Objectives To determine the incidence rate of suicide from 2002 to 2022 among athletes from the National Collegiate Athletic Association (NCAA) and assess for potential differences by, sex, race, division and sport. Methods NCAA athlete deaths over a 20-year period from 2002 to 2022 were identified. Poisson regression models were built to assess changes in incidence rates over time. Linear and quadratic fits between year and suicide incidence for males and females were evaluated. Results Of 1102 total deaths, 128 (11.6%) deaths by suicide were reported (male n=98, female n=30). The overall incidence was 1:71 145 athlete-years (AYs). Over the last decade, suicide was the second most common cause of death after accidents. The proportion of deaths by suicide doubled from the first 10 years (7.6%) to the second 10 years (15.3%). The suicide incidence rate for males increased linearly (5-year incidence rate ratio 1.32 (95% CI 1.14 to 1.53)), whereas a quadratic association was identified among female athletes (p=0.002), with the incidence rate reaching its lowest point in females in 2010–2011 and increasing thereafter. Male cross-country athletes had the highest suicide incidence rate (1:29 815 AYs) and Division I and II athletes had a higher suicide incidence rate than Division III athletes. No significant differences in suicide incidence rates by sex, race or sport were identified. Conclusion Deaths by suicide among NCAA athletes increased in both males and females throughout the 20-year study period, and suicide is now the second most common cause of death in this population. Greater suicide prevention efforts geared towards NCAA athletes are warranted.

Mental health conditions and concussion history reported by a collegiate athlete may contribute to prolonged recovery and symptom severity after concussion. This work examined the potential associations among concussion history, preexisting conditions, and sex relative to initial symptom severity and recovery duration following sport-related concussion (SRC) in a cohort of Division 1 National Collegiate Athletic Association athletes. This prospective cohort study analyzed symptom severity, recovery, and return-to-play (RTP) times reported post-SRC using data collected as part of the Pac-12 Concussion Assessment, Research and Education Affiliated Program and Health Analytics Program. Health history questionnaires that included self-reported history of preexisting conditions were completed at baseline. When consented athletes were diagnosed with a concussion, daily postconcussion symptom scores were evaluated until an athlete was clinically determined to be asymptomatic. Generalized linear and Cox proportional hazards models were used to determine associations between preexisting conditions and recovery and RTP times. Ninety-two concussions met inclusion criteria. Notable differences in initial symptom severity existed between females and males who had mood disorders (effect size [ d ] = 0.51) and attention-deficit hyperactivity disorder (ADHD; d = 0.93). The number of previous concussions was a strong predictor of athletes reporting preexisting mood disorders, depression, anxiety, and ADHD ( p = 0.008–0.04). Females with ≥2 previous concussions required more days to RTP than males ( d = 0.31–0.72). Weekly recovery and RTP probabilities substantially differed between athletes who did or did not have learning disorders (LDs; hazard ratio [HR] _Recovery = 0.32, HR _RTP = 0.22, d = 1.96–2.30) and ADHD (HR _Recovery = 3.38, HR _RTP = 2.74, d = 1.71–4.14). Although no association existed between concussion history and acute symptom severity, collegiate athletes with a history of concussion had higher probabilities of reporting depression, mood disorders, anxiety, and ADHD. Having ADHD or LDs likely strongly affects time to recovery and RTP for collegiate athletes.

Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. Results: 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. Conclusions: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes.

Background: Kickoff plays in American football are associated with an increased risk of concussion compared with other play types. In 2018, the National Collegiate Athletic Association (NCAA) Football Rules Committee altered the kickoff rules so a fair catch inside the 25-yard line results in a touchback, with the ensuing drive starting on the 25-yard line. The intention was to decrease the number of kickoff returns with a corresponding decrease in the rate of concussions on kickoff plays. Purpose: To determine whether the 2018 rule changes had the intended effects in an NCAA Division 1 Conference. Study Design: Cohort study; Level of evidence, 3. Methods: The study population included football athletes in the NCAA Pacific-12 (Pac-12) Conference. Data on the total number of plays, punts, kickoffs, touchbacks, and fair catches were obtained for all in-conference games from the 2016 to 2021 seasons. The number of game concussions and the play type were provided by each conference institution. Incidence of concussions occurring during kickoff plays before (2016-2017) and after (2018-2021) the rule change were compared with a difference-in-difference analysis using Poisson general linear models. Results: There were 242 concussions in 108,774 total plays in the study period, with an overall concussion rate of 2.2 per 1000 plays. The percentage of touchbacks increased significantly from 45% to 51% ( P < .001) and the percentage of fair catches increased from 1% to 7% ( P < .001) from before to after the rule change. Kickoffs accounted for 6% of plays both before and after the rule change and 11% of concussions before and 14% after the change. The mean annual concussion rate (per 1000 plays) on kickoffs was 3.42 before and 5.31 after the rule change (rate difference: 1.89; 95% confidence interval, -1.22 to 5.01). Conclusion: Touchbacks and fair catches increased after the kickoff rule change, but there was not a corresponding decrease in concussions during kickoff plays as anticipated. Concussions occurring during other football plays remained stable.

Objective The Athlete Psychological Strain Questionnaire (APSQ) is a 10-item mental health screen in the International Olympic Committee Sport Mental Health Assessment Tool 1. This study evaluated its accuracy compared to six validated mental health screening tools in collegiate athletes. Methods Division I collegiate athletes (n=2758) completed the APSQ alongside validated screening tools for anxiety (Generalised Anxiety Disorder-7 (GAD-7)), depression (Patient Health Questionnaire-9 (PHQ-9)), sleep disturbance (Athlete Sleep Screening Questionnaire-Sleep Difficulty Score (ASSQ-SDS)), alcohol use (Alcohol Use Disorders Identification Test-Consumption (AUDIT-C)), substance use (Cutting down, Annoyance by Criticism, Guilty feelings and Eye openers Adapted to Include Drugs (CAGE-AID) and disordered eating (Eating Disorder Examination—Questionnaire Short (EDE-QS)) between June 2020 and June 2023. The APSQ’s performance was assessed using sensitivity, specificity and area under the curve (AUC). Results Of 2758 completed APSQ, 720 (26.1%) screened positive. The rates of positive screens for other measures were GAD-7=7.7%, PHQ-9=11.6% (3.1% reported suicidal ideation on item 9), ASSQ-SDS=21.0%, AUDIT-C=14.5%, CAGE-AID=2.9% and EDE-QS=4.8%. Compared with the APSQ, we observed high accuracy for anxiety (GAD-7: AUC=0.92, sensitivity=90%, specificity=83%) and depression (PHQ-9: AUC=0.91, sensitivity=82%, specificity=84%). There was moderate accuracy for disordered eating (EDE-QS: AUC=0.85, sensitivity=82%, specificity=81%), while accuracy was lower for sleep disturbance (ASSQ-SDS: AUC=0.72, sensitivity=52%, specificity=84%), substance use (CAGE-AID: AUC=0.69, sensitivity=61%, specificity=79%) and alcohol use (AUDIT-C: AUC=0.64, sensitivity=46%, specificity=81%). False-negative rates ranged from 9.9% (GAD-7) to 55% (AUDIT-C). Conclusion The APSQ accurately identifies athletes at risk for anxiety and depression but is less accurate for detecting other mental health issues. Adding more specific questions on sleep disturbance and alcohol use to the APSQ could further improve its accuracy.