Understanding Engagement With Digital Mental Health Technology in Mental Health Services: Multicenter Observational Study.

Background: Digital technologies can substantially improve mental health care by facilitating measurement-based care through routine outcome monitoring. However, their effectiveness is constrained by the extent to which these technologies are used by services, clinicians, and clients.

Objective: This study aims to investigate engagement with the Innowell platform, a measurement-based digital mental health technology (DMHT), to gain insights into the individual- and service-level factors influencing engagement.

Using the service needs index to quantify complexity and identify treatment needs across youth mental health service populations: an observational study.

Background: Digital technologies can facilitate comprehensive mental health assessment of an individual's treatment needs, while also enabling data aggregation and analysis at the population or service level. The Service Needs Index (made up of clinical, psychosocial, and comorbidity components) collectively expresses a concise metric for the type, range, and complexity of young people's treatment needs. This study aimed to examine variation in the Service Needs Index across service settings and assess its potential to inform population-level mental health planning.

Dynamic Bayesian network analysis of the social determinants of mental health.

Mental disorders contribute substantially to the global burden of disease, accounting for up to 16.5% of all years of healthy life lost due to disability and premature mortality. Epidemiological evidence indicates that mental health problems are associated with a diverse range of demographic, social, and economic factors, referred to collectively as social determinants; however, the causal mechanisms underlying these associations are widely recognized to be complex and are only incompletely understood.

Towards improved specificity in mental health syndromes: projection-based clustering of depressive phenotypes.

Background: Quantitative attempts to improve syndrome specificity typically produce large heterogenous subgroupings, impacting the validity of treatment and research targets. Assessing barriers to the valid application of existing methods and examining improvements from an interpretable projection-based clustering alternative may improve the precision and reproducibility of our research targets and classification systems.

Methods: This exploratory, cross-sectional, study recruited 2820 participants aged 12-to-25 years, from primary-healthcare services in Australia, between November 2018 and July 2023.

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis.

Background: Digital mental health tools promise to enhance the reach and quality of care. Current tools often recommend content to individuals, typically using generic knowledge-based systems or predictive artificial intelligence (AI). However, predictive AI is problematic for interventional recommendations as cause-effect relationships can be confounded in observed data.

Matching needs to services: Development of a service needs index for determining care pathways in youth mental health.

Objective: To develop a Service Needs Index that measures a young person's needs across domains relevant to care provision and to examine the index's construction under different assumptions.

Methods: A cohort of young people ( = 2193) aged 12-25 years who sought help at youth mental health services across Australia were invited to use a digital platform (Innowell) as part of their care and complete a multidimensional assessment. Using online assessment data from the eligible 1611 individuals (73.

A prognostic model for predicting functional impairment in youth mental health services.

Background: Functional impairment is a major concern among those presenting to youth mental health services and can have a profound impact on long-term outcomes. Early recognition and prevention for those at risk of functional impairment is essential to guide effective youth mental health care. Yet, identifying those at risk is challenging and impacts the appropriate allocation of indicated prevention and early intervention strategies.

Quantifying the interrelationships between physical, social, and cognitive-emotional components of mental fitness using digital technology.

Mental fitness is a construct that goes beyond a simple focus on subjective emotional wellbeing to encompass more broadly our ability to think, feel, and act to achieve what we want in our daily lives. The measurement and monitoring of multiple (often interacting) domains is crucial to gain a holistic and complete insight into an individual's mental fitness. We aimed to demonstrate the capability of a new mobile app to characterise the mental fitness of a general population of Australians and to quantify the interrelationships among different domains of mental fitness.

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.

The temporal dependencies between social, emotional and physical health factors in young people receiving mental healthcare: a dynamic Bayesian network analysis.

Aims: The needs of young people attending mental healthcare can be complex and often span multiple domains (e.g., social, emotional and physical health factors).

Characterising variability in youth mental health service populations: A detailed and scalable approach using digital technology.

Objective: This study utilised digital technology to assess the clinical needs of young people presenting for care at centres across Australia.

Method: 1490 young people (12-25 years) who presented to one of 11 services from four geographical locations (urban New South Wales, urban South Australia, regional New South Wales, and regional Queensland) completed a digital multidimensional assessment at initial presentation. Characteristics were compared between services and geographical locations.

Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care.

Background: Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the first 12 months of care.

Methods: Demographic and clinical information of 2901 young people who accessed mental health care at age 12-25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months).