Digital Mental Health Challenges and the Horizon Ahead for Solutions

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.

Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review

BACKGROUND

Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. Digital phenotyping, which involves using data from smartphones or other digital devices to assess mental health, has emerged as a promising tool for distinguishing between these two disorders.

OBJECTIVE

This systematic review aimed to achieve two goals: (1) to summarize the existing literature on the use of digital phenotyping to directly distinguish between UD and BD and (2) to review studies that use digital phenotyping to classify UD, BD, and healthy control (HC) individuals. Furthermore, the review sought to identify gaps in the current research and propose directions for future studies.

METHODS

We systematically searched the Scopus, IEEE Xplore, PubMed, Embase, Web of Science, and PsycINFO databases up to March 20, 2025. Studies were included if they used portable or wearable digital tools to directly distinguish between UD and BD, or to classify UD, BD, and HC. Original studies published in English, including both journal and conference papers, were included, while reviews, narrative reviews, systematic reviews, and meta-analyses were excluded. Articles were excluded if the diagnosis was not made through a professional medical evaluation or if they relied on electronic health records or clinical data. For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings.

RESULTS

We included 21 studies, of which 11 (52%) focused on directly distinguishing between UD and BD, while 10 (48%) classified UD, BD, and HC. The studies were categorized into 4 groups based on the type of digital tool used: 6 (29%) used smartphone apps, 3 (14%) used wearable devices, 11 (52%) analyzed audiovisual recordings, and 1 (5%) used multimodal technologies. Features such as activity levels from smartphone apps or wearable devices emerged as potential markers for directly distinguishing UD and BD. Patients with BD generally exhibited lower activity levels than those with UD. They also tended to show higher activity in the morning and lower in the evening, while patients with UD showed the opposite pattern. Moreover, speech modalities or the integration of multiple modalities achieved better classification performance across UD, BD, and HC groups, although the specific contributing features remained unclear.

CONCLUSIONS

Digital phenotyping shows potential in distinguishing BD from UD, but challenges like data privacy, security concerns, and equitable access must be addressed. Further research should focus on overcoming these challenges and refining digital phenotyping methodologies to ensure broader applicability in clinical settings.

TRIAL REGISTRATION

PROSPERO CRD42024624202; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024624202.

Users' positive attitudes, perceived usefulness, and intentions to use digital mental health interventions: A systematic literature review and meta-analysis

BACKGROUND

Digital Mental Health Interventions (DMHIs) hold significant potential in addressing gaps in mental health treatment, enhancing mental health literacy, and mitigating associated stigma. However, DMHIs have not been systematically evaluated in terms of potential users' attitudes, perceived usefulness, and intentions to use. Thus, this study aims to consolidate evidence to ascertain users' attitudes, perceived usefulness, and intentions to utilize DMHIs.

METHODS

The meta-analysis reports adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. A comprehensive search of databases: Medline, CINHAL, PsycINFO, SCOPUS, and Web of Science, was conducted. As part of the screening process, Covidence database management software was used. Metaprop command was used to calculate the outcome using a random-effects model. Heterogeneity was assessed using Cochrane chi-square (χ2) and the index of heterogeneity (I2 statistics) test. Sensitivity test and subgroup analysis were performed. Publication bias was examined by funnel plots and Egger's test.

RESULTS

In total, 26 studies were analyzed, including data from 13,923 participants. The overall percentage of users' positive attitudes, perceived usefulness, and intentions to use DHMIs was 0.66 (95 % CI; 0.52, 0.79), 0.73 (95 % CI; 0.64, 0.81), and 0.67 (95 % CI; 0.6, 0.74), respectively. Significant heterogeneity was observed; nonetheless, sensitivity analyses indicated that none of the included individual studies exerted undue influence on the overall pooled prevalence. Assessment of funnel plots and Egger's test (p ≤ 0.895) showed no evidence of publication bias.

CONCLUSION

The results of this meta-analysis indicate that, overall, two-thirds of participants have a positive attitude toward DMHIs, around three-quarters find DMHIs useful, and around two-thirds intend to use them. The findings suggest the need to target users' positive attitudes, perceived utility, and willingness for the improved adoption and sustained use of DMHIs.

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.

A protocol using mixed methods for the impact analysis of the implementation of the EMPOWER project: an eHealth intervention to promote mental health and well-being in European workplaces

INTRODUCTION

Mental health at the workplace has been identified as a major priority by the World Health Organization (WHO). Despite its significance, international studies examining the influence of digital mental health interventions on workplace implementation and outcomes are lacking. The European Platform to Promote Well-being and Health in the Workplace (EMPOWER) platform is an eHealth intervention consisting of a website and web-based app designed to guide employers and employees on the prevention of common health problems, reduce presenteeism and absenteeism in the workplace. The aim of this paper is to describe the rationale and methods that will be used to conduct a maxi impact analysis of the processes undertaken to develop and implement the EMPOWER platform in European workplaces using the Global Impact Analytics Framework (GIAF) methodology.

METHODS AND ANALYSIS

We will undertake a mixed-methods analysis of the impact of the process of implementation in the two phases of implementation (initiation and maturity-the early implementation phase). The primary methodology that will be used for the analysis is the GIAF and toolkit. The GIAF toolkit includes a taxonomy (knowledge map), glossary and checklists to examine and rate the EMPOWER project across various domains of impact: planning, pre-engagement, readiness, usability, dissemination, adoption and uptake. Information will be collected from a range of sources through different methods and used to rate the EMPOWER platform (website and app) on each domain. For reliability and validity, four raters will independently rate the EMPOWER platform using the same information. The analysis will include qualitative and quantitative methods to rate on standardised ladders and scales in the GIAF toolkit. Analysis will include descriptive statistics and non-parametric tests where relevant. The information gained will be reviewed in a subgroup (per country) and group (three country) analysis for formative and key summative learnings. These key learnings will be synthesised to generate organisational learnings and insights for the EMPOWER consortium to improve future intervention implementation processes.

ETHICS AND DISSEMINATION

The impact analysis study protocol has been approved by the Research Ethics Committees of the University of Canberra (ID:202311841) and also the Fundació Sant Joan de Déu (PIC-39-20). The participating countries for the RCT (EMPOWER study) also obtained ethical approval through their respective ethical organisations in the participating countries. The impact analysis is registered with the Open Science Framework ID osf.io/eysc9. The EMPOWER project trial is registered at ClinicalTrial.gov with trial ID NCT04907604. The outcomes of the impact analysis study will be disseminated via conference presentations, peer-reviewed journals and key organisational learnings presented in relevant forums.

TRIAL REGISTRATION NUMBER

NCT04907604.

Telemedicine as an Approach to the Mental Health of Healthcare Workers in Angola

INTRODUCTION

African healthcare professionals face significant mental health challenges; therefore, telemedicine can overcome geographical barriers and improve access to mental healthcare.

OBJECTIVE

The objective of this study was to identify the key factors influencing the adoption of telemedicine as a tool to support healthcare workers' mental health in an Angolan healthcare network and develop a telemedicine model tailored to this context.

METHODOLOGY

This mixed-method study collected quantitative data from a questionnaire applied to healthcare workers (n = 275), which were analyzed using descriptive statistics and logistic regressions. Qualitative data were obtained through structured interviews (n = 5) with psychologists and psychiatrists, and analyzed using thematic analysis in MAXQDA (Version 2022, VERBI Software, Germany), to identify recurring patterns and themes. The data were triangulated to ensure the rigor and consistency of the findings. Participation was anonymous and voluntary, and informed consent was obtained from all participants.

RESULTS

Previous experiences with mental health consultations, perceptions of teleconsultations, and doctor-patient relationship were significant in influencing telemedicine adoption by workers. The thematic analysis revealed four themes: perception and ease of telemedicine use, intention to use, and the perception of mental health in Angola. The key adoption factors for providers included project feasibility, management support, training, payment policies, and adherence to legal, ethical, and deontological standards.

CONCLUSIONS

The adoption of telemedicine for occupational mental health requires culturally adapted interventions and compliance with technological and data protection standards. Hospital management must address infrastructure challenges and mental health perceptions, and implement sustainable strategies that promote occupational well-being within the organization.

Mental Health Shortfalls: Perceptions of Unmet Needs and Barriers and Facilitators to Receiving Care

Understanding the perspectives of individuals with mental health concerns and the influence of barriers and facilitators on treatment access can help mental health professionals ensure that those they serve receive needed care. The current study examined these factors in an online survey among 295 adults self-identifying as diagnosed with a mental health condition, which they felt was uncontrolled by intervention or psychiatric medication and impacted their daily lives. Most participants were not receiving psychotherapy or psychiatric medication, although some were accessing care and indicating unmet needs. A majority expressed a desire for more specialized care. Positive perception of providers, higher distress, and greater stigma tolerance were related to greater openness towards receiving psychotherapy and greater openness to psychotherapy, higher distress, and lower perceived barriers were associated with psychotherapy receipt. The relationship between openness towards psychotherapy and receipt was also stronger for those who perceived lower barriers to care. Advocacy and efforts to promote positive attitudes and reduce barriers by mental health practitioners and professional organizations may help support treatment access.

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.

Factors Influencing the Engagement with Electronic Mental Health Technologies: A Systematic Review of Reviews

Mental disorders impact approximately one-third of the global population, affecting adults, children, and youth worldwide. Recently, electronic mental health (e-mental health) technologies have been proposed to facilitate the provision of mental health care by professionals and other stakeholders, aiming to address the challenges associated with delivering mental health services. The objective of this study was to investigate the existing factors influencing engagement with e-mental health technologies. This study was a systematic review of existing reviews conducted in 2024. PubMed, Scopus, ProQuest, and Cochrane databases were searched. The authors assessed the quality of the studies using the CASP (Critical Appraisal Skills Programme) Checklist. Subsequently, they extracted and analyzed the data, utilizing the Boyatzis thematic analysis approach. The systematic review resulted in 15 papers, all of which exhibited an acceptable level of quality and risk of bias. The thematic analysis classified the data into five main themes: 'Technical', 'Ethical and Legal', 'Clinical', 'Organizational', and 'Social'. The study underscored the significance of ensuring accessibility, affordability, and reimbursement to effectively engage patients with e-mental health services. Additionally, transparency-facilitated by self-certification and user involvement-alongside critical factors like informed consent and privacy safeguards, was presented as playing a pivotal role in the process. Moreover, facilitators, including tailored interventions that consider the specific needs of particular groups and temporary project teams composed of individuals working together on specific initiatives, were identified as essential contributors. Overall, the factors influencing engagement with e-mental health technologies and potential solutions for enhancing such engagement appear to be interconnected.

Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review

BACKGROUND

Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns.

OBJECTIVE

We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues.

METHODS

We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher's Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme.

RESULTS

We included 101 articles, of which 95% (n=96) were published in 2018 or later. Most were reviews (n=22, 21.8%) followed by commentaries (n=17, 16.8%). The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of "black box" algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers' jobs (n=16, 15.8%). Other themes were discussed in 9.9% (n=10) of the identified articles.

CONCLUSIONS

Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders' perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability. Addressing these gaps can inform normative analysis and guide the development of ethical guidelines for responsible CAI use in mental health care.

Mental health help-seeking knowledge, attitudes and behaviour among male elite rugby players: the role of masculine health-related values

Objectives

Masculine norms of toughness and self-reliance can discourage help-seeking among elite athletes who are men. Effective ways to leverage masculine norms for help-seeking is a relatively unexplored area in athlete mental health. The study aimed to investigate how masculine health-related values measured by the Intentions Masculine Values Scale (IMVS) affect help-seeking behaviours among male elite rugby players.

Methods

A cross-sectional survey was conducted among 220 Japanese male elite rugby players in the Japan Rugby League One (response rate: 40.6%). Participants completed an online survey assessing help-seeking knowledge, attitudes and behaviours, along with masculine health-related values using the IMVS. Psychological safety within sports settings was evaluated using the Sport Psychological Safety Inventory (SPSI). Multiple regression analyses examined relationships between help-seeking behaviours and IMVS/SPSI.

Results

The Open and Selfless IMVS value was significantly associated with help-seeking knowledge (β=0.059, p=0.009) and attitudes (β=0.064, p=0.006), increasing recognition of the need for help and willingness to seek it. However, no significant association with actual help-seeking behaviour was found (β=-0.006, p=0.774). The Healthy and Autonomous IMVS value was associated with lower help-seeking behaviour (β=0.060, p=0.010), indicating higher autonomy may inhibit seeking professional support. The SPSI was not significantly related to help-seeking measures.

Conclusion

Findings highlight critical gaps between intention and actual help-seeking behaviour among male elite athletes. Further research is needed to explore additional cultural and organisational factors that may better explain help-seeking behaviour and inform effective intervention strategies.

Incorporating Stakeholder Voices into Data Dashboards: A Qualitative Study of the State of Texas Mental Health Dashboard

The U.S. mental health crisis requires new tools to address mental healthcare needs. Data dashboards are a means of sharing community health data on many topics, including mental health. Unfortunately, many "community" dashboards are designed without stakeholder input. This article outlines the creation of the State of Texas Mental Health Dashboard and how researchers incorporated stakeholder feedback throughout its development. Researchers conducted nine focus groups with community stakeholders from two Texas counties. This feedback illustrated some key differences between designers' priorities and what stakeholders considered most relevant. Designers prioritized access to state and local mental health data. While stakeholders found the data useful, they also advocated for tools to identify community mental health resources. Our findings illustrate how excluding stakeholder voices from the design process could have omitted a key element needed to address mental healthcare needs and provides a process for ensuring that local input drives the design process.

App for independence: A feasibility randomized controlled trial of a digital health tool for schizophrenia spectrum disorders

BACKGROUND

Given the widespread adoption of smartphone technologies, digital health strategies to address schizophrenia spectrum disorders hold considerable promise. However, there are relatively few trials of digital health interventions for schizophrenia. The App for Independence (A4i) is a multi-function digital platform co-designed by people with schizophrenia, their families, and service providers.

HYPOTHESIS

This trial was designed to assess the feasibility of A4i.

STUDY DESIGN

The study was a single-blinded randomized trial. This trial was undertaken to generate feasibility data that might inform the design and utility of future effectiveness and implementation trials. The study took place in Toronto, Canada, with 91 participants randomized to 6 months of A4i use or treatment as usual. Feasibility metrics included recruitment, engagement and retention targets, qualitative and satisfaction data, and a secondary assessment of clinical, quality of life, and treatment adherence outcomes.

STUDY RESULTS

The COVID-19 pandemic markedly affected the recruitment of both primary participants and clinicians. Feasibility outcomes were difficult to interpret, though they presented some useful information for future trials. Engagement objectives were not achieved. However, A4i-user satisfaction ratings and qualitative feedback were positive, and technology engagement was fairly positive despite implementation challenges.

CONCLUSIONS

This study adds to emerging discourse regarding how technologies such as A4i are implemented. It suggests that digital technologies are of interest and are received positively by severe mental illness populations, though more work is needed to understand how they are implemented and the optimal methods for researching them.

Barriers to and facilitators of user engagement with web-based mental health interventions in young people: a systematic review

Many young people (YP) are diagnosed with mental illnesses and require support. Web-based mental health interventions (W-MHIs) have been increasingly utilized by YP, healthcare providers, and parents due to reasons including convenience and anonymity. W-MHIs are effective in improving mental health in YP. However, real-world engagement with W-MHIs remains low. Therefore, understanding barriers/facilitators of user engagement with W-MHIs is necessary to promote W-MHIs and help users gain optimal benefits through higher engagement. This review aims to identify barriers/facilitators of user engagement with W-MHIs in YP aged 10-24 years. A systematic search of five databases for English language, peer-reviewed publications was conducted between January 2010 and February 2023. Studies examining factors influencing user engagement with W-MHIs, described as barriers or facilitators, were included. Study quality was assessed using the Mixed Methods Appraisal Tool. A narrative synthesis was performed. Of 4088 articles identified, 69 studies were included. Barriers/facilitators were reported by young people (63 studies), providers (17 studies), and parents/caregivers (8 studies). YP perceived that usefulness and connectedness were the most common facilitators, whereas low-perceived need was the most reported barrier. Both providers and parents reported that perceived usefulness for YP was the most common facilitator, whereas concerns about program effectiveness and privacy were noted as barriers. This review found that program- and individual-related factors were important determinants of engagement with W-MHIs. This review provides guidance on the future design and development of new interventions, narrowing the gap between existing W-MHIs and unmet needs of users.

Healthcare Professionals' Views on the Use of Passive Sensing and Machine Learning Approaches in Secondary Mental Healthcare: A Qualitative Study

INTRODUCTION

Globally, many people experience mental health difficulties, and the current workforce capacity is insufficient to meet this demand, with growth not keeping pace with need. Digital devices that passively collect data and utilise machine learning to generate insights could enhance current mental health practices and help service users manage their mental health. However, little is known about mental healthcare professionals' perspectives on these approaches. This study aims to explore mental health professionals' views on using digital devices to passively collect data and apply machine learning in mental healthcare, as well as the potential barriers and facilitators to their implementation in practice.

METHODS

Qualitative semi-structured interviews were conducted with 15 multidisciplinary staff who work in secondary mental health settings. Interview topics included the use of digital devices for passive sensing, developing machine learning algorithms from this data, the clinician's role, and the barriers and facilitators to their use in practice. Interview data were analysed using reflexive thematic analysis.

RESULTS

Participants noted that digital devices for healthcare can motivate and empower users, but caution is needed to prevent feelings of abandonment and widening inequalities. Passive sensing can enhance assessment objectivity, but it raises concerns about privacy, data storage, consent and data accuracy. Machine learning algorithms may increase awareness of support needs, yet lack context, risking misdiagnosis. Barriers for service users include access, accessibility and the impact of receiving insights from passively collected data. For staff, barriers involve infrastructure and increased workload. Staff support facilitated service users' adoption of digital systems, while for staff, training, ease of use and feeling supported were key enablers.

CONCLUSIONS

Several recommendations have arisen from this study, including ensuring devices are user-friendly and equitably applied in clinical practice. Being with a blended approach to prevent service users from feeling abandoned and provide staff with training and access to technology to enhance uptake.

PATIENT OR PUBLIC CONTRIBUTION

The study design, protocol and topic guide were informed by a lived experience community group that advises on research projects at the authors' affiliation.

Public Perception on Artificial Intelligence-Driven Mental Health Interventions: Survey Research

BACKGROUND

Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Public perception plays a crucial role in shaping attitudes that can facilitate the adoption of new technologies. Similarly, the acceptance of AI-driven mental health interventions is crucial in determining their effectiveness and widespread adoption. Therefore, it is essential to study public perceptions and usage of existing AI-driven mental health interventions by exploring user experiences and opinions on their future applicability, particularly in comparison to traditional, human-based interventions.

OBJECTIVE

This study aims to explore the use, perception, and acceptance of AI-driven mental health interventions in comparison to traditional, human-based interventions.

METHODS

A total of 466 adult participants from India voluntarily completed a 30-item web-based survey on the use and perception of AI-based mental health interventions between November and December 2023.

RESULTS

Of the 466 respondents, only 163 (35%) had ever consulted a mental health professional. Additionally, 305 (65.5%) reported very low knowledge of AI-driven interventions. In terms of trust, 247 (53%) expressed a moderate level of Trust in AI-Driven Mental Health Interventions, while only 24 (5.2%) reported a high level of trust. By contrast, 114 (24.5%) reported high trust and 309 (66.3%) reported moderate Trust in Human-Based Mental Health Interventions; 242 (51.9%) participants reported a high level of stigma associated with using human-based interventions, compared with only 50 (10.7%) who expressed concerns about stigma related to AI-driven interventions. Additionally, 162 (34.8%) expressed a positive outlook toward the future use and social acceptance of AI-based interventions. The majority of respondents indicated that AI could be a useful option for providing general mental health tips and conducting initial assessments. The key benefits of AI highlighted by participants were accessibility, cost-effectiveness, 24/7 availability, and reduced stigma. Major concerns included data privacy, security, the lack of human touch, and the potential for misdiagnosis.

CONCLUSIONS

There is a general lack of awareness about AI-driven mental health interventions. However, AI shows potential as a viable option for prevention, primary assessment, and ongoing mental health maintenance. Currently, people tend to trust traditional mental health practices more. Stigma remains a significant barrier to accessing traditional mental health services. Currently, the human touch remains an indispensable aspect of human-based mental health care, one that AI cannot replace. However, integrating AI with human mental health professionals is seen as a compelling model. AI is positively perceived in terms of accessibility, availability, and destigmatization. Knowledge and perceived trustworthiness are key factors influencing the acceptance and effectiveness of AI-driven mental health interventions.

The views and experiences of older adults regarding digital mental health interventions: a systematic review of qualitative studies

This systematic review aimed to synthesise qualitative evidence on the views and experiences of older adults in using digital mental health interventions (DMHIs) for the prevention or self-management of mental disorders. We searched PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, PsycINFO, and the first 100 results of Google Scholar for eligible studies and we included 37 papers reporting 35 studies in this Review. Most DMHIs were delivered using mobile apps (n=11), websites (n=6), and video-conferencing tools (n=6). The use of DMHIs in older adults was affected by negative perceptions about ageing and mental health, the digital divide (eg, insufficient digital literacy), personal factors (eg, motivation) and health status, interpersonal influences (eg, guidance and encouragement), intervention features (eg, pace and content), technology-related factors (eg, accessibility), and the perceived benefits and risks of using DMHIs. Future DMHIs for older adults should involve other stakeholders such as health-care professionals, provide content relevant to the needs of older people, be more accessible, and address concerns about privacy and confidentiality.

Digital Mental Health Interventions for Adolescents in Low- and Middle-Income Countries: Scoping Review

BACKGROUND

Digital mental health interventions (DMHIs) are increasingly recognized as potential solutions for adolescent mental health, particularly in low- and middle-income countries (LMICs). The United Nations' Sustainable Development Goals and universal health coverage are instrumental tools for achieving mental health for all. Within this context, understanding the design, evaluation, as well as the barriers and facilitators impacting adolescent engagement with mental health care through DMHIs is essential.

OBJECTIVE

This scoping review aims to provide insights into the current landscape of DMHIs for adolescents in LMICs.

METHODS

The Joanna Briggs Institute scoping review methodology was used, following the recommendations of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Our search strategy incorporated 3 key concepts: population "adolescents," concept "digital mental health interventions," and context "LMICs." We adapted this strategy for various databases, including ACM Digital Library, APA PsycINFO, Cochrane Library, Google Scholar (including gray literature), IEEE Xplore, ProQuest, PubMed (NLM), ScienceDirect, Scopus, and Web of Science. The articles were screened against a specific eligibility criterion from January 2019 to March 2024.

RESULTS

We analyzed 20 papers focusing on DMHIs for various mental health conditions among adolescents, such as depression, well-being, anxiety, stigma, self-harm, and suicide ideation. These interventions were delivered in diverse formats, including group delivery and self-guided interventions, with support from mental health professionals or involving lay professionals. The study designs and evaluation encompassed a range of methodologies, including randomized controlled trials, mixed methods studies, and feasibility studies.

CONCLUSIONS

While there have been notable advancements in DMHIs for adolescents in LMICs, the research base remains limited. Significant knowledge gaps persist regarding the long-term clinical benefits, the maturity and readiness of LMIC digital infrastructure, cultural appropriateness, and cost-effectiveness across the heterogeneous LMIC settings. Addressing these gaps necessitates large-scale, co-designed, and culturally sensitive DMHI trials. Future work should address this.

Using Digital Media to Improve Adolescent Resilience and Prevent Mental Health Problems: Protocol for a Scoping Review

BACKGROUND

Global databases show a high prevalence of mental health problems among adolescents (13.5% among those aged 10-14 years and 14.65% for those aged 15-19 years). Successful coping depends on risk and protective factors and how their interaction influences resilience. Higher resilience has been shown to correlate with fewer mental health problems. Digital mental health interventions may help address these problems.

OBJECTIVE

This protocol serves as a framework for planning a scoping review to map the types of digital communication media and their effectiveness in increasing resilience in youths.

METHODS

The Joanna Briggs Institute guidelines will be used: defining the research questions; identifying relevant studies; study selection (we will select articles based on titles and abstracts); charting the data; collating, summarizing, and reporting the results; and consultation. The synthesis will focus on the type of digital media used to increase adolescent resilience skills and the impact they have on adolescent resilience skills. Quantitative and qualitative analyses will be conducted.

RESULTS

The study selection based on keywords was completed in December 2023, the study screening and review were completed in February 2024, and the results manuscript is currently being prepared. This scoping review protocol was funded by the Center for Higher Education Funding and the Indonesia Endowment Fund for Education.

CONCLUSIONS

The results of the study will provide a comprehensive overview of commonly used digital media types and their effectiveness in increasing youth resilience. Thus, the results of this scoping review protocol can serve as foundational evidence in deciding further research or interventions. This study may also be used as a guideline for mapping and identifying the type and impact of communication media used to increase adolescents' resilience skills.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/58681.

A review on the efficacy of artificial intelligence for managing anxiety disorders

Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.

Engagement challenges in digital mental health programs: hybrid approaches and user retention of an online self-knowledge journey in Brazil

Digital mental health interventions (DMHIs) have surged in popularity over the last few years. However, adherence to self-guided interventions remains a major hurdle to overcome. The current study utilized a phased implementation design, incorporating diverse samples and contexts to delve into the engagement challenges faced by a recently launched online mental health platform in Brazil with self-evaluation forms. Employing an iterative mixed-methods approach, including focus groups, online surveys, and think-aloud protocols, the research aims to evaluate user satisfaction, identify barriers to adherence, and explore potential hybrid solutions. Engagement in the platform was evaluated by descriptive statistics of the number of instruments completed, and qualitative interviews that were interpreted thematically. In the fully self-guided mode, 2,145 individuals registered, but a substantial majority (88.9%) engaged with the platform for only 1 day, and merely 3.3% completed all activities. In another sample of 50 participants were given a choice between online-only or a hybrid experience with face-to-face meetings. 40% of individuals from the hybrid group completed all activities, compared to 8% in the online-only format. Time constraints emerged as a significant barrier to engagement, with suggested improvements including app development, periodic reminders, and meetings with healthcare professionals. While the study identified weaknesses in the number and length of instruments, personalized results stood out as a major strength. Overall, the findings indicate high satisfaction with the mental health platform but underscore the need for improvements, emphasizing the promise of personalized mental health information and acknowledging persistent barriers in a digital-only setting.

Characterization of Psychopathology in Latin American Adolescents Using a Web-Based Screening Tool: Cross-Sectional Study

BACKGROUND

Mental health problems and suicide ideation are common in adolescents. Early detection of these issues could prevent the escalation of mental health-related symptoms in the long term. Moreover, characterizing different profiles of prevalent symptoms in conjunction with emotional regulation strategies could guide the design of specific interventions. The use of web-based screening (WBS) tools has been regarded as a suitable strategy to timely detect symptomatology while improving the appeal, cost, timeliness, and reach of detection in young populations. However, the evidence regarding the accuracy of these approaches is not fully conclusive.

OBJECTIVE

The study aims (1) to examine the capability of a WBS to identify adolescents with psychiatric symptoms and suicidality and (2) to characterize the mental health profiles of a large sample of adolescents using WBS.

METHODS

A total of 1599 Latin American Spanish-speaking adolescents (mean age 15.56, SD 1.34 years), consisting of 47.3% (n=753) female, 98.5% Chilean (n=1570), and 1.5% Venezuelan (n=24) participants, responded to a mental health WBS. A randomized subsample of participants also responded to the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). McNemar χ2 and receiver-operating characteristic curves tested the detection accuracy of WBS contrasted with the MINI-KID. Latent profile analyses explored the symptomatic and emotional regulation profiles of participants.

RESULTS

Both measures showed an adequate level of agreement (area under the curve per symptom domain ranging from 0.70 to 0.89); however, WBS yielded a higher prevalence than MINI-KID for all psychiatric symptoms, except suicide ideation and depression. Latent profile analyses yielded 4 profiles-one of them presented elevated psychopathological symptoms, constituting 11% of the sample (n=175). Rumination (odds ratio [OR] 130.15, 95% CI 51.75-439.89; P<.001), entrapment (OR 96.35, 95% CI 29.21-317.79; P<.001), and defeat (OR 156.79, 95% CI 50.45-487.23; P<.001) contributed significantly to the prediction of latent profile memberships, while cognitive reappraisal did not contribute to the prediction of any latent profile memberships, and expressive suppression was only associated to profile-2 membership.

CONCLUSIONS

WBS is acceptable for the timely detection of adolescents at risk of mental health conditions. Findings from the symptomatic and emotional regulation profiles highlight the need for comprehensive assessments and differential interventions.

Artificial intelligence in positive mental health: a narrative review

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

Understanding University Students' Perspectives towards Digital Tools for Mental Health Support: A Cross-country Study

Background

Organisational and individual barriers often prevent university students from seeking mental health support. Digital technologies are recognised as effective in managing psychological distress and as a source of health-related information, thus representing useful options to address mental health needs in terms of accessibility and cost-effectiveness. However, university students' experiences and perspectives towards such interventions are little known.

Objectives

We thus aimed to expand the existing base of scientific knowledge, focusing on this special population.

Methods

Data were from the qualitative component of "the CAMPUS study", longitudinally assessing the mental health of students at the University of Milano-Bicocca (Italy) and the University of Surrey (UK). We conducted in-depth interviews and thematically analysed the transcripts using the framework approach.

Results

An explanatory model was derived from five themes identified across 33 interviews (15 for Italy, 18 for the UK). Students perceived that social media, apps, and podcasts could deliver relevant mental health content, ranging from primary to tertiary prevention. Wide availability and anonymity were perceived as advantages that make tools suitable for preventive interventions, to reduce mental health stigma, and as an extension of standard treatment. These goals can be hindered by disadvantages, namely lower efficacy compared to face-to-face contact, lack of personalisation, and problematic engagement. Individual and cultural specificities might influence awareness and perspectives on the use of digital technologies for mental health support.

Conclusion

Although considering some specific features, digital tools could be a useful instrument to support the mental health needs of students. Since personal contact remains crucial, digital tools should be integrated with face-to-face interventions through a multi-modal approach.

Integrating science into digital health workflows: A strategic approach for leaders

As the digital health industry becomes increasingly competitive, companies must integrate science cross-functionally to drive innovation and differentiation. However, many companies face significant challenges in effectively leveraging science across their organization. This commentary explores the common obstacles digital health companies encounter when integrating science, including limited C-suite understanding, siloed structures, resource constraints, change resistance, and value demonstration challenges. We provide practical strategies for overcoming each challenge, such as educating leadership on science's strategic importance, fostering cross-functional collaboration, prioritizing high-impact initiatives, facilitating open dialogue, and quantifying science's measurable impact. By proactively addressing these hurdles through targeted solutions, companies can successfully integrate science across teams and functions. Effective cross-functional science integration will enable companies to leverage scientific insights, drive product innovation, build credibility through evidence-based outcomes, and ultimately gain a competitive advantage in the rapidly evolving digital health landscape. Strong scientific leadership that champions integration through clear communication, strategic prioritization, and cultural buy-in is essential for achieving long-term success.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study

BACKGROUND

As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.

OBJECTIVE

The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.

METHODS

We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire.

RESULTS

Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F1-score of 73%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to 8 participants compared to the experts. These individuals had significantly milder symptoms of depression (P<.001) and anxiety (P<.001) compared to those with concordant stage 1b+ ratings.

CONCLUSIONS

This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.

Scope, Characteristics, Behavior Change Techniques, and Quality of Conversational Agents for Mental Health and Well-Being: Systematic Assessment of Apps

BACKGROUND

Mental disorders cause substantial health-related burden worldwide. Mobile health interventions are increasingly being used to promote mental health and well-being, as they could improve access to treatment and reduce associated costs. Behavior change is an important feature of interventions aimed at improving mental health and well-being. There is a need to discern the active components that can promote behavior change in such interventions and ultimately improve users' mental health.

OBJECTIVE

This study systematically identified mental health conversational agents (CAs) currently available in app stores and assessed the behavior change techniques (BCTs) used. We further described their main features, technical aspects, and quality in terms of engagement, functionality, esthetics, and information using the Mobile Application Rating Scale.

METHODS

The search, selection, and assessment of apps were adapted from a systematic review methodology and included a search, 2 rounds of selection, and an evaluation following predefined criteria. We conducted a systematic app search of Apple's App Store and Google Play using 42matters. Apps with CAs in English that uploaded or updated from January 2020 and provided interventions aimed at improving mental health and well-being and the assessment or management of mental disorders were tested by at least 2 reviewers. The BCT taxonomy v1, a comprehensive list of 93 BCTs, was used to identify the specific behavior change components in CAs.

RESULTS

We found 18 app-based mental health CAs. Most CAs had <1000 user ratings on both app stores (12/18, 67%) and targeted several conditions such as stress, anxiety, and depression (13/18, 72%). All CAs addressed >1 mental disorder. Most CAs (14/18, 78%) used cognitive behavioral therapy (CBT). Half (9/18, 50%) of the CAs identified were rule based (ie, only offered predetermined answers) and the other half (9/18, 50%) were artificial intelligence enhanced (ie, included open-ended questions). CAs used 48 different BCTs and included on average 15 (SD 8.77; range 4-30) BCTs. The most common BCTs were 3.3 "Social support (emotional)," 4.1 "Instructions for how to perform a behavior," 11.2 "Reduce negative emotions," and 6.1 "Demonstration of the behavior." One-third (5/14, 36%) of the CAs claiming to be CBT based did not include core CBT concepts.

CONCLUSIONS

Mental health CAs mostly targeted various mental health issues such as stress, anxiety, and depression, reflecting a broad intervention focus. The most common BCTs identified serve to promote the self-management of mental disorders with few therapeutic elements. CA developers should consider the quality of information, user confidentiality, access, and emergency management when designing mental health CAs. Future research should assess the role of artificial intelligence in promoting behavior change within CAs and determine the choice of BCTs in evidence-based psychotherapies to enable systematic, consistent, and transparent development and evaluation of effective digital mental health interventions.

The EPIDIA4Kids protocol for a digital epidemiology study on brain functioning in children, based on a multimodality biometry tool running on an unmodified tablet

Introduction

Neurodevelopment and related mental disorders (NDDs) are one of the most frequent disabilities among young people. They have complex clinical phenotypes often associated with transnosographic dimensions, such as emotion dysregulation and executive dysfunction, that lead to adverse impacts in personal, social, academic, and occupational functioning. Strong overlap exists then across NDDs phenotypes that are challenging for diagnosis and therapeutic intervention. Recently, digital epidemiology uses the rapidly growing data streams from various devices to advance our understanding of health's and disorders' dynamics, both in individuals and the general population, once coupled with computational science. An alternative transdiagnostic approach using digital epidemiology may thus better help understanding brain functioning and hereby NDDs in the general population.

Objective

The EPIDIA4Kids study aims to propose and evaluate in children, a new transdiagnostic approach for brain functioning examination, combining AI-based multimodality biometry and clinical e-assessments on an unmodified tablet. We will examine this digital epidemiology approach in an ecological context through data-driven methods to characterize cognition, emotion, and behavior, and ultimately the potential of transdiagnostic models of NDDs for children in real-life practice.

Methods and analysis

The EPIDIA4Kids is an uncontrolled open-label study. 786 participants will be recruited and enrolled if eligible: they are (1) aged 7 to 12 years and (2) are French speaker/reader; (3) have no severe intellectual deficiencies. Legal representative and children will complete online demographic, psychosocial and health assessments. During the same visit, children will perform additionally a paper/pencil neuro-assessments followed by a 30-min gamified assessment on a touch-screen tablet. Multi-stream data including questionnaires, video, audio, digit-tracking, will be collected, and the resulting multimodality biometrics will be generated using machine- and deep-learning algorithms. The trial will start in March 2023 and is expected to end by December 2024.

Discussion

We hypothesize that the biometrics and digital biomarkers will be capable of detecting early onset symptoms of neurodevelopment compared to paper-based screening while as or more accessible in real-life practice.

Evaluation of the Use of Digital Mental Health Platforms and Interventions: Scoping Review

BACKGROUND

The increasing use of digital mental health (DMH) platforms and digital mental health interventions (DMHIs) is hindered by uncertainty over effectiveness, quality and usability. There is a need to identify the types of available evidence in this domain.

AIM

This study is a scoping review identifying evaluation of the (1) DMH platform/s used; and (2) DMHI/s applied on the DMH platform/s.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guided the review process. Empirical studies that focused on evaluation of the use and application of DMH platforms were included from journal articles (published 2012-2022). A literature search was conducted using four electronic databases (Scopus, ScienceDirect, Sage and ACM Digital Library) and two search engines (PubMed and Google Scholar).

RESULTS

A total of 6874 nonduplicate records were identified, of which 144 were analyzed and 22 met the inclusion criteria. The review included general/unspecified mental health and/or suicidality indications (n = 9, 40.9%), followed by depression (n = 5, 22.7%), psychosis (n = 3, 13.6%), anxiety and depression (n = 2, 9.1%), as well as anxiety, depression and suicidality (n = 1, 4.5%), loneliness (n = 1, 4.5%), and addiction (n = 1, 4.5%). There were 11 qualitative studies (50%), 8 quantitative studies (36.4%), and 3 mixed-methods studies (n = 3, 13.6%). The results contained 11 studies that evaluated the DMH platform/s and 11 studies that evaluated the DMHI/s. The studies focused on feasibility, usability, engagement, acceptability and effectiveness. There was a small amount of significant evidence (1 in each 11), notably the (cost-)effectiveness of a DMHI with significant long-term impact on anxiety and depression in adults.

CONCLUSION

The empirical research demonstrates the feasibility of DMH platforms and DMHIs. To date, there is mostly heterogeneous, preliminary evidence for their effectiveness, quality and usability. However, a scalable DMHI reported effectiveness in treating adults' anxiety and depression. The scope of effectiveness may be widened through targeted strategies, for example by engaging independent young people.

Health professionals' sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis

BACKGROUND

Psychiatric hospitals are increasingly being digitalised. Digitalisation often requires changes at work for health professionals. A positive attitude from health professionals towards technology is crucial for a successful and sustainable digital transformation at work. Nevertheless, insufficient attention is being paid to the health professionals' sentiments towards technology.

OBJECTIVE

This study aims to identify the implemented technologies in psychiatric hospitals and to describe the health professionals' sentiments towards these implemented technologies.

METHODS

A text-mining analysis of semi-structured interviews with nurses, physicians and psychologists was conducted. The analysis comprised word frequencies and sentiment analyses. For the sentiment analyses, the SentimentWortschatz dataset was used. The sentiments ranged from -1 (strongly negative sentiment) to 1 (strongly positive sentiment).

RESULTS

In total, 20 health professionals (nurses, physicians and psychologists) participated in the study. When asked about the technologies they used, the participating health professionals mainly referred to the computer, email, phone and electronic health record. Overall, 4% of the words in the transcripts were positive or negative sentiments. Of all words that express a sentiment, 73% were positive. The discussed technologies were associated with positive and negative sentiments. However, of all sentences that described technology at the workplace, 69.4% were negative.

CONCLUSIONS

The participating health professionals mentioned a limited number of technologies at work. The sentiments towards technologies were mostly negative. The way in which technologies are implemented and the lack of health professionals' involvement seem to be reasons for the negative sentiments.

Assessing diversity and inclusivity is the next frontier in mental health recovery narrative research and practice (Preprint)

Demand for digital health interventions is increasing in many countries. The use of recorded mental health recovery narratives in digital health interventions is becoming more widespread in clinical practice. Mental health recovery narratives are first-person lived experience accounts of recovery from mental health problems, including struggles and successes over time. Helpful impacts of recorded mental health recovery narratives include connectedness with the narrative and validation of experiences. Possible harms include feeling disconnected and excluded from others. Diverse narrative collections from many types of narrators and describing multiple ways to recover are important to maximize the opportunity for service users to benefit through connection and to minimize the likelihood of harm. Mental health clinicians need to know whether narrative collections are sufficiently diverse to recommend to service users. However, no method exists for assessing the diversity and inclusivity of existing or new narrative collections. We argue that assessing diversity and inclusivity is the next frontier in mental health recovery narrative research and practice. This is important, but methodologically and ethically complex. In this viewpoint, we propose and evaluate one diversity and two inclusivity assessment methods. The diversity assessment method involves use of the Simpson Diversity Index. The two inclusivity assessment methods are based on comparator demographic rates and arbitrary thresholds, respectively. These methods were applied to four narrative collections as a case study. Refinements are needed regarding a narrative assessment tool in terms of its practicality and cultural adaptation.

Digital mental health interventions for chronic serious mental illness: Findings from a qualitative study on usability and scale-up of the Life Goals app for bipolar disorder

The Life Goals (LG) application is an evidence-based self-management tool intended to help individuals with bipolar disorder (BD) by aligning symptom coping strategies with personal goals. The program has traditionally been offered in-person or via the web, but has recently been translated into an individualized, customizable mobile intervention to improve access to care and reduce provider burden. The LG app previously showed acceptability with ease of use and satisfaction with user interface, but less success in encouraging self-management. To better understand patient needs, our team conducted semi-structured interviews with 18 individuals with BD who used the LG app for 6 months. These interviews also investigated participant interest in sharing LG app data with their provider through an online dashboard. Using affinity mapping, a collaborative, qualitative data analysis technique, our team identified emerging common themes in the interviews. Through this process, team members identified 494 pieces of salient information from interviews that were mapped and translated into three main findings: (1) many participants found Mood Monitoring and LG modules helpful/interesting and stated the app overall had positive impacts on their mental health, (2) some components of the app were too rudimentary or impersonal to be beneficial, and (3) feedback was mixed regarding future implementation of an LG provider dashboard, with some participants seeing potential positive impacts and others hesitating due to perceived efficacy and privacy concerns. These findings can help researchers improve app-based interventions for individuals with BD by increasing app usage and improving care overall.

Linking music streaming platform advertisements with a digital mental health assessment and interventions

Accessibility issues and low rates of help-seeking hinder engagement with mental health resources and treatment. Pragmatic, (cost-)effective solutions are required to increase engagement with efficacious digital mental health interventions (DMHIs) including for hard-to-reach individuals. As an example, music-based interventions have been positively used in health care to reduce stress, anxiety and depression through music medicine, music therapy and recreational use. Although, enhanced mental health awareness from music listening has yet to be converted into engagement with a DMH assessment (DMHA) and DMHIs. Therefore, a new study is proposed to place linked advertisements on Spotify, the most used music streaming platform. MindSpot's vetted DMHA is suitable to use as an example for linking unto because it measures depression, anxiety, general mental well-being problems and psychological distress in Australian adults and provides access to DMHIs. The primary aim is to provide a convenient, robust and scalable consumer pathway to reduce engagement barriers and maximize facilitation to a vetted DMHA and DMHIs. The proposed study is important because it addresses notorious help-seeking difficulties in the adult population (e.g., young people and men). It also expands outreach to the underserved and the unserved and streamlines the integration of digital solutions with mental health services.

An e-Mental Health Resource for COVID-19-Associated Stress Reduction: Mixed Methods Study of Reach, Usability, and User Perceptions

BACKGROUND

COVID-19 and its public health response are having a profound effect on people's mental health. To provide support during these times, Canada's largest mental health and addiction teaching hospital (Centre for Addiction and Mental Health [CAMH]) launched the Mental Health and COVID-19 Pandemic website on March 18, 2020. This website was designed to be a nonstigmatizing psychoeducational resource for people experiencing mild to moderate distress due to COVID-19 and the public health response to the pandemic.

OBJECTIVE

The aim of this study was to examine the reach, usability, and user perceptions of the CAMH Mental Health and COVID-19 Pandemic website.

METHODS

This study used a mixed methods sequential explanatory design approach, which consisted of the following 2 distinct phases: (1) quantitative data collection and analysis and (2) qualitative semistructured interviews. In phase 1, we analyzed Google Analytics data to understand how many people visited the website and which were the most visited pages. We conducted a survey to identify users' sociodemographic backgrounds, and assess the usability of the website using the System Usability Scale and users' subjective stress levels using the Perceived Stress Scale (PSS-10). For phase 2, we conducted semistructured interviews to explore user experiences; user motivation, engagement, satisfaction, and perception of the stress reduction strategies; reflections of the website's functionality, ease of use, navigation, and design; and recommendations for improvement.

RESULTS

Google Analytics results showed 146,978 unique users from June 2020 to March 2021. Most users were from Canada (130,066, 88.5%). Between February 20, 2021, and June 4, 2021, 152 users completed the survey. Most users identified as white, female, and having at least a college degree. Based on the PSS-10 scores, most participants were experiencing moderate to high stress when they visited the website. Users rated the usability of the website as acceptable. Ten users completed in-depth interviews between May 2021 and June 2021. Positive feedback related to the content was that the website was a trustworthy source of mental health information with helpful evidence-based stress reduction strategies. Areas for improvement included the text heavy design of the website, wider dissemination/marketing, and greater accessibility of the website to meet the needs of diverse populations.

CONCLUSIONS

Adding stress reduction resources to a website from a well-respected institution may be a practical method to increase awareness and access to evidence-based stress reduction resources during times of crisis, where there is severe disruption to usual health care contacts. Efforts to ensure that these resources are more widely accessed, especially by diverse populations, are needed.

Australian Psychologists Experiences with Digital Mental Health: a Qualitative Investigation

Digital mental health is changing the landscape of service delivery by addressing challenges associated with traditional therapy. However, practitioners' use of these resources remains underexamined. This study explored psychologists' attitudes and experiences with digital mental health intervention. Taking a qualitative exploratory approach via thematic analysis, the study sought to answer the following research questions: (i) How do psychologists perceive digital mental health? and (ii) What is their experience using digital mental health as part of routine practice? Ten practising psychologists participated in online semi-structured interviews (approximately 50 min), with interviews then transcribed verbatim. Interview data were analysed according to the six-phase approach to thematic analysis proposed by Braun and Clarke. Three themes were identified: (1) attitudes towards digital mental health; (2) use within routine practice; and (3) perspectives on an effective model for implementation. Practitioners play a major role in the design and delivery of digital mental health services. Barriers and facilitators at the practitioner-level (e.g. knowledge and competence with tools, perceptions on the utility of digital interventions) and the service-level (e.g. government support for digital health) should be considered in the future design of digital mental health resources and service delivery.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41347-022-00271-5.

An AI-based Decision Support System for Predicting Mental Health Disorders

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

Using the Stay Strong App for the Well-being of Indigenous Australian Prisoners: Feasibility Study

Background

The gap between mental health needs and resources for Aboriginal and Torres Strait Islander people, the Indigenous people of Australia, is most marked in the prison population. Indigenous people are overrepresented in Australian prisons. In prison, this group experiences mental disorders to a greater degree than non-Indigenous prisoners. This group has also been found to experience mental disorder at a higher rate than Indigenous people in the community. In addition to pre-existing determinants of poor mental health, these high prevalence rates may reflect poor engagement in mainstream interventions or the efficacy of available interventions. In community populations, the use of digital mental health resources may help to increase access to well-being support. However, culturally appropriate digital tools have not been available to Indigenous people in prisons. The absence of feasibility and efficacy studies of these tools needs to be addressed.

Objective

The aim of this study is to determine the feasibility of the Stay Strong app as a digital well-being and mental health tool for use by Indigenous people in prison.

Methods

Dual government agency (health and corrective services) precondition requirements of implementation were identified and resolved. This was essential given that the Stay Strong app was to be delivered by an external health agency to Indigenous prisoners. Then, acceptability at a practice level was tested using postuse qualitative interviews with clients and practitioners of the Indigenous Mental Health Intervention Program. All Indigenous Mental Health Intervention Program practitioners (10/37, 27%) and client participants who had completed their second follow-up (review of the Stay Strong app; 27/37, 73%) during the study period were invited to participate.

Results

Owing to the innovative nature of this project, identifying and resolving the precondition requirements of implementation was challenging but provided support for the implementation of the app in practice. Acceptability of the app by clients and practitioners at a practice level was demonstrated, with nine themes emerging across the interviews: satisfaction with the current Stay Strong app, supported client goal setting, increased client self-insight, improved client empowerment, cultural appropriateness, enhanced engagement, ease of use, problems with using an Android emulator, and recommendations to improve personalization.

Conclusions

The Stay Strong Custody Project is a pioneering example of digital mental health tools being implemented within Australian prisons. Using the app within high-security prison settings was found to be feasible at both strategic and practice levels. Feedback from both clients and practitioners supported the use of the app as a culturally safe digital mental health and well-being tool for Aboriginal and Torres Strait Islander people in prison.

Human-Computer Interaction in Digital Mental Health

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

A Conceptual Framework to Design Connected Mental Health Solutions in the United Arab Emirates: Questionnaire Study

Background

Connected mental health (CMH) is a field presenting information and communications technology–based mental care interventions that could help overcome many mental care delivery barriers. Culture and background influence people’s attitudes, preferences, and acceptance of such solutions. Therefore, the suitability of CMH solutions to the targeted population is an important factor in their successful adoption.

Objective

The aim of this study is to develop a framework for the design and creation of CMH solutions suitable for the UAE context. The framework is based on investigating enablers and barriers of CMH adoption in the United Arab Emirates, from the mental health professional's (MHP) perspective and from related literature.

Methods

A survey of literature on relevant studies addressing the use of technology for mental care in Arab countries, and a web-based questionnaire-based survey with 17 MHPs practicing in the United Arab Emirates investigating their attitudes and views toward CMH was conducted. Results from the questionnaire and from related studies were analyzed to develop the design framework.

Results

On the basis of findings from the literature survey and analyzing MHP answers to the web-based survey, a framework for the design of CMH solutions for the UAE population was developed. The framework presents four types of recommendation categories: favorable criteria, which included blended care, anonymity, and ease of use; cultural factors including availability in multiple languages, mainly Arabic and English, in addition to religious and cultural considerations; technical considerations, including good-quality communication, availability in formats compatible with mobile phones, and providing technical support; and users’ health and data safety considerations, including users’ suitability testing, confidentiality, and ensuring MHP integrity.

Conclusions

CMH has the potential to help overcome many mental care barriers in the United Arab Emirates in particular and in the Arab world in general. CMH adoption in the United Arab Emirates has a potential for success. However, many factors should be taken into account, mainly cultural, religious, and linguistic aspects.

What is the Current and Future Status of Digital Mental Health Interventions?

The prevalence of mental disorders continues to increase, especially with the advent of the COVID-19 pandemic. Although we have evidence-based psychological treatments to address these conditions, most people encounter some barriers to receiving this help (e.g., stigma, geographical or time limitations). Digital mental health interventions (e.g., Internet-based interventions, smartphone apps, mixed realities -virtual and augmented reality) provide an opportunity to improve accessibility to these treatments. This article summarizes the main contributions of the different types of digital mental health solutions. It analyzes their limitations (e.g., drop-out rates, lack of engagement, lack of personalization, lack of cultural adaptations) and showcases the latest sophisticated and innovative technological advances under the umbrella of precision medicine (e.g., digital phenotyping, chatbots, or conversational agents). Finally, future challenges related to the need for real world implementation of these interventions, the use of predictive methodology, and hybrid models of care in clinical practice, among others, are discussed.

The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review

Suicidal men feel the need to be self-reliant and that they cannot find another way out of relationship or socioeconomic issues. Suicide prevention is of crucial importance worldwide. The much higher rate of suicide in men engenders action. The prelude is a subjective experience that can be very isolating and severely distressing. Men may not realize a change in their thinking and behaviors, which makes it more difficult to seek and get help, thereby interrupting a "downward spiral". Stoicism often prevents men from admitting to their personal struggle. The lack of "quality" connections and "non-tailored" therapies has led to a high number of men "walking out" on traditional clinical approaches. But there are complicated relationships in motivations and formative behaviors of suicide with regards to emotional state, psychiatric disorders, interpersonal life events and suicidal behavior method selection. Middle-aged and older men have alternated as the most at-risk of suicide. There is no one solution that applies to all men, but digital tools may be of assistance (e.g., video conferences, social networks, telephone calls, and emails). Digital interventions require higher levels of effectiveness for distress and suicidality but self-guided approaches may be the most suitable for men especially where linked with an integrated online suicide prevention platform (e.g., quick response with online chats, phone calls, and emails). Furthermore, technology-enabled models of care offer promise to advance appropriate linking to mental health services through better and faster understanding of the specific needs of individuals (e.g., socio-cultural) and the type and level of suicidality experienced. Long-term evidence for suicidality and its evaluation may benefit from progressing human computer-interaction and providing impetus for an eminent integrated digital platform.

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for "pragmatic AI-augmentation" that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice-automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.

Learning Agility of Learning & Development Professionals in the Life-Sciences Field during the Pandemic: An Empirical Study (Preprint)

Background

The COVID-19 pandemic has impacted the life sciences field worldwide. Life sciences organizations (eg, pharmaceutical and med-tech companies) faced a rapidly increasing need for vital medical products, patient support, and vaccine development. Learning and development (L&D) departments play a crucial role in life sciences organizations as they apply learning initiatives to organizational strategy within a constantly evolving sector. During the COVID-19 pandemic, the work of L&D professionals in life sciences organizations changed profoundly during the abrupt shift to remote work, since learning and training normally occur in a face-to-face environment. Given the complex and dynamic situation of the pandemic, both individuals and organizations needed to learn quickly and apply what they learned to solve new, unprecedented problems. This situation presents an opportunity to study how characteristics of learning agility were evidenced by life sciences organizations and individual employees in the remote working mode.

Objective

In collaboration with Life Sciences Trainers & Educators Networks (LTEN), this study investigated the responses and learning agility of L&D professionals and their organizational leadership within the life sciences sector to the work changes due to the pandemic. The study answered the following questions: (1) How did L&D professionals in the life sciences sector respond to the changes in their work environment during the COVID-19 pandemic? (2) How did L&D professionals in the life sciences sector demonstrate learning agility during remote working?

Methods

We adopted a mixed methods approach that included a semistructured interview and a survey. Participants who were life sciences or health care L&D practitioners and in relevant positions were recruited via email through the LTEN and its partner pharmaceutical, biotech, or medical devices organizations. Interviews with 12 L&D professionals were conducted between June and August 2020 through phone or online conferencing, covering 22 open-ended questions to stimulate ideas that could be explored further in the survey. The semistructured interview questions were grounded in theory on learning agility. In total, 4 themes were developed from the interviews, which formed the basis for developing the survey items. The subsequent survey regarding 4 specific themes was conducted from August to October 2020 using Qualtrics. Both interview and survey data were analyzed based on a learning agility framework.

Results

Findings revealed generally positive organizational and individual responses toward the changes brought about by the pandemic. Results also indicated that a disruptive crisis, such as the shift from working in the office to working from home (WFH), required professionals’ learning agility to both self-initiate their own learning and to support the learning agility of others in the organization.

Conclusions

This study was designed to better understand education and training in the life sciences field, particularly during the unique circumstances of the global COVID-19 pandemic. We put forward several directions for future research on the learning agility of L&D professionals in life sciences organizations.