RESUMEN
Executive function influences children's learning abilities and organizes their cognitive processes, behaviors, and emotions. This cross-sectional study examined whether an Indonesian Computer-Based Game (ICbG) prototype could be used as a Computer-Based Game Inventory for Executive Function (CGIEF) in children and adolescents. The study was conducted with 200 children, adolescents, and their parents. The parents completed the Behavior Rating Inventory of Executive Functioning (BRIEF) questionnaire, and the children and adolescents completed the CGIEF. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were performed using LISREL Version 8.80. The construct of CGIEF was valid/fit with normal theory-weighted least squares = 15.75 (p > .05). SEM analysis showed that the theoretical construct of the CGIEF was a valid predictor of executive function. The critical t value of the pathway was 2.45, and normal theory-weighted least squares was 5.74 (p > .05). The construct reliability (CR) for CGIEF was 0.91. Concurrent validity was assessed using the Bland-Altman plot, and the coefficient of repeatability (bias/mean) was nearly zero between the t scores of total executive functions of the CGIEF and BRIEF. This preliminary study showed that the CGIEF can be useful as a screening tool for executive dysfunction, metacognitive deficits, and behavioral regulation problems among children and adolescents in clinical samples.
RESUMEN
The traditional diagnosis of Attention Deficits/Hyperactivity Disorder (ADHD) is through parent-child interviews and observations; therefore, innovative ADHD diagnostic tools that represent this digital era are needed. Virtual reality (VR) is a significant technology that can present a virtual immersive environment; it can provide an illusion of participation in an artificial milieu for children with ADHD. This study aimed to develop an ADHD-VR diagnostic tool construct (Research Domain Construct/RDC) based on the DSM5 ADHD diagnostic criteria, and using the RDC to develop a diagnostic tool with a machine learning (ML) application that can produce an intelligent model to receive some complex and multifaceted clinical data (ADHD clinical symptoms). We aimed to expand a model algorithm from the data, and finally make predictions by providing new data (output data) that have more accurate diagnostic value. This was an exploratory qualitative study and consisted of two stages. The first stage of the study applied the Delphi technique, and the goal was to translate ADHD symptoms based on DSM 5 diagnostic criteria into concrete behavior that can be observed among children in a classroom setting. This stage aimed to gather information, perceptions, consensus, and confirmation from experts. In this study, three rounds of Delphi were conducted. The second stage was to finalize the RDC of the ADHD-VR diagnostic tool with ML, based on the first-stage results. The results were transformed into concrete activities that could be applied in the programming of the ADHD-VR diagnostic tool, followed by starting to input data that were required to build the diagnostic tool. The second stage consisted of more than ten focus-group discussions (FGDs) before it could be transformed into the ADHD-VR diagnostic tool with the ML prototype. First-stage data analysis was performed using Microsoft Excel for Mac. Qualitative data were analyzed using conceptual content analysis with a manifest/latent analysis approach. From the first stage of the study, there were 13 examples of student behaviors that received more than 75% totally agreed or agreed from the experts. The RDC of the ADHD-VR diagnostic tool with machine learning application consisted of three domains and was divided into six sub-domains: reward-related processing, emotional lability, inhibitory, sustained attention, specific timing of playing in order, and arousal. In conclusion, the results of this study can be used as a reference for future studies in a similar context and content, that is, the ADHD-VR diagnostic tool with machine learning based on the constructed RDC.
RESUMEN
The aim of this study was to develop an Indonesian computer-based game prototype, including feasibility testing, targeted on attention deficit/hypersensitivity disorder (ADHD) clinical symptoms and executive function. The study comprised five steps. The first to third steps used an exploratory qualitative research design. The Delphi technique with FGD was applied to collect qualitative data. During the study, seven experts participated in ten FGDs. Feasibility testing was conducted as a one group pre- and post-test design that included ten children with drug-naïve ADHD without other mental or physical disorders. Feasibility data were collected before and after 20 training sessions with the Indonesian computer-based game prototype. The framework analysis was performed for qualitative data. Quantitative data were analyzed using the paired t-test, Pearson's correlation and Spearman's rank-order correlation. Outputs of the exploratory qualitative study were the Indonesian computer-based game prototype constructs and general agreements of the prototype,. The Indonesian computer-based game prototype construct comprised six components: reward-related processing, control inhibition, improved sustained attention, specific timing, increased arousal, and improved emotional regulation. After 20 sessions of training, several indicators decreased significantly, such as CATPRS-teacher rating (18.5 [5.31] vs. 12.9 [5.51], p = 0.047), BRIEF-GEC (64.80 [10.21] vs. 57.50 [7.51], p = 0.02), BRIEF-MI (66.1 [7.61] vs. 58.4 [7.56], p = 0.014), BRIEF-Initiate (66.6 [10.15] vs. 54.1 [6.49], p = 0.008), BRIEF-Working Memory (68.0 [6.89] vs. 60.9 [10.05], p = 0.02), and BRIEF-Organization of Material (60.7 [12.88] vs. 49.3 [11.79], p = 0.04). There was a low to moderate correlation between CATPRS-teacher and -parent rating and several BRIEF domains. Feasibility testing output also included the training procedure guideline. The present study indicated that the Indonesian computer-based game prototype could be used as a framework to develop a fixed computer-based game intervention for children with ADHD. However, further randomized controlled studies need to be conducted to show its effectiveness.
RESUMEN
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder among children resulting in disturbances in their daily functioning. Virtual reality (VR) and machine learning technologies, such as deep learning (DL) application, are promising diagnostic tools for ADHD in the near future because VR provides stimuli to replace real stimuli and recreate experiences with high realism. It also creates a playful virtual environment and reduces stress in children. The DL model is a subset of machine learning that can transform input and output data into diagnostic values using convolutional neural network systems. By using a sensitive and specific ADHD-VR diagnostic tool prototype for children with DL model, ADHD can be diagnosed more easily and accurately, especially in places with few mental health resources or where tele-consultation is possible. To date, several virtual reality-continuous performance test (VR-CPT) diagnostic tools have been developed for ADHD; however, they do not include a machine learning or deep learning application. A diagnostic tool development study needs a trustworthy and applicable study design and conduct to ensure the completeness and transparency of the report of the accuracy of the diagnostic tool. The proposed four-step method is a mixed-method research design that combines qualitative and quantitative approaches to reduce bias and collect essential information to ensure the trustworthiness and relevance of the study findings. Therefore, this study aimed to present a brief review of a ADHD-VR digital game diagnostic tool prototype with a DL model for children and the proposed four-step method for its development.