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The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.

Australia’s headspace initiative is world-leading in nation-wide youth mental healthcare reform for young people aged 12 to 25 years, now with 16 years of implementation. This paper examines changes in the key outcomes of psychological distress, psychosocial functioning, and quality of life for young people accessing headspace centres across Australia for mental health problems. Routinely collected data from headspace clients commencing an episode of care within the data collection period, 1 April 2019 to 30 March 2020, and at 90-day follow-up were analysed. Participants came from the 108 fully-established headspace centres across Australia, and comprised 58,233 young people aged 12–25 years first accessing headspace centres for mental health problems during the data collection period. Main outcome measures were self-reported psychological distress and quality of life, and clinician-reported social and occupational functioning. Most headspace mental health clients presented with depression and anxiety issues (75.21%). There were 35.27% with a diagnosis: overall, 21.74% diagnosed with anxiety, 18.51% with depression, and 8.60% were sub-syndromal. Younger males were more likely to present for anger issues. Cognitive behavioural therapy was the most common treatment. There were significant improvements in all outcome scores over time (P<0.001). From presentation to last service rating, over one-third had significant improvements in psychological distress and a similar proportion in psychosocial functioning; just under half improved in self-reported quality of life. Significant improvement on any of the three outcomes was shown for 70.96% of headspace mental health clients. After 16 years of headspace implementation, positive outcomes are being achieved, particularly when multi-dimensional outcomes are considered. A suite of outcomes that capture meaningful change for young people’s quality of life, distress and functioning, is critical for early intervention, primary care settings with diverse client presentations, such as the headspace youth mental healthcare initiative.

Background Mental health outcome measures are used to monitor the quality and effectiveness of mental health services. There is also a growing expectation for implementation of routine measurement and measures being used by clinicians as a feedback monitoring system to improve client outcomes. The recent focus in Australia and elsewhere targeting mental health services to young people aged 12–25 years has meant that outcome measures relevant to this age range are now needed. This is a shift from the traditional divide of child and adolescent services versus adult services with a transitioning age at 18 years. This systematic review is the first to examine mental health outcome measures that are appropriate for the 12 to 25 year age range. Methods MEDLINE and PsychINFO databases were systematically searched to identify studies using mental health outcome measures with young people aged 12 to 25 years. The search strategy complied with the relevant sections of the PRISMA statement. Results A total of 184 published articles were identified, covering 29 different outcome measures. The measures were organised into domains that consisted of eight measures of cognition and emotion, nine functioning measures, six quality of life measures, and six multidimensional mental health measures. No measures were designed specifically for young people aged 12 to 25 years and only two had been used by clinicians as a feedback monitoring system. Five measures had been used across the whole 12 to 25 year age range, in a range of mental health settings and were deemed most appropriate for this age group. Conclusions With changes to mental health service systems that increasingly focus on early intervention in adolescence and young adulthood, there is a need for outcome measures designed specifically for those aged 12 to 25 years. In particular, multidimensional measures that are clinically meaningful need to be developed to ensure quality and effectiveness in youth mental health. Additionally, outcome measures can be clinically useful when designed to be used within routine feedback monitoring systems.

Background This study provides the first comprehensive empirical evidence of developmental changes in the social influences on seeking mental health care, both in-person and online, during the critical lifestages for mental health of adolescence and young adulthood. Methods Main source of help-seeking influence was determined via self-report for all young people accessing youth-targeted mental health services in Australia for a first episode of care over a 12 month period during 2013. This comprised 30,839 young people who accessed in-person services and 7,155 clients of the online service. Results Results show a major developmental shift in help-seeking influence across the age range, which varied for males and females, and a striking difference between the online and in-person service modalities. The dominant influence online, regardless of age, was the young person themself. In contrast, for in-person services, the dominant influence during adolescence was family, but this changed markedly in late adolescence to favour self-influence, with a lessor, but still substantial effect of family. The influence of friends was surprisingly low. Conclusions To support young people with mental health problems to access mental health care, the personal connection of parents and family needs to be engaged to encourage in-person service use through better mental health literacy, particularly for adolescents. In the online environment, ways to ensure that young people themselves are guided to appropriate services are required.

Despite a high level of research, policy, and practice interest in help-seeking for mental health problems and mental disorders, there is currently no agreed and commonly used definition or conceptual measurement framework for help-seeking. A systematic review of research activity in the field was undertaken to investigate how help-seeking has been conceptualized and measured. Common elements were used to develop a proposed conceptual measurement framework. The database search revealed a very high level of research activity and confirmed that there is no commonly applied definition of help-seeking and no psychometrically sound measures that are routinely used. The most common element in the help-seeking research was a focus on formal help-seeking sources, rather than informal sources, although studies did not assess a consistent set of professional sources; rather, each study addressed an idiosyncratic range of sources of professional health and community care. Similarly, the studies considered help-seeking for a range of mental health problems and no consistent terminology was applied. The most common mental health problem investigated was depression, followed by use of generic terms, such as mental health problem, psychological distress, or emotional problem. Major gaps in the consistent measurement of help-seeking were identified. It is evident that an agreed definition that supports the comparable measurement of help-seeking is lacking. Therefore, a conceptual measurement framework is proposed to fill this gap. The framework maintains that the essential elements for measurement are: the part of the help-seeking process to be investigated and respective time frame, the source and type of assistance, and the type of mental health concern. It is argued that adopting this framework will facilitate progress in the field by providing much needed conceptual consistency. Results will then be able to be compared across studies and population groups, and this will significantly benefit understanding of policy and practice initiatives aimed at improving access to and engagement with services for people with mental health concerns.

The 'My youth mental health Session Experience' (MySE) measure was developed by headspace, Australia's National Youth Mental Health Foundation, in collaboration with young people, for use as a routine session experience measure across its national centre service network. The measure fills a gap in measures needed to implement measurement-informed care in youth mental health care. Routinely collected data from 37,201 young people aged 12 to 25 years who commenced an episode of care at one of the 150 headspace centres between 1 July 2021 and 30 June 2022 were used to validate the five-item measure. MySE demonstrated high internal consistency invariant over age and gender groups. There was one latent factor of session experience that all MySE items relate to, although this factor does not adequately capture all the information present in the individual items. A significant age effect showed that young adults reported more positive session experiences than adolescents. MySE demonstrated strong psychometric properties and is suitable for use in youth mental health care as a routine session-by-session experience measure. Such measures are needed to routinely inform clinicians of how young people are experiencing their treatment sessions, thereby contributing to better retention, engagement, and client outcomes through measurement-informed care.

Background Informal carers suffer from worse health outcomes than non-carers due to their caregiving role. Yet, in a society carers health is as important as that of their care recipients. This study investigated the self-assessed mental and general health outcomes of informal carers in Australia. It evaluated the influence of carers’ personal social capital- a logically linked sequence of their social behaviour such as community participation, social support and trust in others- on their health outcomes. The study estimated the magnitude of small area level variation at Statistical Area Level 1 (SA1) along with individual level variation in carers’ health outcomes. Methods The study used a multilevel mixed effects cross-sectional design using data from the Household Income and Labour Dynamics of Australia survey, wave 14. It included Australians aged 15 years and older that were surveyed in the year 2014. The sample consisted of 12,767 individuals and 5004 SA1s. The outcome measures included- mental health, general health and physical functioning, domains of the Short Form 36 Questionnaire , a widely used multi-dimensional measure of health-related quality of life. Results Informal carers suffered from poor mental (Beta = − 0.587, p  = 0.003) and general health (Beta = − 0.670, p  = 0.001) outcomes compared to non-carers in Australia. These health outcomes exhibited significant variation acrossSA1s in Australia, with 12–13% variation in general and mental health. However, within small local areas, differences at the individual level, accounted for most of the variation in outcomes. Moreover, levels of community participation, personal social connection and trust, as perceived by individuals in the communities, had a positive influence on both mental and general health of carers and non-carers, and were more beneficial for carers compared to non-carers. Conclusion It seems that the positive influence of social capital for carers helps them in coping with the negative impact of their caregiving duty on health outcomes. Findings suggested that some targeted community support programs for carers to build on their personal social cohesion and trust in their community could help in improving their poor health profiles. Moreover, improved informal carers’ health may help the health system in better managing their resources.

The high level of psychological distress in young people is a growing concern. However, there are few national surveys that describe the trajectories of mental health and wellbeing through adolescence into early adulthood. Further, existing research has largely focused exclusively on mental ill-health, with little focus on positive mental health. This study provides the first national profile of the mental health and wellbeing of Australians aged 12-25 years. Participants completed the National Youth Mental Health survey in 2018 (n  = 3832), 2020 (n  = 974) or 2022 (n  = 961). We applied Keyes' Complete Mental Health (CMH) framework to derive categories of mental health and wellbeing, and examine rates of CMH over time, by age and gender. While approximately half of those surveyed reported flourishing (high wellbeing without mental illness), rates of flourishing declined between 2018 and 2022. Rates of flourishing generally decreased with age, and flourishing was more prevalent amongst males than females. The findings provide a unique contrast of youth mental health pre-, during and post- the COVID-19 pandemic. While rates of psychological distress are consistently high, the proportion of youth reporting flourishing highlights the need to consider all aspects of psychological functioning to accurately understand and respond to the mental health needs of young people.

Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.

Purpose Timely access to effective treatment is a primary goal for mental health services; however, when demand exceeds available resources, services may place clients on a waitlist or restrict services. This paper aims to identify approaches used by mental health services to manage service demand and waitlists. Design/methodology/approach A review of research literature between 2009 and 2019 was conducted using the Medline, PsycINFO, CINAHL, Embase and Cochrane databases. Articles were screened and assessed against inclusion criteria and the methodological quality of included studies was assessed using the Mixed Methods Appraisal Tool. Findings In total, 20 articles were located that met the inclusion criteria. Five demand management approaches were identified, namely, walk-in models, triage processes, multi-disciplinary care, patient-led approaches and service delivery changes. Research limitations/implications This review identifies effective approaches that services can consider adapting to their local setting; however, further research is needed to demonstrate the clinical effectiveness of services provided under these models. Originality/value This review makes a valuable contribution to mental health care service delivery by detailing the strategies that services have adopted to manage demand and, where available, comparative outcomes with traditional service delivery models.