Measuring the validity of two continuous performance tests: different parameters and scoring indices

Today, there are numerous versions of the continuous performance test (CPT) used in clinical and research settings. Although CPTs may constitute a similar group of tasks with a common paradigm, they are very different in the parameters they measure (Conners, 1995). To learn more about the effects of different CPT versions as well as the numerous scoring indices, two very different CPTs, the Connersâ Continuous Performance Test-Second Edition (CCPT-II) and the Gordon Diagnostic System (GDS), were compared with a population of children and adolescents exhibiting ADHD and normal controls. Major findings were as follows: (a) the CCPT-II and GDS measures were not able to separate children with ADHD from normal controls; (b) individual variables from neither the CCPT-II nor the GDS were able to adequately differentiate children with ADHD and normal controls; and (c) score profiles obtained from the overall group of children and adolescents did not successfully separate the ADHD group from normal controls using the CCPT-II and GDS

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

Essentials of assessment with brief intelligence tests/ Homack

x, 189 hal.; 21 cm

Essentials of assessment with brief intelligence tests/ Homack

x, 189 hal.; 21 cm

Essentials of assessment with brief intelligence tests/ Homack

x, 189 hal.; 21 cm