Sport Specialization Classification and Injury Risk in Female Athletes

Abstract

Background:

Description of possible detrimental effects of sport specialization specific to adolescent female athletes is limited in current literature with no consensus on sport specialization classification.

Hypothesis:

Specialized female athletes will have higher rates of injury, body image issues, and menstrual dysfunction, regardless of the specialization classification utilized.

Study Design:

Cross-sectional.

Level of Evidence:

Level 3.

Methods:

Retrospective data was obtained from questionnaires from female athletes in local high schools (n = 229; 13–18 years of age). The 3-point specialization scale was used to analyze differences in injury rates, body image issues, and menstrual dysfunction within low, moderate, and highly specialized athletes. When comparing accuracy of specialization scales in identifying high risk athletes, three peer-reviewed specialization classification scales were utilized—a 3-point scale, a 6-point scale, and a binary self-selection scale. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for studied variables (a priori p ≤ 0.05).

Results:

Of 229 athletes surveyed, 219 (95.6%) completed the 3-point specialization classification questions and were included in the study. 91 athletes (41.6%) were categorized as low specialization (LS), 59 (26.9%) were moderately specialized (ModS), and 69 (31.5%) were highly specialized (HS). ModS athletes were more likely to have a history of stress fractures (SFx) compared to LS athletes (p = 0.02; OR 3.62; 95% CI 1.27–10.26). Compared to LS athletes, HS athletes were more likely to have injury history (p = 0.01; OR 2.93; 95% CI 1.38–6.24) and a history of concussion (p < 0.01; OR 5.00; 95% CI 1.86–13.42).

Conclusion:

Among female high school athletes, higher levels of specialization are associated with greater risk of injuries overall, and greater risk of concussions and SFx. This study did not demonstrate significant associations between specialization and body image issues or menstrual dysfunction.

Clinical Relevance:

This study further strengthens the association between injury and sport specialization and suggests that combining specialization scales better improves risk stratification which overall aids in preventing athlete injury.

Since the 1990s, researchers have noted a slow decline in multisport (MultiS) athletes and a steady increase in the number of adolescent athletes choosing to focus on a single sport (SS).^2,5,9 This trend may be due to the misperception that early focus on a SS increases the likelihood of earning college scholarships and of achieving national sport success.^6,17 The concept and impact of “labeling” children as gifted or talented at an early age has also been cited as a probable motivating factor for parents. ¹⁷ The impact of media portrayals of professional athletes, of sporting goods advertising, and of the inappropriate application of a “10,000-hour” or “10-year” rule applicable to musicians and other disciplines have also been discussed. ¹⁷

Athletes specialize as early as middle school, prompting the American Academy of Pediatrics Council on Sports Medicine to issue a statement warning in 2007 about the dangers of overtraining and the increased risk of injury associated with focus on a SS.^3,16 These recommendations were reiterated in 2016. ⁴ Using data from the 2010 to 2016 National Hospital Ambulatory Medical Care Survey, a 2019 study estimated an annual average of 1.5 million visits to the emergency department for sport or recreational injury by patients aged 5 to 14 years. ²⁶ After excluding injuries that occurred on the playground and other unspecified settings, approximately 1.3 million visits were attributed to a sport-related injury. ²⁶

Sport specialization has been associated with an increased risk of overuse injury and lower extremity injury.^{2,15,18,20,25} In 2016, Bell et al ² found that athletes aged 13 to 18 years who trained in a SS for >8 months in a year had a significantly higher likelihood of concussion and knee, hip, and overuse injury. ²⁵ In addition to increased injury risk, specialized athletes demonstrate detrimental psychosocial consequences. In the 2020 study of athletes aged 12 to 17 years by Giusti et al, ¹¹ specialized athletes reported reduced sense of accomplishment, greater exhaustion, and devaluation of their respective sport when compared with nonspecialized athletes.

Sport specialization has been studied extensively and subsequent recommendations have been made to promote injury prevention in specialized athletes.^{7,9,15,18,25,27} However, research related to detrimental effects of specialization in female athletes specifically is limited, although these effects may be more significant than for their male counterparts. In 2020, Post et al ²³ studied basketball players aged 12 to 18 years and found that female athletes reported an overuse injury 4 times more frequently than male athletes. Despite evidence that female athletes who specialize demonstrate increased risk of injury, recommendations regarding specialization in predominantly or exclusively female sports have not been well described. In addition, while studies emphasize the association between specialization and injury risk in female athletes, few studies have examined the association with menstrual changes, stress fracture (SFx), bone mineral density, or disordered eating.^22,25

Currently, there is no consensus on the definition of sport specialization. Sport specialization has been defined as “athletes limiting their athletic participation to one sport which is practiced, trained for, and competed in.”^10,13 This definition resulted in athletes being categorized as specialized if they played only a SS, regardless of the amount of time throughout the year spent participating in that sport. The definition evolved with the proposal of a 6-point questionnaire-based classification system developed by Jayanthi et al ¹⁴ in 2011 (Table 1). Classifying athletes as specialized or nonspecialized is binary in this system and does not provide precise risk stratification or a detailed categorization of the level of sport specialization. Subsequently, the 3-point specialization scale was proposed by Jayanthi et al^13,15 in 2015 and placed athletes on a specialization continuum. The 3-point classification system has been highly regarded for its ability to consider that some SS athletes may participate recreationally or sporadically, and may not have the increased injury risks associated with year-round SS athletes.^13,15 While the 3-point system has been used widely, there are still studies that classify athletes with the SS versus MultiS classification or with the 6-point scale.^1,2,6,19 Additional research is needed to determine whether the 3-point scale is superior to other specialization classification systems and whether it appropriately stratifies injury risk.^2,17,18

Table 1.

List of 3- and 6-point specialization classification questions ^a

1. Have you quit other sports to focus on 1 sport?	Yes/No
2. Do you consider your primary sport more important than other sports?	Yes/No
3. Do you train or participate in your primary sport more than 8 months out of the year?	Yes/No
4. Do you train more than 75% of the time in your primary sport?	Yes/No
5. Do you train to improve skill and miss time with friends as a result?	Yes/No
6. Do you regularly travel out of state for your primary sport?	Yes/No

^aResponses to all questions were used to determine the 6-point scale classification. Responses to questions 1 to 3 were used to determine the 3-point scale classification.

The purpose of this study was to determine the association between sport specialization, injury risk, menstrual dysfunction, and body image issues in adolescent female athletes. A secondary aim was to compare 3 specialization scales and their abilities to appropriately identify high-risk athletes. The authors hypothesized that if an athlete was categorized as specialized on any of the scales, she would have greater risk for injury, menstrual dysfunction, and body image issues.

Methods

Data for this study were obtained from a retrospective review of preparticipation questionnaires distributed to female athletes aged 13 to 18 years from local all-female high schools in August 2019. This study was approved by the Institutional Review Board at our institution [Reference no. 2020-495].

Questionnaire

A sport preparticipation questionnaire was completed by all athletes. The questionnaire included 34 questions with an approximate completion time of 3 to 5 minutes. Questions were related to menstrual history, body image concerns, diet, eating habits, and vitamin intake. Questions relating to bone health included a history of SFx and low bone mineral density diagnoses. Athletes were asked to classify themselves as SS or MultiS athletes, and asked to list their primary sport if they self-classified as MultiS. Participants listed all sports played, the number of months per year each sport was played, practice schedules, weight training participation, concussion history, and injury history. The 3- and 6-point specialization questions were also included in the questionnaire. The questions that comprise the 3- and 6-point scale are shown in Table 1.^{2,13 -15} All 6 questions in the table are used for the 6-point scale, while only the first 3 questions are used in the 3-point scale. Using the 6-point scale, a response of “yes” to 4 or more questions classifies an athlete as specialized, while a response of “yes” to 3 or fewer questions classifies an athlete as nonspecialized. ¹² Using the 3-point scale, each “yes” response contributes 1 point; points are summed to obtain a classification (3 points for high specialization [HS], 2 points for moderate specialization [ModS], 0-1 point for low specialization [LS]).^2,14,15 The 3-point specialization scale was used in our study to identify possible associated risk factors of specialization. The SS versus MultiS classification and the 6-point scale were evaluated against the 3-point scale.

Statistical Analysis

Frequencies, proportions (%), and means ± standard deviation were calculated using Microsoft Excel, version 1909. Odds ratios (ORs) and 95% confidence intervals (CI) were calculated using MedCalc statistical software (v.18.2.1), and were used to determine associations between specialization and injury, irregular menses, body image concerns, and bone health (a priori P ≤ 0.05).

Results

Data from 229 female athletes were obtained; 10 participants were excluded due to incomplete specialization responses, leaving 219 for inclusion in the final analysis. Figure 1 presents the CONSORT flow diagram. Figure 2 details the distribution of athletes using each of the 3 specialization classification scales. Results reported in this manuscript compared responses between LS, ModS, and HS athletes as categorized by the 3-point scale, as that classification is commonly used and recognized in sports literature and allows for insights into injury risk and other dysfunction in female athletes.

Figure 1.

CONSORT flow diagram.

Figure 2.

Classification of athletes studied, separated by classification system. HS, high specialization; LS, low specialization; ModS, moderate specialization.

An analysis of responses to questions in the preparticipation questionnaire according to the 3-point specialization classification is included in Table 2. The average number of menstrual cycles in a 12-month period and the average duration of menses was analyzed. Athletes who reported using contraceptives and athletes who had started menarche the same calendar year were not included in the analysis. Using the 3-point specialization scale, there was no significant difference in the number of menstrual cycles in the last 12 months in ModS or HS athletes compared with LS athletes. When comparing ModS and HS athletes with their LS counterparts, there was no significant difference in the number of menstrual cycles in the last 12 months (P = 0.22; 95% CI, -0.35 to 1.55 and P = 0.66; 95% CI, -0.69 to 1.09, respectively). There was also no significant difference in the average duration of menses in ModS or HS athletes compared with LS athletes (P = 0.14; 95% CI, -0.70 to 0.81 and P = 0.83; 95% CI, -1.00 to 0.81, respectively). When these same athletes were analyzed using the self-reported SS versus MultiS classification, there was no significant difference in the number of menstrual cycles in the last 12 months (P = 0.83; 95% CI, -1.00 to 0.81) or the average menses duration in days (P = 0.13; 95% CI, -0.69 to 0.09). The 6-point scale also showed no significant difference between the 2 groups of athletes in the number of menstrual cycles in the last 12 months (P = 0.34; 95% CI, -0.43 to 1.23) or the average menses duration in days (P = 0.59; 95% CI, -0.46 to 0.26)

Table 2.

Data results from select questionnaire responses ^a

	3-Point Scale Low Specialization (n = 91)	3-Point Scale Moderate Specialization b (n = 59)	3-Point Scale High Specialization b (n = 69)	6-Point Scale Specialized c (n = 95)	6-Point Scale Nonspecialized (n = 124)	Self-classification Single-Sport Specialized d (n = 158)	Self-classification Multisport Nonspecialized (n = 61)
Menstrual History
Average number of periods in 12 months (excludes those on oral contraceptive pills)	10.6 ± 3.0	11.2 ± 2.7 P = 0.22; 95% CI, -0.35 to 1.55	10.8 ± 2.6 P = 0.66; 95% CI, -0.69 to 1.09	10.6 ± 3.0 P = 0.34; 95% CI, -0.43 to 1.23	11.0 ± 2.5	10.9 ± 2.7 P = 0.83; 95% CI, -1.00 to 0.81	10.8 ± 2.8
Average number of days periods last	5.6 ± 1.2	5.3 ± 1.2 P = 0.14; 95% CI, -0.70 to 0.10	5.8 ± 3.2 P = 0.58; 95% CI, -0.52 to 0.92	5.5± 1.2 P = 0.59; 95% CI, -0.46 to 0.26	5.4 ± 1.2	5.5 ± 1.2 P = 0.13; 95% CI, -0.69 to 0.09	5.2 ± 1.1
Nutrition/Wellness
Worry about their weight	24 (26.4%)	14 (23.7%) P = 0.71; 95% CI, -11.90 to 16.08	10 (14.5%) P = 0.07; 95% CI, -1.02 to 23.68	18 (18.9%) P = 0.35; 95% CI, -5.93 to 15.86	30 (24.2%)	38 (23.9%) P = 0.25; 95% CI, -5.64 to 17.49	10 (16.7%)
Bone Health
Report history of ≥1 stress fracture	6 (6.6%)	12 (20.3%) P = 0.02; OR, 3.62; 95% CI, 1.27-10.26	8 (11.6%) P = 0.27; OR, 1.86; 95% CI, 0.61-5.63	12 (12.6%) P = 0.76; OR, 1.14; 95% CI, 0.50-2.58	14 (11.3%)	17 (10.7%) P = 0.38; OR, 0.68; 95% CI, 0.28-1.62	9 (15.0%)
Sport Injuries
Report ≥1 concussion while playing a sport	6 (6.6%)	4 (6.8%) P = 0.96; OR, 1.03; 95% CI, 0.28-3.82	18 (26.1%) P < 0.01; OR, 5.00; 95% CI, 1.86-13.42	21 (22.1%) P < 0.01; OR, 4.74; 95% CI, 1.92-11.71	7 (5.6%)	16 (10.1%) P = 0.05; OR, 0.45; 95% CI, 0.20-1.01	12 (20%)
Report ≥1 injury while playing a sport	14 (15.4%)	14 (23.7%) P = 0.20; OR, 1.71; 95% CI, 0.75-3.91	24 (34.8%) P = 0.01; OR, 2.93; 95% CI, 1.38-6.24	31 (32.6) P = 0.01; OR, 2.25; 95% CI, 1.20-4.21	22 (17.7%)	37 (23.3%) P = 0.60; OR, 0.83; 95% CI, 0.42-1.65	16 (26.7%)
Top 2 injury locations	1. Hand (28.6%) 2. Ankle (21.4%)	1. Ankle (50%) 2. Hand (28.6%)	1. Ankle (25%) 2. Head (20.8%)	1. Ankle (32.3%) 2. Head (22.6%)	1. Hand (31.8%) 2. Ankle (27.3%)	1. Ankle (32.4%) 2. Hand (18.9%)	1. Ankle (25%) 2. Hand (18.9%) and shoulder (18.9%)
After injury - quit sport that caused injury	6 (42.9%)	3 (21.4%) P = 0.23; OR, 0.36; 95% CI, 0.07-1.91	4 (16.7%) P = 0.09; OR, 0.27; 95% CI, 0.06-1.20	2 (6.5%) P < 0.01; OR,0.07; 95% CI, 0.01-0.36	11 (50%)	10 (27.0%) P = 0.52; OR,1.60; 95% CI, 0.38-6.84	3 (18.8%)
After injury - continue to feel pain and lasting effects from previous injury today	4 (28.6%)	3 (21.4%) P = 0.66; OR, 0.68; 95% CI, 0.12-3.83	9 (37.5%) P = 0.58; OR, 1.50; 95% CI, 0.36-6.23	11 (35.5%) P = 0.32; OR,1.87; 95% CI, 0.54-6.46	5 (22.7%)	P = 0.91; OR, 0.93; 95% CI, 0.26-3.32

^aStatistically significant (P ≤ 0.05) are bolded.

^bP values are compared with 3-point low specialized athletes.

^cP values compare 6-point specialized athletes with nonspecialized athletes.

^dP values compare single-sport specialized athletes with multisport nonspecialized athletes.

Athletes were asked whether they worried about their weight. The 3-point specialization scale showed no difference in the percentage of ModS (P = 0.71; 95% CI, -11.91 to 16.08) or HS athletes (P = 0.07; 95% CI, -1.02 to 23.68) who worried about their weight compared with LS athletes. Per the self-reported SS versus MultiS classification, there was no significant difference between the proportions of specialized and nonspecialized athletes who reported worrying about their weight (P = 0.25; 95% CI, -5.64 to 17.49). There was also no significant difference when athletes were compared using the 6-point specialization scale (P = 0.35; 95% CI, -5.93 to 15.86).

The number of athletes reporting at least 1 SFx was analyzed. The number of ModS athletes, per the 3-point scale, who had at least 1 SFx was significantly higher than for LS athletes (P = 0.02; OR, 3.62; 95% CI, 1.27-10.26). HS athletes did not have a significantly higher percentages of SFx when compared with LS athletes (P = 0.27; OR, 1.86; 95% CI, 0.61-5.63). Athletes who self-classified as specialized on the SS versus MultiS scale and those deemed specialized by the 6-point scale were not more likely to have a history of SFx when compared with nonspecialized counterparts (P = 0.38; OR, 0.68; 95% CI, 0.28-1.62 and P = 0.76; OR, 1.14; 95% CI, 0.50-2.58, respectively).

ModS athletes were not more likely to have a history of at least 1 concussion compared with LS athletes (P = 0.96; OR, 1.03; 95% CI, 0.28-3.82), unlike HS athletes who did have significantly higher rates of concussion than LS athletes (P < 0.01; OR, 5.00; 95% CI, 1.86-13.42). Specialized athletes using the self-classification scale and the 6-point scale were more likely to have at least 1 concussion compared with their nonspecialized counterparts (P = 0.05; OR, 0.45; 95% CI, 0.20-1.01 and P < 0.01; OR, 4.74; 95% CI, 1.92-11.71, respectively).

Via the 3-point scale, HS athletes were more likely to report at least 1 injury while playing a sport compared with LS athletes(P = 0.01; OR, 2.93; 95% CI, 1.38-11.71). There was no significant difference between ModS and LS athletes who reported an injury (P = 0.20; OR, 1.71; 95% CI, 0.75-3.91). The binary self-reported scale showed no difference between SS and MultiS athletes (P = 0.60; OR, 0.83; 95% CI, 0.42-1.65). However, specialized athletes, based on the 6-point scale, were more likely to have a history of a sport injury (P = 0.01; OR, 2.25; 95% CI, 1.20-4.21).

Athletes who reported at least 1 previous injury were asked whether they quit the sport that caused the injury. There was so significant difference in the number of ModS or HS previously injured athletes who quit the sport that caused their injury (P = 0.23; OR, 0.36; 95% CI, 0.07-1.91 and P = 0.09; OR, 0.27; 95% CI, 0.06-1.20, respectively). When the SS versus MultiS self-selection scale was applied, there was no statistically significant difference in the number of SS athletes who quit their sport after injury when compared with MultiS athletes (P = 0.52; OR, 1.60; 95% CI, 0.38-6.84). Specialized athletes on the 6-point scale who reported history of sport injury had a statistically significant higher percentage of previously injured athletes who then quit their sport compared with nonspecialized athletes (P < 0.01; OR, 0.07; 95% CI, 0.01-0.36).

When asked whether athletes currently experienced pain from their sport injury, there was no significant difference using any of the 3 scales. ModS and HS athletes had no difference compared with LS athletes (P = 0.66; OR, 0.68; 95% CI, 0.12-3.83 and P = 0.58; OR, 1.50; 95% CI, 0.36-6.23, respectively). The self-selection scale showed no significant difference between the proportion of SS athletes and MultiS athletes that continued to experience pain (P = 0.91; OR, 0.93; 95% CI, 0.26-3.32). When athletes were analyzed using the 6-point scale, specialized athletes were not more likely to have pain or other persistent symptoms from an injury (P = 0.32; OR, 1.87; 95% CI, 0.54-6.46).

Discussion

The authors hypothesized that more specialized athletes would be at higher risk for negative outcomes such as higher rate of injury, of body image issues, and of menstrual dysfunction, regardless of the specialization classification utilized. Analysis of athletes using the 3-point specialization scale determined that ModS athletes were more likely to have a history of SFx than LS athletes, while HS athletes were more likely to have a history of concussion and of injury while playing a sport than ModS or LS athletes.

Utilization of different specialization classifications resulted in different numbers of athletes who were classified as specialized. The association with increased concussions in more HS athletes was demonstrated using all 3 scales. However, previous history of SFx was not significantly higher in more specialized athletes when using the SS versus MultiS self-selection scale or when using the 6-point scale. Injury history was significantly associated with specialization when using the 3- and 6-point scale, but not when using the SS versus MultiS classification.

Some of our findings are consistent with previous studies and with consensus statements on female sport specialization, which found that any sort of specialization, as determined by whichever scale a study used, is associated with an increased risk of injury.^12,20,22 In studies that utilized the 3-point specialization scale, HS athletes were more likely to report a previous history of injury than LS athletes.^22,24,25 This is consistent with our study, which demonstrated an increased risk of injury, including concussion, among HS female athletes when compared with their LS counterparts.

The female athlete triad components are menstrual irregularity, low energy availability, and low bone mineral density. ⁸ This study did not demonstrate significant association between menstrual dysfunction and sport specialization. Higher rates of SFx, a marker of decreased bone density, were seen in ModS athletes compared with LS athletes, although his difference was insignificant when comparing HS athletes with LS athletes.

This study found that 25% of the athletes quit the sport that caused their injury, and 31% were experiencing lasting effects from their previous sports injuries such as residual pain and weakness. Although sport participation can improve overall health and psychological well-being, specialization may have paradoxically negative effects if it contributes to an athlete becoming sedentary after suffering an injury or stopping sport participation altogether.^15,20,21,27

Given the lack of general consensus on the definition of sport specialization, it is difficult to determine the overall prevalence of specialization in female athletes. Our study found the same inconsistency in that specialized athletes by one scale who then had increased risk for a certain outcome would not have the same statistically significant difference in outcome when applying a different scale. When applying the 3-point scale, this study identified inconsistencies in determining long-term injury effects or the likelihood of quitting a sport after injury. Overall, the 6-point classification proved superior to the SS versus MultiS classification as overall injury risk was not significant when the SS versus MultiS self-classification measurement was applied. However, given that 12% of the HS athletes on the 3-point scale were not considered specialized with the 6-point scale, this classification alone may not be comprehensive enough to be accurate. This is possibly because the 6-point scale, although gathering more information, interprets specialization with a binary result rather than using a specialization continuum such as that used by the 3-point scale.

The same difference in conclusions depending on the scale used was seen when analyzing which athletes quit the sport that contributed to their injury. All 3 classification scales lead to difference conclusions on the risk involved when specialized. Interestingly, neither the 6-point scale nor the binary SS versus MultiS self-classification scale demonstrated increased SFx in specialized athletes. In contrast, increased SFx risk was identified in higher specialized athletes with the 3-point scale utilized.

Limitations

There are major limitations to this study. Our smaller sample size limited the strength of associations found and the ability to find correlations in different subcategorizations of this cohort. This retrospective review is subject to recall bias that does influence accuracy of questionnaire-based scales. Participants completed questionnaires without examiner intervention or review; in adolescent subjects, this may contribute to incomplete response or limited comprehension of the questionnaires. However, this may be minimized by the simple format and short duration of the questionnaires. This study was also limited to female athletes from high schools in a specific geographic location, and results may not be generalizable to all female athletes. In addition, the questionnaire filled out by participants was developed by the authors and did not undergo a validation process.

Conclusion

Among female high school athletes, our study demonstrated that higher levels of specialization are associated with greater risk of injury including concussions and SFx. These findings contribute additional data and support to other studies which demonstrate negative outcomes associated with specialization. Although the 3-point specialization scale is the classification commonly used in current studies, consideration should be given to other methods of classifying who is and is not a specialized athlete and therefore at risk for certain outcomes.