ABSTRACT
Introduction: Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare. Method: We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence. Results: Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low. Discussion: The review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.
ABSTRACT
OBJECTIVE: This scoping review aims to provide a comprehensive summary of the biological, psychological, and sociological risk factors for intimate partner violence (IPV) victimization and perpetration reported after the onset of the COVID-19 pandemic. INTRODUCTION: IPV is a significant public health concern, characterized by various forms of violence inflicted by intimate partners. The onset of the COVID-19 pandemic significantly increased the global prevalence of IPV. While prior research has identified factors linked to IPV, the risk factors reported in the literature during this period have not been systematically mapped. Additionally, the similarities and differences in risk factors between perpetration and victimization have not been well delineated. INCLUSION CRITERIA: This review will focus on individuals aged 12 years or older involved in dyadic romantic relationships. Primary studies and systematic reviews published from the year 2020 will be included. Full-text papers, preprints, theses, and dissertations published in English will be included. Studies focusing on factors unrelated to IPV risk will be excluded. Non-systematic reviews, opinion pieces, and protocols will also be excluded. METHODS: Following the JBI methodology for scoping reviews, systematic searches will be conducted for both peer-reviewed and gray literature. Independent reviewers will screen records, select eligible studies, and extract data using a standardized form. Key risk factors will be mapped to explore their interplay. REVIEW REGISTRATION: Open Science Framework https://osf.io/c2hkm.
