ADHD Among High-Level Athletes and Its Potentially Beneficial Role in Athlete Development
Bibliographic record
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a relatively common neurodevelopmental condition affecting 2.5% of the general adult population. Some researchers have suggested that certain ADHD traits may benefit one’s athletic performance. Therefore, its prevalence might be even higher among athletes, especially high-level athletes. Yet, we know little about how common ADHD is among athletes from different skill levels. In North America, the assessment of ADHD among athletes has relied on medication reports and prior medical diagnoses. However, some adults are unaware of having the condition and, thus, do not have a diagnosis. Using self-reports to assess ADHD in adult athletes may improve our estimation of ADHD rates. In this cross-sectional study, 138 students from a Canadian university above 18 years old answered a survey containing the Adult ADHD Self-Report Scale (ASRS v1.1) along with sports background and demographic questions to investigate the potential prevalence of ADHD among athletes using a standardized self-report scale and to test for skill group differences in ADHD. We found ADHD symptoms were present in adult athletes at a rate approximately 4 times greater than previous studies assessing ADHD through medical reports/diagnoses. Further, we found a higher ADHD rate in intermediate/high-level athletes than low-level athletes. These findings may suggest potential benefits of ADHD for athletes, particularly at higher skill levels, and that ADHD rates among athletes may be underestimated. This study warrants further research to better understand the potential athletic development benefit for athletes with ADHD and contributes to a more comprehensive narrative of ADHD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".