Developing attention deficits/hyperactivity disorder-virtual reality diagnostic tool with machine learning for children and adolescents.

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.

Anxiety and Its Associated Factors During the Initial Phase of the COVID-19 Pandemic in Indonesia.

Introduction: Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus which has not been identified previously in humans. The disease leads to respiratory problems, systemic disorders, and death. To stop the virus transmission, physical distancing was strongly implemented, including working and school from home (WFH & SFH). The limitation altered daily routines and needs advanced to adapt. Many have felt uncomfortable and this could have triggered anxiety symptoms. This study aimed to evaluate the proportion of significant anxiety symptoms and its association with COVID-19-related situations in an Indonesian context during the initial months of the pandemic. Methods: An online community survey was distributed through social media and communication platforms, mainly WhatsApp, targeting people >18 years old in Indonesia. Anxiety symptoms were assessed using Generalized Anxiety Disorder-7 (Indonesian Version). Demographical data and information on social situation related to the COVID-19 pandemic were collected. The proportion of clinically significant anxiety symptoms was calculated and the association with demographic and social factors was assessed using chi square test (χ2) and logistic regression for multivariate analysis. Results: Out of 1215 subjects that completed the survey, 20.2% (n = 245) exhibited significant anxiety symptoms. Several factors, such as age (AOR = 0.933 CI 95% = 0.907-0.96), sex (AOR = 1.612 CI 95% = 1.097-2.369), medical workers (AOR = 0.209 CI 95% = 0.061-0.721), suspected case of COVID-19 (AOR = 1.786 CI 95% = 1.001-3.186), satisfaction level of family support (AOR = 3.052 CI 95% = 1.883-4.946), and satisfaction level of co-workers (AOR = 2.523 CI 95% = 1.395-4.562), were associated with anxiety. Conclusion: One out of five Indonesian people could have suffered from anxiety during the COVID-19 pandemic. The riskiest group being young females, people who had suspected cases of COVID-19, and those with less satisfying social support. Nevertheless, health workers were found to have a lesser risk of developing anxiety. Accessible information and healthcare, social connection, supportive environment, and mental health surveillance are important to prevent bigger psychiatric problems post-pandemic.

Developing and feasibility testing of the Indonesian computer-based game prototype for children with attention deficit/hyperactivity disorder.

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.

A Four-Step Method for the Development of an ADHD-VR Digital Game Diagnostic Tool Prototype for Children Using a DL Model.

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.

Brief Research Report on Adolescent Mental Well-Being and School Closures During the COVID-19 Pandemic in Indonesia.

The COVID-19 pandemic does not affected only physical but also mental health and socioeconomic part. The social distancing, social quarantine, school from home, and work from becomes a new normal these days. Being adolescence, the above conditions may be challenging due to their developmental milestones. Therefore, this brief report aimed to preliminary identify proportion of adolescents' emotional and behavior problems and several factors related to it during COVID-19 pandemic in Indonesia. The findings might raise some understanding of youth mental well-being and programs that can be applied in schools and community in general to overcome the issues. The study was designed as cross sectional and used online survey that started on April 2020. During April 15-May 10, 2020, there were 113 adolescents participated on this survey. Strength and Difficulties Questionnaire (SDQ) 11-17 years old was used to assess adolescent emotional and behavior problems; and specific life experience questionnaire was designed to collect other independents variables (Cronbach's α = 0.75). All participants fulfilled the online informed consent before they started to complete the questionnaire. All data was analyzed by using SPSS version 20 for Mac. The average age of research subjects were 14.07 (2.18) years old; 98.2% was school from home. There was 14.2% of the total research subject at risk on total difficulties problems; 38.1% of adolescent was at risk on peer-relationship problems, 28.3% at risk on pro-social behavior problems, 15% at risk on conduct behavior and 10.6% at risk on emotional problems. The number of adolescent that perceived worse to significantly worse self-mental well-being prior COVID-19 increased during COVID-19 pandemic in Indonesia (p < 0.05). There was significantly association between having mental health information and conduct behavior (OR = 10.34, 95%CI = 1.27-78.86); Subjective anxiety due to COVID-19 pandemic and pro-social behavior problems (OR = 2.37, 95% CI = 1.00-5.63), parental support and total difficulties (OR = 0.09, 95% CI = 0.14-0.60) and pro-social behavior problems (OR = 0.09, 95% CI = 0.01-0.82); friends support during COVID-19 pandemic and conduct behavior (OR = 0.20, 95% CI = 0.04-1.00) and pro-social behavior problems (OR = 0.14, 95% CI = 0.02-0.75). To be concluded, during phase 1 and 2 COVID-19 pandemic and school closures in Indonesia, adolescents were at risk for having emotional and behavior problems. Therefore, maintain clear mental health information, keep them on connection with school by designing an optimal tele-education, tele-consultation, and virtual activity programs to accommodate adolescents' biopsychosocial needs in the near future.