Athletic Training and Sports Health Care

Original Research 

The Association of Sport Specialization, Overuse Injury, and Travel With Daytime Sleepiness in Youth Athletes

Eric G. Post, PhD, ATC; Stephanie M. Trigsted, PhD, ATC; Daniel A. Schaefer, MS, CSCS; Lisa A. Cadmus-Bertram, PhD; Andrew M. Watson, MD, MS; Timothy A. McGuine, PhD, ATC; M. Alison Brooks, MD, MPH; David R. Bell, PhD, ATC

Abstract

Purpose:

To examine the association of sport specialization, overuse injury, and regular travel with daytime sleepiness in youth club sport athletes.

Methods:

A total of 647 youth athletes (63.5% female; age: 14.0 ± 1.5 years) completed an anonymous questionnaire assessing sport specialization status, overuse injury history, travel, and daytime sleepiness. Sport specialization was classified as low, moderate, or high. Daytime sleepiness was assessed using the Pediatric Daytime Sleepiness Scale (PDSS).

Results:

High levels of specialization were associated with higher PDSS scores compared to low specialization (16.1 ± 0.5 vs 14.6 ± 0.6, P = .04). Athletes with an overuse injury in the previous year had significantly higher PDSS scores compared to uninjured athletes (16.3 ± 0.7 vs 14.2 ± 0.4, P = .001). Athletes who traveled regularly for their sport had higher PDSS scores than athletes who did not travel regularly (16.0 ± 0.5 vs 14.5 ± 0.5, P = .003).

Conclusions:

Parents, coaches, and medical providers involved in youth sports should be aware of the potential association between sport specialization and increased daytime sleepiness.

[Athletic Training & Sports Health Care. 201X;X(X):XX–XX.]

Abstract

Purpose:

To examine the association of sport specialization, overuse injury, and regular travel with daytime sleepiness in youth club sport athletes.

Methods:

A total of 647 youth athletes (63.5% female; age: 14.0 ± 1.5 years) completed an anonymous questionnaire assessing sport specialization status, overuse injury history, travel, and daytime sleepiness. Sport specialization was classified as low, moderate, or high. Daytime sleepiness was assessed using the Pediatric Daytime Sleepiness Scale (PDSS).

Results:

High levels of specialization were associated with higher PDSS scores compared to low specialization (16.1 ± 0.5 vs 14.6 ± 0.6, P = .04). Athletes with an overuse injury in the previous year had significantly higher PDSS scores compared to uninjured athletes (16.3 ± 0.7 vs 14.2 ± 0.4, P = .001). Athletes who traveled regularly for their sport had higher PDSS scores than athletes who did not travel regularly (16.0 ± 0.5 vs 14.5 ± 0.5, P = .003).

Conclusions:

Parents, coaches, and medical providers involved in youth sports should be aware of the potential association between sport specialization and increased daytime sleepiness.

[Athletic Training & Sports Health Care. 201X;X(X):XX–XX.]

Youth sports is a rapidly growing industry, with an estimated 15 billion dollars being spent in 2017, a growth of more than 50% since 2010.1 Despite this rapid increase in the youth sport market, there is an alarming trend of increased obesity and decreased physical activity in children.2 One potential explanation for this decrease in physical activity despite the rapid increase in the youth sport market could be the encouragement of youth athletes to specialize in a single sport to participate on year-round extramural club teams, which often require significant fees and expenses to join and travel to compete at local and regional tournaments with other club teams.3,4 This may price a large population of potential participants out of physical activity while providing excessive sport volume to the youth athletes who are able to join those teams.5 Recent research has found the prevalence of specialization to range between 13% and 37%, depending on the specific sample of youth athletes.6–10 Specialization has been widely established as a risk factor for overuse injury in a variety of youth athlete populations.3,6–10 However, potential non-physical consequences of specialization have not been examined.

Decreases in the amount or quality of sleep have been shown to have consequences for cognitive function and a variety of aspects of health and well-being.11–15 However, chronic sleep loss is increasingly common in the adolescent population because most high school students are currently failing to meet sleep quantity recommendations.13 The Pediatric Daytime Sleepiness Scale (PDSS) was developed to provide an easy to use measure of daytime sleepiness among youth populations.13 Daytime sleepiness is highly correlated with decreased sleep duration and thus the PDSS serves as a quick, inexpensive, and accurate measure of impaired sleep in children and adolescents.13 Using this measure, high levels of daytime sleepiness have been found to be associated with poor academic performance, low school enjoyment, low total sleep time, and increased rates of illness and absenteeism in multiple samples of student populations.12,13

The highly organized and travel-intensive nature of modern youth sports may make it even more difficult for youth athletes to get sufficient sleep. Highly specialized youth athletes participate in organized athletics for more months out of the year and more hours per week than their non-specialized counterparts,8 and these additional time commitments may cause specialized youth athletes to delay or reduce their sleep to complete normal academic and social commitments. Additionally, sport specialization often involves participation on non-scholastic club teams that frequently travel to compete at “elite” or “select” tournaments or competitions, which may result in frequent disruptions in sleep environment and routine among the youth athletes on those teams. To date, research in the area of sport specialization has focused on the potential consequences of specialization and year-round sport participation on injury, but has not examined the potential effects of specialization on other aspects of well-being in youth athletes.

Therefore, the purpose of this study was to examine the relationship between sport specialization and daytime sleepiness in youth club sport athletes. We hypothesized that high levels of specialization would be associated with higher levels of daytime sleepiness, even after controlling for potential covariates such as sex, age, previous injury history, and regular travel out of state. A secondary purpose was to examine the potential independent associations of overuse injury and regular sport travel with daytime sleepiness. We hypothesized that youth athletes who travel out of state regularly for their sport or have sustained an overuse injury in the past year would display greater sleepiness than athletes who do not travel regularly for their sport or had not had an overuse injury in the past year.

Methods

Participants

The Institutional Review Board at the University of Wisconsin–Madison approved this study and procedures. Youth club sport athletes were recruited to complete an anonymous questionnaire. To be included in the study, participants were required to be between 12 and 18 years of age and active on a youth sports team in the past 12 months. Participants were recruited at club team tournaments, competitions, and events around the state of Wisconsin. These tournaments, competitions, and practices took place at various local venues that frequently host large events for non-interscholastic club teams. Because the questionnaire was anonymous, parents and athletes were provided an information sheet describing the study before providing oral consent to participate. For participants who were minors (younger than 18 years), oral consent was obtained from both the athlete and a parent. Participants who were 18 years old were required to provide oral consent but did not need a parent to also provide consent.

Questionnaire

Youth sport athletes were invited to complete a self-administered and anonymous questionnaire that consisted of demographics, sport specialization status, daytime sleepiness, regular travel, and sport-related injury history in the previous 12 months. Questionnaires were completed on-site by the youth athletes at the tournament, competition, or practice and took approximately 15 minutes to complete. Sport specialization status was determined using a widely used 3-point specialization scale.2,6,8,10,16 These three questions are based on the definition of sport specialization as “year-round intensive training in a single sport at the exclusion of other sports”10 and were as follows: “Have you quit other sports to focus on one sport?,” “Do you train more than 8 months out of the year in one sport?,” and “Do you consider your primary sport more important than your other sports?” During data analysis, a categorical classification system was used to assess the sport specialization questions (yes = 1, no = 0), with a score of 3 considered high specialization, a score of 2 considered moderate specialization, and a score of 0 or 1 considered low specialization.

Daytime sleepiness was assessed using the PDSS, a reliable and previously validated tool that provides a score of daytime sleepiness (0 to 32) with higher scores indicating increased sleepiness.13 Regular travel out of state was determined by a yes/no response to the following question: “Do you regularly travel out of state for your primary sport?” Overuse injuries were defined as gradual-onset injuries occurring during sports in the previous 12 months and requiring the athlete to seek medical care. Additionally, each questionnaire was reviewed individually with the participant by an athletic trainer to make sure injuries and sport participation were accurately recalled, correctly classified, and met the criteria above.

The University of Wisconsin Survey Center, an internationally recognized organization in the field of survey design and best practices, helped to design the format of the questionnaire and develop the individual questions to meet best practices in survey design. Specifically, the Survey Center held multiple meetings with the study authors to determine the goals of the project and then conducted several rounds of revisions on the authors' original survey draft to accomplish two major goals: clarity (rewording of questions to ensure they were easily understandable, free of jargon, and of the appropriate reading level for this age group) and ease of use (consistent formatting throughout the questionnaire, arrows and other clues to direct the participant to the next section or question, and eliminating redundant questions to improve response time).

Statistical Analysis

Data were summarized by means and standard deviations, frequencies and proportions (%), and least-square means and standard errors. Univariate analyses (one-way analysis of variance and independent t tests) were used to examine differences in PDSS score based on the independent variables of interest (specialization status, overuse injury history, and regular travel) and potential covariates (sex and day of week that the questionnaire was completed). Assumptions of normality for univariate analyses were determined via visual inspection of histograms and calculation of skewness/kurtosis values for both overall sample PDSS score and PDSS score for each level of the variables of interest.

Multivariable regression analysis was used, with PDSS score as the dependent variable and specialization status, overuse injury, regular travel, sex, age, and day of the week (weekday vs weekend) entered as variables in the model. Separate least-square mean estimates were calculated for each variable of interest (specialization status, overuse injury history, and regular travel), adjusting for all other variables. Age was entered into the model as a covariate because it has been previously shown to be associated with both specialization status3,8 and PDSS score.12,13,17 Day of the week (weekend vs weekday) and sex were identified as additional covariates during the univariate analysis, which revealed significantly greater PDSS scores among athletes who completed their questionnaire on a weekend and among female athletes, respectively.

The multivariable regression model was assessed to determine whether it met the assumptions of linear regression using the Global Validation of Linear Models Assumptions package18 in R and via visual inspection of quantile-quantile and residual plots. Post-hoc pairwise Tukey's HSD tests were used to compare least-squares means estimates of PDSS scores between levels of the three independent variables (specialization status, overuse injury history, and regular travel). Statistical significance was set at a two-sided a priori P value of less than .05, and all analyses were performed in R statistical software (R Foundation for Statistical Computing, Vienna, Austria).

Results

A total of 647 youth club sport athletes (63.5% female; age: 14.0 ± 1.5 years) completed the questionnaire. Participant demographics and the frequency of primary sport participation can be found in Table 1. Overall, 38.9% (n = 252) of athletes were considered highly specialized, 15.1% (n = 98) had sustained an overuse injury in the previous 12 months, and 40.8% (n = 263) reported regularly traveling out of state for their sport competitions. The mean PDSS score for the entire sample was 15.3 ± 6.2.

Participant Demographics

Table 1:

Participant Demographics

Results of the univariate analyses are presented in Table 2. Significant differences in PDSS scores were observed for all three independent variables. Highly specialized athletes had greater PDSS scores than both moderate and low specialization athletes. There were no differences in PDSS scores between low and moderately specialized athletes. Athletes who reported an overuse injury in the previous 12 months had greater PDSS scores than athletes without a history of overuse injury. Athletes who reported traveling regularly out of state for their primary sport had greater PDSS scores than athletes who did not travel regularly. Small to medium effect sizes were observed for the univariate differences in PDSS scores for specialization status, overuse injury, regular travel, sex, and day of week.

Results of Univariate Analysis

Table 2:

Results of Univariate Analysis

Least-square means and standard error estimates from the multivariable regression analyses are presented in Table 3. Highly specialized athletes had significantly higher PDSS scores compared to low specialization athletes (16.1 ± 0.5 vs 14.6 ± 0.6, P = .04), even after adjusting for sex, age, previous overuse injury, regular travel out of state, and day of week (weekend vs week-day) (Figure 1). There were no significant differences in estimated PDSS scores between moderate and low specialization athletes (P = .68) or between moderate and high specialization athletes (P = .17). Athletes who had sustained an overuse injury in the previous year had significantly higher PDSS scores compared to uninjured athletes (16.3 ± 0.7 vs 14.2 ± 0.4, P = .001), even after adjusting for all potential covariates. Similarly, athletes who reported traveling regularly for their sport reported higher PDSS scores than athletes who did not travel regularly (16.0 ± 0.5 vs 14.5 ± 0.5, P = .003).

Results of Multivariable Regression Analyses

Table 3:

Results of Multivariable Regression Analyses

Differences in Pediatric Daytime Sleepiness Scale (PDSS) score by specialization category.

Figure 1.

Differences in Pediatric Daytime Sleepiness Scale (PDSS) score by specialization category.

Discussion

The main finding of this study was that high levels of specialization were associated with greater levels of daytime sleepiness compared to low specialization. This difference was significant even after adjusting for a variety of factors that have been previously shown to influence both specialization rates and PDSS scores. Because a minimal clinically important difference for the PDSS has not been established and the effect sizes we observed were small to moderate, it is difficult to determine whether the differences we observed are clinically meaningful or simply statistically significant. However, Meyer et al.19 recently attempted to establish PDSS score cut-points for predicting various negative conditions and found a score of greater than 15 to be a predictive cut-point for poor sleep quality. Using this cut-point, athletes in this study who were classified as highly specialized, reported a previous overuse injury, or traveled regularly for sport would all be at increased risk of poor sleep quality. Multiple previous studies have consistently found an association between high levels of specialization and increased risk of injury, and overuse-type injuries in particular.7–10 Additionally, multiple position statements have warned that specialization may also be linked to consequences such as psychological burnout, but until this study there has been little to no data examining the effects of specialization on aspects of well-being other than injury.20–22 The results of this study suggest that sport specialization has potential consequences for the well-being of youth athletes ranging beyond just physical injury. However, caution should be observed when interpreting these results as more than preliminary findings due to the cross-sectional nature of this study and the small to moderate effect sizes observed.

Injury history was another factor found to be independently associated with daytime sleepiness. Specifically, athletes who reported an overuse injury in the previous year reported higher levels of daytime sleepiness compared to athletes with no history of overuse injury. Our findings are in agreement with previous research, which has observed sleep to be both a risk factor and a consequence of injury.23–26 Previous prospective and retrospective studies of adolescent athletes have found lack of sleep to be a risk factor for injury.23,24 Additionally, impaired sleep and daytime sleepiness has been observed as a consequence of certain types of injury, specifically concussion.25,26 More broadly, there is significant evidence that musculoskeletal injuries have a negative effect on quality of life, one aspect of which is sleep.27–29 To our knowledge, the current study is the first to find an association between a measure of impaired sleep and overuse injuries in particular.

Athletes who reported traveling regularly out of state for their primary sport reported increased levels of daytime sleepiness compared to athletes who did not travel regularly. Modern youth sport has transitioned away from neighborhood and community-based recreational teams toward a model of highly competitive “elite” or “select” club teams that travel frequently to compete against other club teams at regional showcases and tournaments.30 This travel often occurs on the weekends, when adolescents would typically be “catching up” on sleep that they did not get during the week. Young athletes who travel to these tournaments and showcases often stay at hotels near the tournament site, and thus their sleep may be impaired by both sleeping in an unfamiliar location and the disruption of their typical weekend sleep routine. There has been a significant amount of research on the topic of travel in elite athletics, with long-distance and interstate travel shown to impair both sleep and sport performance.31–33 Despite the significant research devoted to the effects of travel on sleep in professional and elite sport settings, there has been little to no examination of travel-related sleep disturbance in youth athletes. Additionally, although much of the previous research has focused on the effects of long-haul travel across multiple time zones, we focused on regional travel across state lines because we believed this better represented the most common type of sport-related travel among this population.

Impaired sleep has been shown to have a variety of negative effects in both adolescent and athletic populations. Raniti et al.14 found that poor sleep quality and decreased sleep were both significant contributors to the increase in depressive symptoms seen during adolescence. Multiple studies have shown a decreased risk of adolescent motor vehicle accidents in school districts with later high school start times and increased sleep duration among the students in that district.34,35 Decreased nighttime sleep has also been linked with a variety of poor academic outcomes, such as decreased academic achievement, performance, and alertness during class.12,13,36,37 High levels of daytime sleepiness have been found to be associated with poor academic performance, low school enjoyment, low total sleep time, and increased rates of illness and absenteeism in multiple samples of youth student populations.12,13 Finally, a recent meta-analysis of 11 longitudinal studies with more than 24,000 total participants concluded that adolescents with short sleep durations were more than twice as likely to be overweight or obese than adolescents who slept for longer durations.15 In light of these consequences, further longitudinal examination is needed to determine the true nature and magnitude of the associations between year-round, highly specialized youth sport participation and increased daytime sleepiness that were observed in this study.

This study has several important limitations to note. First, there is no established minimal clinically important difference for the PDSS and the effect sizes observed in this study were small to moderate.38 Therefore, although our results may be statistically significant, it is difficult to determine whether the differences we observed are clinically meaningful. However, highly specialized athletes, athletes with a previous overuse injury, and athletes who traveled regularly for sport all displayed mean PDSS scores greater than 15, which has been previously established as a cut-point for predicting poor sleep quality.19 Future work should attempt to establish measures of minimal clinically important difference for the PDSS to determine clinically relevant differences between populations or treatments. Second, as mentioned earlier, the cross-sectional study design limits the conclusions that can be drawn regarding causality. For example, it is possible that sustaining an injury impaired sleep, but it could also be that impaired sleep predisposed an athlete to injury. Additionally, overuse injury was defined as having happened in the previous year, so athletes may not have been symptomatic at the time of the questionnaire, further limiting any conclusions about cause and effect between sleepiness and injury. Finally, participants in this study consisted of a convenience sample recruited from local sport tournaments and events. Without a randomly selected sample, it is possible that our study population was biased in some way with regard to the variables of interest. We attempted to adjust for various factors that were not evenly distributed (age, sex) in an effort to control for the effects of a non-random sample. Additionally, the mean PDSS score for the entire sample was 15.3 ± 6.2, which is similar to previous cross-sectional samples of American adolescents reported by Drake et al.13 (15.3 ± 6.2) and Polos et al.17 (16.0 ± 6.1). The similarity in the overall mean PDSS score observed in this study to previous samples of American adolescents indicates that our sample was typical in regard to daytime sleepiness.13,17

Implications for Clinical Practice

High levels of specialization, overuse injury history in the previous year, and regular travel out of state for sport were all associated with higher levels of daytime sleepiness among youth athletes. Although the overall mean PDSS score resembled previous samples of American adolescents, the mean PDSS scores of specialized athletes, athletes who reported an overuse injury, and athletes who traveled regularly were all greater than previously established cut-offs for poor sleep quality. Youth athletes, parents, and other youth sports stakeholders should be aware of the potential consequences beyond just physical injury that may exist as a result of the intense demands of highly specialized sport participation.

References

  1. Gregory S. How kids' sports became a $15 billion industry. TIME Magazine. 2017:September4.
  2. Pasulka J, Jayanthi N, McCann A, Dugas LR, LaBella C. Specialization patterns across various youth sports and relationship to injury risk. Phys Sportsmed. 2017;45:344–352. doi:10.1080/00913847.2017.1313077 [CrossRef]
  3. Post EG, Bell DR, Trigsted SM, et al. Association of competition volume, club sports, and sport specialization with sex and lower extremity injury history in high school athletes. Sports Health. 2017;9:518–523. doi:10.1177/1941738117714160 [CrossRef]
  4. Post EG, Green NE, Schaefer DA, et al. Socioeconomic status of parents with children participating on youth club sport teams. Phys Ther Sport. 2018;32:126–132. doi:10.1016/j.ptsp.2018.05.014 [CrossRef]
  5. Jayanthi NA, Holt DB, LaBella CR, Dugas LR. Socioeconomic factors for sports specialization and injury in youth athletes. Sports Health. 2018;10:303–310. doi:10.1177/1941738118778510 [CrossRef]
  6. Bell DR, Post EG, Trigsted SM, Hetzel S, McGuine TA, Brooks MA. Prevalence of sport specialization in high school athletics: a 1-year observational study. Am J Sports Med. 2016;44:1469–1474. doi:10.1177/0363546516629943 [CrossRef]
  7. McGuine TA, Post EG, Hetzel SJ, Brooks MA, Trigsted S, Bell DR. A prospective study on the effect of sport specialization on lower extremity injury rates in high school athletes. Am J Sports Med. 2017;45:2706–2712. doi:10.1177/0363546517710213 [CrossRef]
  8. Post EG, Trigsted SM, Riekena JW, et al. The association of sport specialization and training volume with injury history in youth athletes. Am J Sports Med. 2017;45:1405–1412. doi:10.1177/0363546517690848 [CrossRef]
  9. Hall R, Barber Foss K, Hewett TE, Myer GD. Sport specialization's association with an increased risk of developing anterior knee pain in adolescent female athletes. J Sport Rehabil. 2015;24:31–35. doi:10.1123/jsr.2013-0101 [CrossRef]
  10. Jayanthi NA, Labella C, Fischer D, Pasulka J, Dugas LR. Sports specialized intensive training and the risk of injury in young athletes: a clinical case-control study. Am J Sports Med. 2015;43:794–801. doi:10.1177/0363546514567298 [CrossRef]
  11. Mihalik JP, Lengas E, Register-Mihalik JK, Oyama S, Begalle RL, Guskiewicz KM. The effects of sleep quality and sleep quantity on concussion baseline assessment. Clin J Sport Med. 2013;23:343–348. doi:10.1097/JSM.0b013e318295a834 [CrossRef]
  12. Perez-Chada D, Perez-Lloret S, Videla AJ, et al. Sleep disordered breathing and daytime sleepiness are associated with poor academic performance in teenagers: a study using the Pediatric Daytime Sleepiness Scale (PDSS). Sleep. 2007;30:1698–1703. doi:10.1093/sleep/30.12.1698 [CrossRef]
  13. Drake C, Nickel C, Burduvali E, Roth T, Jefferson C, Pietro B. The pediatric daytime sleepiness scale (PDSS): sleep habits and school outcomes in middle-school children. Sleep. 2003;26:455–458.
  14. Raniti MB, Allen NB, Schwartz O, et al. Sleep duration and sleep quality: associations with depressive symptoms across adolescence. Behav Sleep Med. 2017;15:198–215. doi:10.1080/15402002.2015.1120198 [CrossRef]
  15. Fatima Y, Doi SAR, Mamun AA. Longitudinal impact of sleep on overweight and obesity in children and adolescents: a systematic review and bias-adjusted meta-analysis. Obes Rev. 2015;16:137–149. doi:10.1111/obr.12245 [CrossRef]
  16. Post EG, Thein-Nissenbaum JM, Stiffler MR, et al. High school sport specialization patterns of current Division I athletes. Sports Health. 2016;9:148–153. doi:10.1177/1941738116675455 [CrossRef]
  17. Polos PG, Bhat S, Gupta D, et al. The impact of Sleep Time-Related Information and Communication Technology (STRICT) on sleep patterns and daytime functioning in American adolescents. J Adolesc. 2015;44:232–244. doi:10.1016/j.adolescence.2015.08.002 [CrossRef]
  18. Peña EA, Slate EH. Global validation of linear model assumptions. J Am Stat Assoc. 2006;101:341. doi:10.1198/016214505000000637 [CrossRef]
  19. Meyer C, Barbosa DG, Junior GJF, et al. Proposal of cutoff points for pediatric daytime sleepiness scale to identify excessive daytime sleepiness. Chronobiol Int. 2018;35:303–311. doi:10.1080/07420528.2017.1400980 [CrossRef]
  20. LaPrade RF, Agel J, Baker J, et al. AOSSM Early Sport Specialization Consensus Statement. Orthop J Sport Med. 2016;4:1–8.
  21. Smucny M, Parikh SN, Pandya N. Consequences of single sport specialization in the pediatric and adolescent athlete. Orthop Clin North Am. 2015;46:249–258. doi:10.1016/j.ocl.2014.11.004 [CrossRef]
  22. Bergeron MF, Mountjoy M, Armstrong N, et al. International Olympic Committee consensus statement on youth athletic development. Br J Sports Med. 2015;49:843–851. doi:10.1136/bjsports-2015-094962 [CrossRef]
  23. Milewski MD, Skaggs DL, Bishop GA, et al. Chronic lack of sleep is associated with increased sports injuries in adolescent athletes. J Pediatr Orthop. 2014;34:129–133. doi:10.1097/BPO.0000000000000151 [CrossRef]
  24. von Rosen P, Frohm A, Kottorp A, Fridén C, Heijne A. Multiple factors explain injury risk in adolescent elite athletes: applying a biopsychosocial perspective. Scand J Med Sci Sports. 2017;27:2059–2069. doi:10.1111/sms.12855 [CrossRef]
  25. Davis-Hayes C, Gossett JD, Levine WN, et al. Sex-specific outcomes and predictors of concussion recovery. J Am Acad Orthop Surg. 2017;25:818–828. doi:10.5435/JAAOS-D-17-00276 [CrossRef]
  26. Ponsford JL, Parcell DL, Sinclair KL, Roper M, Rajaratnam SMW. Changes in sleep patterns following traumatic brain injury: a controlled study. Neurorehabil Neural Repair. 2013;27:613–621. doi:10.1177/1545968313481283 [CrossRef]
  27. Cho C-H, Song K-S, Hwang I, Warner JJP. Does rotator cuff repair improve psychologic status and quality of life in patients with rotator cuff tear?Clin Orthop Relat Res. 2015;473:3494–3500. doi:10.1007/s11999-015-4258-1 [CrossRef]
  28. McGuine TA, Winterstein AP, Carr K, Hetzel S. Changes in health-related quality of life and knee function after knee injury in young female athletes. Orthop J Sport Med. 2014;2:1–7.
  29. Mcguine TA, Winterstein A, Carr K, Hetzel S, Scott J. Changes in self-reported knee function and health-related quality of life after knee injury in female athletes. Clin J Sport Med. 2012;22:334–340. doi:10.1097/JSM.0b013e318257a40b [CrossRef]
  30. Malina RM. Early sport specialization: roots, effectiveness, risks. Curr Sports Med Rep. 2010;9:364–371. doi:10.1249/JSR.0b013e3181fe3166 [CrossRef]
  31. Leatherwood WE, Dragoo JL. Effect of airline travel on performance: a review of the literature. Br J Sports Med. 2013;47:561–567. doi:10.1136/bjsports-2012-091449 [CrossRef]
  32. Gupta L, Morgan K, Gilchrist S. Does elite sport degrade sleep quality? A systematic review. Sports Med. 2017;47:1317–1333. doi:10.1007/s40279-016-0650-6 [CrossRef]
  33. Nédélec M, Halson S, Abaidia AE, Ahmaidi S, Dupont G. Stress, sleep and recovery in elite soccer: a critical review of the literature. Sports Med. 2015;45:1387–1400. doi:10.1007/s40279-015-0358-z [CrossRef]
  34. Vorona RD, Szklo-Coxe M, Wu A, Dubik M, Zhao Y, Ware JC. Dissimilar teen crash rates in two neighboring southeastern Virginia cities with different high school start times. J Clin Sleep Med. 2011;7:145–151.
  35. Danner F, Phillips B. Adolescent sleep, school start times, and teen motor vehicle crashes. J Clin Sleep Med. 2008;4:533–535.
  36. Curcio G, Ferrara M, De Gennaro L. Sleep loss, learning capacity and academic performance. Sleep Med Rev. 2006;10:323–337. doi:10.1016/j.smrv.2005.11.001 [CrossRef]
  37. Wolfson AR, Carskadon MA. Sleep schedules and daytime functioning in adolescents. Child Dev. 1998;69:875–887. doi:10.1111/j.1467-8624.1998.tb06149.x [CrossRef]
  38. Meyer C, Ferrari GJ, Barbosa DG, Andrade RD, Pelegrini A, Felden ÉPG. Analysis of daytime sleepiness in adolescents by the Pediatric Daytime Sleepiness Scale: a systematic review. Rev Paul Pediatr. 2017;35:351–360. doi:10.1590/1984-0462/;2017;35;3;00015 [CrossRef]

Participant Demographics

VariableN%
Sex
  Female41163.5
  Male23636.5
Age (y)
  1210916.9
  1317426.9
  1413621.0
  1510716.5
  167812.1
  17284.3
  18152.3
Specialization status
  Low17126.4
  Moderate22434.6
  High25239.0
Overuse injury in past 12 months
  Yes9815.1
  No54984.9
Travel regularly out of state
  Yes26340.7
  No38259.0
  N/A20.3
Primary sport
  Baseball203.1
  Basketball14722.7
  Cheer/dance20.3
  Cross-country50.8
  Football233.6
  Gymnastics10.2
  Ice hockey487.4
  Lacrosse10.2
  Soccer10916.8
  Softball172.6
  Swimming111.7
  Tennis10.2
  Track71.1
  Volleyball22634.9
  Wrestling162.4
  Nonea132.0

Results of Univariate Analysis

VariablePDSS Score (Mean ± SD)Effect Sizea (95% CI)P
Specialization status0.39 (0.20 to 0.59)< .001
  Low13.99 ± 5.9
  Moderate14.99 ± 6.2
  High16.37 ± 6.2b,c
Overuse injury in past 12 months0.45 (0.23 to 0.66)< .001
  Yes17.75 ± 6.7
  No14.82 ± 6.0
Travel regularly out of state0.33 (0.17 to 0.49)< .001
  Yes16.43 ± 6.2
  No14.43 ± 6.0
Sex0.18 (0.02 to 0.34).027
  Female15.65 ± 6.4
  Male14.58 ± 5.6
Day of week that survey was completed0.31 (0.09 to 0.53).006
  Weekday13.65 ± 6.1
  Weekend15.53 ± 6.1

Results of Multivariable Regression Analyses

VariablePDSS ScoreP

Least-Squares Mean EstimateaStandard Error Estimate
Low specialization14.60.60.68b
Moderate specialization15.10.52.17c
High specialization16.10.50.04d
Overuse injury in previous 12 months–no14.2.35
Overuse injury in previous 12 months–yes16.30.67.001
Travel regularly out of state for primary sport–no14.50.50
Travel regularly out of state for primary sport–yes16.00.47.002
Authors

From the School of Exercise and Nutritional Sciences, San Diego State University, San Diego, California (EGP); and the Departments of Kinesiology (SMT, DAS, LAC-B, DRB) and Orthopedics and Rehabilitation (AMW, TAM, MAB, DRB), University of Wisconsin–Madison, Madison, Wisconsin.

Supported by a grant that was co-funded by the American Medical Society for Sports Medicine and American College of Sports Medicine. Dr. Cadmus-Bertram's time is supported by NIH# 1K07CA178870.

The authors have no financial or proprietary interest in the materials presented herein.

Correspondence: Eric G. Post, PhD, ATC, ENS Building, Room 309, 5500 Campanile Drive, San Diego State University, San Diego, CA 92182. E-mail: epost@sdsu.edu

Received: September 05, 2018
Accepted: December 05, 2018
Posted Online: March 26, 2019

10.3928/19425864-20190219-01

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