Melissa Aji

Background: Nighttime fears are highly prevalent and are associated with sleep disturbances and later anxiety disorders. Despite their impact, research and treatment options remain limited. Few studies have examined the clinical profile of this population. Families have few intervention options, especially those that are accessible and evidence-based. This mixed-methods study aimed to [1] assess the clinical profile of children experiencing nighttime fears, and [2] explore parents’ needs and preferences to inform the co-design of a digital, parent-led exposure-based intervention. Methods: A co-design approach ( N = 44) was used involving an online assessment battery with parents of children aged 7–12 years experiencing nighttime fears ( n = 34; M child age = 9.6 years; 56% female), two phases of semi-structured interviews with a separate group of parents ( n = 5; median child age = 10.7 years; 60% female), and a focus group with psychologists ( n = 5; median age: 31.0; 60% female). The assessment battery included a diagnostic instrument assessing DSM-5 psychiatric disorders, and a measure of sleep disturbances. Interviews explored both parents’ experiences and their feedback on a prototype intervention. Psychologists discussed clinical insights and treatment approaches. Quantitative data were analysed descriptively; qualitative data were analysed using framework analysis. Results: Separation Anxiety Disorder (24%) and Specific Phobia – Natural/Environment type (23%) were the most common primary diagnoses. Sleep disturbances were prevalent, with 70% scoring in the pathological range for initiating and maintaining sleep. Qualitative findings revealed significant emotional distress for both children and parents, limited access to care and the need for clear guidance. Psychologists emphasised the central role of parents and the need for structured support. Conclusions: Children with nighttime fears often meet criteria for psychiatric disorders and experience significant sleep issues. A co-designed and parent-led digital intervention may address a critical gap for families.

Background: Mental disorders are the leading cause of disability in young people (aged 12‐30 years), and their incidence constitutes a major health crisis. Primary youth mental health services are struggling to keep up due to overwhelming demand, the complexity and severity of young people presenting for care, and a shortage of qualified mental health professionals (MHPs). Artificial intelligence (AI) tools have the potential to facilitate necessary improvements to diagnosis, triage, and care planning for young people with emerging mental disorders. Objective: The objective of the present scoping research was to examine beliefs and attitudes underlying MHP acceptance of AI tools in youth mental health services. Methods: In total, 57 MHPs (mean age 35.35, SD = 9.50 years, 72% female (n = 39)) with experience working with youth populations (age 12-30) took part in study 1 that involved completing a web-based survey about the acceptability of using AI in early intervention services. During study 2, 15 MHPs also participated in 1-hour semistructured Zoom interviews. Attitudes toward the use of 2 novel AI prototypes (both of which provide recommendations for care coordination based on previously published data analyses) in youth mental health were explored. Quantitative data were interpreted using descriptive statistics, and qualitative analysis followed the thematic analysis approach. Results: MHPs were more likely to agree than disagree that AI will improve youth mental health care overall (eg, n=37, 64% participants somewhat or strongly agree that the field of mental health will improve with AI). Despite voicing concerns regarding data security and privacy, MHPs also acknowledged a need for AI to improve the “signal-to-noise ratio” in services and address delays to care for those with severe and complex problems. Such problems were seen as pervasive across the youth mental health system and emphasize the serious costs of delaying the development and implementation of novel tools. All participating MHPs discussed the potential negative impacts of not adopting novel tools. Conclusions: MHP acceptance and uptake of novel AI tools in youth mental health services will be driven by a more complex cost-benefit analysis of both adopting and not adopting, rather than solely on their design. The costs of delay are clear, and so researchers and MHPs have a shared imperative to develop useful and meaningful clinical tools and to work jointly on integrating them into practice. Limitations of our sample (including low sample size limiting generalizability) notwithstanding, these findings should inform the future design and implementation of such tools.

Sleep-related fears (i.e. fears related to sleep, nighttime and/or occurring before sleep) are linked to the development of anxiety and sleep problems in children. Parent- and child-report measurement tools are key to increasing understanding and facilitating better identification for treatment to prevent disorder development. We conducted a systematic review with the aim of identifying the breadth of parent- and child-report measures that include an assessment of sleep-related fears in children from 7 to 12 years old (stage 1) and reviewing the psychometric properties of measures that more comprehensively assess sleep-related fears (i.e. 3 or more items) using the COSMIN checklist (stage 2) to provide recommendations for measures (stage 3). A systematic search was performed using MEDLINE, EMBASE, ERIC and PsycINFO in accordance with PRISMA guidelines. At stage 1, we retrieved 14,495 records. Of these, 66 papers met eligibility and included 43 distinct measures. At stage 2, 11 out of the 43 measures met criteria for more comprehensive measurement of sleep-related fears. Findings demonstrated mixed and incomplete data and very little high-quality evidence. No measures met criteria for recommendation and the majority of measures require further validation studies.

BACKGROUND Mental disorders are the leading cause of disability in young people (aged 10-24-years), and their incidence constitutes a major health crisis. Primary youth mental health services are struggling to keep up due to overwhelming demand, the complexity and severity of young people presenting for care and a shortage of qualified mental health professionals (MHPs). Artificial Intelligence (AI) tools have potential to facilitate necessary improvements to diagnosis, triage and care planning for young people with emerging mental disorders. OBJECTIVE The objective of the current study was to examine beliefs and attitudes underlaying MHP acceptance of AI tools in youth mental health services. METHODS Fifty-seven MHPs (Mage = 35.35, 54% Male) with experience working with youth populations (age 12 to 30) completed online surveys about the acceptability of two novel AI prototypes. Attitudes towards the use of AI in youth was also explored in one-hour semi-structured interviews. Fifteen MHPs also participated in online one-hour semi-structured interviews. Quantitative data was interpreted using descriptive statistics, and qualitative analysis followed the Thematic Analysis approach. RESULTS MHPs were more likely to agree than disagree that AI will improve youth mental health care overall (e.g. 64.4% somewhat or strongly agree that the field of mental health will improve with AI). Despite voicing concerns regarding data security and privacy, MHPs also acknowledged a need for AI to improve the “signal-to-noise ratio” in services and address delays to care for those with severe and complex problems. Such problems were seen as pervasive across the youth mental health system and emphasise the serious costs of delaying development and implementation of novel tools. All participating MHPs discussed the potential negative impacts of not adopting novel tools. CONCLUSIONS MHP acceptance and uptake of novel AI tools in youth mental health services will be driven by a more complex cost-benefit analysis of both adopting and not adopting, rather than solely on their design. The costs of delay are clear and so researchers and MHPs have a shared imperative to develop useful and meaningful clinical tools, and to work jointly on integrating them into practice. These findings should inform the future design and implementation of such tools.

Anxiety disorders in children lead to substantial impairment in functioning and development. Even the most effective gold standard treatments for childhood anxiety have 50% remission rates, suggesting a critical need to improve current treatments. Optimising exposure, the key component of anxiety treatments, represents a promising way to do so. This chapter explains how to optimise exposure outcomes for childhood anxiety through inhibitory learning theory. This chapter describes the background of inhibitory learning, including its different components and the empirical evidence supporting it. We then discuss how to improve the formation of inhibitory associations through enhancing expectancy violation, the proposed mechanism underlying inhibitory learning. Strategies to enhance inhibitory learning for child anxiety treatment are provided. These include strategies to enhance the formation of inhibitory associations, such as psychoeducation, eliminating safety signals, deepened extinction, occasional reinforced extinction, and affect-based strategies. Additionally, strategies to enhance retrieval include variability, multiple contexts, and retrieval cues. Suggestions are made on how to adapt these strategies to child populations. Further, a clinical guide for using inhibitory learning strategies in child anxiety treatment is included as an appendix. This details how clinicians can utilise these strategies to enhance current treatments, including examples of case studies and scripts.

Background: Mental health conditions are considered the leading cause of disability, sickness absence, and long-term work incapacity. eHealth interventions provide employees with access to psychological assistance. There has been widespread implementation and provision of eHealth interventions in the workplace as an inexpensive and anonymous way of addressing common mental disorders. Objective: This updated review aimed to synthesize the literature on the efficacy of eHealth interventions for anxiety, depression, and stress outcomes in employee samples in organizational settings and evaluate whether their effectiveness has improved over time. Methods: Systematic searches of relevant articles published from 2004 to July 2020 of eHealth intervention trials (app- or web-based) focusing on the mental health of employees were conducted. The quality and bias of all studies were assessed. We extracted means and SDs from publications by comparing the differences in effect sizes (Hedge g) in standardized mental health outcomes. We meta-analyzed these data using a random-effects model. Results: We identified a tripling of the body of evidence, with 75 trials available for meta-analysis from a combined sample of 14,747 articles. eHealth interventions showed small positive effects for anxiety (Hedges g=0.26, 95% CI 0.13-0.39; P<.001), depression (Hedges g=0.26, 95% CI 0.19-0.34; P<.001), and stress (Hedges g=0.25, 95% CI 0.17-0.34; P<.001) in employees’ after intervention, with similar effects seen at the medium-term follow-up. However, there was evidence of no increase in the effectiveness of these interventions over the past decade. Conclusions: This review and meta-analysis confirmed that eHealth interventions have a small positive impact on reducing mental health symptoms in employees. Disappointingly, we found no evidence that, despite the advances in technology and the enormous resources in time, research, and finance devoted to this area for over a decade, better interventions are being produced. Hopefully, these small effect sizes do not represent optimum outcomes in organizational settings. Trial Registration: PROSPERO CRD42020185859; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=185859

Objective: Parliamentary inquiries into health-related issues empower everyday Australians to contribute to the development and reform of health policy. We explored how patient and family/carers concerns are translated by this process, using a less well-known disease, narcolepsy, as an example. Methods: Written submissions made to the Parliamentary Inquiry into Sleep Health Awareness in Australia 2018 by self-identified patients or family/carers with narcolepsy (n = 13) were extracted and thematically analysed using the Framework Approach. Each submission was systematically coded and abstracted into emergent themes before being evaluated against the final policy recommendations. Results: Although patients and their family/carers prioritised issues that affected their daily lives (i.e. mental health sequela, workplace accommodations), the policy recommendations in the report focused mainly on issues of healthcare infrastructure, funding and engagement. Our analysis highlighted several barriers that patients and their family/carers face when contributing to this part of healthcare policy formation. Conclusions: Our findings suggest that the parliamentary inquiry process in its current form is not an ideal vehicle by which patients and family/carers can contribute or influence healthcare policy. Despite calling for submissions from patients and their family/carers, the final report and subsequent health policy recommendations made by the inquiry do not appear to be patient-centric or reflective of the submissions written by these stakeholders. Increased transparency, development of processes to balance stakeholder priorities and improved accessibility for stakeholders to participate are needed if health-related parliamentary inquiries are to produce healthcare policy that ultimately meets the needs of patients and family/carers.

This pilot trial aimed to provide evidence for whether the integration of a wearable device with digital behavioral therapy for insomnia (dBTi) improves treatment outcomes and engagement.One hundre...

Background: Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps. Objective: The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. Methods: A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL). Results: A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial. Conclusions: This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation.

BACKGROUND Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps. OBJECTIVE The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. METHODS A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL). RESULTS A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial. CONCLUSIONS This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation. CLINICALTRIAL