related variables and academic performance among first-year college students: implications for sleep and other behaviors. J Am Coll Health. 2000; 49(3): 125–131. doi: 10.1080/07448480009596294. [DOI] [PubMed] [Google Scholar]
- 31.Lack LC. Delayed sleep and sleep loss in university students. J Am Coll Health. 1986; 35(3): 105–110. doi: 10.1080/07448481.1986.9938970. [DOI] [PubMed] [Google Scholar]
- 32.Hosick MB. College athletes graduate at record high rates. NCAA. November 14, 2018. http://www.ncaa.org/about/resources/media-center/news/college-athletes-graduate-record-high-rates. Accessed May 29, 2019. [Google Scholar]
- 33.Bonnar D, Bartel K, Kakoschke N, Lang C. Sleep interventions designed to improve athletic performance and recovery: a systematic review of current approaches. Sports Med. 2018; 48(3): 683–703. doi: 10.1007/s40279-017-0832-x. [DOI] [PubMed] [Google Scholar]
- 34.Leproult SK, L’Hermite-Baleriaux M, Copinschi G, Penev PD, Van Cauter E. Leptin levels are dependent on sleep duration: relationships and sympathovagal balance, carbohydrate regulation, cortisol, and thyroptin. J Clin Endrocrinol Metab. 2004: 89(11): 5762–5771. doi: 10.120/jc.2004-1003. [DOI] [PubMed] [Google Scholar]
- 35.Benington JH, Heller HC. Restoration of brain energy metabolism as the function of sleep. Prog Neurobiol. 1995; 45(4): 347–360. [DOI] [PubMed] [Google Scholar]
- 36.Fullagar HH, Skorski S, Duffield R, Hammes D, Coutts AJ, Meyer T. Sleep and athletic performance: the effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Med. 2015;45(2):161–186. doi: 10.1007/s40279-014-0260-0. [DOI] [PubMed] [Google Scholar]
- 37.Reilly T, Piercy M. The effect of partial sleep deprivation on weight-lifting performance. Ergonomics. 1994;37(1):107–115. doi: 10.1080/00140139808963628. [DOI] [PubMed] [Google Scholar]
- 38.Min H The risk factors of abusive relationships for nontraditional students. J Am Coll Health. 2019; 67(2): 174–179. doi: 10.1080/07448481.2018.1468333. [DOI] [PubMed] [Google Scholar]
- 39.StataCorp LP. College Station, TX: StataCorp LP; 2015. Stata Surv Data Ref Man Release. 14. [Google Scholar]
- 40.Hanton S, Fletcher D, Coughlan G. Stress in elite sport performers: a comparative study of competitive and organizational stressors. J Sports Sci. 2005; 23(10): 1129–1141. doi: 10.1080/02640410500131480. [DOI] [PubMed] [Google Scholar]
- 41.Comeaux E Mentoring as an intervention strategy. J Study Sports Athletes Educ. 2010;4(3):257–275. doi: 10.1179/ssa.2010.4.3.257. [DOI] [Google Scholar]
- 42.Meyer SK. NCAA academic reforms: maintaining the balance between academics and athletics. In: Phi Kappa Phi Forum. Vol 85. Honor Society of Phi Kappa Phi; 2005:15–19. [Google Scholar]
- 43.Simons HD, Bosworth C, Fujita S, Jensen M. The athlete stigma in higher education. Coll Stud J. 2007; 41(2): 251–274. [Google Scholar]
- 44.Turner RW, Southall RM, Eckard W. Athlete graduation rate gaps at Division-I state flagship universities: an exploratory analysis emphasizing Black males. Spectr J Black Men. 2015;3(2):1–25. [Google Scholar]
- 45.Turner RW II. Not For Long: The Life and Career of the NFL Athlete. New York, NY: Oxford University Press; 2018. [Google Scholar]
- 46.Fenton GB. Athletes and academia: the effect of the NCAA 2003 academic standards on university support services. ProQuest; 2006. [Google Scholar]
- 47.Watson JC. College student-athletes’ attitudes toward help-seeking behavior and expectations of counseling services. J Coll Stud Dev. 2005; 46(4): 442–449. doi: 10.1353/csd.2005.0044. [DOI] [Google Scholar]
- 48.Commission on Intercollegiate Athletics. A call to action: reconnecting college sports and higher education. https://www.knightcommission.org/wp-content/uploads/2008/10/2001_knight_report.pdf. Knight Foundation technical report. Published June 2001. Accessed May 29, 2019.
- 49.Maniar SD, Curry LA, Sommers-Flanagan J, Walsh JA. Student-athlete preferences in seeking help when confronted with sport performance problems. Sport Psychol. 2001; 15(2): 205–223. [Google Scholar]
- 50.Brewer B, Vanraalte J, Petitpas A, D. Bachman A, A. Weinhold R. Newspaper portrayals of sport psychology in the United States, 1985-1993. Sport Psychol. 1998; 12: 89–94. doi: 10.1123/tsp.12.1.89. [DOI] [Google Scholar]
- 51.Greenspan M, Andersen MB. Providing psychological services to student athletes: a developmental psychology model. Sport Psychol Interv. 1995: 177–191. [Google Scholar]
- 52.Broughton E, Neyer M. Advising and counseling student athletes. New Dir Stud Serv. 2001; 93: 47–53. [Google Scholar]
- 53.Fletcher TB, Benshoff JM, Richburg MJ. A systems approach to understanding and counseling college student-athletes. J Coll Couns. 2003; 6(1): 35–45. doi: 10.1002/j.2161-1882.2003.tb00225.x. [DOI] [Google Scholar]
- 54.Nédélec M, Halson S, Abaidia A- E, Ahmaidi S, Dupont G. Stress, sleep and recovery in elite soccer: a critical review of the literature. Sports Med. 2015; 45(10): 1387–1400. doi: 10.1007/s40279-015-0358-z. [DOI] [PubMed] [Google Scholar]
- 55.Dorrian J, Rogers NL, Dinges DF. Psychomotor vigilance performance: neurocognitive assay sensitive to sleep loss. Univ Pa. January 2005:32. [Google Scholar]
- 56.Drummond SPA, Anderson DE, Straus LD, Vogel EK, Perez VB. The Effects of Two Types of Sleep Deprivation on Visual Working Memory Capacity and Filtering Efficiency [published online ahead of print April 18, 2012].. PLoS One. doi: 10.1371/journal.pone.003565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Wolfson AR, Spaulding NL, Dandrow C, Baroni EM. Middle school start times: the importance of a good night’s sleep for young adolescents. Behav Sleep Med. 2007; 5(3): 194–209. doi: 10.1080/15402000701263809. [DOI] [PubMed] [Google Scholar]
- 58.Selby R, Weinstein HM, Bird TS. The health of university athletes: attitudes, behaviors, and stressors. J Am Coll Health. 1990; 39(1): 11–18. doi: 10.1080/07448481.1990.9936208. [DOI] [PubMed] [Google Scholar]
- 59.Choe EK, Consolvo S, Watson NF, Kientz JA. Opportunities for computing technologies to support healthy sleep behaviors. In: CHI 2011 - 29th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts 2011. British Columbia, Canada: Conference on Human Factors in Computing Systems, 2011: 3053–3062. [Google Scholar]