The usefulness of virtual, augmented, and mixed reality technologies in the diagnosis and treatment of attention deficit hyperactivity disorder in children: an overview of relevant studies

Diagnosis of ADHD using virtual reality and artificial intelligence: an exploratory study of clinical applications

Introduction

Diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) is based on clinical evaluation of symptoms by a psychiatrist, referencing results of psychological tests. When diagnosing ADHD, the child's behavior and functionality in real-life situations are critical components. However, direct observation by a clinician is often not feasible in practice. Therefore, such information is typically gathered from primary caregivers or teachers, which can introduce subjective elements. To overcome these limitations, we developed AttnKare-D, an innovative digital diagnostic tool that could analyze children's behavioral data in Virtual Reality using Artificial Intelligence. The purpose of this study was to explore the utility and safety of AttnKare-D for clinical application.

Method

A total of 21 children aged between 6 and 12 years were recruited for this study. Among them, 15 were children diagnosed with ADHD, 5 were part of a normal control group, and 1 child was excluded due to withdrawal of consent. Psychological assessments, including K-WISC, Conners CPT, K-ARS, and K-CBCL, were conducted for participants and their primary caregivers. Diagnoses of ADHD were confirmed by child and adolescent psychiatrists based on comprehensive face-to-face evaluations and results of psychological assessments. Participants underwent VR diagnostic assessment by performing various cognitive and behavioral tasks in a VR environment. Collected data were analyzed using an AI model to assess ADHD diagnosis and the severity of symptoms.

Results

AttnKare-D demonstrated diagnostic performance with an AUC of 0.893 when compared to diagnoses made by child and adolescent psychiatrist, showing a sensitivity of 0.8 and a specificity of 1.0 at a cut-off score of 18.44. AttnKare-D scores showed a high correlation with K-ARS scores rated by parents and experts, although the correlation was relatively low for inattention scores.

Conclusion

Results of this study suggest that AttnKare-D can be a useful tool for diagnosing ADHD in children. This approach has potential to overcome limitations of current diagnostic methods, enhancing the accuracy and objectivity of ADHD diagnoses. This study lays the groundwork for further improvement and research on diagnostic tools integrating VR and AI technologies. For future clinical applications, it is necessary to conduct clinical trials involving a sufficient number of participants to ensure reliable use.

Practitioner Review: Clinical utility of the QbTest for the assessment and diagnosis of attention-deficit/hyperactivity disorder - a systematic review and meta-analysis

BACKGROUND

Several computerised cognitive tests (e.g. continuous performance test) have been developed to support the clinical assessment of attention-deficit/hyperactivity disorder (ADHD). Here, we appraised the evidence-base underpinning the use of one of these tests - the QbTest - in clinical practice, by conducting a systematic review and meta-analysis investigating its accuracy and clinical utility.

METHODS

Based on a preregistered protocol (CRD42022377671), we searched PubMed, Medline, Ovid Embase, APA PsycINFO and Web of Science on 15th August 2022, with no language/type of document restrictions. We included studies reporting accuracy measures (e.g. sensitivity, specificity, or Area under the Receiver Operating Characteristics Curve, AUC) for QbTest in discriminating between people with and without DSM/ICD ADHD diagnosis. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). A generic inverse variance meta-analysis was conducted on AUC scores. Pooled sensitivity and specificity were calculated using a random-effects bivariate model in R.

RESULTS

We included 15 studies (2,058 participants; 48.6% with ADHD). QbTest Total scores showed acceptable, rather than good, sensitivity (0.78 [95% confidence interval: 0.69; 0.85]) and specificity (0.70 [0.57; 0.81]), while subscales showed low-to-moderate sensitivity (ranging from 0.48 [0.35; 0.61] to 0.65 [0.52; 0.75]) and moderate-to-good specificity (from 0.65 [0.48; 0.78] to 0.83 [0.60; 0.94]). Pooled AUC scores suggested moderate-to-acceptable discriminative ability (Q-Total: 0.72 [0.57; 0.87]; Q-Activity: 0.67 [0.58; 0.77); Q-Inattention: 0.66 [0.59; 0.72]; Q-Impulsivity: 0.59 [0.53; 0.64]).

CONCLUSIONS

When used on their own, QbTest scores available to clinicians are not sufficiently accurate in discriminating between ADHD and non-ADHD clinical cases. Therefore, the QbTest should not be used as stand-alone screening or diagnostic tool, or as a triage system for accepting individuals on the waiting-list for clinical services. However, when used as an adjunct to support a full clinical assessment, QbTest can produce efficiencies in the assessment pathway and reduce the time to diagnosis.

Systematic Review and Meta-Analysis: Clinical Utility of Continuous Performance Tests for the Identification of Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

We aimed to quantify the clinical utility of continuous performance tests (CPTs) for the diagnosis of attention-deficit/hyperactivity disorder (ADHD) compared to a clinical diagnosis in children and adolescents.

METHOD

Four databases (MEDLINE, PsycINFO, EMBASE, and PubMed) were screened until January 2023. Risk of bias of included results was judged with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). We statistically pooled the area under the curve, the sensitivity, and the specificity of 3 commonly used CPTs subscales: omission/inattention, commission/impulsivity, and total number of errors/ADHD subscales (PROSPERO registration: CRD42020168091).

RESULTS

A total of 19 studies using commercially available CPTs were identified. Results from up to 835 control individuals and 819 cases were combined in the summary receiver operating characteristic (ROC) curve analyses (sensitivity and specificity pooling), and up to 996 cases and 1,083 control individuals in the area under the curve (AUC) analyses. Clinical utility as measured by AUCs could be considered as barely acceptable (between 0.7 and 0.8) for the most part, with the best results for the total/ADHD score, followed by omissions/inattention, and poorest for commission/impulsivity scores. A similar pattern was found when pooling sensitivity and specificity: 0.75 (95% CI = 0.66-0.82) and 0.71 (0.62-0.78) for the total/ADHD score; 0.63 (0.49-0.75) and 0.74 (0.65-0.81) for omissions; and 0.59 (0.38-0.77) and 0.66 (CI = 0.50-0.78) for commissions.

CONCLUSION

At the clinical level, CPTs as a stand-alone tool have only a modest to moderate ability to differentiate ADHD from non-ADHD samples. Hence, they should be used only within a more comprehensive diagnostic process.

STUDY PREREGISTRATION INFORMATION

A systematic review of screening tools for ADHD in children and adolescents; https://www.crd.york.ac.uk/prospero/; CRD42020168091.

A systematic review on the use of technology to enhance the academic achievements of children with attention deficit hyperactivity disorder in language learning

This review investigates the use of technology for children and adolescents with Attention Deficit Hyperactivity Disorder and their academic achievements in language learning, as well as their attitudes towards technology use. The goal is to integrate this information in order to learn how technology is provided to this population regarding first and second language learning, and also how effective and appropriate its use is and how it is perceived by stakeholders in the learning process. A systematic review was conducted, and after the final screening process, 15 studies were analysed, the majority of which were about first language learning (n = 11). Most studies found that technology use has a positive impact on students' language skills and that students tend to find it more motivating and engaging. While these technologies seem to be promising tools for improving skills, further studies must be conducted, especially in the field of second language learning.

Effectiveness of Metaverse Space-Based Exercise Video Distribution in Young Adults: Randomized Controlled Trial

BACKGROUND

In response to the serious lack of physical activity among young adults, recent attempts have been made to encourage young people to exercise through exercise video distribution. However, merely distributing videos does not lead to improved physical activity levels. Metaverse space, which enables web-based interaction through avatars, allows users to watch exercise videos in the same space as other avatars.

OBJECTIVE

This study explored whether exercise video distribution using metaverse space is effective in improving physical activity levels, along with mental health and locomotive function, among young people.

METHODS

In this parallel-group randomized controlled trial participants were recruited using printed poster displays. A total of 48 young adults aged between 18 and 30 years were assigned to 3 groups of 16 each: the metaverse, YouTube, and control group. To encourage exercise, the metaverse group was given an exercise video each week with a load of around 4-8 metabolic equivalents of tasks (METs) for 8 videos delivered in the metaverse space. The YouTube group was sent a URL on YouTube every week to view exercise videos with the same content as the metaverse group. The control group was given no special instructions. The intervention period was 8 weeks. Pre- and postintervention physical activity, well-being, locomotive syndrome risk tests, and social capital were measured. Although this study was not blinded to the participants, the measurers did not know to which group the participants belonged. Mixed model repeated-measures analyses and a post hoc Wilcoxon signed rank sum test were performed to detect the effects of the intervention in all groups.

RESULTS

The results of the mixed model repeated-measures analyses showed a significant interaction between groups and before and after the intervention for total physical activity (metaverse group: pre 737.1, SD 609.5 METs/week, post 1575.4, SD 1071.8 METs/week; YouTube group: pre 661.7, SD 710.7 METs/week, post 911.9, SD 1103.3 METs/week; and control group: pre 930.6, SD 665.1 METs/week, post 844.7, SD 701.8 METs/week; P=.04) but none for the indicators of well-being (P=.40), locomotive function scale (P=.17), and social capital (P=.23). A post hoc test showed a significant increase in physical activity in the metaverse group before and after the intervention (P=.006).

CONCLUSIONS

This study is the first to show that delivering exercise videos through metaverse space can increase physical activity in young adults by providing a gathering space for individuals similarly motivated for exercise practice. This way, the sense of isolation during exercise is reduced compared with merely distributing videos on YouTube. The use of metaverse space in health promotion is likely to spread, and this study provides a useful reference for its exploration.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06019156; https://ichgcp.net/clinical-trials-registry/NCT06019156.

Being in Virtual Reality and Its Influence on Brain Health-An Overview of Benefits, Limitations and Prospects

BACKGROUND

Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value.

OBJECTIVE

Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users.

METHODS

This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience.

RESULTS

More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals.

CONCLUSIONS

VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience.

Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review

The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities.

Realizing consumers' existential dreams via product marketing and mixed reality: a perspective based on affective neuroscience theories

In an era of swift societal changes and escalating consumerism, this paper presents an exploration of an innovative approach that integrates product marketing strategies, mixed reality (MR) technology, and affective neuroscience theories to actualize consumers' existential dreams. MR, with its unique capacity to blend the virtual and real worlds, can enhance the consumer experience by creating immersive, personalized environments that resonate with consumers' existential aspirations. Insights from affective neuroscience, specifically the brain's processing of emotions, guide the development of emotionally engaging marketing strategies, which strengthen the connection between consumers, products, and brands. These integrated strategies not only present a novel blueprint for companies to deepen consumer engagement but also promise more fulfilling and meaningful consumer experiences. Moreover, this approach contributes to societal well-being and prosperity, marking a significant stride in the field of marketing.

Full-sized realistic 3D printed models of liver and tumour anatomy: a useful tool for the clinical medicine education of beginning trainees

BACKGROUND

Simulation-based medical education (SBME) and three-dimensional printed (3DP) models are increasingly used in continuing medical education and clinical training. However, our understanding of their role and value in improving trainees' understanding of the anatomical and surgical procedures associated with liver surgery remains limited. Furthermore, gender bias is also a potential factor in the evaluation of medical education. Therefore, the aim of this study was to evaluate the educational benefits trainees receive from the use of novel 3DP liver models while considering trainees' experience and gender.

METHODS

Full-sized 3DP liver models were developed and printed using transparent material based on anonymous CT scans. We used printed 3D models and conventional 2D CT scans of the liver to investigate thirty trainees with various levels of experience and different genders in the context of both small group teaching and formative assessment. We adopted a mixed methods approach involving both questionnaires and focus groups to collect the views of different trainees and monitors to assess trainees' educational benefits and perceptions after progressing through different training programs. We used Objective Structured Clinical Examination (OSCE) and Likert scales to support thematic analysis of the responses to the questionnaires by trainees and monitors, respectively. Descriptive analyses were conducted using SPSS statistical software version 21.0.

RESULTS

Overall, a 3DP model of the liver is of great significance for improving trainees' understanding of surgical procedures and cooperation during operation. After viewing the personalized full-sized 3DP liver model, all trainees at the various levels exhibited significant improvements in their understanding of the key points of surgery (p < 0.05), especially regarding the planned surgical procedure and key details of the surgical procedures. More importantly, the trainees exhibited higher levels of satisfaction and self-confidence during the operation regardless of gender. However, with regard to gender, the results showed that the improvement of male trainees after training with the 3DP liver model was more significant than that of female trainees in understanding and cooperation during the surgical procedure, while no such trend was found with regard to their understanding of the base knowledge.

CONCLUSION

Trainees and monitors agreed that the use of 3DP liver models was acceptable. The improvement of the learning effect for practical skills and theoretical understanding after training with the 3DP liver models was significant. This study also indicated that training with personalized 3DP liver models can improve all trainees' presurgical understanding of liver tumours and surgery and males show more advantage in understanding and cooperation during the surgical procedure as compared to females. Full-sized realistic 3DP models of the liver are an effective auxiliary teaching tool for SBME teaching in Chinese continuing medical education.

Virtual Reality Technology to Enhance Conventional Rehabilitation Program: Results of a Single-Blind, Randomized, Controlled Pilot Study in Patients with Global Developmental Delay

Global developmental delay (GDD) is a complex disorder that requires multimodal treatment involving different developmental skills. The objective of this single-blind, randomized, controlled pilot study is to evaluate the feasibility and effectiveness of conventional rehabilitation programs integrated with the BTs-Nirvana virtual reality system. Patients with GDD aged 12 to 66 months were enrolled and treated for a 48-session cycle. Patients were randomized into two groups, (1) conventional treatment and (2) conventional treatment supplemented with the use of BTs-Nirvana, in a 1:1 ratio. Before and after treatments, areas of global development were tested with the Griffiths-III Mental Developmental Scale and the clinical indicator of global improvement were measured with the Clinical Global Impressions-Improvement (CGI-I). Feasibility was confirmed by the high retention rate. The experimental group presented a significantly improvement in General Quotient (GQ) after treatment (GQ, p = 0.02), and the effect of the two treatments was significantly different in both the GQ (t =2.44; p = 0.02) and the Foundations of Learning subscale (t =3.66; p < 0.01). The overall improvement was also confirmed by the CGI-I (p = 0.03). According to these preliminary data, virtual reality can be considered a useful complementary tool to boost the effectiveness of conventional therapy in children with GDD.

Current development of biosensing technologies towards diagnosis of mental diseases

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Immersive virtual reality for improving cognitive deficits in children with ADHD: a systematic review and meta-analysis

Virtual reality (VR) shows great potential in treating and managing various mental health conditions. This includes using VR for training or rehabilitation purposes. For example, VR is being used to improve cognitive functioning (e.g. attention) among children with attention/deficit-hyperactivity disorder (ADHD). The aim of the current review and meta-analysis is to evaluate the effectiveness of immersive VR-based interventions for improving cognitive deficits in children with ADHD, to investigate potential moderators of the effect size and assess treatment adherence and safety. The meta-analysis included seven randomised controlled trials (RCTs) of children with ADHD comparing immersive VR-based interventions with controls (e.g. waiting list, medication, psychotherapy, cognitive training, neurofeedback and hemoencephalographic biofeedback) on measures of cognition. Results indicated large effect sizes in favour of VR-based interventions on outcomes of global cognitive functioning, attention, and memory. Neither intervention length nor participant age moderated the effect size of global cognitive functioning. Control group type (active vs passive control group), ADHD diagnostic status (formal vs. informal) and novelty of VR technology were not significant moderators of the effect size of global cognitive functioning. Treatment adherence was similar across groups and there were no adverse effects. Results should be cautiously interpreted given the poor quality of included studies and small sample.

Effectiveness of Augmented and Virtual Reality-Based Interventions in Improving Knowledge, Attitudes, Empathy and Stigma Regarding People with Mental Illnesses-A Scoping Review

Interventions adopting augmented and virtual reality (AR/VR) modalities allow participants to explore and experience realistic scenarios, making them useful psycho-educational tools for mental illnesses. This scoping review aims to evaluate the effectiveness of AR/VR interventions in improving (1) knowledge, (2) attitudes, (3) empathy and (4) stigma regarding people with mental illnesses. Literature on published studies in English up till April 2022 was searched within several databases. Sixteen articles were included. The majority of studies were conducted in the West (93.8%), within undergraduates (68.8%) but also amongst high school students, patients, caregivers, public including online community, and covered conditions including psychotic illnesses, dementia, anxiety and depression. A preponderance of these included studies which employed AR/VR based interventions observed improvements in knowledge (66.7%), attitudes (62.5%), empathy (100%) and reduction of stigma (71.4%) pertaining to people with mental illnesses. In the context of relatively limited studies, extant AR/VR based interventions could potentially improve knowledge, attitudes, empathy and decrease stigma regarding people with mental illness. Further research needs to be conducted in larger and more diverse samples to investigate the relatively beneficial effects of different AR/VR modalities and the durability of observed improvements of relevant outcomes of interests over time for different mental conditions.

Taking modern psychiatry into the metaverse: Integrating augmented, virtual, and mixed reality technologies into psychiatric care

The landscape of psychiatry is ever evolving and has recently begun to be influenced more heavily by new technologies. One novel technology which may have particular application to psychiatry is the metaverse, a three-dimensional digital social platform accessed via augmented, virtual, and mixed reality (AR/VR/MR). The metaverse allows the interaction of users in a virtual world which can be measured and manipulated, posing at once exciting new possibilities and significant potential challenges and risks. While the final form of the nascent metaverse is not yet clear, the immersive simulation and holographic mixed reality-based worlds made possible by the metaverse have the potential to redefine neuropsychiatric care for both patients and their providers. While a number of applications for this technology can be envisioned, this article will focus on leveraging the metaverse in three specific domains: medical education, brain stimulation, and biofeedback. Within medical education, the metaverse could allow for more precise feedback to students performing patient interviews as well as the ability to more easily disseminate highly specialized technical skills, such as those used in advanced neurostimulation paradigms. Examples of potential applications in brain stimulation and biofeedback range from using AR to improve precision targeting of non-invasive neuromodulation modalities to more innovative practices, such as using physiological and behavioral measures derived from interactions in VR environments to directly inform and personalize treatment parameters for patients. Along with promising future applications, we also discuss ethical implications and data security concerns that arise when considering the introduction of the metaverse and related AR/VR technologies to psychiatric research and care.

Extended reality for mental health: Current trends and future challenges

Virtual and augmented reality have been used to diagnose and treat several mental health disorders for decades. Technological advances in these fields have facilitated the availability of commercial solutions for end customers and practitioners. However, there are still some barriers and limitations that prevent these technologies from being widely used by professionals on a daily basis. In addition, the COVID-19 pandemic has exposed a variety of new scenarios in which these technologies could play an essential role, like providing remote treatment. Disorders that traditionally had received less attention are also getting in the spotlight, such as depression or obsessive-compulsive disorder. Improvements in equipment and hardware, like Mixed Reality Head Mounted Displays, could help open new opportunities in the mental health field. Extended reality (XR) is an umbrella term meant to comprise Virtual reality (VR), mixed reality (MR), and augmented reality (AR). While XR applications are eminently visual, other senses are being explored in literature around multisensory interactions, such as auditory, olfactory, or haptic feedback. Applying such stimuli within XR experiences around mental disorders is still under-explored and could greatly enrich the therapeutic experience. This manuscript reviews recent research regarding the use of XR for mental health scenarios, highlighting trends, and potential applications as well as areas for improvement. It also discusses future challenges and research areas in upcoming topics such as the use of wearables, multisensory, and multimodal interaction. The main goal of this paper is to unpack how these technologies could be applied to XR scenarios for mental health to exploit their full potential and follow the path of other health technologies by promoting personalized medicine.

Future of mental health in the metaverse

The metaverse and non-fungible tokens (NFTs) were some of the hottest tech terms in 2021, according to a Google Trends search. Our review aims to describe the metaverse and NFTs in the context of their potential application in the treatment of mental health disorders. Advancements in technology have been changing human lives at an ever-increasing pace. Metaverse, also known as the three-dimensional (3D) internet, is the convergence of virtual reality (VR) and physical reality in a digital space. It could potentially change the internet as we know it, with NFTs as the key building blocks in the new expansive virtual ecosystem. This immersive 3D virtual world boasts the features of the real world with the added ability to change the surrounding environment according to individual needs and requirements. VR, augmented reality (AR) and mixed reality (MR) have been employed as tools in the treatment of various mental health disorders for the past decade. Studies have reported positive results on their effectiveness in the diagnosis and treatment of mental health disorders. VR/AR/MR have been hailed as a solution to the acute shortage of mental health professionals and the lack of access to mental healthcare. But, on the flip side, young adults tend to spend a significant amount of time playing 3D immersive games and using social media, which can lead to insecurity, anxiety, depression, and behavioural addiction. Additionally, endless scrolling through social media platforms negatively affects individuals' attention span as well as aggravating the symptoms of adolescents with attention deficit hyperactivity disorder.

We aimed to explore the ramifications of expanding applications of the metaverse on mental health. So far, no other review has explored the future of mental health in the context of the metaverse.

An immersive virtual reality-based application for treating ADHD: A remote evaluation of acceptance and usability

Introduction

Virtual reality (VR) is a digital technology currently considered to implement rehabilitation programs for children with ADHD, a disorder characterised by inattention, overactivity and impulsiveness. This study presents the results of the acceptance and usability of a VR application developed for children with ADHD aiming to provide an environment capable of supporting the development of the different attentional components. Due to COVID-19 restrictions, this study had the secondary aim of assessing whether a remote evaluation was feasible and meaningful.

Methods

A sample of 20 clinical experts (neuro and psychomotor therapists of the developmental age) was involved in assessing the proposed environment. Two different tools have been applied: the Technology Acceptance Model (TAM-3) questionnaire and a semi-structured interview were self-administered. Six sessions were planned in total, and each one lasted 30 min.

Results

With respect to the acceptance of the system, the mean of the answers given is for most of the constructs greater than 4, showing agreement among experts. Cronbach alpha and correlations of subscales seem to confirm the reliability of measures. According to results from the interviews, the developed application has shown versatility in being able to be applied to the heterogeneity of the disorder and it was also possible to obtain valuable insights on possible additional features and functionalities. Regarding the secondary aim, the collected outcomes were positive: all the participants were satisfied with what they could perceive about the application.

Conclusions

The results of this work pave the way for a future validation study with children due to the active participation of clinicians and their unanimous positive judgement confirming that the application was considered user-friendly and well accepted.

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.