RESUMO
BACKGROUND: At present, clinicians diagnose that the clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in children is mainly on the basis of the information provided by their parents, the behaviour of children in clinical clinics and the assessments of clinical rating scales and neuropsychological tests. Notably, no unified standard exists currently for analysing the results of various measurement tools for diagnosing ADHD. Therefore, clinicians interpret the results of clinical rating scales and neuropsychological tests entirely based on their clinical experience. METHODS AND SUBJECTS: To provide guidance for clinicians on how to analyse the results of various clinical assessment tools when diagnosing ADHD, this study assessed children with ADHD and children in the control group using two clinical assessment scales-parent rating scale (PSQ) and Child Behavior Checklist (CBCL)-and one neuropsychological test (Integrated Visual and Auditory Continuous Performance Testing). The two-sample t-test (FDR correction) screened the parameters of the three assessment tools with significant inter-group differences. LibSVM was used to establish a classification prediction model for analysing the accuracy of ADHD prediction using parameters of the three assessment tools and weight values of each parameter for classification prediction. RESULTS: A total of 19 parameters (16 from clinical rating scales, 3 from neuropsychological tests) with significant inter-group differences were screened. The accuracy of classification modelling was higher for the clinical rating scales (61.635%) than for the neuropsychological test (59.784%), whereas the accuracy of classification modelling was higher for the clinical rating scales combined with the neuropsychological test (70.440%) than for the former two parameters alone. The three parameters with the highest weight values were learning problem (0.731), hyperactivity/impulsivity (0.676) and activity capacity (0.569). The three parameters with the lowest weight values are integrated control force (0.028), visual attention (0.028) and integrated attention (0.034). CONCLUSION: Our study findings indicate that the diagnosis of ADHD should be based on multidimensional assessment. For a more accurate diagnosis of ADHD, assessments and that more assessment parameters should be developed on the basis of different dimensions of physiology or psychology in the future to obtain a more accurate diagnosis of ADHD. Furthermore, the predictive model for ADHD may improve our understanding and help in optimisation of the treatment of such a condition.
RESUMO
Background and objective: The development of objective assessment tools for attention deficit hyperactivity disorder (ADHD) has become a hot research topic in recent years. This study was conducted to explore the feasibility and availability of virtual reality (VR) for evaluating symptoms of ADHD. Methods: School-aged children were recruited. The children with ADHD or without ADHD were assigned into the ADHD group or Control group, respectively. They were all evaluated using the Conners' Parent Rating Scale (CPRS), Child Behavior Checklist (CBCL), Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), and a VR test. Results: The correct items, incorrect items, and the accuracy rate of the VR test of the children with ADHD were significantly different with those of the children in the Control group. The correct items, incorrect items, total time, and accuracy of the VR test were significantly correlated with the scores of IVA-CPT (auditory attention and visual attention), CPRS (impulsion/hyperactivity and ADHD index), and CBCL (attention problems and social problems), respectively. Discussion: The results supported the discriminant validity of the VR test for evaluating ADHD in school-age children suffering from learning problems. The VR test results are associated with the commonly used clinical measurements results. A VR test is interesting for children and therefore it attracts them to complete the test; whilst at the same time, it can also effectively evaluate ADHD symptoms.
