Injury and illness burden in sports has traditionally been quantified using severity and time-loss metrics, which focus narrowly on days missed from sport. These measures fail to capture the full spectrum of health consequences, particularly those associated with non-time-loss injuries, chronic conditions, and lingering impairments following return to play. To address this gap, we propose the disability-adjusted sporting year (DASY), a novel metric adapted from the public health framework of disability-adjusted life years (DALYs). The DASY incorporates both the immediate and long-term health impact of sport-related injuries and illnesses by summing years of sport lost (YSL) and years lived with injury (YLI), weighted by condition-specific disability weights derived from broad population surveys. By anchoring assessments in athlete health rather than sport participation alone, the DASY enables a more comprehensive, equitable, and athlete-centered approach to surveillance, clinical decision making, and policy development. This manuscript outlines the theoretical foundation, calculation framework, and potential applications of the DASY metric, with the goal of advancing how health burden is evaluated in sport. By shifting the focus from return-to-play timelines to overall health loss, the DASY aligns athlete welfare more closely with broader public health standards and offers a robust tool for comparative risk assessment across sporting contexts.

Little is known about the long-term health outcomes associated with sustained elite performance once female Olympians retire from their Olympic careers. The aim of this study was to determine and compare (1) musculoskeletal health, (2) general health and (3) current physical activity (PA) behaviours between a global sample of female retired Olympians and general population controls. A cross-sectional survey comprised four sections: (1) background information, (2) injury history, (3) current musculoskeletal health and (4) general health. A total of 1488 retired female Olympians and 998 female controls completed the survey. Poisson regression analysis was used to determine prevalence ratios (aPR) with 95

Background While sleep is essential for elite athletes, poor sleep is a common issue in this population. Brief Behavioral Therapy for Insomnia (BBTI) is a 4-session adaptation of Cognitive Behavioral Therapy for Insomnia (CBT-I) that has been feasibly delivered to retired athletes. Whether BBTI is feasible and acceptable for actively competing elite athletes with subthreshold insomnia symptoms remains unclear. Methods This mixed-methods study comprised two phases: (1) focus group informed by the COM-B and Theoretical Domains Framework to guide BBTI adaptation, and (2) prospective, non-randomized pilot study of the adapted protocol (REST USA: Recovery and Elite Sleep Training for Team USA), evaluating a priori feasibility benchmarks and preliminary self-report and objective sleep outcomes via linear mixed models. Results Twenty-two senior winter national team athletes with subthreshold insomnia symptoms were enrolled. Retention and clinician fidelity were excellent. Benchmarks for recruitment, sleep diary completion, and actigraphy compliance were unmet, with assessment burden and device usability identified as primary barriers. Linear mixed models (modified intent-to-treat; n = 20) revealed preliminary signals of improvement at post-intervention in sleep difficulty (ASSQ: β=-0.69, SE = 0.29, p=.023), sleep behaviors (ASBQ: β=-0.84, SE = 0.25, p=.002), dysfunctional sleep beliefs (DBAS: β=-0.69, SE = 0.26, p=.013), training distress (TDS: β=-0.54, SE = 0.25, p=.036), and anxiety (GAD-7: β=-0.53, SE = 0.25, p=.044). Improvements in sleep beliefs and training distress were sustained at 2-month follow-up. The proportion of athletes flagging for clinically significant sleep disturbance decreased from 46% to 19%. Conclusion REST USA demonstrated strong acceptability and preliminary target engagement, with clear and addressable pathways for refining feasibility in subsequent trials.

Context: Per-diem athletic trainers (ATs) are essential for providing emergency care at athletic events, yet they often face inconsistent access to critical resources and unclarity about who is responsible for supplying them. Purpose: To explore ATs' perspectives and identify essential resources needed for safe and effective patient care in the per-diem setting. Methods: Twenty ATs with recent per-diem experiences developed a standardized list of resources using the nominal group technique. Results: Eight resources were consistently identified across all groups: an automated external defibrillator (AED), emergency action plan (EAP), detailed job outline, ice, splints, tape, gauze, and band-aids. Of these, an AED, EAP and a detailed job outline were ranked as most important across all groups. In addition, a comprehensive resource list, encompassing items and ideas from all five groups, was created. Interpretation: Establishing standardized resource expectations can help ensure organizational accountability for resource provision and maintenance, improve emergency preparedness, and support ATs in delivering high-quality care. These findings highlight gaps in communication and responsibility that may affect patient safety and provide a foundation for future work to tailor resource lists to specific event types and environments.

OBJECTIVE:Conduct a comprehensive scoping review of sport science and medicine (SSM) research in highly trained, elite and world-class female athletes. DESIGN:Scoping review. DATA SOURCES:PubMed (MEDLINE, EBSCOhost), Scopus, SPORTDiscus (EBSCOhost), Web of Science and base-search (grey literature). ELIGIBILITY:Peer-reviewed and grey literature informing the health and performance of highly trained, elite and/or world-class female athletes (>16 years of age), with mixed-group studies retained when data were reported separately. Sports that require physical exertion or highly skilled movements were included. The search was restricted to English with no date limits. RESULTS:3,867 (1970-2025) records including 3,250 (84%) original studies reporting 4,374,571 participants [31% female (n=1,368,170)] were reviewed. 3,221 (99%) studies included athletes [world class, n=433 (13%); elite, n=1,873 (58%); highly trained, n=1,302 (40%)] across 102 different sport disciplines from 74 different countries. 135 (4%) studies included athletes with disabilities, 58 (2%) retired and 37 (1%) masters athletes. Physiology (n=1,059 studies, 33%), performance analysis (n=947, 29%) and injury (n=820, 25%) were the most common SSM general themes; equipment (n=84, 3%), law (n=17, 1%) and business (n=8, 0%) were least common. Health-related SSM themes included musculoskeletal (n=364, 22%), mental (n=243, 15%) and cardiovascular (n=183, 11%) health; pelvic floor (n=26, 2%), breast health (n=11, 1%) and (peri-)menopause (n=2, 0%) were least common. CONCLUSION:Research in high-performing female athletes is increasing, however persistent gaps remain in participant representation, geographical coverage and health-related themes. Targeted, co-created and multidisciplinary research approaches are needed to improve equity, relevance and impact in female athlete health and performance.

Objective:To determine if paediatric athletes exhibit differences in positive screenings on the Sport Mental Health Assessment Tool (SMHAT-1) compared with adults. Methods:Team USA Olympic and Paralympic paediatric athletes ≤21 years (n=589; 58.8% female; age=19.5±1.7 years) completed the SMHAT-1 between January 2021 and September 2024. A comparative sample of Team USA adult athletes (n=493, 54.2% female; age=27.3±4.9 years) matched for sex and sport was randomly sampled, and the percentage of athletes with a positive screening on each questionnaire was calculated. χ2 analysis compared the proportion of positive screenings between paediatric and adult samples. Piecewise linear regression assessed the association between age and total questionnaire scores. Results:The proportion of positive screenings was lower for paediatric athletes for the Alcohol Use Disorders Identification Test Consumption (AUDIT-C) (χ2(1)=21.2, p<0.001) and Athlete Psychological Strain Questionnaire (APSQ) (χ2(1)=4.47, p=0.034). Increasing age of paediatric athletes was associated with higher AUDIT-C (β=0.41, p<0.001) and APSQ scores (β=0.35, p<0.001); however, a negative deviation was observed in AUDIT-C (β=-0.44, p<0.001) and APSQ scores (β=-0.38, p=0.002) among adults. Further, increasing age of paediatric athletes was associated with increased General Anxiety Disorder-7 (β=0.14, p=0.045) and ASSQ (β=0.12, p=0.029) scores; however, there was no significant deviation from this age trend in adults for either score (p>0.05). Conclusion:Paediatric athletes produce a similar proportion of positive screenings to adults on subcomponents of the SMHAT-1, but they show a lower proportion of positive screenings for alcohol misuse and psychological strain. The changes in SMHAT-1 subcomponent scores among paediatric athletes warrant continued exploration to determine how the interaction of psychosocial development and elite sport may impact mental health.

Sports medicine epidemiology has advanced considerably over the past two decades, with standardized surveillance systems and consensus statements improving the quality of data collection and reporting. Yet the field continues to face structural challenges, including small cohorts, heterogeneous samples, and rare outcomes that undermine reproducibility and limit generalizability. In practice, researchers and clinicians frequently rely on implicit expert judgment to bridge these gaps, but such judgments are often undocumented and irreproducible. Expert elicitation offers a structured, transparent approach to formalizing this knowledge into quantitative priors that can complement empirical data within Bayesian analyses. This commentary introduces expert elicitation to sports medicine epidemiology, drawing on applications of the Sheffield Elicitation Framework (SHELF) in our ongoing work. We highlight three key areas where elicitation can strengthen research and practice: 1) studies involving small, sport-specific cohorts, such as Paralympic athletes; 2) analyses of rare or severe events, including catastrophic injuries and sudden illnesses; and 3) underpowered intervention trials, where structured priors can improve interpretation and guide future prevention and treatment strategies. We also share practical insights from our pilot work, including strategies for framing questions, conducting warm-up and challenge exercises, and using real-time visualization to improve accuracy and engagement. Expert elicitation is not without challenges, requiring careful facilitation and appropriate expertise, but it provides a rigorous, reproducible method for transforming clinical judgment into usable data. Wider adoption of this methodology could accelerate progress in athlete health research by formalizing knowledge that already shapes practice but remains largely untapped.

Objective To (1) describe the incidence and characteristics of injuries and illnesses reported by Team USA athletes competing in the 2024 Paris Summer Olympic Games (PSOG) and Paralympic Games (PSPG); (2) compare injury and illness incidence between Olympic and Paralympic cohorts and (3) quantify the burden of respiratory and thermoregulatory illnesses under standard (post-COVID) public health conditions.Methods The United States Olympic & Paralympic Committee (USOPC) Injury and Illness Surveillance system was used to document the details of all injuries and illnesses reported by any of the 862 athletes, alternates and guides competing for Team USA in the PSOG and PSPG. Illness and injury incidence per 1000 athlete-days (AD) and incidence ratios (IR) were calculated, both with 95% CI.Results Team USA Paralympic athletes sustained 20.5 injuries per 1000 AD versus 14.6 among Olympic athletes (IR (95% CI): 1.4 (1.1 to 1.8)). Illness incidence was 15.7 per 1000 AD among Paralympic athletes versus 8.3 in Olympic athletes (IR (95% CI): 1.9 (1.4 to 2.6)). Among Team USA Paralympic athletes, injuries sustained outside of sport settings accounted for 38.5% (30/78) of all Paralympic injuries, the largest single setting category. The most common system affected by illness at both PSOG and PSPG was the respiratory system, with 7.4% of all athletes reporting a respiratory illness. Despite concerns ahead of the Games related to extreme heat, only two (0.3%) Olympic athletes and zero (0.0%) Paralympic athletes from Team USA reported a heat-related illness.Conclusion Injury and illness rates were higher among Team USA Paralympic athletes compared with Olympic athletes during the Paris 2024 Games. Respiratory illnesses were the most frequent medical problem, despite a pre-Games prevention campaign, highlighting the need for stronger infection-control strategies at future Games. The absence of heat illness suggests current heat-mitigation measures were effective. Comprehensive injury-prevention and illness-prevention strategies are needed for both Olympic and Paralympic athletes, with additional attention to the environmental and accessibility risks that disproportionately affect Paralympic competitors.

Context: Authors of extensive research have exposed health care disparities regarding socioeconomic status (SES) and race and ethnicity demographics. Previous researchers have shown significant differences in access to athletic training services (athletic trainer [AT] access) in the secondary school setting based on SES, but with limited samples. Objective: To investigate differences in AT access based on race and ethnicity and SES on a national scale. Design: Cross-sectional study. Setting: Database study using secondary analysis. Data were collected from the National Center for Education Statistics, Athletic Training Location and Services database, and US Census Bureau. Patients or Other Participants: A total of 10 983 public schools. Main Outcome Measure(s): Descriptive data were summarized by measures of central tendency. A 1-way analysis of variance determined differences between school characteristics (median household income, percentage of students eligible for free and reduced lunch, percentage of White students, and percentage of non-White students) based on AT access: full-time (FT-AT), part-time (PT-AT), and no AT (no-AT). A Bonferroni pairwise comparison was used for variables with significant main effects. Results: Across all schools included in the study, 43.8% had no-AT (n = 4812), 23.5% had PT-AT access (n = 2581), and 32.7% had FT-AT access (n = 3590). Significant effects were found between AT access and median household income (P < .001), the percentage of students eligible for free and reduced lunch (P < .001), the percentage of White students (P < .001), and the percentage of non-White students (P < .001). Schools with FT-AT access had a higher SES than PT-AT and no-AT schools. Significant differences existed between AT access groups and the race and ethnicity of schools. Schools with FT-AT access had a significantly lower percentage of non-White students (31.3%) than schools with no-AT (46.0%; P < .001). No significant differences between FT-AT and PT-AT access based on race and ethnicity demographics presented (P >= .13). Conclusions: Schools with higher SES had greater AT access, whereas schools with a higher percentage of nonWhite students were more likely to have no AT access, demonstrating the disparities in health care extends to athletic health care as well. To increase AT access, future initiatives should address the inequities where larger minority populations and counties of lower SES exist.

Background: Previous research has reported higher rates of both injury and illness among Paralympic athletes compared with Olympic athletes during the Winter Olympic and Paralympic Games, but no studies have directly compared injury and illness incidence between Olympic and Paralympic athletes competing in a Summer Games. Purpose: To compare injury and illness rates between Olympic and Paralympic Team USA athletes competing in the Tokyo 2020 Olympic and Paralympic Games. Study Design: Descriptive epidemiology study. Methods: All injuries and illnesses that occurred among the Team USA athletes competing in the Tokyo 2020 Summer Olympic or Paralympic Games were documented. A total of 701 Team USA athletes (53.6% female) competed in the Tokyo 2020 Summer Olympic Games, across 34 different sports. For the Tokyo 2020 Summer Paralympic Games, a total of 245 athletes (51.6% female) competed across 20 sports. Incidence rates (IRs) per 1000 athlete-days were calculated according to sex, sport, anatomic location, and illness type. IR ratios (IRRs) were calculated to compare IRs between male and female athletes and between Olympic and Paralympic athletes. Results: Overall, there were no differences in injury incidence (IRR, 1.18; 95% CI, 0.84-1.68) or illness incidence (IRR, 0.68; 95% CI, 0.41-1.15) between Olympic and Paralympic athletes. Male Paralympic athletes were less likely to sustain an illness compared with female Paralympic athletes (IRR, 0.35; 95% CI, 0.11-0.90). Conclusion: There were no differences in injury or illness rates between Olympic and Paralympic Team USA athletes competing at the Tokyo 2020 Summer Games, contrary to previous comparisons among winter sport athletes. These results challenge the prevailing notion that Summer Paralympic athletes are at greater injury and illness risk, suggesting that factors beyond Olympic or Paralympic Games participation influence health concerns.

Injury and illness surveillance is essential for understanding the relative risks of sports participation to develop effective strategies to optimize athlete health, wellness, and performance. Epidemiological studies examining injuries and illnesses among Team USA youth athletes are limited, particularly among athletes competing in Winter sports. The purpose of this study was to characterize the injury and illness incidence rate among Team USA athletes participating in the 2024 Winter Youth Olympic Games (YOG). Injuries and illnesses among 101 Team USA youth athletes (40.6

In the field of sports injury epidemiology, the metric known as injury burden has been posited as a measure that combines information on the incidence of an injury (quantified by injury rate per a standard measure of exposure) with the severity of the injury (denoted by the average time lost due to the injury). Injury burden is calculated as the product of these two variables and has been recently encouraged to be included in sports injury epidemiology and injury prevention research (1). Often, time loss associated with sport-related injuries presents a non-normal distribution with outliers, extreme values, and, perhaps most notably, zero inflation. Consequently, certain injury burden assessments have utilized median time loss (TLmed) to represent the central tendency of time loss (see [2], for example). Nonetheless, recent discourse suggests that mean time loss (TLx̅) may offer a more accurate representation than TLmed for the computation of injury burden, a point we believe lacks adequate justification at this time. This editorial will explore two pivotal concerns: first, the mathematical justification for the preference of TLx̅ compared to TLmed in injury burden calculations, and second, brief conceptual deliberations regarding injury burden that warrant attention within the discipline of sports epidemiology. Mean or Median Time Loss? Calculating injury burden, as currently defined, requires a method to represent the typical duration of time loss associated with an injury. This value is then multiplied by the frequency of occurrence of that injury (i.e., its incidence) to estimate the average time loss relative to injury occurrence. Assuming for the sake of argument that time loss adequately reflects injury severity, this approach is theoretically sound. However, determining how to best represent "typical" time loss remains contentious. Conventional statistical principles suggest that the mean is an unsuitable measure of central tendency for skewed distributions; the median is likely a more accurate reflection of the "center" of the time loss distribution, whereas the mean would overestimate the "typical" time loss in a positively skewed distribution. Nonetheless, there is an ongoing debate in the literature about handling non-normal time loss distributions, which is problematic as these are more common than approximately normal distributions when measuring time loss in sports medicine. The proposed reasoning for the superiority of TLx̅ in the calculation of injury burden (3), even in the face of a non-normal time loss distribution, relies on the linear relationship between mean injury burden and total time loss from sport, in contrast to the absence of such a linear relationship for median injury burden and time loss from sport (3). To examine this contention, we generated example data to illustrate this linear relationship (Figure A–D; reproducible R code for data generation is available upon reasonable request to the corresponding author). As shown, there is a linear relationship between mean injury burden and total time loss from sport. This perfect linear association is present because the mean injury burden is essentially the total time lost scaled by a constant factor (as explained by the axis labels, which detail the derivation for each variable). Consequently, this association between a variable and itself, which may be characterized as an endogeneity issue, raises concerns about whether this constitutes sufficient evidence of greater validity in using TLx̅. An analogous scenario would be modeling the relationship between body mass index (BMI = body mass • height−2) and body mass to establish mathematical validity or using both BMI and body mass to determine or describe an outcome concurrently. To further illustrate the necessity of a constant scaling variable (i.e., exposure) for maintaining the linear association, we examined the impact of differing exposures between cohorts on injury burden. We generated new data for three unique injury cohorts with varying mean and total time loss and, importantly, with different exposure quantities (a common real-world scenario). Replotting mean injury burden against total time loss revealed that the strict linear relationship no longer exists (Figure E).Figure: (A–D) Demonstrating that the linear association between total time loss and mean injury burden is a function of endogeneity with scaling. (A) The y-axis represents mean injury burden. (B) The y-axis represents the mean injury burden reduced to its components, mean time loss, and injury incidence. (C) The y-axis represents mean time loss and incidence reduced to its components, total time loss over the number of injuries, and a number of injuries over exposures. (D) The y-axis has been simplified (number of injuries canceled) to demonstrate that the mean injury burden is the total time loss over the number of exposures. Note that the y-axis variable in D was calculated separately as the total time loss divided by the exposure multiplied by 1000. (E) When assessing the association between total time loss and mean injury burden when each injury has a different exposure (i.e., variable scaling variable), the association becomes decidedly nonlinear. (F–H) The uniform (F), normal (G), and skewed (H) distribution of three injury scenarios, each with approximately the same mean time loss and total time loss.Conceptual Considerations for Injury Burden It may be the case that what is meant by Fuller (3) is that the mathematical validity of injury burden calculated with TLx̅ lies in its depiction of the total amount of time lost in the population per unit of exposure rather than the use of mean time loss per se. However, this is more of a conceptual question and line of argumentation. In fact, injury burden, as calculated as the mean days lost per injury multiplied by the injury incidence, is numerically equivalent to the total days lost per unit of exposure (this is effectively what has been demonstrated in the linear relationship depicted in Figure A–D). As such, if injury burden is being conceptualized as the total number of days lost in a population for a unit of exposure, then it can be computed as such, and the calculation of the product of mean days lost and incidence is unnecessary. If, however, injury burden is conceptualized as the product of the average — that is, a measure of the central tendency of the time loss distribution — and the frequency of the injury event, then the correct measure of central tendency should be chosen for the time loss distribution. If the most appropriate representation of the central tendency is the mean, then the calculation numerically coincides with the former concept (i.e., total days lost per unit of exposure). If the correct representation of the central tendency of the time loss distribution, however, happens to be the median, then this will be numerically distinct from the former conceptualization and, indeed, require its own calculation beyond just using the total days lost in the population. One may use other calculations to represent the central tendency if there are concerns of outliers or time loss, such as the trimmed or winterized mean, but this too would be numerically distinct from the total days lost in population relative to exposure (necessarily, as trimming or winsorizing removes some of the observed days lost from the calculation). In the case that the concept of injury burden truly is a measure of central tendency of the time loss distribution multiplied by incidence, the question remains as to which metric (TLx̅ or TLmed) would be the more reasonable to use. To explore this issue, we again generated data representing three separate injury scenarios with equal numbers of total injuries, with each distribution having approximately equivalent mean time loss and total time loss but with contrasting time loss distributions (Figure F–H). Each time loss distribution has an approximately equal mean injury burden, but the third injury distribution (Figure H) has a lower median injury burden. It then becomes vital to consider whether we are comfortable maintaining that each time loss distribution represents the same "burden" — perhaps a metric that captures this inherent difference in time loss distributions is more valuable. Moreover, the burden of injury may extend beyond only a consideration of time loss (4), which requires further consideration, although this is outside the scope of the present narrative. Summary and Recommendations We do not herein suggest that using TLx̅ or TLmed is necessarily more valid than the other in calculating injury burden, only that they can represent a different notion or conception of what the typical burden for an injury is. We, at this time, therefore, support the following practices in reporting injury burden: 1) reporting both mean (using TLx̅ in the injury burden calculation) and median (using TLmed in the injury burden calculation) injury burden; or 2) if either metric is chosen, the researchers must present an adequate rationale for why; 3) we encourage researchers to include raw data or sufficient summary data (at least incidence, total, mean and median time loss, and exposure), so that researchers may more readily recalculate burden metrics as necessary. Finally, we also suggest that sports medicine and sports injury epidemiology continue to expand measures to approximate the burden of injury and illness, potentially emulating best practices from public health and epidemiology, such as the use of health-adjusted life years (5,6).

The Sport Mental Health Assessment Tool-1 (SMHAT-1) screens for athlete mental health concerns, yet little is known about outcomes following positive screens. Objective:This study examined the follow-up outcomes of Team USA athletes who exceeded thresholds on the SMHAT-1 questionnaire prior to the 2024 Paris Olympic and Paralympic Games. Methods:A total of 847 SMHAT-1 assessments were completed (Paralympic, 26.7%; women, 52%). Questionnaires exceeding established thresholds were classified as a positive screen, which elicited follow-up by a Team USA mental health provider. During follow-up, mental health providers recorded follow-up outcomes using one of nine predefined outcomes. Outcomes were analysed for athletes with a single positive screen and for all unique positive screen combinations. Results:450 (53.1%) athletes had a positive screen. The most common outcomes were: 'discussion without further action' (31.8%), 'athlete already connected to outside services' (26.0%) and 'inability to contact the athlete' (20.4%). However, 43.1% of follow-ups required a service to be provided to the athlete. Conclusion:While many positive screens did not require new clinical interventions, the proportion of athletes receiving follow-up care (43.1%) or were already in care (26.0%), underscores the need for diverse psychological resources to support elite athlete mental health. The inability to reach 20.4% of athletes following a positive screen suggests that alternative approaches for the deployment of the SMHAT-1 may be needed to allow for immediate clinician follow-up care. These findings support ongoing SMHAT-1 use and highlight the need for sufficient psychological services resources to meet athletes' follow-up needs.

The effects of injury and illness on sports performance remain incompletely understood in Olympic athletes. This study investigated whether sustaining an injury or illness at the 2024 Paris Summer Olympic Games affected the probability of winning a medal, which combinations of injuries or illnesses were most impactful on the probability of winning a medal, and how injury or illness influenced athletes’ final percentile ranking. Data from injury and illness events among Team USA athletes were merged with final event results and ex ante (i.e., based on forecasts) market-derived probabilities of success. Logistic and general linear regression models were used to assess the impact of injury and illness on outcomes, controlling for the expected probability of success. Results showed no significant effect of injury or illness on the probability of medaling (p = 0.945). However, sustaining an injury or illness was significantly associated with a lower percentile rank finish (p = 0.004), with a stronger effect among athletes with lower initial probabilities of success (p = 0.013). These findings highlight the measurable impact of injury and illness beyond only time loss and reinforce the importance of robust injury and illness prevention strategies for elite athletes.

Introduction The Sport Mental Health Assessment Tool-1 (SMHAT-1) was developed to screen elite athletes for mental health concerns. Previous work demonstrated high false negative rates (FNRs) for the initial triage step of the tool, but given the novelty of the deployment of the SMHAT-1 during large multisport, multinational competitions, replication of these findings was justified and required. This study, therefore, aimed to recalculate classification performance metrics at the triage step and investigate potential reasons for the high FNRs observed.Methods All athletes (n=847) completed steps 1 and 2, including an additional Posttraumatic Stress Disorder questionnaire, and FNRs were calculated. Exploratory analysis, including an exploratory factor analysis (EFA), was used to investigate the latent constructs being captured.Results Classification performance metrics indicated FNRs ranging from 0% (Patient Health Questionnaire-9 (PHQ9) and PHQ9 Item 9) to 63.16% (Brief Eating Disorder in Athletes Questionnaire (BEDAQ)), consistent with previous findings. The EFA identified nine latent factors in step 2, with each instrument appearing to mostly favour its own independent factor, highlighting a range of distinct latent constructs. An exploratory mixed graphical model revealed some step 2 clusters that were not closely linked with the Athlete Psychological Strain Questionnaire (APSQ) items.Conclusions These findings confirm previous concerns regarding the APSQ’s sensitivity and underscore the challenge of using a single triage tool to capture the broad spectrum of mental health issues assessed by the SMHAT-1. Future work should consider a bespoke triage tool to better capture the diverse mental health needs of elite athletes.

Purpose: Physical consequences of youth sport specialization have been established, yet the psychosocial demands remain largely unexplored. Hypothesis: Missing time with friends due to sport demands and frequent out-of-state travel is associated with sport specialization classification for each high school grade, and these associations would remain after stratifying for gender and sport level. Study Design: Cross-sectional. Methods: Six hundred sixty-eight (349 female) Division I and club sport athletes from a large midwestern university completed a retrospective survey assessing sport specialization classification and the travel and social demands of sport participation for each high school grade. To measure the social and travel demands of high school sport participation, participants were asked if they missed time with friends because of sports training and if they regularly traveled outof-state for any sport, to which they responded "yes" or "no". Chi-square tests evaluated associations between frequent out-ofstate travel and missing time with friends due to sport with sport specialization classification (low, moderate, high). Stratifications such as gender (male or female) and college sport level (club or Division I) were further tested in the analyses. Results: A significant association was found between specialization classification and missing time with friends in each high school grade (p- values < 0.001). Similarly, there was a significant association between specialization classification and frequent out-of-state travel in each high school grade (p-values < 0.001). Significant associations remained after stratifying by gender or college sport level. Conclusions: College athletes who reported themselves as highly specialized athletes in high school were more likely to miss time with friends and frequently travel out-of-state due to the demands of their sport compared to less specialized athletes, regardless of gender or college sport level. Findings from this study may help researchers and clinicians understand additional pressures specialized athletes may face that could lead to burnout and eventual sport drop out. This information can help clinicians encountering young athletes who are considering specialization, or have already specialized in sport, inform them and their parents of the potential consequences.