BACKGROUND AND HYPOTHESIS:Digital remote monitoring (DRM) captures service users' health-related data remotely using devices such as smartphones and wearables. Data can be analyzed using advanced statistical methods (eg, machine learning) and shared with clinicians to aid assessment of people with psychosis' mental health, enabling timely intervention. Such methods show promise in detecting early signs of psychosis relapse. However, little is known about clinicians' views on the use of DRM for psychosis. This study explores multi-disciplinary staff perspectives on using DRM in practice. STUDY DESIGN:Fifty-nine mental health professionals were interviewed about their views on DRM in psychosis care. Interviews were analyzed using reflexive thematic analysis. Study Results: Five overarching themes were developed, each with subthemes: (1) the perceived value of digital remote monitoring; (2) clinicians' trust in digital remote monitoring (3 subthemes); (3) service user factors (2 subthemes); (4) the technology-service user-clinician interface (2 subthemes); and (5) organizational context (2 subthemes). CONCLUSIONS:Participants saw the value of using DRM to detect early signs of relapse and to encourage service user self-reflection on symptoms. However, the accuracy of data collected, the impact of remote monitoring on therapeutic relationships, data privacy, and workload, responsibility and resource implications were key concerns. Policies and guidelines outlining clinicians' roles in relation to DRM and comprehensive training on its use are essential to support its implementation in practice. Further evaluation regarding the impact of digital remote monitoring on service user outcomes, therapeutic relationships, clinical workflows, and service costs is needed.

This paper reviews the current evidence and synthesizes fifteen years of real-world development, testing, and implementation of digital mental health interventions (DMHIs) for young people. Drawing on the work of Orygen Digital, we outline the evolution of interventions including the Moderated Online Social Therapy (MOST) platform, the Mello app, and a suite of virtual reality-based therapies; all developed to meet the complex clinical, developmental, and service needs of youth aged 12 to 25.We identify ten key challenges and opportunities encountered in designing, developing and implementing these DMHIs: (1) meaningful co-design; (2) sustained user engagement; (3) personalization and transdiagnostic targeting; (4) optimizing intensities of human support; (5) leveraging peer support and social networking; (6) embedding DMHIs in clinical services; (7) blending digital and face-to-face care; (8) building data infrastructure and learning health systems; (9) developing sustainable and scalable business models; and (10) preparing DMHIs for large language models. Each theme reflects both achievements and persistent challenges, and is illustrated through a synthesis of the current evidence and real-world insights from our clinical trials and national-scale service implementations.Our approach is grounded in various frameworks including clinical staging, self-determination theory, supportive accountability, and minimally disruptive medicine. Emerging innovations such as just-in-time adaptive interventions, extended reality (XR) therapies, stratified treatment models, and large language models offer promising future pathways for greater personalization, engagement, effectiveness, and scalability.Our findings highlight the potential value of context-sensitive, co-designed, and system-integrated DMHIs, while also emphasising enduring limitations such as variable engagement, implementation barriers, and population-specific adaptation. Moving beyond controlled efficacy trials toward agile, real-world learning health systems will be essential to realising the full potential of DMHIs in transforming youth mental health care.

BACKGROUND:Digital health interventions have the potential to improve the efficacy and accessibility of mental health services for people with severe mental health problems, but their integration into routine practice is a challenge. The real-world implementation of digital health interventions should be considered alongside digital intervention development. However, little is known about the quality of implementation research in this area, including the extent to which implementation science theories, models and frameworks are used. The aim of this review was to synthesise evidence regarding the application of theories, models and frameworks in research investigating the implementation of digital health interventions in services for people with severe mental health problems. Secondary aims were to consider the contexts within which studies had been undertaken and the degree of service user involvement in this research. METHODS:A scoping review method was employed. Electronic databases were systematically searched for published papers in English and reference lists of included studies were hand searched. Included studies used an implementation science theory, model, or framework to understand, guide or evaluate the implementation of digital health interventions in services for people with severe mental health problems. RESULTS:Twelve eligible studies were identified. Studies were primarily undertaken in community mental health services with staff participants and there was variation in the types of digital interventions that were investigated. Eight different implementation science theories, models, and frameworks were used and were mainly employed to guide qualitative analysis. Most studies were undertaken in the early exploratory stages of implementation projects and there was little evidence regarding factors affecting the longer-term sustainment of digital health interventions in practice. Only one study reported the inclusion of service users in the design of the implementation study. CONCLUSIONS:The use of implementation theories, models, and frameworks in efforts to implement digital health interventions in routine care for people with severe mental health problems is limited. Researchers should consider integrating such approaches throughout the research process and ensure service users are involved in this work. Further research regarding implementation processes, and the reach and sustainment of digital health interventions in routine practice, is required.

Introduction: Youth mental health services are characterised by high demand and modest clinical outcomes. While digital mental health interventions (DMHIs) have been shown to be clinically effective, the relationship between DMHI use and outcome is unclear. The current study sought to identify the factors affecting the relationship between DMHI use and depression and anxiety symptom improvement in sub-groups of young people. Method: An observational cohort design included young people aged 12-25 years engaging with a DMHI (MOST) from October 2020 to October 2023. The primary outcome was improvement at 12 weeks on the Patient Health Questionnaire-4 (PHQ4). DMHIs were combinations of self-paced digital cognitive-behavioural therapy content, social network interactions, and professional support. A machine learning clustering algorithm was used to identify distinct user clusters based on baseline characteristics and multiple logistic regression models examined the relationship between DMHI usage and improvement. Results: Two distinct user clusters emerged, differing by symptom severity, age, service setting, and concurrent external treatment. 46.7% of "Severe" users and 39.8% of "Mild-Moderate" users significantly improved. Greater use of therapy content and professional support interactions were associated with improvement for the MildModerate group only (OR = 1.16, 95% CI: 1.04-1.30, p = 0.008). Conclusion: While a greater proportion of users in the Severe group significantly improved, increased MOST use was associated with symptom improvement only for the Mild-Moderate group. These findings highlight the complexity of the relationship between DMHI use and outcome. Other unmeasured mediating or moderating factors such concurrent 'offline' treatment may help explain the results. Further research is required to better understand the relationship between DMHI use and clinical outcomes.

Online virtual worlds are platforms that allow users, represented as avatars, to meet and interact with other users in real time within 3D virtual environments. These platforms have potential utility as vehicles to deliver/receive clinical services, especially as a preference to video-conferencing-based telehealth. However, commercial virtual worlds (e.g.,“Second Life”) are often deemed unsuitable due to privacy and safety concerns. The aim of this study was therefore to co-develop and test a bespoke virtual world platform to deliver routine youth mental health services. We undertook a participatory-design process to develop the platform (Orygen Virtual Worlds) involving 10 young people with lived experience of mental health difficulties, researchers, software designers and mental health clinicians. We then tested two types of clinic-led interventions delivered through the virtual world (a structured therapy group and an individual therapy) in a public youth mental health service setting in Australia. Participants were patients receiving treatment in the service. The main outcomes were acceptability and feasibility; we also measured symptom change, usability, presence and therapeutic alliance. We conducted qualitative interviews post-intervention with the participants and analysed these interviews using thematic analysis. 15 young people were recruited to the structured group (27% consented from referred) and 8 were recruited to the individual therapy (36% consented from referred). Drop out was higher in the individual therapy than the structured group therapy (38% versus 80%). Acceptability ratings were high for both therapy approaches and there were no significant safety events attributed to using the platform. There were no significant pre-post differences in the symptom outcome measures in either the structured group intervention or individual therapy. The platform was perceived as being comfortable and safe, enjoyable, fun and interactive, and was not confusing to navigate or difficult to use. The qualitative themes included the platform being fun and engaging, making treatment more accessible, providing a safe and inclusive place, fostering connections, positively impacting wellbeing and providing a catalyst for real life functional change. Young people perceived decreased barriers, increased comfort with help-seeking and reduced social stress facilitated by the avatar, communication options (emoji, text, voice) and accessibility from home. Our findings indicate that online virtual world platforms, such as the one we have designed, hold considerable promise for providing interventions for young people in clinical services. Virtual worlds can provide fun and engaging experiences of therapeutic interventions for young people with mental health difficulties which are safe and inclusive, especially for harder to reach groups.

This systematic review and meta-analysis examined the efficacy of digital mental health apps and the impact of persuasive design principles on intervention engagement and outcomes. Ninety-two RCTs and 16,728 participants were included in the meta-analyses. Findings indicate that apps significantly improved clinical outcomes compared to controls (g = 0.43). Persuasive design principles ranged from 1 to 12 per app (mode = 5). Engagement data were reported in 76% of studies, with 25 distinct engagement metrics identified, the most common being the percentage of users who completed the intervention and the average percentage of modules completed. No significant association was found between persuasive principles and either efficacy or engagement. With 25 distinct engagement metrics and 24% of studies not reporting engagement data, establishing overall engagement with mental health apps remains unfeasible. Standardising the definition of engagement and implementing a structured framework for reporting engagement metrics and persuasive design elements are essential steps toward advancing effective, engaging interventions in real-world settings.

BACKGROUND:Digital remote monitoring (DRM) utilises devices such as smartphones and wearables to remotely collect health-related data, providing insights into the mental health of individuals with psychosis. This data can be shared with mental health services to aid clinical assessment. DRM has been found to effectively identify early signs of psychosis relapse, enabling clinicians to intervene earlier and improve outcomes for service users. However, there are challenges to its implementation in services. This study used Normalisation Process Theory (NPT) as a framework to examine mental health professionals' expectations regarding the barriers and facilitators to implementing DRM in psychosis care. METHODS:Semi-structured interviews were conducted with 59 multi-disciplinary mental health professionals from nine UK National Health Service mental health Trusts/Health Boards. Interviews were inductively thematically analysed, then deductively analysed by mapping themes to the core constructs of NPT. FINDINGS:Findings were similar across all settings and applicable to three NPT constructs (coherence, cognitive participation and collective action) and their subcomponents. One inductive theme, 'own experiences of technology' was not captured by NPT. Participants understood DRM's purpose for detecting early signs of relapse. However, several barriers to implementation were identified: uncertainty about professional roles, resource issues, concerns about inaccurate DRM data, complexity of the technology, security/privacy issues, and concerns about using DRM with certain clinical presentations. Suggested implementation strategies included staff training and ongoing technical support, developing guidance regarding professionals' responsibilities, using an in-house 'DRM expert' to lead its integration within services, enhancing clinician's knowledge of the evidence base for DRM in psychosis care, and actively involving both clinicians and service users in DRM system development. Interpretation Findings identify key factors and actionable implementation strategies essential for successful early adoption of DRM in routine care. By addressing these considerations, implementation effectiveness can be optimised, ultimately improving outcomes for people with psychosis. Funding Wellcome Trust, National Institute for Health and Care Research.

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

Youth mental health services are characterised by high demand and modest clinical outcomes. While digital mental health interventions (DMHIs) have been shown to be clinically effective, the relationship between DMHI use and outcome is unclear. The current study sought to identify the factors affecting the relationship between DMHI use and depression and anxiety symptom improvement in sub-groups of young people. An observational cohort design included young people aged 12-25 years engaging with a DMHI (MOST) from October 2020 to October 2023. The primary outcome was improvement at 12 weeks on the Patient Health Questionnaire-4 (PHQ4). DMHIs were combinations of self-paced digital cognitive-behavioural therapy content, social network interactions, and professional support. A machine learning clustering algorithm was used to identify distinct user clusters based on baseline characteristics and multiple logistic regression models examined the relationship between DMHI usage and improvement. Two distinct user clusters emerged, differing by symptom severity, age, service setting, and concurrent external treatment. 46.7% of "Severe" users and 39.8% of "Mild-Moderate" users significantly improved. Greater use of therapy content and professional support interactions were associated with improvement for the Mild-Moderate group only (OR = 1.16, 95% CI: 1.04-1.30, p = 0.008). While a greater proportion of users in the Severe group significantly improved, increased MOST use was associated with symptom improvement only for the Mild-Moderate group. These findings highlight the complexity of the relationship between DMHI use and outcome. Other unmeasured mediating or moderating factors such concurrent 'offline' treatment may help explain the results. Further research is required to better understand the relationship between DMHI use and clinical outcomes.

AIMS:Education is a key goal of young people experiencing mental ill-health and is crucial for many aspects of enjoyable, meaningful lives. However, the completion of education can be a challenge. This paper evaluated the expanded implementation of a targeted education support programme for young people with mental ill-health. METHODS:A retrospective chart audit of the 125 young people accessing a range of mental health services in a metropolitan region referred to the intervention between January 2022 and June 2023 was conducted. Education, demographic and administrative data were collected. The primary outcome was engagement in education (both secondary and higher), measured as both maintaining education and engaging in new educational opportunities. Fifteen clinicians were also surveyed on their perspectives on the intervention. RESULTS:One-hundred and twenty-two referrals were accepted, 93 young people engaged and 70 were supported to engage with education. Half of the young people who were not engaged in education prior to participating were successfully supported to re-engage. However, the intervention was less integrated between referring services than during a previous pilot phase. Clinicians viewed the intervention as contributing to the development of generalisable skills and enhancing efficacy of care, but viewed a lack of co-location at every site as a substantial barrier to integration. CONCLUSIONS:In a large sample embedded in a real-world setting, the current paper demonstrates positive outcomes of supported education within youth mental healthcare. Further studies are needed to demonstrate efficacy with control groups and to explore the perspectives of young people and carers.

BackgroundDigital mental health interventions (DMHIs) to monitor and improve the health of people with psychosis or bipolar disorder show promise; however, user engagement is variable, and integrated clinical use is low. ObjectiveThis prospectively registered systematic review examined barriers and facilitators of clinician and patient engagement with DMHIs, to inform implementation within real-world settings. MethodsA systematic search of 7 databases identified empirical studies reporting qualitative or quantitative data about factors affecting staff or patient engagement with DMHIs aiming to monitor or improve the mental or physical health of people with psychosis or bipolar disorder. The Consolidated Framework for Implementation Research was used to synthesize data on barriers and facilitators, following a best-fit framework synthesis approach. ResultsThe review included 175 papers (150 studies; 11,446 participants) describing randomized controlled trials; surveys; qualitative interviews; and usability, cohort, and case studies. Samples included people with schizophrenia spectrum psychosis (98/150, 65.3% of studies), bipolar disorder (62/150, 41.3% of studies), and clinicians (26/150, 17.3% of studies). Key facilitators were a strong recognition of DMHIs’ relative advantages, a clear link between intervention focus and specific patient needs, a simple, low-effort digital interface, human-supported delivery, and device provision where needed. Although staff thought patients would lose, damage, or sell devices, reviewed studies found only 11% device loss. Barriers included intervention complexity, perceived risks, user motivation, discomfort with self-reflection, digital poverty, symptoms of psychosis, poor compatibility with existing clinical workflows, staff and patient fears that DMHIs would replace traditional face-to-face care, infrastructure limitations, and limited financial support for delivery. ConclusionsIdentified barriers and facilitators highlight key considerations for DMHI development and implementation. As to broader implications, sustainable business models are needed to ensure that evidence-based DMHIs are maintained and deployed. Trial RegistrationPROSPERO CRD42021282871; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282871

ABSTRACTAimAccurate and appropriate cognitive screening can significantly enhance early psychosis care, yet no screening tools have been validated for the early psychosis population and little is known about current screening practices, experiences, or factors that may influence implementation. CogScreen is a hybrid type 1 study aiming to validate two promising screening tools with young people with first episode psychosis (primary aim) and to understand the context for implementing cognitive screening in early psychosis settings (secondary aim). This protocol outlines the implementation study, which aims to explore the current practices, acceptability, feasibility and determinants of cognitive screening in early psychosis settings from the perspective of key stakeholders.MethodsYoung people with first episode psychosis (n = 350), caregivers (minimum n = 10) and service providers (minimum n = 12) will be recruited from primary and specialist early psychosis services in Melbourne, Adelaide and Sydney, Australia. Two implementation science frameworks will inform data collection and analysis: the Theoretical Framework of Acceptability and the Consolidated Framework for Implementation Research. A mixed‐methods design will be employed to collect and analyse data from questionnaires with young people, interviews with all stakeholder groups, and administrative processes. Quantitative data will be analysed using descriptive statistics. Qualitative data will be analysed through content analysis using deductive and inductive coding.Results and DiscussionThis protocol paper presents the rationale and methods for the CogScreen implementation study.ConclusionTogether with accuracy findings, results from the implementation study will provide insights about the practices, experiences, enablers and barriers to cognitive screening in early psychosis services.

Background: General Practitioners (GPs) play a key role in the treatment of adolescent depression and anxiety, but their capacity to provide effective care may be compromised by long wait times. Aim: To explore GPs[&prime] referral practices to mental health treatment and services for adolescents with depression and/or anxiety, their knowledge of wait times and the perceived impact of these on GPs and their patients and proposed acceptable wait times. Design, Setting and Methods: An online survey of 192 GPs in Australia who treated adolescents (12 to 17 years old) with depression and/or anxiety. Results: GPs most frequently referred adolescents with depression and/or anxiety to psychologists and the mean estimated wait times 57 days (SD: 47.9), which was four times their proposed acceptable wait time (M: 6.7 days, SD: 27.0). The frequency of medication prescribing almost doubled during the wait time when compared to routine practice (14.6% versus 8.3%, respectively). Almost all GPs (81.8%) increased their level of care due to long wait times but had limited training in adolescent mental health and knowledge on appropriate strategies to do so effectively. Conclusion: The findings signify the discrepancies between GPs[&prime] preferences for mental health treatment availability in Australia and what they perceive to be acceptable and the experience of their adolescent patients who they have referred to specialist care. Greater knowledge of wait time strategies, adolescent mental health training, and improved communication between referrer and referred services may help GPs to enhance the quality of primary care provided to adolescent patients with depression and/or anxiety in Australia. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by a generous donation from the Buxton Family Foundation, Australian Unity, the Frontiers Technology Clinical Academic Group Industry Connection Seed Funding Scheme, and the UNSW Medicine, Neuroscience, Mental Health and Addiction Theme and SPHERE Clinical Academic Group Collaborative Research Funding. BOD is supported by an NHMRC MRFF Investigator Fellowship (MRF1197249). AEW is supported by an NHMRC Investigator Fellowship (2017521). This work is supported by a NHMRC MRFF Million Minds Grant (MRF2035416). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Human Research Ethics Committee of the University of New South Wales gave ethical approval for this work (HC220107). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the Chief Investigator bridianne.odea@flinders.edu.au

RationaleLung T1 MRI is a potential method to assess cystic fibrosis (CF) lung disease that is safe, quick, and widely available, but there are no data in children with mild CF lung disease.ObjectiveAssess the ability of lung T1 MRI to detect abnormalities in children with mild CF lung disease.MethodsWe performed T1 MRI, multiple breath washout (MBW), chest computed tomography (CT), and spirometry in a cohort of 45 children with mild CF lung disease (6-11 years of age).Main ResultsDespite mean normal ppFEV1 values, the majority of children with CF in this study exhibited mild lung disease evident in lung clearance index (LCI) measured by MBW, chest CT Brody scores, and percent normal lung perfusion (%NLP) measured by T1 MRI. The %NLP correlated with chest CT Brody scores, as did LCI, but %NLP and LCI did not correlate with each other. Analysis of the Brody subscores showed that %NLP and LCI largely correlated with different Brody subscores.ConclusionsT1 MRI can detect mild CF lung disease in children and correlates with chest CT findings. The %NLP from T1 MRI and LCI correlate with different chest CT Brody subscores, suggesting they provide complementary information about CF lung disease.

AIM:Educational attainment is consistently highly valued by young people with mental ill health, yet maintenance and completion of education is a challenge. This paper reports on the implementation of a supported education programme for youth mental health. METHODS:Between 10 October 2019 and 10 October 2020, a supported education programme was delivered within primary and tertiary youth mental health services. A description of the programme, context, and adjustments required due to COVID-19 is presented, and the educational outcomes of young people referred to the programme were explored. Two case studies are also presented. RESULTS:The programme received 71 referrals over this period, of which 70.4% had not yet completed secondary school and 68% were experiencing multiple mental health conditions. Overall outcomes were positive, with 47.5% of the 40 young people who chose to engage with the programme maintaining or re-engaging with education. However, the remainder of those who engaged withdrew from the programme, often reporting challenges due to COVID-19 such as social isolation or increased uncertainty. Additionally, a number of young people declined or disengaged from the programme to focus on employment. CONCLUSION:This report of the experience of integrating a supported employment programme in Australian youth mental health services reinforces the need for such support, and provides preliminary evidence for its successful implementation as part of routine care. The disengagement in response to COVID-19 highlights the real-world challenges of the pandemic, while young people's voicing of employment goals indicates the need for combined educational and vocational support-to assist transition and progression between these goals.

This systematic review and meta-analysis examined the efficacy of digital mental health apps and the impact of persuasive design principles on engagement and clinical outcomes. Of 119 eligible randomised controlled trials, 92 studies (n=16,728) were included in the analysis. Results demonstrated that mental health apps significantly improved clinical outcomes compared to control groups (g = 0.43). Apps used between 1 and 12 persuasive design principles (mode = 5). Notably, only 76% of studies reported engagement data. Twenty-five engagement metrics were identified across studies and grouped into 10 categories. Meta-regression and correlation analyses found no significant association between persuasive design principles and app efficacy or engagement. Future research should prioritise standardising and documenting engagement metrics and persuasive design principles; differentiating between engagement with mental health apps and real-world behavioural change and exploring the integration of persuasive design with behaviour change models to more accurately assess their influence on engagement and outcomes.

Background Integrating innovative digital mental health interventions within specialist services is a promising strategy to address the shortcomings of both face-to-face and web-based mental health services. However, despite young people’s preferences and calls for integration of these services, current mental health services rarely offer blended models of care. Objective This pilot study tested an integrated digital and face-to-face transdiagnostic intervention (eOrygen) as a blended model of care for youth psychosis and borderline personality disorder. The primary aim was to evaluate the feasibility, acceptability, and safety of eOrygen. The secondary aim was to assess pre-post changes in key clinical and psychosocial outcomes. An exploratory aim was to explore the barriers and facilitators identified by young people and clinicians in implementing a blended model of care into practice. Methods A total of 33 young people (aged 15-25 years) and 18 clinicians were recruited over 4 months from two youth mental health services in Melbourne, Victoria, Australia: (1) the Early Psychosis Prevention and Intervention Centre, an early intervention service for first-episode psychosis; and (2) the Helping Young People Early Clinic, an early intervention service for borderline personality disorder. The feasibility, acceptability, and safety of eOrygen were evaluated via an uncontrolled single-group study. Repeated measures 2-tailed t tests assessed changes in clinical and psychosocial outcomes between before and after the intervention (3 months). Eight semistructured qualitative interviews were conducted with the young people, and 3 focus groups, attended by 15 (83%) of the 18 clinicians, were conducted after the intervention. Results eOrygen was found to be feasible, acceptable, and safe. Feasibility was established owing to a low refusal rate of 25% (15/59) and by exceeding our goal of young people recruited to the study per clinician. Acceptability was established because 93% (22/24) of the young people reported that they would recommend eOrygen to others, and safety was established because no adverse events or unlawful entries were recorded and there were no worsening of clinical and social outcome measures. Interviews with the young people identified facilitators to engagement such as peer support and personalized therapy content, as well as barriers such as low motivation, social anxiety, and privacy concerns. The clinician focus groups identified evidence-based content as an implementation facilitator, whereas a lack of familiarity with the platform was identified as a barrier owing to clinicians’ competing priorities, such as concerns related to risk and handling acute presentations, as well as the challenge of being understaffed. Conclusions eOrygen as a blended transdiagnostic intervention has the potential to increase therapeutic continuity, engagement, alliance, and intensity. Future research will need to establish the effectiveness of blended models of care for young people with complex mental health conditions and determine how to optimize the implementation of such models into specialized services.

Background:Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support. Objective:This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs). Methods:This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted. Results:The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback. Conclusions:Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

ABSTRACTBackground and AimsSince the onset of the COVID‐19 pandemic, a significant rise in mental ill health has been observed globally in young people, particularly those in their final years of secondary school. Students' negative experiences coincide with a critical transitional period which can disrupt milestones in social and educational development. This study aimed to use innovative population‐level data to map the impact of the pandemic on students entering higher education.MethodsPre‐pandemic (2019/2020) and pandemic (2020/2021) tertiary education application data were obtained from the Victorian Tertiary Admissions Centre. Prevalence of applications for special consideration related to mental ill health were compared between cohorts across various geographical areas and applicant demographic subgroups. Relative risk regression models were used to understand the role of different risk factors.ResultsRates of mental health‐related special consideration applications increased by 38% among all applications (pre‐pandemic: 7.8%, n = 56 916; pandemic: 10.8%, n = 58 260). Highest increases were observed among students in areas with both extended and close‐quarter lockdown experiences, and areas impacted by 2019/2020 black summer bushfires. The increases were higher among Year 12 students and students with other special consideration needs (e.g., physical condition, learning disability). Slightly higher increases were observed in areas with higher socio‐economic status, which may potentially be related to inequality in mental health service access.ConclusionAs consequences of mental health difficulties and academic disruption in youth can be long lasting, it is critical to establish a mental health support framework both in and outside of higher education to facilitate young people's recovery from the pandemic.