A web-based video messaging intervention for suicide prevention in men: study protocol for a five-armed randomised controlled trial.

BACKGROUND: More than 50% of people who die by suicide have not been in contact with formal mental health services. The rate of people who fly 'under the radar' of mental health services is higher among men than women, indicating a need to improve engagement strategies targeted towards men who experience suicidal thoughts and/or behaviours. In Australia, a range of mental health support services exist, designed specifically for men, yet, a substantial proportion of men do not use these services. The aim of this study is to evaluate whether a brief online video-based messaging intervention is an effective approach for encouraging men with suicidal thoughts and/or behaviours to engage with existing support services. METHODS: Informed by a literature review, surveys, and consultation with men with a lived experience of suicidal thoughts and/or behaviours, we designed five video-based messages that will be used in this five-arm randomised controlled trial. A total of 380 (76 per arm) men aged 18 years or older with suicidal thoughts who are not currently accessing formal mental health services will be recruited online and randomly assigned to watch one of the five web-based video messages. After viewing the video, men will be presented with information about four existing Australian support services, along with links to these services. The primary outcome will be help-seeking, operationalised as a click on any one of the four support service links, immediately after viewing the video. Secondary outcomes include immediate self-reported help-seeking intentions in addition to self-reported use of the support services during a 1-week follow-up period. We will also use the Discrete Choice Experiment methodology to determine what aspects of support services (e.g. low cost, short appointment wait times) are most valued by this group of men. DISCUSSION: This study is the first to evaluate the effectiveness of a brief web-based video messaging intervention for promoting engagement with existing support services among men with suicidal thoughts who are not currently receiving formal help. If found to be effective, this would represent a scalable, cost-effective approach to promote help-seeking for this at-risk population. Limitations and strengths of this study design are discussed.

Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study.

INTRODUCTION: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. METHODS AND ANALYSIS: The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. ETHICS AND DISSEMINATION: Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). TRIAL REGISTRATION NUMBER: ACTRN12621001223820.

The three ghosts of medical AI: Can the black-box present deliver?

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.

Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes 2 contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.