The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

Predicting responsiveness to a dialectical behaviour therapy skills training app for recurrent binge eating: A machine learning approach

OBJECTIVE

Smartphone applications (apps) show promise as an effective and scalable intervention modality for disordered eating, yet responsiveness varies considerably. The ability to predict user responses to app-based interventions is currently limited. Machine learning (ML) techniques have shown potential to improve prediction of complex clinical outcomes. We applied ML techniques to predict responsiveness to a dialectical behaviour therapy-based smartphone app for recurrent binge eating.

METHOD

Data were collected as part of a randomised controlled trial (RCT). The present sample was based on data from 576 participants with recurrent binge eating. 10 common classification and regression approaches were used to predict outcomes that represent key stages of the user experience, including initial intervention uptake, app adherence, study drop-out, and symptom change. Models were developed using 69 self-reported baseline variables (i.e., demographic, clinical, psychological) and several app usage variables (i.e., number of modules completed) as predictors.

RESULTS

All models, using only baseline predictors, performed sub-optimally at predicting engagement (AUCs = 0.48-0.61; R2 = 0.00-0.04) and symptom level change (R2 = 0.00-0.07). Incorporating usage data improved prediction of study dropout (AUC = 0.69-0.76).

CONCLUSION

ML models were unable to accurately predict responsiveness using self-reported baseline predictors alone. Predicting outcomes with greater precision may require consideration of how predictors change over time and interact with a user's context. Modelling usage pattern data appears to improve prediction of dropout, highlighting the potential value of tracking intervention usage to identify individuals at risk of disengagement.

Designing smartphone-based cognitive assessments for schizophrenia: Perspectives from a multisite study

Introduction

Cognitive deficits represent a core symptom of schizophrenia and a principal contributor to illness disability, yet evaluating cognition in routine clinical settings is often not feasible as cognitive assessments take longer than a standard doctor's visit. Using smartphones to assess cognition in schizophrenia offers the advantages of convenience in that patients can complete assessments outside of the clinic, temporality in that longitudinal trends can be identified, and contextuality in that cognitive scores can be interpreted with other measures captured by the phone (e.g. sleep). The current study aims to design a battery of cognitive assessments corresponding to the MATRICs Consensus Battery of Cognition (MCCB), in partnership with people living with schizophrenia.

Methodology

Focus group discussions (FGDs) and interviews were conducted with people diagnosed with schizophrenia across three sites (Boston, Bhopal, and Bangalore) to help design, refine, and assess the proposed smartphone battery of cognitive tests on the mindLAMP app. Interviews were conducted between December 2023 and March 2024. Inductive thematic analysis was used to analyze data.

Results

Participants found the app and its proposed cognitive assessments to be acceptable, helpful, and easy to use. They particularly found the gamified nature of the cognitive tests to be appealing and engaging. However, they also proposed ways to further increase engagement by including more information about each cognitive test, more visual instructions, and more information about scoring. Across all sites, there were many similarities in themes.

Discussion & conclusion

People living with schizophrenia, from different sites in the US and India, appear interested in using smartphone apps to track their cognition. Thematic analysis reinforces the importance of feedback and data sharing, although this presents a challenge, given the novel nature of smartphone-based cognitive measures that have not yet been standardized or validated.

The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality

The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and generative artificial intelligence, including large language models. While industry setbacks and methodological critiques have highlighted gaps in evidence and challenges in scaling these technologies, emerging solutions rooted in co-design, rigorous evaluation, and implementation science offer promising pathways forward. This paper underscores the dual necessity of advancing the scientific foundations of digital mental health and increasing its real-world applicability through five themes. First, we discuss recent technological advances in digital phenotyping, virtual reality, and generative artificial intelligence. Progress in this latter area, specifically designed to create new outputs such as conversations and images, holds unique potential for the mental health field. Given the spread of smartphone apps, we then evaluate the evidence supporting their utility across various mental health contexts, including well-being, depression, anxiety, schizophrenia, eating disorders, and substance use disorders. This broad view of the field highlights the need for a new generation of more rigorous, placebo-controlled, and real-world studies. We subsequently explore engagement challenges that hamper all digital mental health tools, and propose solutions, including human support, digital navigators, just-in-time adaptive interventions, and personalized approaches. We then analyze implementation issues, emphasizing clinician engagement, service integration, and scalable delivery models. We finally consider the need to ensure that innovations work for all people and thus can bridge digital health disparities, reviewing the evidence on tailoring digital tools for historically marginalized populations and low- and middle-income countries. Regarding digital mental health innovations as tools to augment and extend care, we conclude that smartphone apps, virtual reality, and large language models can positively impact mental health care if deployed correctly.

Implementation of an Online Mental Health Website for the Early Intervention in Psychosis Services, Developed for the Early Youth Engagement (EYE-2) Trial: A Cross-Sectional Survey Study of Clinical Barriers and Facilitators to Normalisation

INTRODUCTION

Disengagement is a problem for early intervention in psychosis (EIP) services. Access to trusted information on a website might help to overcome some of the problems associated with disengagement. Clinician and organisational engagement are integral to the implementation and uptake of online resources.

AIMS AND OBJECTIVES

A theory-driven approach used the normalisation process theory (NPT) to investigate the implementation of an NHS psychoeducational website developed for the Early Youth Engagement Project (EYE-2). The aim was to establish barriers and facilitators to website use.

METHODS

A cross-sectional survey study was used; 36 EIP clinicians in Sussex were asked about their attitudes towards introducing the website and using it in appointments. Accessibility, usability and internet skills were also measured.

RESULTS

A key implementation barrier was lack of familiarity with the website and its content, which inhibited use. Poorer scores in the NPT 'Collective Action' construct relating to Skillset Workability and Relational Integration (staff confidence and ability) suggested that clinicians lacked confidence in their skills and ability to introduce the website in clinical sessions. Findings suggest that clinicians, might have lower operational skills compared to the general population.

CONCLUSION

Embedding of nominated digital leads in teams as well as appropriate training is required to promote familiarity, confidence and enhance digital skills. Larger studies are required to establish the replicability of our findings.

Sexual trauma and interest in mobile health among women with mental illness in Ghana

Sexual violence against vulnerable populations is common worldwide. Many survivors of sexual assault experience long-term mental health difficulties. This study evaluated sexual violence exposure among women with mental illness in Ghana and examined their readiness to engage in mobile health interventions. We surveyed 200 women receiving inpatient or outpatient treatment at a large psychiatric hospital. Survey results indicated that 41.0 % reported having experienced sexual violence in the past. Over two-thirds of respondents had a high probability of PTSD (68.4 %) and these proportions were higher among those who experienced sexual violence (77.5 %). The majority were interested in mobile health resources that could provide them with support (73.2 %). Respondents' top topics of interest were information about managing stress and improving mood. The skill they were most interested in was relaxation. Video and audio content were rated as preferred intervention modalities. Most of the sample reported owning a mobile phone (86.4 %), with most being smartphones (76.1 %). Almost all respondents reported having access to electricity (99.5 %), a majority had a data plan (86.2 %), and all reported daily mobile phone use (100.0 %). Our findings suggest that there are significant unmet mental health needs among female survivors of sexual violence who are already receiving care in Ghana; most female survivors of sexual assault are open to using mobile health interventions; and most women with mental illness have access to the resources necessary for deployment of mobile interventions in their communities. Smartphone applications that leverage video and audio content may be particularly suitable for this context.

Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders

OBJECTIVE

Smartphone technology presents a promising path toward expanding access to evidence-based eating disorder assessment and treatment. Despite rapid technological advances, research has yet to harness these systems in ways that make personalized digital health care a clinical reality. In this forum, we review extant research testing smartphone intervention and monitoring tools for eating disorders and explore innovative ways integrating this technology with AI can enhance assessment, symptom detection, and intervention efforts.

METHOD

We highlight three capabilities of smartphones that hold promise for delivering personalized and maximally effective digital health tools: (1) passive sensing and digital phenotyping; (2) natural language processing of reflections from in-app homework tasks; and (3) closed-loop adaptive interventions. We discuss how these capabilities can augment current assessment and treatment efforts and draw on literature from other fields to inform research questions for the eating disorder field.

RESULTS

Evidence from other fields demonstrates the feasibility of constructing data-driven models from smartphone sensor data and textual input from in-app CBT activities to predict clinical outcomes. These models may inform closed-loop interventions, enabling apps to deliver timely, personalized support in response to real-time changes in a user's needs.

CONCLUSION

The eating disorder field can draw on lessons from other fields to evaluate smartphone technology that leverages AI to enhance personalization. Realizing the potential of these tools will require addressing challenges related to engagement, trust, data governance, and clinical integration. The testable research questions presented here offer a roadmap to guide future large-scale, collaborative efforts aimed at transforming eating disorder care.

Analyzing metaverse-based digital therapies, their effectiveness, and potential risks in mental healthcare

The metaverse, defined as a collective virtual shared space created by the convergence of augmented reality (AR), virtual reality (VR), and the Internet, offers new opportunities for mental healthcare by delivering immersive and engaging digital therapies. This study examines the current landscape of metaverse-based mental healthcare applications, analyzing their effectiveness and potential risks. Using a systematic literature review (SLR) and case study research, four digital therapeutic applications-NightWare, Freespira, EndeavorRx, and Sleepio-were evaluated for their ability to address conditions such as PTSD, anxiety, and ADHD. The results indicate that metaverse-based therapies can provide significant benefits, with clinical validation supporting their effectiveness. However, concerns around user privacy, accessibility, and long-term efficacy remain challenges. Overall, metaverse-based digital therapies represent a promising shift in mental healthcare, offering innovative, personalized, and scalable solutions. Further research is needed to address ethical issues, improve accessibility, and confirm the long-term impact of these interventions.

A digital therapeutic (CT-152) as adjunct to antidepressant medication: A phase 3 randomized controlled trial (the Mirai study)

OBJECTIVE

Digital therapeutics (DTx) are a new treatment class for major depressive disorder (MDD). This study evaluated the effectiveness and safety of a novel DTx, CT-152 (Rejoyn™), for MDD adjunctive to antidepressant medication monotherapy.

METHODS

Adults aged 22-64 years with MDD having an inadequate response to current antidepressant medication monotherapy were enrolled in a phase 3 multicenter, randomized, blinded, sham-controlled, remote trial with a 6-week intervention and 4-week extension. Delivered via smartphone apps, the CT-152 group received a cognitive-emotional and behavioral therapeutic intervention; the control group received a sham app with a working memory task. Both groups received supportive text messages and continued current antidepressant medication. The primary outcome was Montgomery-Åsberg Depression Rating Scale (MADRS) score change from baseline to week 6. Treatment-emergent adverse events (TEAEs) were assessed.

RESULTS

Overall, 386 participants were randomly assigned (CT-152, n = 194; sham, n = 192). In the primary efficacy analysis of participants with ≥1 treatment session and ≥ 1 MADRS assessment post-baseline (n = 354), MADRS score changed -9.03 in the CT-152 group and - 7.25 in the sham (difference - 1.78, P = 0.0568). These results were consistent with data from additional patient and clinician scales. In a supportive analysis of the intent-to-treat sample (n = 386), the between-group difference in 6-week MADRS change from baseline was -2.12 (P = 0.0211), favoring CT-152. No TEAEs or discontinuations were considered related to CT-152, and no deaths occurred.

CONCLUSIONS

CT-152 resulted in depression symptom improvement and a favorable safety profile. Based on these data, CT-152 became the first FDA-authorized prescription DTx for the adjunctive treatment of MDD.

Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis

BACKGROUND AND HYPOTHESIS

Digital remote monitoring (DRM) captures service users' health-related data remotely using devices such as smartphones and wearables. Data can be analyzed using advanced statistical methods (eg, machine learning) and shared with clinicians to aid assessment of people with psychosis' mental health, enabling timely intervention. Such methods show promise in detecting early signs of psychosis relapse. However, little is known about clinicians' views on the use of DRM for psychosis. This study explores multi-disciplinary staff perspectives on using DRM in practice.

STUDY DESIGN

Fifty-nine mental health professionals were interviewed about their views on DRM in psychosis care. Interviews were analyzed using reflexive thematic analysis. Study Results: Five overarching themes were developed, each with subthemes: (1) the perceived value of digital remote monitoring; (2) clinicians' trust in digital remote monitoring (3 subthemes); (3) service user factors (2 subthemes); (4) the technology-service user-clinician interface (2 subthemes); and (5) organizational context (2 subthemes).

CONCLUSIONS

Participants saw the value of using DRM to detect early signs of relapse and to encourage service user self-reflection on symptoms. However, the accuracy of data collected, the impact of remote monitoring on therapeutic relationships, data privacy, and workload, responsibility and resource implications were key concerns. Policies and guidelines outlining clinicians' roles in relation to DRM and comprehensive training on its use are essential to support its implementation in practice. Further evaluation regarding the impact of digital remote monitoring on service user outcomes, therapeutic relationships, clinical workflows, and service costs is needed.

Are we all singing from the same song sheet? Standardizing terminology used in inter-professional telehealth education and practice: a mixed method study

BACKGROUND

Telehealth interventions have proven essential in maintaining healthcare delivery during the global pandemic. However, its broader adoption within different healthcare settings has been impacted by inconsistent and non-standardized terminology, which poses challenges to global implementation and stakeholder communication. This article addresses these barriers by analyzing telehealth-related terms and developing a detailed clinical guide to aid inter-professional health educators in adopting standardized terminology, improving clarity, and fostering collaboration.

METHODS

A mixed-methods approach was used, comprising four phases. Phase 1 included weekly online journal club sessions (February to August 2024) focused on digital health topics, where relevant terms were discussed and extracted. Phase 2 involved detailed transcription analysis to identify telehealth-related terms based on their frequency of use and relevance to digital health. Phase 3 was a systematic literature review to contextualize and refine the identified terms. Phase 4 entailed expert validation, where five digital health professionals reviewed the proposed terminology and provided refinements. Additionally, terms were cross-referenced with the Medical Subject Headings (MeSH) database to evaluate their existing definitions.

RESULTS

A total of 314 telehealth terms were identified through discussions in the International Journal Club in Digital Health (IJC DH) and a literature review. Approximately 90.44% of these terms were sourced from 12 journal club sessions, covering topics such as Digital Health, Digital Psychiatry, Neurorehabilitation, and Robotic Surgery. The literature review contributed 30 unique terms, with further analysis revealing that 73% of the terms were not defined in the MeSH database. This finding underscores the evolving nature of telehealth and the need for terminology standardization. Expert reviews validated most proposed definitions, though specific terms required additional discussion.

CONCLUSIONS

The resulting standardized terminology guide enhances inter-professional collaboration in telehealth by providing clear and consistent definitions. This guide reduces miscommunication, facilitates interdisciplinary research and practice, and can be integrated into educational curricula to prepare future healthcare professionals for the complexities of digital health. By addressing terminology gaps, this study supports the advancement of telehealth education and improves patient care outcomes.

Digital phenotyping for mental health based on data analytics: A systematic literature review

Even though mental health is a human right, mental disorders still affect millions of people worldwide. Untreated and undertreated mental health conditions may lead to suicide, which generates more than 700,000 deaths annually around the world. The broad adoption of smartphones and wearable devices allowed the recording and analysis of human behaviors in digital devices, which might reveal mental health symptoms. This analysis constitutes digital phenotyping research, referring to frequent and constant measurement of human phenotypes in situ based on data from smartphones and other personal digital devices. Therefore, this article presents a systematic literature review providing a computer science view on data analytics for digital phenotyping in mental health. This study reviewed 5,422 articles from ten academic databases published up to September 2024, generating a final list of 74 studies. The investigated databases are ACM, IEEE Xplore, PsycArticles, PsycInfo, Pubmed, Science Direct, Scopus, Springer, Web of Science, and Wiley. We investigated ten research questions, considering explored data, employed devices, and techniques for data analysis. This review also organizes the application domains and mental health conditions, data analytics techniques, and current research challenges. This study found a growing research interest in digital phenotyping for mental health in recent years. Current approaches still present a high dependence on self-reported measures of mental health status, but there is evidence of the employment of smartphones for leveraging passive data collection. Traditional machine learning techniques are the main explored strategies for analyzing the large amount of collected data. In this regard, published approaches deeply focused on data analysis, generating opportunities concerning the implementation of resources for assisting individuals suffering from mental disorders.

Mental Health Impacts of Self-Help Interventions for the Treatment and Prevention of Eating Disorders. A Meta-Analysis

OBJECTIVE

Self-help programs are recommended as a first step in the management of eating disorders. Yet, whether self-help interventions have broader mental health benefits beyond symptom and risk reduction remains unclear. As randomized controlled trials (RCTs) also assess general mental health secondary to eating disorder symptoms, we conducted a meta-analysis to investigate whether and to what extent pure self-help interventions for eating disorders produce improvements in these secondary outcomes.

METHOD

Twenty-seven RCTs of pure self-help interventions for the prevention or treatment of eating disorders were included. Mean age ranged from 16 to 46 years. Most self-help interventions were based on cognitive-behavioral therapy. Most interventions were delivered via digital means (Internet, apps, etc.). Random effects meta-analyses were conducted on six outcomes: depression, anxiety, general distress, quality of life, self-esteem, and psychosocial impairment. Analyses were stratified based on pre-selected (at risk/symptomatic) and clinical samples.

RESULTS

For pre-selected samples (k = 18), significant pooled effects favoring self-help over controls were observed for depression (g = 0.24), anxiety (g = 0.23), distress (g = 0.23) and self-esteem (g = 0.18). Effects remained robust when adjusting for risk of bias. Non-significant effects were observed for quality of life and impairment. Crucially, > 80% of trials on pre-selected samples delivered a waitlist control. For clinical samples (k = 9), significant pooled effects favoring self-help were found for distress (g = 0.39), impairment (g = 0.39), and quality of life (g = 0.29), although these results should be interpreted with caution as the number of studies was low.

CONCLUSION

Self-help interventions produce small improvements in those mental health symptoms that are typically comorbid with eating disorders.

Application of Machine Learning Techniques to the Prediction of Onset and Persistence of Binge Eating: A Prospective Study

OBJECTIVE

Machine learning (ML) techniques have shown promise for enhancing prediction of clinical outcomes; however, its application to predicting binge eating has been scarcely explored. We applied ML techniques to predict binge eating onset (vs. continued absence) and persistence (vs. remission) over time.

METHOD

Data were used from a larger prospective study of 1106 participants who were assessed on a range of putative risk, maintaining, and protective factors at baseline and 8 months follow-up. Nine ML models for classification were developed and compared against a generalised linear model (GLM) for predicting onset (n = 334) and persistence (n = 623) outcomes using 39 self-reported baseline variables as predictors.

RESULTS

All models performed poorly at predicting onset (AUC = 0.49-0.61) and persistence (AUC = 0.50-0.59) outcomes, with ML models demonstrating comparable performance to the GLM.

CONCLUSION

We suspect that poor ML performance may have been a result of the limited set of self-reported baseline predictors used to generate prediction models. Improved predictive accuracy and optimisation of ML models in future research may require consideration of a larger, more disparate set of predictors that also incorporate various data types, such as neuroimaging, physiological, or smartphone sensor data.

Mental health clinicians' practices and perspectives of eating disorder apps

Smartphone apps have the potential to play an integral role in the management of eating disorders. However, despite evidence of efficacy, apps have yet to be widely adopted and integrated into clinical practice. This study sought to understand mental health clinicians' practices and perspectives of eating disorder apps. One-hundred-eighteen mental health clinicians (67 % Psychologists, 10 % Psychiatrists, 10 % Counsellors, 6 % Psychiatric Nurses; 7 % other) responded to a survey assessing current practices, knowledge, and attitudes related to apps in practice. Nearly two-thirds of clinicians (63 %) had used/discussed apps with clients; the most common reasons were that apps complement their services (80 % endorsed) and are efficient methods for monitoring symptoms and progress (89 % endorsed). The most common reason for clinicians who had not used/discussed apps with clients was not knowing which apps were appropriate to recommend (82 % endorsed). Nearly 50 % of clinicians reported little to no knowledge of current eating disorder apps, as well as limited confidence in using them effectively in practice. However, 8 in 10 clinicians were open to incorporating apps in treatment, and most agreed that apps can be beneficial for psychoeducation, skill utilization, relapse prevention, and offering support when access to other help options is limited. Overall, findings show that clinicians are enthusiastic about the potential of apps to assist in the management of eating disorders, but clear barriers to their adoption and integration in practice remain. We outline actionable strategies aimed at addressing these barriers and facilitating the broader integration of eating disorder apps into practice.

The impact of AI-driven sentiment analysis on patient outcomes in psychiatric care: A narrative review

This article addresses the pressing question of how advanced analytical tools, specifically artificial intelligence (AI)-driven sentiment analysis, can be effectively integrated into psychiatric care to enhance patient outcomes. Utilizing specific search phrases like "AI-driven sentiment analysis," "psychiatric care," and "patient outcomes," a comprehensive survey of English-language publications from the years 2014-2024 was performed. This examination encompassed multiple databases such as PubMed, PsycINFO, Google Scholar, and IEEE Xplore. Through a comprehensive analysis of qualitative case studies and quantitative metrics, the study uncovered that the implementation of sentiment analysis significantly improves clinicians' ability to monitor and respond to patient emotions, leading to more tailored treatment plans and increased patient engagement. Key findings indicated that sentiment analysis improves early mood disorder detection, personalizes treatments, enhances patient-provider communication, and boosts treatment adherence, leading to better mental health outcomes. The significance of these findings lies in their potential to revolutionize psychiatric care by providing healthcare professionals with real-time insights into patient feelings and responses, thereby facilitating more proactive and empathetic care strategies. Furthermore, this study highlights the broader implications for healthcare systems, suggesting that the incorporation of sentiment analysis can lead to a paradigm shift in how mental health services are delivered, ultimately enhancing the efficacy and quality of care. By addressing barriers to new technology adoption and demonstrating its practical benefits, this research contributes vital knowledge to the ongoing discourse on optimizing healthcare delivery through innovative solutions in psychiatric settings.

A scoping review of large language models for generative tasks in mental health care

Large language models (LLMs) show promise in mental health care for handling human-like conversations, but their effectiveness remains uncertain. This scoping review synthesizes existing research on LLM applications in mental health care, reviews model performance and clinical effectiveness, identifies gaps in current evaluation methods following a structured evaluation framework, and provides recommendations for future development. A systematic search identified 726 unique articles, of which 16 met the inclusion criteria. These studies, encompassing applications such as clinical assistance, counseling, therapy, and emotional support, show initial promises. However, the evaluation methods were often non-standardized, with most studies relying on ad-hoc scales that limit comparability and robustness. A reliance on prompt-tuning proprietary models, such as OpenAI's GPT series, also raises concerns about transparency and reproducibility. As current evidence does not fully support their use as standalone interventions, more rigorous development and evaluation guidelines are needed for safe, effective clinical integration.

Framework to Assist Stakeholders in Technology Evaluation for Recovery (FASTER) to Mental Health and Wellness

Mental health mobile applications (apps) have the potential to expand the provision of mental health and wellness services to underserved populations. There is a lack of guidance on how to choose wisely from the thousands of mental health apps without clear evidence of safety, efficacy, and consumer protections. We propose the Framework to Assist Stakeholders in Technology Evaluation for Recovery (FASTER) to Mental Health and Wellness to support agencies and individuals working in mental health, as well as users of mental health apps, in making informed decisions recommending the use of, or using mental health and wellness apps. The framework was developed through a systematic process including a review of existing frameworks and the literature, interviews with key informants, public stakeholder feedback, and iterative application and refinement of the framework to 45 apps. It comprises three sections: Section 1. Risks and Mitigation Strategies, assesses the integrity and risk profile of the app; Section 2. Function, focuses on descriptive aspects related to accessibility, costs, developer credibility, evidence and clinical foundation, privacy/security, usability, functions for remote monitoring of the user, access to crisis services, and artificial intelligence (AI); and Section 3. Mental Health App Features focuses on specific mental health app features, such as journaling and mood tracking. The framework facilitates an assessment of the level of risk posed by the app against the evidence on the effectiveness of the app and its safety features, recognizing that given vast variations in health apps, a 'one size fits all' approach is likely to be insufficient. Future application, testing, and refinements may be required to determine the framework's suitability and reliability across multiple mental health conditions.

Impact of cybersickness and presence on treatment satisfaction and clinical outcomes in virtual reality-based biofeedback for depression and anxiety

Virtual Reality-Biofeedback (VR-BF) has emerged as a novel digital intervention for reducing anxiety and depressive symptoms. This study aimed to assess the relationship between cybersickness, presence, treatment satisfaction, and symptom change following VR-BF. In this prospective clinical study, 80 drug-naive adults were enrolled and classified into two groups: those with subclinical depressive and anxiety symptoms (n = 40) and healthy controls (n = 40). All participants completed three sessions of a self-developed VR-BF intervention over four weeks. Clinical outcomes related to depression and anxiety symptoms were assessed using established psychological scales, along with post-intervention evaluations of presence, cybersickness, and treatment satisfaction. Higher presence was associated with greater reductions in anxiety (ΔSTAI: β = -0.24, SE = 0.06, P < 0.001), stress (ΔVAS: β = -0.37, SE = 0.13, P = 0.008), and depressive symptoms (ΔPHQ-9: β = -0.07, SE = 0.02, P = 0.008), and with greater treatment satisfaction (β = 0.07, SE = 0.01, P < 0.001). Cybersickness was inversely correlated with presence (ρ = -0.38, P < 0.001) and satisfaction (β = -0.11, SE = 0.04, P = 0.013) and was associated with smaller improvements in anxiety (ΔSTAI: β = 0.62, SE = 0.30, P = 0.044) and depressive symptoms (ΔPHQ-9: β = 0.28, SE = 0.12, P = 0.019). Female sex and older age were associated with greater clinical improvement and higher satisfaction. These findings underscore the relevance of experiential process factors in VR-BF and support further development of user-centered, tolerable, and clinically effective VR-based interventions.

Evaluating the evidence: a systematic review of reviews of the effectiveness and safety of digital interventions for ADHD

BACKGROUND

Attention-Deficit/Hyperactivity Disorder (ADHD) impacts academics, work and social relationships. Digital interventions, such as virtual reality, games, app and other, offer accessible therapeutic options, yet understanding their efficacy and potential adverse effects is crucial for safe use. The objective of this study is to identify and analyze the efficacy and adverse effects reported in systematic reviews of digital interventions for ADHD.

METHODS

We conducted a systematic review of systematic reviews to assess the reported efficacy and safety of digital interventions for ADHD. We searched for relevant publications in Scopus, PubMed, PsycINFO and Cochrane Library. Both study selection and data extraction were performed in duplicate to ensure accuracy and reduce bias. This review followed PRISMA 2020 guidelines, PRISMA-harms checklist, and we used AMSTAR-2 to assess the quality and risk of bias of the included reviews.

RESULTS

A total of 26 systematic reviews on digital interventions for ADHD were included. These reviews collectively involved 34,442 participants, with the majority focusing on children and adolescents. The digital interventions analyzed included video games, computerized cognitive training, virtual reality, apps, and others. The outcomes reported various positive effects, such as improvements in inattention and executive function, though evidence was generally low quality. Adverse effects were reported in 8 of the 26 included reviews (30,1%), and included physical discomfort, emotional reactions, and behavioral issues, such as video game addiction.

CONCLUSIONS

This systematic review of systematic reviews indicates that while digital interventions for ADHD show potential benefits, their effectiveness remains inconclusive due to low evidence quality. Adverse effects, particularly from video games, have been reported but are inconsistently documented. Future research should focus on rigorous safety assessments, standardized reporting, and long-term effectiveness.

TRIAL REGISTRATION

This systematic review is registered in Prospero: CRD42024521084.

Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Background

Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (NLP) and acoustic signal processing may support ongoing BD assessment within a mobile health (mHealth) framework.

Objective

Using both acoustic and NLP-based features from the speech of people with BD, we built an app-based tool and tested its feasibility and performance to remotely assess the individual clinical status.

Methods

We carried out a pilot, observational study, sampling adults diagnosed with BD from the caseload of the Nord Milano Mental Health Trust (Italy) to explore the relationship between selected speech features and symptom severity and to test their potential to remotely assess mental health status. Symptom severity assessment was based on clinician ratings, using the Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale (MADRS) for manic and depressive symptoms, respectively. Leveraging a digital health tool embedded in a mobile app, which records and processes speech, participants self-administered verbal performance tasks. Both NLP-based and acoustic features were extracted, testing associations with mood states and exploiting machine learning approaches based on random forest models.

Results

We included 32 subjects (mean [SD] age 49.6 [14.3] years; 50% [16/32] females) with a MADRS median (IQR) score of 13 (21) and a YMRS median (IQR) score of 5 (16). Participants freely managed the digital environment of the app, without perceiving it as intrusive and reporting an acceptable system usability level (average score 73.5, SD 19.7). Small-to-moderate correlations between speech features and symptom severity were uncovered, with sex-based differences in predictive capability. Higher latency time (ρ=0.152), increased silences (ρ=0.416), and vocal perturbations correlated with depressive symptomatology. Pressure of speech based on the mean intraword time (ρ=-0.343) and lower voice instability based on jitter-related parameters (ρ ranging from -0.19 to -0.27) were detected for manic symptoms. However, a higher contribution of NLP-based and conversational features, rather than acoustic features, was uncovered, especially for predictive models for depressive symptom severity (NLP-based: R2=0.25, mean squared error [MSE]=110.07, mean absolute error [MAE]=8.17; acoustics: R2=0.11, MSE=133.75, MAE=8.86; combined: R2=0.16; MSE=118.53, MAE=8.68).

Conclusions

Remotely collected speech patterns, including both linguistic and acoustic features, are associated with symptom severity levels and may help differentiate clinical conditions in individuals with BD during their mood state assessments. In the future, multimodal, smartphone-integrated digital ecological momentary assessments could serve as a powerful tool for clinical purposes, remotely complementing standard, in-person mental health evaluations.

Early psychosis service user views on digital remote monitoring: a qualitative study

BACKGROUND

Current approaches to mental healthcare for people with severe mental health problems are limited by sporadic monitoring and symptom recall bias. Emotional and behavioural markers generated by digital health technologies (DHTs) offer the potential to enhance quality of care and clinical decision-making. This study explored early psychosis service users' views and experiences of using a digital remote monitoring tool (ClinTouch app).

METHODS

Qualitative framework analysis was undertaken with interview data collected from participants who took part in the Actissist proof-of-concept and subsequent randomised controlled trial studies to understand the experiences of participants using the ClinTouch app (n = 8).

RESULTS

Data were summarised into four key themes. The following three themes were established a priori: (1) awareness of mood and symptoms; (2) acceptability of ClinTouch; and (3) improvements and recommendations. The fourth theme was established a posteriori: (4) integrating ClinTouch into clinical practice. More specifically, participants felt ClinTouch was an acceptable and useful tool for symptom monitoring. ClinTouch facilitated an increased awareness of mood and symptoms, which enabled participants to self-reflect and develop understanding of their own experiences.

CONCLUSIONS

This study shed light on early psychosis service users' experiences with using the ClinTouch digital remote monitoring app. ClinTouch was viewed as acceptable for monitoring symptoms, safe and easy to use, showed potential of integration with clinical care, and facilitated increased awareness and understanding of symptoms. Improvements including personalised question items and interactive features were suggested. Future developments of digital remote monitoring apps should include a more refined item set and personalisation features.

CLINICAL TRIAL NUMBER

ISRCTN34966555, Registration Date: 12/06/2014; ISRCTN76986679, Registration Date: 07/02/2018.

Self-Guided Psychological Interventions for the Treatment and Prevention of Eating Disorders: A Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Self-guided interventions may broaden the dissemination of evidence-based prevention and treatment protocols for eating disorders. We conducted a meta-analysis comparing self-guided prevention and treatment approaches for eating disorders to (1) control groups and (2) professionally guided self-help programs.

METHODS

Forty-six trials were included. Interventions ranged from web, to app, to CD-ROM to book-based programs. Random effects meta-analyses were conducted on numerous symptom and risk outcomes.

RESULTS

Only four trials recruited an unselected sample, with self-guided programs reducing shape/weight concerns over control groups (g = 0.18). Among high risk/symptomatic samples (k = 25), self-guided interventions reduced several behavioural and cognitive symptoms (gs = 0.31-0.50) over control groups, with effects being robust when adjusting for higher risk of bias and small sample trials. Among clinical samples (k = 17), evidence for the effectiveness of self-guided interventions over control groups on symptom measures was only found for binge-eating disorder, as too few studies sampled other diagnostic subtypes. Among 10 trials that compared guided to unguided self-help, we observed a significant effect in symptom reduction in favour of guided self-help (g = -0.26). Dropout did not differ between guided and unguided self-help (OR = 0.97).

CONCLUSION

Self-guided interventions may be an effective, low intensity intervention format for high risk individuals or for binge-eating disorder presentations.

Methodological quality in randomised clinical trials of mental health apps: systematic review and longitudinal analysis

QUESTION

This study investigated the methodological rigour of randomised controlled trials (RCTs) of mental health apps for depression and anxiety, and whether quality has improved over time.

STUDY SELECTION AND ANALYSIS

RCTs were drawn from the most recent meta-analysis of mental health apps for depression and anxiety symptoms. 20 indicators of study quality were coded, encompassing risk of bias, participant diversity, study design features and app accessibility measures. Regression models tested associations between year of publication and each quality indicator.

FINDINGS

176 RCTs conducted between 2011 and 2023 were included. Methodological concerns were common for several quality indicators (eg, <20% were replication trials, <35% of trials reported adverse events). Regression models revealed only three significant changes over time: an increase in preregistration (OR=1.27; 95% CI 1.10, 1.46) and reporting of adverse events (OR=1.32; 95% CI 1.11, 1.56), and a decrease in apps reported to be compatible with iOS and/or Android (OR=0.78; 95% CI 0.64, 0.96). Results were unchanged when excluding outliers. Results were similar when excluding three high-quality studies published between 2011 and 2013, with additional evidence for an increase in modern missing data methods (OR=1.22; 95% CI 1.04, 1.42) and studies reporting intention-to-treat analysis (OR=1.20; 95% CI 1.03, 1.39).

CONCLUSIONS

Findings provide minimal evidence of improvements in the quality of clinical trials of mental health apps, highlighting the need for higher methodological standards in future research to ensure the reliability and generalisability of evidence for these digital tools.

Approach to Design and Evaluate Digital Tools to Enhance Young Adult Participation in Clinical Trials: Co-Design and Controlled Intercept Study

BACKGROUND

Certain populations are underrepresented in clinical trials, limiting the generalizability of new treatments and their efficacy and uptake in these populations. It is essential to identify and understand effective strategies for enrolling young adults in clinical trials, as they represent a vital and key demographic for future clinical trial participation.

OBJECTIVE

This study aimed to develop, test, and evaluate digital tools designed to encourage the participation of young adults in the clinical trial process. An interdisciplinary approach, incorporating social listening, qualitative focus groups, and co-design workshops, was used to achieve this goal.

METHODS

Digital tools were designed and evaluated using a 4-phase approach that included: (1) social listening to characterize lived experiences with COVID-19 trials as self-reported by online users, (2) qualitative focus groups with young adults to explore specific lived attitudes and experiences related to COVID-19 clinical research hesitancy and engagement, (3) a series of cocreation and co-design workshops to build digital tools aimed at encouraging clinical trial participation, and (4) a controlled intercept study to assess the usability and specific outcome measures of the co-designed digital tools among young adults.

RESULTS

A significantly higher change in the likelihood of participating in a clinical trial post exposure was observed among study participants when exposed to prototypes of a mobile app (Δ=0.74 on a 10-point scale, P<.01) and website (Δ=0.93, P<.01) compared to those exposed to a Facebook ad (Δ=0.21) but not a digital flyer (Δ=0.58). Furthermore, those exposed to the mobile app (x̅=5.76, P=.04) and electronic flier (x̅=5.72, P=.04), but not the website (x̅=5.55), exhibited significantly higher postexposure interest in learning about clinical trials when compared to participants exposed to the Facebook (Meta) ad (x̅=5.06). Participants in the intercept study were more likely to consider joining a clinical trial after seeing a mobile app (Δ=0.74, P<.01) or website (Δ=0.93, P<.001) compared to a Facebook ad (Δ=0.21), but the digital flyer (Δ=0.58) did not show a significant difference. In addition, those who saw the mobile app (x̅=5.76, P=.04) or the digital flyer (x̅=5.72, P=.04) showed more interest in learning about clinical trials than those who saw the Facebook ad (x̅=5.06), though the website (x̅= 5.55) did not significantly impact interest.

CONCLUSIONS

Mobile apps and web pages co-designed with young diverse adults may represent effective digital tools to advance shared goals of encouraging inclusive clinical trials.

Current limitations in technology-based cognitive assessment for severe mental illnesses: a focus on feasibility, reliability, and ecological validity

Cognitive impairments are frequently observed in subjects with severe mental illnesses (SMI), leading to a remarkable impact in their real-world functioning. Well-validated and gold standard instruments are available for the assessment of cognitive deficits, but different limitations should be considered, such as the need for specific training, lengthy administration times, practice effects, or reliance on subjective reports. Recent advances in digital technologies, such as ecological momentary assessments (EMA), virtual reality (VR), and passive digital phenotyping (DP), offer promising complementary approaches for capturing real-world cognitive functioning. In the current mini-review, we examine current research gaps that limit the application of these technologies, with a specific focus on feasibility, reliability and ecological validity. EMA may capture real-world functioning by increasing the number of evaluations throughout the day, but its use might be hindered by high participant burden and missing data. Furthermore, to achieve an accurate interpretation of EMA, studies should account for sampling and moment selection biases and the presence of several confounding factors. DP faces significant ethical and logistical challenges, including privacy and informed consent concerns, as well as challenges in data interpretation. VR could serve as a platform for both more ecologically valid cognitive assessments and rehabilitation interventions, but current barriers include technological and psychometric limitations, underdeveloped theoretical frameworks, and ethical considerations. Addressing these issues is crucial for ensuring that these novel technologies can effectively serve as valuable complements to traditional neuropsychological cognitive batteries.

Emerging strategies and clinical recommendations for the management of novel depression subtypes

INTRODUCTION

The phenomenology of depression is characterized by a wide array of emotional, cognitive, and physical symptoms that significantly disrupt an individual's life. Societal changes, driven by technological advancements, economic pressures, environmental concerns including climate change, and shifting cultural norms, have influenced how depression manifests and is understood. These developments have led to the identification of new depression subtypes, highlighting the need for personalized treatment approaches based on individual symptoms and underlying causes.

AREAS COVERED

The authors provide a comprehensive narrative review of the literature on managing novel depression subtypes, focusing on both pharmacological and non-pharmacological treatments. Specifically, scenarios recorded were related to i) depression in adolescents and young adults; ii) depression and social disconnection; iii) depression and alcohol/substance use disorder; iv) depression and gender dysphoria; v) depression, stressful events, and other environmental factors.

EXPERT OPINION

In the novel depression subtypes discussed, individualized treatment approaches tailored to the individual's specific circumstances are necessary. While selective serotonin reuptake inhibitors (SSRIs) and serotonin and noradrenaline reuptake inhibitors (SNRIs) remain the cornerstone of treatment for many forms of depression, atypical antidepressants such as trazodone, and emerging therapies like ketamine, neuromodulation techniques, and personalized psychotherapy offer hope for those with complex or treatment-resistant presentations.

User journey method: a case study for improving digital intervention use measurement

BACKGROUND

Many digital mental health interventions meet low levels of use. However, current use measurement methods do not necessarily help identify which intervention elements are associated with dropout, despite this information potentially facilitating iterative intervention development. Here, we suggest improving the comprehensiveness of intervention use measurement with the user journey method, which evaluates every intervention element to identify intervention-specific use barriers.

METHODS

We applied user journey method in a clinical trial that investigated the efficacy of a novel game-based intervention, Meliora, for adult Major Depressive Disorder. We modelled the intervention for its four technological (Recruitment, Website, Questionnaires, Intervention Software) and two interpersonal elements (Assessment, Support). We then applied the user journey method to measure how many users proceeded from one element to the next combining social media analytics, website use data, signup data, clinical subject coordinator interview data, symptom questionnaire data, and behavioral intervention use data. These measurements were complemented with the qualitative analysis of the study discovery sources and email support contacts.

RESULTS

Recruitment: The intervention recruitment reached at least 145,000 Finns, with social media, word-of-mouth, and news and web sources being the most effective recruitment channels. Website: The study website received 16,243 visitors, which led to 1,007 sign-ups.

ASSESSMENT

895 participants were assessed and 735 were accepted. Intervention Software: 498 participants were assigned to the active intervention or comparator, of whom 457 used them at least once: on average, for 17.3 h (SD = 20.4 h) on 19.7 days (SD = 20.7 d) over a period of 38.9 days (SD = 31.2 d). The 28 intervention levels were associated with an average dropout rate of 2.6%, with two sections exhibiting an increase against this baseline. 150 participants met the minimum adherence goal of 24 h use. Questionnaires: 116 participants completed the post-intervention questionnaire.

SUPPORT

313 signed-up participants contacted the researchers via email.

CONCLUSION

The user journey method allowed for the comprehensive evaluation of the six intervention elements, and enabled identifying use barriers expediting iterative intervention development and implementation.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05426265. Registered 28 June 2022, https://clinicaltrials.gov/ct2/show/NCT05426265 .

Mental Health Professionals' Technology Usage and Attitudes Toward Digital Health for Psychosis: Comparative Cross-Sectional Survey Study

Background

Digital health technologies (DHTs) for psychosis have been developed and tested rapidly in recent years. However, research examining mental health professionals' views on the use of DHTs for people with psychosis is limited. Given the increased accessibility and availability of DHTs for psychosis, an up-to-date understanding of staff perception of DHTs for psychosis is warranted.

Objective

In this study, we aimed to investigate mental health professionals' usage of technology and their perception of service users' technology usage; their views toward the use of DHTs for psychosis, including their concerns; and barriers for implementing DHTs in a mental health setting.

Methods

Two cross-sectional surveys were distributed to mental health care staff who had experience of working with individuals experiencing psychosis in the United Kingdom. Survey 1 (from April 2018 to September 2020) was distributed to 3 local UK National Health Service (NHS) trusts in Northwest England; survey 2 was administered nationally across 31 UK NHS trusts or health boards (from November 2022 to March 2024). The COVID-19 pandemic occurred between the 2 survey periods. Data were analyzed descriptively.

Results

A total of 155 and 352 participants completed surveys 1 and 2, respectively. Staff reported high levels of technology ownership and usage in both surveys. In general, staff expressed positive views regarding the use of DHTs for psychosis; however, barriers and concerns, including affordability, digital literacy, and potential negative effects on service users' mental health, were also reported. We did not find notable changes in terms of staff use of digital technology in clinical practice over time.

Conclusions

Staff sampled from a broad and diverse range expressed consistent optimism about the potential implementation of DHTs in practice, though they also noted some concerns regarding safety and access. While the COVID-19 pandemic is frequently regarded as a catalyst for the adoption of digital health care tools, the sustainability of this transition from traditional to digital health care appeared to be suboptimal. To address staff concerns regarding safety and potentially facilitate the implementation of DHTs, systematic evaluation of adverse effects of using DHTs and dissemination of evidence are needed. Organizational support and training should be offered to staff to help address barriers and increase confidence in recommending and using DHTs with service users.

Dynamic Updating of Psychosis Prediction Models in Individuals at Ultra-High Risk of Psychosis

BACKGROUND

The performance of psychiatric risk calculators can deteriorate over time due to changes in patient population, referral pathways, and medical advances. Such temporal biases in existing models may lead to suboptimal decisions when translated into clinical practice. Methods are available to correct this bias, but no research has been conducted to investigate their utility in psychiatry.

METHODS

We aimed to analyze the performance of model updating methods for predicting psychosis onset by 1 year in 780 individuals at ultra-high risk (UHR) of psychosis from the UHR 1000+ cohort, a longitudinal cohort of UHR individuals recruited to research studies at Orygen, Melbourne, Australia, between 1995 and 2020. Model updating was performed using a yearly adjusted model (recalibration), a continuously updated model (refitting), and a continuous Bayesian updating model (dynamic updating) and compared with a static logistic regression prediction model (original) regarding calibration, discrimination, and clinical net benefit.

RESULTS

The original model was poorly calibrated over the entire validation period. All 3 updating methods improved the predictive performance compared with the original model (recalibration: p = .009; refitting: p = .020; dynamic updating: p = .001). The dynamic updating method demonstrated the best predictive performance (Harrell's C-index = 0.71; 95% CI, 0.60 to 0.82), calibration slope (slope = 1.12; 95% CI, 0.46 to 1.87), and clinical net benefit over the entire validation period.

CONCLUSIONS

Dynamic updating of psychosis prediction models may help to mitigate decreases in performance over time. Therefore, existing psychosis prediction models need to be monitored for temporal biases to mitigate potentially harmful decisions.

Use of e-Mental Health Tools for Suicide Prevention in Clinical Practice by Mental Health Professionals in NSW, Australia: Cross-Sectional Survey

BACKGROUND

Suicide is a significant global health concern. In the context of increased demand for mental health services and workforce shortages, exacerbated by the COVID-19 pandemic, electronic mental health (eMH) tools represent a promising means of augmenting mental health care generally and for suicide prevention specifically. A significant research gap exists however with respect to the use and uptake of eMH tools, especially electronic mental health tools for suicide prevention (eMH-SP).

OBJECTIVE

This study aimed to investigate the use of eMH tools by Australian mental health professionals, both in general and with respect to suicide prevention specifically, examining changes in use since COVID-19. Further, it explored factors associated with frequent use of eMH-SP, including sociodemographic and professional characteristics.

METHODS

A web-based cross-sectional survey was conducted across 15 local health districts (LHDs) in New South Wales, Australia, from May 2022 to July 2023. The sample was drawn from over 10,000 mental health professionals working in government services statewide. The survey explored the use of electronic mental health tools for general mental health issues (eMH-gen) and eMH-SP, explored the changes in the use of both since COVID-19, and used multivariable logistic regression to identify factors associated with the current use of eMH-SP.

RESULTS

Among 469 participants, increased use since COVID-19 was reported by over half (247/469, 52.7%) for eMH-gen, and by approximately one-third (141/386, 36.6%) for eMH-SP. The proportion reporting frequent use increased significantly from before to after COVID-19 for both eMH-gen (243/469, 51.8% to 283/469, 60.3%; P<.001) and eMH-SP (152/386, 39.4% to 170/385, 44.2%; P=.01). Since COVID-19, the most frequently used types of eMH tools for eMH-gen and eMH-SP, respectively, were information sites (231/469, 49.3% and 130/385, 33.8%), phone/online counseling (173/469, 36.9% and 130/385, 33.8%), and apps (145/469, 30.9% and 107/385, 27.8%). Professionals more likely to use eMH-SP frequently were females (odds ratio [OR] 3.32, 95% CI 1.88-5.87; P<.001) compared with males; peer workers (OR 2.17, 95% CI 1.0-4.71; P<.001) compared with nurses; those located in regional/rural LHDs (OR 1.65, 95% CI 1.04-2.61; P=.03) compared with metropolitan LHDs; and those practicing in emergency health care settings (OR 8.31, 95% CI 2.17-31.75; P=.03) compared with inpatient settings.

CONCLUSIONS

The study's findings highlight the increasing adoption of eMH tools and delivery of remote care by mental health professionals and provide valuable new insights into sociodemographic factors associated with the use of eMH for suicide prevention specifically. Continued research on the role eMH is playing is essential for guiding policy, optimizing resources, and enhancing mental health care and suicide prevention efforts.

Conversational AI in Pediatric Mental Health: A Narrative Review

BACKGROUND/OBJECTIVES

Mental health disorders among children and adolescents represent a significant global health challenge, with approximately 50% of conditions emerging before age 14. Despite substantial investment in services, persistent barriers such as provider shortages, stigma, and accessibility issues continue to limit effective care delivery. This narrative review examines the emerging application of conversational artificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental considerations required for implementation.

METHODS

We searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pediatric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, developmental considerations, implementation contexts, and ethical frameworks.

RESULTS

The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimensions rather than simple modifications of adult-oriented systems.

CONCLUSIONS

Conversational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively address the unique mental health needs of young people while mitigating potential risks.

Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial

Background

Artificial intelligence (AI) chatbots have been customized to deliver on-demand support for people with mental health problems. However, the effectiveness of AI chatbots in tackling mental health problems among the general public in Hong Kong remains unclear.

Objective

This study aimed to develop a local AI chatbot and compare the effectiveness of the AI chatbot with a conventional nurse hotline in reducing the level of anxiety and depression among individuals in Hong Kong.

Methods

This study was a pilot randomized controlled trial conducted from October 2022 to March 2023, involving 124 participants allocated randomly (1:1 ratio) into the AI chatbot and nurse hotline groups. Among these, 62 participants in the AI chatbot group and 41 in the nurse hotline group completed both the pre- and postquestionnaires, including the GAD-7 (Generalized Anxiety Disorder Scale-7), PHQ-9 (Patient Health Questionnaire-9), and satisfaction questionnaire. Comparisons were conducted using independent and paired sample t tests (2-tailed) and the χ2 test to analyze changes in anxiety and depression levels.

Results

Compared to the mean baseline score of 5.13 (SD 4.623), the mean postdepression score in the chatbot group was 3.68 (SD 4.397), which was significantly lower (P=.008). Similarly, a reduced anxiety score was also observed after the chatbot test (pre vs post: mean 4.74, SD 4.742 vs mean 3.4, SD 3.748; P=.005), respectively. No significant differences were found in the pre-post scores for either depression (P=.38) or anxiety (P=.19). No statistically significant difference was observed in service satisfaction between the two platforms (P=.32).

Conclusions

The AI chatbot was comparable to the traditional nurse hotline in alleviating participants' anxiety and depression after responding to inquiries. Moreover, the AI chatbot has shown potential in alleviating short-term anxiety and depression compared to the nurse hotline. While the AI chatbot presents a promising solution for offering accessible strategies to the public, more extensive randomized controlled studies are necessary to further validate its effectiveness.

Testing a Dashboard Intervention for Tracking Digital Social Media Activity in Clinical Care of Individuals With Mood and Anxiety Disorders: Protocol and Design Considerations for a Pragmatic Randomized Trial

BACKGROUND

Mood and anxiety disorders are prevalent mental health diagnoses. Numerous studies have shown that measurement-based care, which is used to monitor patient symptoms, functioning, and treatment progress and help guide clinical decisions and collaboration on treatment goals, can improve outcomes in patients with these disorders. Including digital information regarding patients' electronic communications and social media activity is an innovative approach to augmenting measurement-based care. Recent data indicate interest and willingness from both mental health clinicians and patients to share this type of digital information in treatment sessions. However, the clinical benefit of systematically doing this has been minimally evaluated.

OBJECTIVE

This study aims to develop an electronic dashboard for tracking patients' digital social activity and a protocol for a pragmatic randomized trial to test the feasibility and efficacy of using the dashboard in real-world clinical care of patients with depression or anxiety disorders.

METHODS

We developed a personalized electronic dashboard that tracks patients' electronic communications and social media activity, visualizes data on these interactions through key graphics and figures, and provides a tool that can be readily integrated into routine clinical care for use by clinicians and patients during treatment sessions. We then designed a randomized trial to evaluate the feasibility and effectiveness of using the electronic dashboard in real-world care compared to treatment as usual. The trial included patients aged ≥12 years with a mood or anxiety disorder who were receiving treatment in outpatient psychiatry clinics in the Johns Hopkins Health System and the Kennedy Krieger Institute. The primary outcome includes changes in patient-rated depression symptoms. Secondary outcomes include changes in patient-rated anxiety symptoms and overall functioning. Exploratory analyses examine the impact of the intervention on measures of therapeutic alliance and the detection of clinically actionable targets.

RESULTS

We successfully developed an electronic dashboard for tracking patients' electronic communications and social media activity, and we implemented a protocol for evaluating the feasibility and efficacy of using the dashboard in routine care for mood or anxiety disorders. The protocol was approved by the Johns Hopkins University School of Medicine Institutional Review Board. In this study, we report the technological, ethical, and pragmatic considerations in developing the dashboard and testing it in a real-world setting.

CONCLUSIONS

The integration of an electronic dashboard to monitor digital social activity in mental health care treatment is novel. This study examines the feasibility and effectiveness of the dashboard and the challenges in implementing this protocol. The lessons learned from developing and implementing the study will inform ongoing discussions about the value of gathering collateral information on patients' digital social activity and how to do so in a way that is acceptable and clinically effective.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03925038; https://clinicaltrials.gov/study/NCT03925038.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/63279.

A Systematic Review of Parental Involvement in Digital Cognitive Behavioural Therapy Interventions for Child Anxiety

Cognitive behavioural therapy (CBT) is an efficacious intervention for child anxiety that has been translated into accessible digital formats, many of which involve parents in treatment. However, the value of parental involvement in treatment is not clearly understood. This systematic review examined characteristics of parental involvement (i.e., format and content of sessions, level of therapist guidance, and parent session compliance) in digital CBT for child anxiety (mean child age ≤ 12 years) and their relation to child outcomes (primary disorder remission, clinician-, parent-, and child-rated anxiety, and global functioning). Systematic searches in CINAHL, Embase, ERIC, PsychINFO, PubMed, and Scopus up to 14th August 2023, and citation searching, identified 27 articles (as 23 studies) assessing 14 interventions. Two were parent-only programmes for 3-6-year-old children, with the remaining being parent-child programmes targeting older children. Parents were actively involved as coaches/collaborators, assisting their children with anxiety management and exposure, and less often as co-clients working on their own difficulties. Benefits of treatment were observed across interventions, except for one, psychoeducation-based programme. Higher remission rates were more frequently observed in interventions delivered in controlled settings, those with fewer parent-only sessions, or those incorporating more parent- or therapist-led exposure sessions. Most studies were conducted with affluent samples, limiting generalisability, and several received a high risk of bias rating. Future research should examine parent and family related mechanisms of change and modify interventions for improved adherence, such as restricting the number of modules parents are required to complete and teaching key therapeutic strategies such as exposure early in the programme.

AI-Augmented Psychosocial Interventions: A Bibliometric Review and Implications for Nursing

PURPOSE

To map out the current artificial intelligence (AI)-informed psychosocial interventions research landscape, with a focus on main themes, trends, and prospective future directions.

METHOD

A bibliometric analysis extracted articles that had been published between 2007 and 2024 from the Web of Science database. Software used to process results were Bibliometrix and VOSviewer.

RESULTS

A total of 207 articles published by 86 different sources were obtained. A publication of high recurrence source was the Journal of Medical Internet Research. The United States showed high research activity in link strength, volume of articles, and citation frequency. Key themes identified were machine learning, mental health, cognitive-behavioral therapy, and personalization. Emerging trends since 2020 show growing interest in ChatGPT and AI-driven therapy.

CONCLUSION

Bibliometric analysis suggests increased application of AI in psychosocial interventions in mental health. Integrating AI with existing therapies and the development of novel digital tools indicate a future for mental health care that is personalized and innovative. The advent of advanced language models, such as ChatGPT, has opened new horizons in AI-supported mental health care. This preliminary analysis provides a foundational understanding of the current landscape while identifying key areas for further research. [Journal of Psychosocial Nursing and Mental Health Services, xx(xx), xx-xx.].

The Efficacy of the SinHumo App Combined With a Psychological Treatment to Quit Smoking: A Randomized Clinical Trial

INTRODUCTION

This study assessed the efficacy of the SinHumo App combined with a cognitive-behavioral smoking cessation treatment on 12-month follow-up abstinence, compared with the same smoking cessation treatment and a control App.

AIMS AND METHODS

A sample of 288 treatment-seeking people who smoke were randomized: SinHumo App plus smoking cessation treatment (n = 140) and control App plus smoking cessation treatment (n = 148). The primary outcome was 7-day point prevalence abstinence (PPA) at the 12-month follow-up. Secondary outcomes were abstinence rates at the end of the intervention and 3- and 6-month follow-ups, cigarette per day (CPD) reduction over the 12-month follow-up, intervention engagement, and satisfaction.

RESULTS

Intention-to-treat analyses showed nonsignificant differences in self-reported 7-day PPA at the 12-month follow-up (37.1 and 42.6%, respectively; OR = 0.80). No significant differences were found in abstinence at the end of the treatment (68.6 vs. 62.8%) nor on 7-day PPA at 3- (35.7 vs. 45.9%) and 6-month (35.0 vs. 41.2%) follow-up. Complete case and multiple imputation analyses yielded similar results for abstinence outcomes. A significant reduction in CPD across the 12-month follow-up in the subsample of participants who smoked was observed, but nonsignificant differences between conditions were found. Higher engagement with the SinHumo App was a significant predictor of 12-month abstinence. Satisfaction with the intervention was high and similar in both groups.

CONCLUSIONS

High abstinence rates over the 12-month follow-up and satisfaction were found in both conditions. The inclusion of the SinHumo App did not improve abstinence rates in the intervention.

IMPLICATIONS

Scarce research has examined the long-term efficacy of smoking cessation treatments, including Apps, to support the quitting process. The present randomized controlled trial contributes to the existing literature about including information and communication technologies in behavior change interventions. The development of effective smoking cessation apps and information and communication technologies-based interventions is crucial for reducing the prevalence of smoking, as these interventions have the potential to reach a large number of people who smoke and reduce access-related barriers to treatment.

Randomized controlled study of a digital data driven intervention for depressive and generalized anxiety symptoms

As mental health disorders like Major Depressive Disorder and Generalized Anxiety Disorder rise globally, effective, scalable, and personalized treatments are urgently needed. This 16-week prospective, decentralized, randomized, waitlist-controlled study investigated the effectiveness of a digital data-driven therapeutic integrating wearable sensor data with a mobile app to deliver personalized CBT-based interventions for individuals with depressive and generalized anxiety symptoms. 200 adults were randomized to intervention or control groups, with 164 completing the study. The intervention group demonstrated significant reductions in depressive (mean change = -5.61, CI = -7.14, -4.08) and anxiety symptoms (mean change = -5.21, CI = -6.66, -3.76), compared to the control group, with medium-to-large effect sizes (r = 0.64 and r = 0.62, P < 0.001). Notably, these improvements were also observed in participants with clinically significant depression and anxiety, further reinforcing the potential of digital therapeutics in targeting more severe cases. These findings, combined with high engagement levels, suggest that data-driven digital health interventions could complement traditional treatments, though further research is needed to assess their long-term impact.

Toward a New Conceptual Framework for Digital Mental Health Technologies: Scoping Review

Background

Digital mental health technologies (DMHTs) are becoming more widely available and are seen as having the potential to improve the quality of mental health care. However, conversations around the potential impact of DMHTs can be impacted by a lack of focus on the types of technologies that are available. Several frameworks that could apply to DMHTs are available, but they have not been developed with comprehensive methods and have limitations.

Objective

To address limitations with current frameworks, we aimed to identify existing literature on the categorization of DMHTs, to explore challenges with categorizing DMHTs for specific purposes, and to develop a new conceptual framework.

Methods

We used an iterative approach to develop the framework. First, we completed a rapid review of the literature to identify studies that provided domains that could be used to categorize DMHTs. Second, findings from this review and associated issues were discussed by an expert working group, including professionals from a wide range of relevant settings. Third, we synthesized findings to develop a new conceptual framework.

Results

The rapid review identified 3603 unique results, and hand searching identified another 3 potentially relevant papers. Of these, 24 papers were eligible for inclusion, which provided 10 domains to categorize DMHTs. The expert working group proposed a broad framework and based on the findings of the review and group discussions, we developed a new conceptual framework with 8 domains that represent important characteristics of DMHTs. These 8 domains are population, setting, platform or system, purpose, type of approach, human interaction, human responsiveness, and functionality.

Conclusions

This conceptual framework provides a structure for various stakeholders to define the key characteristics of DMHTs. It has been developed with more comprehensive methods than previous attempts with similar aims. The framework can facilitate communication within the field and could undergo further iteration to ensure it is appropriate for specific purposes.

Promises and Pitfalls of Internet Search Data in Mental Health: Critical Review

Unlabelled

The internet is now integral to everyday life, and users' web-based search data could be of strategic importance in mental health care. As shown by previous studies, internet searches may provide valuable insights into an individual's mental state and could be of great value in early identification and helping in pathways to care. Internet search data can potentially provide real-time identification (eg, alert mechanisms for timely interventions). In this paper, we discuss the various problems related to the use of these data in research and clinical practice, including privacy concerns, integration with clinical information, and technical limitations. We also propose solutions to address these issues and provide possible future directions.

User Personas for eHealth Regarding the Self-Management of Depressive Symptoms in People Living With HIV: Mixed Methods Study

BACKGROUND

eHealth has enormous potential to support the self-management of depressive symptoms in people living with HIV. However, a lack of personalization is an important barrier to user engagement with eHealth. According to goal-directed design, personalized eHealth requires the identification of user personas before concrete design to understand the goals and needs of different users.

OBJECTIVE

This study aimed to identify user personas for eHealth regarding the self-management of depressive symptoms in people living with HIV and explore the goals and needs of different user personas for future eHealth.

METHODS

We used an explanatory sequential mixed methods design at the First Hospital of Changsha City, Hunan Province, China, from April to October 2022. In the quantitative phase, 572 people living with HIV completed validated questionnaires with questions related to demographics, self-efficacy, self-management abilities of depressive symptoms, and eHealth literacy. Latent profile analysis was performed to identify different user personas. In the qualitative phase, 43 one-to-one semistructured interviews across different user personas were conducted, transcribed verbatim, and analyzed using conventional content analysis. The findings from both phases were integrated during the interpretation phase.

RESULTS

Three types of user personas could be identified, including "high-level self-managers" (254/572, 44.4%), "medium-level self-managers" (283/572, 49.5%), and "low-level self-managers" (35/572, 6.1%). High-level self-managers had relatively high levels of self-efficacy, self-management abilities of depressive symptoms, and eHealth literacy. High-level self-managers had a positive attitude toward using eHealth for the self-management of depressive symptoms and desired access to self-management support for depressive symptoms from eHealth with high usability. Medium-level self-managers had relatively medium levels of self-efficacy, self-management abilities of depressive symptoms, and eHealth literacy. Medium-level self-managers felt burdened by using eHealth for the self-management of depressive symptoms and preferred to access self-management support for HIV from eHealth with privacy. Low-level self-managers had relatively low levels of self-efficacy, self-management abilities of depressive symptoms, and eHealth literacy. Low-level self-managers had an acceptable attitude toward using eHealth for the self-management of depressive symptoms and desired access to professional guidance from eHealth with privacy and no cost ("free of charge").

CONCLUSIONS

The 3 user personas shed light on the possibility of personalized eHealth to support the self-management of depressive symptoms in different people living with HIV. Further research is needed to examine the generalizability of the user personas across study sites.

The relationships between mental health and social media addiction, and between academic burnout and social media addiction among Chinese college students: A network analysis

Background

The rapid growth in the use of social media applications on the internet has significant impacts on mental health, especially among university students. This study aims to explore the network characteristics and core symptoms between social media addiction, mental health issues (anxiety, depression, stress), and academic burnout. Understanding these relationships is crucial for enhancing psychological interventions and improving academic performance in the digital age.

Methods

A cross-sectional study was conducted with undergraduates and doctoral students from Air Force Medical University. Participants (n = 432) completed self-report scales, including the Depression Anxiety and Stress Scale, Academic Burnout Inventory, and Bergen Social Media Addiction Scale. Network analysis was performed using R to model the relationships between study variables, employing Gaussian Graphical Models and the least absolute shrinkage and selection operator technique for robust network estimation.

Results

Bridge Expected Influence results indicate that "Depression" is consistently the most central node, playing a critical role in the network connecting social media addiction, academic burnout, and psychological stress. The study reveals a significant positive correlation between social media addiction and both academic burnout and mental health issues. Particularly, excessive use of social media can distract students, leading to academic burnout, and exacerbating feelings of anxiety and depression.

Conclusion

Social media addiction has a significant impact on the psychological health and academic performance of college students. This study highlights the potential risks associated with social media usage behaviors and provides a scientific basis for developing related intervention measures. These interventions aim to help college students reduce their dependence on social media, thereby restoring a healthy state of life and study.

Designing a Smartphone-Based Virtual Reality App for Relaxation: Qualitative Crossover Study

BACKGROUND

Accumulating evidence supports the use of virtual reality (VR) in mental health care, with one potential application being its use to assist individuals with relaxation exercises. Despite studies finding support for the potential of VR to effectively aid in relaxation, its implementation remains limited outside of specialized clinics. Known barriers are insufficient knowledge regarding VR operation, lack of availability of VR relaxation apps tailored to local health care systems, and cost concerns. Unfortunately, many VR relaxation apps are designed exclusively for stand-alone headsets, limiting accessibility for a broad audience.

OBJECTIVE

We aimed to design an accessible, smartphone-based VR relaxation app based on user preferences. This paper describes the assessment of 2 stand-alone VR relaxation apps and the resulting smartphone-based VR relaxation app design.

METHODS

Overall, 30 participants (n=23, 77% women; n=7, 23% men) took part in 2 separate VR sessions, assessing 1 of the 2 VR relaxation apps (Flowborne and Calm Place) in each session. After each session, participants were presented with open-ended questions to assess their experiences via a web-based survey tool. These questions explored positive and negative features, shortcomings, and suggestions for improvements while also allowing space for additional remarks concerning the 2 VR relaxation apps. Three of the authors analyzed the responses using inductive thematic analysis, a process comprising 6 phases.

RESULTS

Across both the apps, 5 recurring themes and 13 recurring subthemes were identified in the participants' answers: audio (music and sounds, guidance), visuals (content, realism, variation and dynamics in the environment), features (language, options, feedback and instructions, duration, exercise), implementation (technical aspects, cybersickness, acceptability and usability), and experience. We analyzed the participants' findings and conducted a literature review, which served as the basis for developing the app. The resulting app is a Dutch-language, smartphone-based VR relaxation app, with customization options including 3 types of relaxation exercises, 2 guiding voices, and 3 different environments. Efforts have been made to ensure maximum variation and dynamism in the environments. Calming music and nature sounds accompany the exercises. The efficacy and effectiveness of the resulting app design were not assessed.

CONCLUSIONS

This study provides insights into key features of VR relaxation apps, which were subsequently used for the development of a novel smartphone-based VR relaxation app. Further research concerning the effectiveness of this app, along with a broader evaluation of the efficacy and user feedback for smartphone-based VR relaxation apps, is needed. More generally, there is a clear need for more research on the impact of interactivity, biofeedback, and type of environment in VR relaxation.

Digital mental health treatment implementation playbook: successful practices from implementation experiences in American healthcare organizations

Introduction

Digital mental health treatments (DMHTs) have begun to be implemented in some healthcare systems across the United States. These implementations are conducted as business arrangements. Thus, information on successful or unsuccessful implementations is not published or disseminated. This slows progress, as experiences and learnings are siloed within each organization, hindering or preventing learning across implementations and slowing the progress. To address this, the Society for Digital Mental Health established a DMHT Implementation Workgroup, with the goal of developing a DMHT Playbook that describes current best practices in DMHT implementation in American healthcare settings.

Methods

The workgroup was comprised of representatives from 7 healthcare systems and 10 DMHT companies that have conducted implementations, along with other stakeholders and technical experts. The workgroup met virtually to discuss implementation of effective DMHT implementation processes and inform the development of an interview guide, which was then administered to another 20 key opinion leaders with DMHT implementation experience. Concepts and thematic constructs were extracted by experts in qualitative data analysis. These findings were discussed and refined by the Workgroup based on the Workgroup's experience.

Results

The resulting playbook includes detailed methods, processes and procedures, representing practices that have been successful for implementing DMHTs in healthcare settings.

Discussion

The workgroup recognizes that DMHT implementation is a rapidly evolving field. The successful practices for DMHT implementation described in this playbook may be useful for improving the efficiency of future DMHT implementations in American healthcare systems. However, the authors caution that as the field rapidly evolves, successful implementation practices will likely evolve as well.

Physician Perspectives on the Potential Benefits and Risks of Applying Artificial Intelligence in Psychiatric Medicine: Qualitative Study

BACKGROUND

As artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice.

OBJECTIVE

This study aimed to characterize physician perspectives on the potential impact AI tools will have in psychiatric medicine.

METHODS

We interviewed 42 physicians (21 psychiatrists and 21 family medicine practitioners). These interviews used detailed clinical case scenarios involving the use of AI technologies in the evaluation, diagnosis, and treatment of psychiatric conditions. Interviews were transcribed and subsequently analyzed using qualitative analysis methods.

RESULTS

Physicians highlighted multiple potential benefits of AI tools, including potential support for optimizing pharmaceutical efficacy, reducing administrative burden, aiding shared decision-making, and increasing access to health services, and were optimistic about the long-term impact of these technologies. This optimism was tempered by concerns about potential near-term risks to both patients and themselves including misguiding clinical judgment, increasing clinical burden, introducing patient harms, and creating legal liability.

CONCLUSIONS

Our results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.

Improving the Utility, Safety, and Ethical Use of a Passive Mood-Tracking App for People With Bipolar Disorder Using Coproduction: Qualitative Focus Group Study

BACKGROUND

Coproduction with users of new digital technology, such as passive mood monitoring, is likely to improve its utility, safety, and successful implementation via improved design and consideration of how such technology fits with their daily lives. Mood-monitoring interventions are commonly used by people with bipolar disorder (BD) and have promising potential for digitization using novel technological methods.

OBJECTIVE

This study aims to explore how a passive behavioral monitoring platform, Remote Assessment of Disease and Relapse, would meet the needs of people with BD by specifically considering purpose and function, diversity of need, personal preference, essential components and potential risks, and harms and mitigation strategies through an iterative coproduction process.

METHODS

A total of 17 people with BD were recruited via national charities. We conducted 3 web-based focus groups as a part of an iterative coproduction process in line with responsible research and innovation principles and with consideration of clinical challenges associated with BD. Data were analyzed thematically. Results were cross-checked by someone with lived experience of BD.

RESULTS

Focus groups were transcribed and analyzed using thematic analysis. Six themes were identified as follows: (1) the purpose of using the app, (2) desired features, (3) when to use the app, (4) risks of using the app, (5) sharing with family and friends, and (6) sharing with health care professionals.

CONCLUSIONS

People with BD who are interested in using passive technology to monitor their mood wish to do so for a wide variety of purposes, identifying several preferences and potential risks. Principally, people with BD wished to use this novel technology to aid them in self-managing their BD with greater insight and a better understanding of potential triggers. We discuss key features that may aid this functionality and purpose, including crisis plans and sharing with others. Future development of passive mood-monitoring technologies should not assume that the involvement of formal mental health services is desired.

Effectiveness of Virtual Reality Technology Interventions in Improving the Social Skills of Children and Adolescents With Autism: Systematic Review

BACKGROUND

Virtual reality (VR) technology has shown significant potential in improving the social skills of children and adolescents with autism spectrum disorder (ASD).

OBJECTIVE

This study aimed to systematically review the evidence supporting the effectiveness of VR technology in improving the social skills of children and adolescents with ASD.

METHODS

The search for eligible studies encompassed 4 databases: PubMed, Web of Science, IEEE, and Scopus. Two (XY and JW) researchers independently assessed the extracted studies according to predefined criteria for inclusion and exclusion. These researchers also independently extracted information regarding gathered data on the sources, samples, measurement methods, primary results, and data related to the main results of the studies that met the inclusion criteria. The quality of the studies was further evaluated using the Physiotherapy Evidence Database scale.

RESULTS

This review analyzed 14 studies on using VR technology interventions to improve social skills in children and adolescents with ASD. Our findings indicate that VR interventions have a positive effect on improving social skills in children and adolescents with ASD. Compared with individuals with low-functioning autism (LFA), those with high-functioning autism (HFA) benefited more from the intervention. The duration and frequency of the intervention may also influence its effectiveness. In addition, immersive VR is more suitable for training complex skills in individuals with HFA. At the same time, nonimmersive VR stands out in terms of lower cost and flexibility, making it more appropriate for basic skill interventions for people with LFA. Finally, while VR technology positively enhances social skills, some studies have reported potential adverse side effects. According to the quality assessment using the Physiotherapy Evidence Database scale, of the 14 studies, 6 (43%) were classified as high quality, 4 (29%) as moderate quality, and 4 (29%) as low quality.

CONCLUSIONS

This systematic review found that VR technology interventions positively impact social skills in children and adolescents with ASD, with particularly significant effects on the enhancement of complex social skills in individuals with HFA. For children and adolescents with LFA, progress was mainly observed in basic skills. Immersive VR interventions are more suitable for the development of complex skills. At the same time, nonimmersive VR, due to its lower cost and greater flexibility, also holds potential for application in specific contexts. However, the use of VR technology may lead to side effects such as dizziness, eye fatigue, and sensory overload, particularly in immersive settings. These potential issues should be carefully addressed in intervention designs to ensure user comfort and safety. Future research should focus on optimizing individualized interventions and further exploring the long-term effects of VR interventions.

TRIAL REGISTRATION

International Platform of Registered Systematic Review and Meta-analysis Protocols INPLASY202420079U1; https://inplasy.com/inplasy-2024-2-0079/.