Many children and adolescents do not achieve adequate sleep durations. The prevalence of sleep problems has been estimated at 7% for typically developing children (Corkum, Tannock, & Moldofsky, 1998) and as high as 45% for representative samples of children, including participants with various diagnoses in proportion to what would be expected in the population (Sher-Fen Gau, 2006). For children with ADHD, the prevalence of sleep problems has been estimated at between 25-50% (Corkum, Tannock, & Moldofsky, 1998). Given the important role that sleep plays in children with ADHD, a brief and effective screener is needed to aid clinicians in assessing for sleep problems, especially when the referral for a neuropsychological evaluation concerns ADHD or any other neurodevelopmental disorder for which presenting concerns involve symptoms that overlap with ADHD. While the developers of the BEARS have demonstrated its utility as a screening tool, there is currently no independent published research replicating this finding. The current study aimed to replicate the findings of the BEARS developers by demonstrating its utility as a sensitive screening tool for sleep problems. It was predicted that the BEARS would demonstrate high sensitivity in identifying children with sleep problems.

Data from 54 school aged children (aged 6-147-13, Mage = 9.83) was analysed. Children were administered the BEARS, and caregivers completed the BEARS and Children's Sleep Habits Questionnaire (CSHQ), as part of a larger study.

Binomial logistic regression model was statistically significant, x2(2) = 20.508, p < .0005. The model explained 46.8% (Nagelkerke R2) of the variance and correctly classified 70.8% of cases. Sensitivity was 78.6%, specificity was 60.0%, positive predictive value was 73.3%, and negative predictive value was 66.7%. Both predictor variables, parent reported BEARS (p = .001) and child-reported BEARS (p = .049), were significant. Children with higher BEARS parent report scores had 3.27 times higher odds, and those with higher self-report scores had 2.88 times higher odds, of exceeding the CSHQ cut-off than those with lower scores. ROC curve analysis revealed that the BEARS parent and self-report scores had excellent diagnostic utility (Hosmer et al., 2013) for accurately classifying children who exceeded the cut-off on the CSHQ from those who did not (area under the curve [AUC] = 0.849, SE = 0.054, 95% CI = .742 to .956, p < .001).

The results of the current study indicate that the BEARS has excellent diagnostic utility for accurately classifying sleep problems. Additionally, it is quick to administer making it a practical screening tool for clinicians to include as part of a comprehensive neuropsychological assessment.