The importance of understanding sleep and circadian physiology for enhancing athletic performance and health of the collegiate athlete is becoming more widely acknowledged (14,23,26,31). Poor sleep is prevalent among this cohort, and it is estimated that approximately 40% of collegiate athletes obtain less than 7 hours of sleep during weekdays, while 51% report high levels of daytime sleepiness (22). Sleep extension in college basketball players over multiple weeks improves sprint time, shooting accuracy, reaction time, as well as overall ratings of physical and mental well-being during practice (23), while a 1-week sleep extension period improved serving accuracy by 17% among college tennis players (31). Together, these data suggest a likely benefit of optimal sleep in improving objective markers of athletic performance. However, whether improved sleep translates to reduced incidence of sport-related injury is not clear (14,24,34,35).
To the best of our knowledge, few studies have characterized sleep and circadian measures in college football players, and none have examined the relationship between subjective and objective sleep metrics and the incidence of injury. Still, although data examining quality and quality of sleep are lacking, recent reports suggest that the prevalence of sleep disordered breathing among college players ranges between 8 and 55% (6,19). Importantly, the injury rate among National Collegiate Athletic Association (NCAA) football players between 2004 and 2014 was 7.29/1,000 athlete exposures (17). This preliminary evidence of sleep disordered breathing supports the continued investigation of suspected sleep-related problems that characterize this cohort and the potential impact poor sleep outcomes have on athletic injury during the competitive season.
The purpose of this study was threefold. First, we examined subjective sleep metrics (sleep quality, insomnia severity, daytime sleepiness, sleep apnea risk, and circadian preference) using preclinical sleep and circadian questionnaires in college football players before the start of the competitive season. Second, we assessed objective sleep characteristics during the season with wrist actigraphy and examined the relationship between preseason subjective sleep measures and in-season objective sleep characteristics. Finally, we examined the relationship between subjective and objective sleep measures and the occurrence of injury during the competitive season.
Experimental Approach to the Problem
The study was completed with a convenience sample from a single NCAA Division I Football Championship Subdivision program. Before the start of the 2017 season (July 2017), subjects completed 5 clinical sleep and circadian screening questionnaires on the same day: (a) Pittsburgh Sleep Quality Index (PSQI), (b) Insomnia Severity Index (ISI), (c) Epworth Sleepiness Scale (ESS), (d) Sleep Medicine Associates of Maryland (SMAM) Sleep Apnea Risk Questionnaire, and (e) Morningness-Eveningness Questionnaire (MEQ). After completion of surveys, subjects were provided wrist actigraphy monitors and instructed to wear the watches continuously over an ∼17-week period (119-day collection window) throughout the duration of the football preseason and season (from July to November 2017). Obtained sleep through the season was observational such that the athletes were instructed to maintain their normal training and sleep behaviors with no restrictions or modifications. Finally, a retrospective analysis of injury characteristics that resulted in time loss from participation was conducted.
A total of 94 men (mean ± SD: age: 19.6 ± 1.7 years; range: 18–23 years) participated in this study. There were no exclusion criteria for participation. The Towson University Institutional Review Board approved the study protocol, and all subjects provided written informed consent.
Clinical Sleep and Circadian Screening Questionnaires: Pittsburgh Sleep Quality Index
The PSQI is a 19-item self-report questionnaire indexing 7 clinically formulated sleep domains: sleep duration, sleep disturbance, sleep latency, daytime disturbance, habitual sleep efficiency, sleep quality, and use of sleep medications. Scores on each of the 7 PSQI subscales are used to calculate a global score that ranges from 0 to 21. Scores ≤5 are associated with good sleep quality, and scores >5 are associated with poor sleep quality (4). Subjective sleep characteristics at the start of the season were also obtained from the PSQI.
Insomnia Severity Index
The ISI assesses the nature, severity, and impact of insomnia. The index evaluates 7 dimensions using a 5-point Likert scale. These include severity of sleep onset, sleep maintenance, early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, noticeability of sleep problems by others, and distress caused by sleep difficulties. The total score can range from 0 to 28 that can be interpreted as absence of insomnia (0–7); subthreshold insomnia (8–14); moderate insomnia (15–21); and severe insomnia (22–28). The index has been determined to be a valid outcome for insomnia research (1).
Epworth Sleepiness Scale
The ESS is a validated 8-item questionnaire that measures subjective sleepiness (16). Subjects are asked to rate how likely they are to fall asleep while (a) sitting and reading, (b) watching television, (c) sitting inactive in a public place, (d) being a passenger in a car for at least 1 hour, (e) lying down to rest in the afternoon, (f) sitting and talking to someone, (g) sitting quietly after lunch, and (h) driving a car stopped in traffic. Each question was scored from 0 to 3. Total scores range from 0 (unlikely to fall asleep in any situation) to 24 (high chance of falling asleep in all situations). High risk of daytime sleepiness was defined as an ESS total score >10.
Sleep Medicine Associates of Maryland Sleep Apnea Risk Questionnaire
The SMAM Sleep Apnea Risk Questionnaire assesses the risk of sleep apnea. Subjects are asked about their history of snoring (no or mild—0 points; moderate/inconsistent—2 points; and severe/consistent—8 points), whether they have been told that they have “pauses” in breathing during sleep (no—0 points; yes, but infrequent—2 points; yes, inconsistent but most nights—8 points; and yes, severely so—10 points), the extent to which they are overweight (no—0 points; yes, <20 lbs—2points; yes, 21-50 lbs—3 points; and yes, >50 lbs—8 points), and whether they have a history of high blood pressure (6 points), stroke (1 point), heart disease (1 point), morning headaches (1 point), 3 awakenings/night (7 points), excessive fatigue (1 point), depression (1 point), and diabetes (1 point). The sum of these items is added to their ESS total score to generate a total score. Sleep consultations are suggested for scores that range between 15 and 19, while score >19 suggests significant risk of sleep apnea and scheduling of a sleep study.
The Morningness-Eveningness Questionnaire (MEQ) was used to determine circadian preference (i.e., identifies when an individual would prefer to wake up or start sleep, rather than when he/she may actually be scheduled to). The questionnaire consists of 19 multiple choice questions that address the daily sleep-wake habits (e.g., During the first half hour after you wake up in the morning, how do you feel?) and times of day that one prefers to engage in certain activities (e.g., At what time would you prefer to take a test that you know is going to be mentally exhausting and will last 2 hours?). Each question is ranked on a 4- to 5-point scale. The sum score ranges from 16 to 86. Scores of 41 and below indicate “evening types,” scores of 59 and above indicate “morning types,” and scores 42–58 indicate “intermediate types.” The MEQ has been previously validated in a group of 18- to 34-year-old adults against individual differences in the circadian variation of oral temperature (15).
Subjects were provided with an actigraph wristwatch (Actiwatch 2; Philips/Respironics, Pittsburg, PA) and given verbal instructions to wear the watch at all times except when showering. Throughout the collection window, 1 or 2 research assistants collected watches on a biweekly basis, so that data could be downloaded and actigraphs recharged. The goal was to return the watch within 48 hours to begin the next 2-week collection period. This was designed to allow for an anticipated minimum of 85 days of actigraphy collection per athlete. Estimates of rest/activity states were assessed in 1-minute bins using Respironics Actiware V5 software (Philips/Respironics) for each nighttime major sleep episode available (major nighttime sleep is defined as initiation of a major sleep interval occurring between the hours of 21:00–05:00 lasting at least 3 hours). A nighttime sleep episode was excluded if the actigraph was off for all or part of the nighttime sleep episode. Average objective sleep characteristics were calculated for any obtained major nighttime sleep episode for each athlete. Sleep efficiency was calculated as the total time asleep (sleep duration) divided by the time in bed. Compliance scores for wearing the watch were calculated as the percentage of number of days the actigraph was worn divided by the total number of collection days for the athlete.
Throughout the competitive season, members of the university sports medicine staff recorded injury data for all subjects into an electronic medical record database (Athletic Training Systems; Keffer Development Services, Grove City, PA). Injury characteristics included body part or category (e.g., knee, ankle, and environmental), injury type (e.g., sprain, strain, fracture, concussion, heat exhaustion, and upper respiratory illness), activity at time of injury (i.e., game, practice, or nonathletic), and days of time loss. Based on previous research, an injury was defined as any event that required medical intervention by an athletic trainer and resulted in complete restriction from 1 or more practices or games (21). Subjects were included in the injury group after an initial incidence of injury; multiple injuries to the same subject were not included in the multivariate analyses. All injury data were abstracted from the electronic medical record database by one of the authors (J.N.W.).
Descriptive statistics were calculated for all variables. Data are reported as mean ± SD unless otherwise indicated. Sleep and circadian questionnaires were also compared based on provided clinical threshold scores as well as categorical scores. Pearson product-moment correlations were used to examine relationships between variables. Linear regression was used to identify whether scores on the clinical sleep screening questionnaires predicted objective sleep duration during the season. Logistic regression models, with “injured/noninjured” as the dependent variable, were used to examine the association between subjective and objective sleep metrics and injury. Scores on clinical sleep and circadian screening questionnaires were analyzed as continuous variables and using cutpoints that indicate the boundary into a clinical range and a recommendation for a follow-up consultation with an appropriate health care professional. Subjective sleep duration and objective sleep duration were analyzed as continuous variables and using the cutpoint for the recommended minimum number of hours of sleep per night for young adults. All analyses were adjusted for age and body mass index, 2 potential confounders previously reported to be associated with injuries in previous studies (12,13). The limit for statistical significance was set at p ≤ 0.05. Data analysis was conducted using Statistica (version 13.0; Statsoft, Tulsa, OK).
Clinical Sleep and Circadian Screening Questionnaires
Table 1 provides results from the sleep disruption and circadian preference questionnaires. Ninety-four athletes completed at least 1 questionnaire, although missing data on 5 questionnaires precluded the assessment scores above clinical threshold and these data were subsequently discarded. In total, 88.3% (83 of 89) of subjects had complete data for all 4 sleep screening questionnaires. Questionnaire-specific sample sizes are included in Table 1. Of these, 67.4% (60 of 89) of athletes had scores that were above the clinical range threshold on at least one of the 4 questionnaires to indicate sleep disorder risk. Specifically, the percentage of athletes scoring above the clinical range threshold were 25.5, 4.4, 22.5, and 28.0% for the PSQI, ISI, ESS, and SMAM, respectively. Average scores on the PSQI, ISI, ESS, and SMAM sleep questionnaires were all below clinical range thresholds (Table 1). The 23 athletes who scored above threshold on the PSQI had a mean score of 7.00 ± 1.48. For the ISI, the 4 athletes who scored above threshold had a mean score of 16.75 ± 1.26 and were all within the moderate severity clinical insomnia range. An additional 25 athletes had ISI scores ranging from 8 to 14, which would be classified as subthreshold insomnia. No individuals were identified as having severe clinical insomnia. The 20 athletes who scored above threshold on the ESS had a mean score of 12.60 ± 1.76, while 26 athletes identified as being at risk of sleep apnea had a mean SMAM score of 10.19 ± 5.11. There was a weak positive correlation between SMAM and ESS total scores (r = 0.28, p = 0.007) while a strong positive correlation was found between the PSQI and ISI (r = 0.61, p < 0.001). The athletes were classified as morning types (12%), intermediate types (80%), or evening types (8%).
Objective Sleep Characteristics
Objective sleep data from actigraphy was obtained from 88 athletes due to lost or damaged actigraphs. Of those, 4 subjects were excluded due to full noncompliance with instructions. This resulted in a total sample of 84 subjects for the objective sleep analyses. On average, there was 51 ± 24% compliance for wearing the actigraphs during the 17 weeks. All subjects had at least 5 days of recorded major nighttime sleep episodes with an average of 30.7 ± 16.3 major nighttime sleep episodes (range: 5–65). A major nighttime sleep episode was defined by the initiation of a major sleep interval occurring between the hours of 21:00–05:00 lasting at least 3 hours. Table 2 provides the average objective sleep characteristics for major nighttime sleep episodes during the ∼17 weeks. Only 7.1% (6 of 84) of the athletes obtained the recommended daily sleep duration of 7–9 hours for young adults. The mean sleep duration (6 hours and 4 minutes) was ∼1 hour less than the minimum recommended daily amount. The mean objective sleep efficiency (89.85 ± 3.12%) for the group was above the daily recommended 85%; however, 6% (5 of 84) of the athletes had a sleep efficiency values that were less than recommended.
Relationships Between Objective Sleep Characteristics, Clinical Sleep Screening Questionnaires, and Subjective Sleep Characteristics
Higher scores on the ISI predicted lower average objective sleep durations (N = 80, R2 = 0.08, p = 0.009). Similarly, higher scores on the ESS predicted lower average objective sleep durations (N = 79, R2 = 0.16, p < 0.001). No associations were found between objective sleep duration and PSQI or SMAM global questionnaire scores. Of the 84 subjects who had objective and subjective sleep characteristic metrics, 39.3% (33 of 84) of athletes self-reported sleeping less than the recommended minimum 7 hours per night for young adults. Alternatively, when assessing sleep objectively, 92.8% (78 of 84) of players slept less than 7 hours per night. Although subjective sleep duration was predictive of objective sleep time (R2 = 0.14, p < 0.001), the duration subjectively reported before the start of the season was 1 hour and 12 minutes longer than the period measured objectively during the ∼17 weeks. Bedtime measured objectively was roughly 60 minutes later than subjective bedtime, whereas objective and subjective wake times differed by only 3 minutes.
Injury Characteristics and the Associations Between Subjective and Objective Sleep Characteristics, Clinical Sleep Screening Questionnaires, and Injury Incidence
Forty-five athletes sustained a total of 68 injuries. Lower extremity injuries comprised 33.8% (n = 23) of all injuries, followed by the head and neck (30.9%, n = 21) and upper extremity (14.7%, n = 10). Environmental (heat exhaustion) and general medical conditions each comprised 8.9% (both, n = 6) of all injuries. Over half (54.4%, n = 37) of all injuries occurred during practice with the remaining reported during games (36.8%, n = 25) and nonathletic activities (8.8%, n = 6). Thirty-two injuries (47.1%) resulted in time loss of ≤1 week while 24 (35.3%) led to time loss for a period between 8 days and 1 month; 12 (17.6%) injuries resulted in time loss of ≥1 month.
Table 3 presents the associations between subjective and objective sleep measures and injury in collegiate football players. As shown, no significant associations were found between injury and subjective and objective sleep duration (continuous total minutes and total duration <7 hours) or measures attained from clinical sleep and circadian screening questionnaires (total scores and cutpoints for recommended clinical follow-up consultation).
The purpose of this study was threefold: (a) to characterize subjective sleep metrics in college football players at the start of preseason, (b) examine the relationship between preseason subjective sleep measures and objective characteristics during the season, and (c) determine the association between subjective and objective sleep metrics and incidence of time-loss injury during the competitive season.
Across this sample of collegiate football players, average sleep metric scores for self-reported sleep quality, insomnia severity, daytime sleepiness, and sleep apnea risk were below clinical thresholds and, when considered along with individual sleep metrics (e.g., sleep timing, sleep duration, time in bed, and sleep onset latency) derived from the PSQI, were indicative of overall good sleep quality and quantity as well as a low risk of sleep disorders. Yet, the group-averaged data obscured a very important observation, namely, that approximately 66% of the sample had at least 1 self-reported global score above the clinical threshold, indicating a potential need for a follow-up sleep consult. We acknowledge that using questionnaires that have been validated in the general population, such as the PSQI or ESS, may not have the appropriate sensitivity or specificity for detecting unique sleep problems in competitive athletes (2,7,29). For example, Driller et al. (7) compared ESS and PSQI global scores between elite-level athletes and nonathletes who did not have a diagnosed sleep disorder. Although no differences in ESS scores were observed between the groups, the global score for the PSQI was significantly lower in athletes, indicating they had better overall sleep quality. By contrast, results from the Athlete Sleep Behavior Questionnaire, a survey designed to examine sleep behaviors in athletes, indicated that elite athletes had worse sleep behaviors and habits than their nonathletic counterparts. Furthermore, items which asked subjects to rate their level of concern and worry about sport performance while in bed, as well as the frequency of going to bed with sore muscles, were 2 of the questions with the highest scores in the athletic group (7). Thus, it is reasonable to suggest that questionnaires traditionally used in the general population may not be specific to the unique challenges encountered by competitive athletes and therefore may lead to an underreporting of athletes at risk of sleep disturbances. Despite this potential limitation, our finding that roughly 2-thirds of this athletic cohort had at least 1 self-reported global score above the clinical threshold is concerning.
As noted previously, athletes commonly have reduced sleep quality and quantity (9,22,30). Looking at individual subjective sleep metrics provided on the PSQI, the athletes, on average, reported sleep characteristics consistent with those recommended by sleep experts. For young adults aged 18–25 years, the National Sleep Foundation recommends 7–9 hours of sleep per night, a sleep onset latency of 30 minutes or less, and a sleep efficiency ≥85%; all indicators of good sleep quality (27). By contrast, the mean sleep duration derived from actigraphy (6 hours and 4 minutes) for our sample was 76 minutes less than the duration reported subjectively and, in turn, 1 hour less than the recommended amount of 7–9 hours for young healthy adults (27). This supports previous evidence of athletes having reduced sleep quantity. These data indicate that subjective sleep indices alone may not provide an accurate picture of overall sleep duration for the season. It is important to note that subjective sleep was measured before the start of the season and thus may reflect sleep patterns specific to off-season and preseason periods (spring through early summer) while the objective sleep across the ∼17 weeks may be more representative of typical sleep behaviors during the season. Interestingly, the difference in sleep duration between the objective and subjective measures seemed to be the result of an athlete's bedtime more than the time of awakening, suggesting that sleep extension interventions may be more effective if focused on bedtime behaviors and implementing earlier target bedtimes.
Although it is widely accepted that athletes are not getting the quality and quantity of sleep to perform optimally (9,20,30), it is still unknown how much sleep is ideal for athletes. Although Mah et al. (23) recommended 8.5 hours of sleep for optimal athletic performance, as evidenced by improvements in mood, reaction time, and specific assessments of athletic performance in college basketball players after sleep extension, Bompa and Haff (3) recommend 9–10 hours of sleep. Importantly, none of the football players assessed in this study obtained the recommended daily sleep duration for athletes. In addition, and even more concerning, is that roughly 40% of athletes self-reported sleeping less than the recommended minimum 7 hours per night for young adults. Identifying markers of sleep disturbance early in the season can inform interventions designed to improve academic and athletic performance, as well as health. Scores indicating sleep disruption on questionnaires may not be indicative of a specific sleep disorder, per se, but potentially they may serve as a factor associated with the athlete-specific schedule (training, away games, and practice schedules). Ultimately, having an individual follow-up with a clinician is a key factor in determining whether an athlete truly has a sleep disorder requiring treatment or whether the individual just has poor sleep hygiene.
Recent investigations have brought increased focus on a potential link between sleep and athletic injuries (14,24,33,34). To the best of our knowledge, however, we are the first to examine both subjective and objective sleep and the relation to the incidence of injury in an athletic cohort. Notably, we found no evidence that suboptimal sleep duration and quality in this cohort of collegiate football players was predictive of injuries sustained during the competitive season. These findings are in contrast to some previous studies, which reported associations between sleep measures and elevated injury risk in adolescent athletes. Specifically, Milewski (24) found that adolescent athletes (n = 112) who self-reported <8 hours of sleep per night were 1.7 times more likely to experience an injury than those who slept ≥8 hours a night. Similarly, among 340 elite adolescent athletes, those reaching the recommended sleep duration of 8 hours of sleep on the weekdays reduced the odds of injury by 61%, compared with those who reported less than 8 hours of sleep (34). Finally, von Rosen et al. (33) monitored 496 adolescent elite athletes repeatedly over 52 weeks. Although no significant difference in risk of injury was observed between those athletes who reported sleeping ≤8 hours compared with those sleep more than 8 hours, an increase in training load and intensity and a decrease in sleep volume resulted in a 2.25 times higher risk of first reported injury compared with no change in these variables. Given that these previous findings are in contrast to those of the current investigation, several methodological differences that may limit direct comparisons warrant mention. First, these previous investigations examined sleep in younger and more heterogeneous populations comprised both male and female adolescent athletes from either middle school and high school cohorts or high school only. In addition, cohorts included elite or nonelite athletes from multiple sports, whereas our investigation examined football players only. Only 1 study reported including American football players in their sample, although they represented less than 2% (7 of 496) of the entire cohort (33). Notably, previous epidemiological studies have reported football to have one of the highest overall injury rates, in particular at the collegiate level (18,28). Furthermore, increasing age and female sex have been shown to be risk factors for elevated risk of sports-related injury in athletic populations (8,35). Consequently, additional efforts to examine the association between sleep and injury in collegiate athletes across a multitude of sports are warranted.
Additional methodological variations across studies include differences in injury definition and the methods used to ascertain injury data (self-report or medical records). Milewski et al. (24) defined injury as any event that required a medical evaluation, treatment by a member of the athletic training staff, or both, whereas both studies by von Rosen et al. (33,34) defined injury as “any physical complaint resulting in reduced training volume, experience of pain, difficulties participating in normal training or competition, or reduced performance in sport.” Markedly, all 3 studies did not specify time loss as a criterion for injury inclusion, which is contrast to the definition used in the current study. Our injury definition thus may limit comparisons with these studies that used broader injury definitions. Notably, the use of inconsistent injury definitions has been noted as an important limiting factor in injury prevention research to date (25). Finally, all 3 aforementioned studies used self-report questionnaires to ascertain injury data, whereas we abstracted all injury characteristics from an electronic medical record database maintained by the university sports medicine staff. Recently, authors have questioned the accuracy of self-reported injury details, in particular over increasing lengths of time (5,11). Taken together, these differences further limit any direct comparisons between our study and those studying adolescent athletes.
As stated, no measure of sleep quantity or quality was predictive of injury in our sample of collegiate football players. Notably, our results are similar to the only published study to date that investigated the association between sleep and injury risk in collegiate athletes. Specifically, Hayes et al. (14) did not find sleep quantity, derived from the PROMIS sleep disturbance questionnaire, to be a significant predictor of in-season injury among 297 cross-country runners. Instead, runners who self-reported poor quality sleep had a higher risk of an in-season injury. However, this relationship was attenuated and became nonsignificant after controlling for weekly training mileage and presence of preseason injury (14). Similar to studies of adolescent athletes, the data were based on self-reported survey responses, and a very broad injury definition, irrespective of time loss, was used. These methodological differences, coupled with variations in the physical demands and injury characteristics (e.g., acute, chronic, contact, or noncontact) associated with these 2 sports, limit direct comparisons with the current study. Nonetheless, our findings support those from Hayes et al. in that sleep duration is not an independent risk factor for injury in collegiate athletes. In summary, although the discrepant results across studies can be potentially explained by the numerous methodological differences, the multifactorial nature of injuries cannot be underestimated. Previous reports have identified numerous extrinsic and intrinsic risk factors associated with injury and thus simplifying injury risk to sleep alone may not be feasible (32). Although the major strength of our study was that we captured both subjective and objective sleep metrics in our cohort, we did not have the appropriate means to evaluate biomechanical movement patterns, nutrition status, self-esteem, training load, and stress, all of which have been identified as factors related to athletic injury. Consequently, future prospective studies that measure sleep, in combination with other established risk factors, and its relation to injury are warranted.
This study has several limitations. First, self-report sleep characteristics were only provided before the start of the season. It is likely that these scores reflect sleep patterns specific to off-season and preseason periods only and are not consistent with the sleep behaviors of athletes during the season. Nonetheless, our finding that roughly 40% of athletes self-reported sleeping less than the minimum recommended duration for young adults is noteworthy. Furthermore, we expect that this percentage would likely increase during the competitive season as the time demands associated with in-season competition and academic coursework are raised. Second, using subjective questionnaires at 1 time point only requires subjects to correctly estimate their sleep behaviors to provide accurate responses regarding sleep quality and quantity. It is reasonable to suggest that if subjective sleep had also been assessed with a daily sleep diary, responses would have been more accurate and correlated with objective sleep measures. Third, our investigation of objective sleep characteristics was limited by our finding of variable compliance from athletes instructed to wear the wrist actigraphs. The average number of available days, or days an athlete had the watch in their possession to wear to record a major nighttime sleep episode, was 56.6 ± 17.2 days (range, 18–92 days). Of the 94 subjects in this study, objective sleep data were obtained from 88 athletes due to unworn, damaged, or lost actigraphs. The average number of days worn, or days an athlete actually wore the watch during a major nighttime sleep episode, was 29.3 ± 17.1 days (range, 0–65 days). Initially, athletes were instructed to wear the actigraph at all times. However, because of actigraph damage during training, practice, and games, the study investigators adapted the original instructions partially through the season to allow subjects the option to not wear the actigraphs during football-related activities. This modification likely led to decreased compliance as athletes may have been more inclined to not wear the actigraphs for extended periods of time. Importantly, we acknowledge that low compliance could skew the interpretation of the actigraph data such that there may have not been enough data to meet standards of predictive reporting associated with actigraphy. Traditionally, at least 3–7 consecutive days are needed to represent an individual's habitual sleeping behavior. However, when analyzing the objective sleep metrics with individuals having low compliance (<7 days of sleep) removed, objective sleep duration variables were similar. Moving forward, when assessing sleep in college athletes, frequent check-ins for compliance and intervention adherence would be advised. Finally, our analysis of injury data was retrospective in nature, and we used an injury definition that included time loss as a criterion. Although using a more broad injury definition would have resulted in an increased the number of “injured” subjects in our analyses, we chose our definition to be consistent with previous injury prevention studies in collegiate populations (21).
In conclusion, the results from this study indicate a potential need for clinician consultation with athletes identified as having clinically relevant values on sleep screening questionnaires before the start of the season. Subjective sleep indices alone may not provide an accurate picture on overall sleep characteristics for athletes such that athletes may subjectively overreport the amount of sleep they obtain. When implementing sleep-behavioral (e.g., measuring sleep objectively with actigraphy or prescribing sleep hygiene interventions) monitoring or interventions, frequent checking and verification of adherence and compliance may be needed. Finally, findings from the current study suggest that sleep measured subjectively before the start of a competitive season or objectively throughout the season may not be associated with elevated injury risk in collegiate football players.
As the importance of sleep becomes more relevant for athletes, coaches, athletic trainers, and strength and conditioning professionals, special considerations should be taken when assessing and interpreting sleep characteristics and implementing sleep interventions in college athletes. Sleep screening assessments using validated sleep questionnaires may only highlight an already known issue of poor sleep hygiene practices in both elite athletes and college students. Regardless of clinical threshold criteria for sleep disorder risk, roughly 40% of this population self-reported that they did not attain the recommended amount of sleep for young adults, let alone the suggested amount for athletes. Alarmingly, this percentage (92.8%) was even higher when sleep was measured objectively through actigraphy. Taken together, these findings suggest that this population would benefit from sleep hygiene education and sleep intervention implementation. When working with the collegiate athlete population, as compliance may be an issue, it may be helpful for researchers and members of the sports medicine team (athletic trainers and strength and conditioning professionals) to provide ample oversight of both staff and athletes to maintain accountability and effective implementation of assessments and interventions. Previous research has demonstrated sleep measures to be associated with elevated injury risk in adolescent athletes (24,33,34). To the best of our knowledge, this study is only the second to examine the relationship between sleep metrics and injury in collegiate athletes. Our findings suggest that neither sleep quantity nor quality is associated with injury risk in collegiate football players. Despite these results, the authors support the promotion of proper sleep hygiene in athletic populations as poor sleep habits may influence a number of other factors related to injury risk.
The authors acknowledge the leadership of the Department of Athletics and the Dean of the College of Health Professions, for unwavering support of the TRACS. The authors also thank Coach Ambrose and the members of the football team who participated in this study. The authors have no conflicts of interest to disclose. The results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association. Support for this study came from the Military Operational Medicine Research Program (MOMRP). Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70-25.
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