The Clinical Validation of the Athlete Sleep Screening Questionnaire: an Instrument to Identify Athletes that Need Further Sleep Assessment
Bibliographic record
Abstract
BACKGROUND: Previous research has established that general sleep screening questionnaires are not valid and reliable in an athlete population. The Athlete Sleep Screening Questionnaire (ASSQ) was developed to address this need. While the initial validation of the ASSQ has been established, the clinical validity of the ASSQ has yet to be determined. The main objective of the current study was to evaluate the clinical validity of the ASSQ. METHODS: Canadian National Team athletes (N = 199; mean age 24.0 ± 4.2 years, 62% females; from 23 sports) completed the ASSQ. A subset of athletes (N = 46) were randomized to the clinical validation sub-study which required subjects to complete an ASSQ at times 2 and 3 and to have a clinical sleep interview by a sleep medicine physician (SMP) who rated each subjects' category of clinical sleep problem and provided recommendations to improve sleep. To assess clinical validity, the SMP category of clinical sleep problem was compared to the ASSQ. RESULTS: The internal consistency (Cronbach's alpha = 0.74) and test-retest reliability (r = 0.86) of the ASSQ were acceptable. The ASSQ demonstrated good agreement with the SMP (Cohen's kappa = 0.84) which yielded a diagnostic sensitivity of 81%, specificity of 93%, positive predictive value of 87%, and negative predictive value of 90%. There were 25.1% of athletes identified to have clinically relevant sleep disturbances that required further clinical sleep assessment. Sleep improved from time 1 at baseline to after the recommendations at time 3. CONCLUSIONS: Sleep screening athletes with the ASSQ provides a method of accurately determining which athletes would benefit from preventative measures and which athletes suffer from clinically significant sleep problems. The process of sleep screening athletes and providing recommendations improves sleep and offers a clinical intervention output that is simple and efficient for teams and athletes to implement.
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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 | 0.007 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".