Mental Health and the Youth Athlete: An Analysis of the HeartBytes Database
Bibliographic record
Abstract
Abstract Background There is a high rate of mental health conditions among the youth athlete population; however, there is limited information about the impact mental health disorders can have on their overall health and athletic performance. Methods Data was collected by Simon’s Heart, a nonprofit organization that coordinates pre-participation examinations (PPE) for adolescents. Multivariable logistic regression adjusting for age, gender, race, BMI, asthma, anemia, and participation in sports was performed to assess the relationship between mental health disorders and each outcome. Results The HeartBytes dataset is composed of screening data from 7425 patients ranging from 12 to 20 years old. We identified 565 patients (7.6%) diagnosed with ADHD and 370 patients (5.0%) diagnosed with anxiety/depression. Screened participants without a diagnosis of ADHD, anxiety, or depression were significantly more likely to play sports compared to those with these conditions (75.4% vs 59.4%, P < 0.001 for ADHD; 89.4% vs 72.4%, P < 0.001 for anxiety/depression). Those with anxiety/depression not on an antidepressant (OR: 2.09, CI: 1.31–3.19, P < 0.01), but not those on an antidepressant (OR: 1.87, CI: 0.96–3.33, P = 0.05), were more likely to report chest pain or dyspnea with exercise. Those with ADHD not on a stimulant (aOR 1.91, CI 1.22–2.89, p < 0.01), but not those on a stimulant (aOR 1.40, CI 0.82–2.24, p = 0.19) were more likely to report palpitations. ECG abnormalities were not more prevalent regardless of anxiety/depression, ADHD, or medical therapy with an antidepressant or a stimulant. Conclusion Young athletes with anxiety/depression were less likely to participate in sports than healthy individuals. This may be due to deterrence as a result of the symptoms, such as dyspnea or chest pain, they experience during exercise. However, those taking antidepressants had fewer symptoms during exercise. Given that exercise has been shown to improve depression/anxiety, increasing the rate of antidepressant use may lead to less symptoms, more exercise, and an overall improvement in the mental health conditions in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".