INTRODUCTION
Youth sport participation is increasing in North America1 and Europe.2,3 Recent studies indicate that more than 44 million youth athletes are participating in some form of organized sports in the United States alone.4,5 In addition, the amount of NCAA athletes set an all-time record and surpassed 520,000 in the 2021 to 2022 school year.6 It is demonstrated that there are many advantages to youth sport participation, including physical improvement7 and psychosocial growth.8 More precisely, the advantages for youth involve motor skill acquisition,9 cognitive progress,10 and psychological development.11 Hence, abundant advantages exist for youth to participate in organized sports.
When youth sport participation turns into intensive, early-age sport specialization, however, there is cause for concern.12,13 The potential disadvantages span a broad spectrum of topic areas. The common themes include overtraining,14,15 dropout,16 burnout,17 and injuries.18,19 Among the listed categories, injuries can be further broken down into additional subcategories. For instance, studies have explored the incidence of recurrent,20 overuse,21,22 serious overuse,23 acute,24 and nonacute25 injuries. This specific study investigates a handful of the above-referenced subcategories with an emphasis on the incidence of upper extremity injuries (UEIs)26,27 and lower extremity injuries (LEIs).28,29 Before concentrating on methods with attention to UEIs and LEIs, it is important to put forth a working definition for sport specialization.
At this point in time, there is not a consistent definition of sport specialization in the literature.30 For the purpose of this research initiative, sport specialization was defined as “year-round intensive training in a single sport to the exclusion of other sports.”31,32 In short, the above-mentioned definition of sport specialization can be divided into 3 components: (1) single-sport focus, (2) sport exclusion, and (3) year-round involvement. The collegiate athletes were then classified by specialization category based on their responses to a questionnaire. The precise aim of this study was to determine whether an association existed between early sport specialization and the incidence of UEIs and LEIs among collegiate athletes. Data were collected from a large sample of NCAA athletes through the Sport, Action, Finding, and Evaluation (SAFE) Consortium. Before undertaking this study, the SAFE investigators hypothesized that highly specialized athletes would have a higher incidence of UEIs and LEIs than those classified under low specialization.
METHODS
The SAFE Consortium is a multisite partnership between an assortment of colleges with the goal to better understand the effects of early sport specialization on athletes’ long-term health. For this particular initiative, a retrospective cohort design was selected due to the nature of the data required for principal analysis. This study’s protocol was approved by the Institutional Review Board at each site and the Human Research Protection Program, Office of Research Regulatory Support at Michigan State University (HRPP ID: 00007883). Participants provided informed consent before participation. All participants were rostered on a collegiate sports team. The SAFE investigators collected data from all schools in the consortium during the 2022 to 2023 school year. Colleges were chosen based on NCAA division classification (1 Division I, 1 Division II, and 1 Division III) to acquire a diverse sample of collegiate athletes from the NCAA’s 3 levels of competition.
Participants
Potential participants were recruited by the SAFE investigators and were provided with study information written by research program staff. All athletes who were rostered in an NCAA sport for the 2022 to 2023 school year were eligible for enrollment in the study. The inclusion criterion for statistical analysis was that each participant had completed the questionnaire in its entirety. Therefore, only incomplete evaluations were excluded from the study.
Questionnaire
All participants completed an anonymous questionnaire that contained a collection of both independent and interconnected items. Participants were asked to report demographic information, sport participation, specialization status, and injury history. Demographic information integrated questions regarding sex, age, high school size, class standing in college, and NCAA division classification. High school size was organized by total enrollment using a published continuum (1000 students = large).33 Average weekly time commitment was calculated in 3 ways. Participants were asked to report the number of hours per week spent in organized sports (team responsibilities), unorganized sports (free-time activities), and any physical education classes (academic events). These questions concentrated on sport participation before and after NCAA registration.
Specialization category was determined by 2 methods. First, participants had to self-identify as either multi-sport or single-sport athletes. Second, research program staff classified participants with an established 3-point scale that has been validated.34,35 The scale contained the following 3 questions: “Did you pick a main sport?” (single-sport focus), “Did you quit other sports to focus on a main sport?” (sport exclusion), and “Did you train more than 8 months of the year?” (year-round involvement). These questions emphasized sport participation before enrollment onto an NCAA roster. For each of these 3 questions, a “yes” response was counted as 1 point, and a “no” response was counted as 0 points (see Figure 1). Specialization category was then retrospectively assigned according to the total number of points each NCAA athlete accumulated (0-1 = low specialization, 2 = moderate specialization, and 3 = high specialization). Participants were also asked to identify a main sport and record their age at specialization.
Specialization scale.
Injury history incorporated questions concerning injury rate, injury location (head/neck, shoulder, elbow, hand/wrist, hip/pelvis/thigh, low back, knee, leg, or foot/ankle), injury type (contact, noncontact, overuse, or acute), reinjury rate, surgery rate, and number of missed days of sport participation due to an injury. Research program staff further assigned injury location to UEI and LEI clusters in preparation for statistical analysis. Participants were asked to document any injury before and after registration with the NCAA. While the full questionnaire has not been validated, its components have been used in recent studies.36,37
Statistical Analysis
Data were summarized by means and standard deviations (SDs), frequencies and proportions (%), medians and interquartile ranges (IQRs), and F-statistics. Models for specialization additionally controlled for hours per week spent in athletic commitments. The primary analysis was a comparison between intensity of sport specialization and likelihood of subsequent physical injury.
Research program staff further used χ2 tests to determine whether there were associations between groups of 2 categorical variables. ANOVA was used to assess for differences in average weekly time commitment between the 3 specialization categories. Significance was set a priori at P
RESULTS
Of the 240 athletes who started the questionnaire, 211 (92 male, 119 female) completed it in its entirety (average age = 19.98 ± 1.42) (see Figure 2). The majority of the athletes classified themselves as juniors (58 (27.5%)), followed by freshmen (55 (26.0%)), sophomores (50 (23.7%)), seniors (39 (18.5%)), and others (9 (4.3%)) (see Figure 3). Most athletes were from Division II (109 (51.6%)). The rest were distributed between Division I (66 (31.3%)) and Division III (36 (17.1%)). Specialization status per category was high (102 (48.3%)), moderate (69 (32.7%)), and low (40 (19.0%)) (see Table 1). Notably, athletes from Division I were more likely to be highly specialized than those from Division II (χ2 (1, N = 175) = 9.37, P = 0.002) and Division III (χ2 (1, N = 102) = 6.52, P = 0.01). Average age at specialization was calculated to be slightly above 14 years (14.29 ± 2.21).
Flow diagram.
Specialization category by class.
Athlete Characteristics
| N | % | |
| Sex | ||
| Male | 92 | 43.6 |
| Female | 119 | 56.4 |
| NCAA division classification | ||
| Division I | 66 | 31.3 |
| Division II | 109 | 51.6 |
| Division III | 36 | 17.1 |
| Specialization category | ||
| Low | 40 | 19.0 |
| Moderate | 69 | 32.7 |
| High | 102 | 48.3 |
Highly specialized athletes were more likely than low specialized athletes to report an injury of any kind (χ2 (1, N = 142) = 19.81, P 2 (1, N = 142) = 7.17, P = 0.007) and LEIs (χ2 (1, N = 142) = 4.11, P = 0.04) when compared with low specialized athletes. Moderate specialization was associated with a higher incidence of LEIs (χ2 (1, N = 109) = 4.69, P = 0.03), but not UEIs (χ2 (1, N = 109) = 3.78, P = 0.052), in contrast to low specialization. Markedly, there was no significant difference in the number of hours per week spent in athletic commitments between all 3 specializations (F(2, 208) = 0.90, P = 0.41).
Over a quarter (57 (27%)) underwent surgery to ameliorate an injury or reinjury. High specialization was associated with a greater likelihood of surgery to treat an injury when compared with low specialization (χ2 (1, N = 142) = 6.76, P = 0.009). However, moderate specialization was not more likely to be associated with surgical incidence when evaluated against low specialization (χ2 (1, N = 109) = 2.32, P = 0.13). Finally, return to play exhibited a rising trend with each advance in specialization category. For low specialized athletes, they missed 85 days, on average, of practice and competition. Similarly, athletes classified under moderate specialization were away for 110 days. Return to play for highly specialized athletes was the longest at 112 days.
DISCUSSION
To the knowledge of the SAFE investigators, this is the first retrospective cohort study to examine the relationship between intensity of sport specialization and risk of injury through a multicenter framework with a concentration on NCAA athletics. Recent retrospective cohort studies have explored the risk of UEIs and LEIs related to sport specialization; however, some involved only high schools, single colleges, or did not include surgical incidence or return to play.38,39
This study adds to the literature both by replicating past results in sports medicine and orthopedics and by providing new information specific to the rate of UEIs and LEIs, surgical incidence, sport participation, and return to play. When contrasted against low specialized athletes, high specialization was associated with a greater likelihood of UEIs and LEIs. Relatedly, highly specialized athletes were more likely to require surgery. Moderate specialization was not associated with a higher incidence of surgery. Furthermore, moderately specialized athletes were more likely to report LEIs but not UEIs. Overall, athletes classified as highly specialized were injured more often than the moderate and low categories. Importantly, there was no significant difference in the number of hours per week spent in athletic commitments across all 3 categories. Finally, return to play increased in an incremental manner from low toward high specialization.
The previously mentioned findings are sufficient to modify existing clinical practice by implementing recommendations at the developmental level of sport. Some specific recommendations relate to end user education, periodization, monitoring, and cross-training. Sports medicine professionals should educate athletes, parents, coaches, and athletic trainers about the impact of high specialization on UEIs, LEIs, and surgical incidence. They should also educate them on the benefits of periodization, which is the structuring of training and competition schedules to allow for peak performance at certain times while emphasizing periods of rest and recovery to prevent overuse injuries.40
In addition, athletes should be surveilled closely for signs of overuse injuries by scheduling regular checkups with sports medicine professionals and educating them on the importance of self-monitoring.41 Finally, athletes should also be strongly encouraged to participate in different sports or activities to engage other muscle groups and prevent overuse; cross-training may also be incorporated into their training regimens. Taken as a whole, these approaches form a robust basis for re-evaluating training practices and developing policies within the SAFE Consortium and beyond.
Limitations
The study has 2 main limitations. First, participants self-reported their own injuries. Second, the questionnaire asked participants about any injuries they sustained. Thus, the data are subject to recall bias. To reduce the potential for recall bias, athletic department personnel and research program staff were available to field any questions from participants as they completed the questionnaire. In an effort to mitigate the limitation of self-reported injuries, future studies could use athletes’ medical records to obtain a precise account of their injuries. Furthermore, to address the challenge of recall bias, future studies could be conducted in a longitudinal format where injuries are recorded in real time. This would ensure injuries are reported as they occur rather than relying on memory recall.
Future Directions
The implications of this study for future research are wide-ranging. Since the SAFE investigators examined current athletes retrospectively, only relatively short-term effects of early sport specialization on injuries were ascertained. Further research is recommended to understand the longer-term effects of early-age sport specialization on risk of injury and quality of life. Research is also needed to determine the cross-training activities that most effectively prevent overuse injuries in each sport without diminishing athletic performance. This balance is critical to protecting athletes from injury without decreasing the attainment of athletic scholarships. Finally, since it may be reasonably anticipated that despite all these efforts, some athletes may still specialize early, more research focused on developing and testing interventions to prevent overuse injuries in highly specialized athletes remains necessary.
CONCLUSIONS
In this large sample of NCAA athletes from the SAFE Consortium, highly specialized athletes were more likely to report an injury of any kind. They also missed more days of practice and competition than low specialized athletes. Regarding the types of injuries sustained, highly specialized athletes were at a greater risk of sustaining UEIs and LEIs, despite no significant difference in average weekly time commitment across specialization categories. Among moderately specialized athletes, LEIs, but not UEIs, were more likely to occur when compared with those classified under low specialization. Rates of surgery to treat an injury were significantly greater in highly specialized athletes.
ACKNOWLEDGMENTS
The authors acknowledge the assistance of the Data Manager, Barry R. DeCicco, MS, at Michigan State University, Center for Statistical Training and Consulting. In addition, the authors are grateful for the support of the Director for Research Development, Furqan B. Irfan, MD, PhD, at Michigan State University, College of Osteopathic Medicine.
References