The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review

Is society caught up in a Death Spiral? Modeling societal demise and its reversal

Just like an army of ants caught in an ant mill, individuals, groups and even whole societies are sometimes caught up in a Death Spiral, a vicious cycle of self-reinforcing dysfunctional behavior characterized by continuous flawed decision making, myopic single-minded focus on one (set of) solution(s), denial, distrust, micromanagement, dogmatic thinking and learned helplessness. We propose the term Death Spiral Effect to describe this difficult-to-break downward spiral of societal decline. Specifically, in the current theory-building review we aim to: (a) more clearly define and describe the Death Spiral Effect; (b) model the downward spiral of societal decline as well as an upward spiral; (c) describe how and why individuals, groups and even society at large might be caught up in a Death Spiral; and (d) offer a positive way forward in terms of evidence-based solutions to escape the Death Spiral Effect. Management theory hints on the occurrence of this phenomenon and offers turn-around leadership as solution. On a societal level strengthening of democracy may be important. Prior research indicates that historically, two key factors trigger this type of societal decline: rising inequalities creating an upper layer of elites and a lower layer of masses; and dwindling (access to) resources. Historical key markers of societal decline are a steep increase in inequalities, government overreach, over-integration (interdependencies in networks) and a rapidly decreasing trust in institutions and resulting collapse of legitimacy. Important issues that we aim to shed light on are the behavioral underpinnings of decline, as well as the question if and how societal decline can be reversed. We explore the extension of these theories from the company/organization level to the society level, and make use of insights from both micro-, meso-, and macro-level theories (e.g., Complex Adaptive Systems and collapsology, the study of the risks of collapse of industrial civilization) to explain this process of societal demise. Our review furthermore draws on theories such as Social Safety Theory, Conservation of Resources Theory, and management theories that describe the decline and fall of groups, companies and societies, as well as offer ways to reverse this trend.

Improving the mental health of farmers: what types of remote support are acceptable, feasible, and improve outcomes? A feasibility RCT

BACKGROUND

The farming community have high rates of poor mental health, and are relatively 'hard to reach' with mental health services. The aim of this study was therefore to undertake a feasibility RCT, based on two mental health interventions. These were (1) CBT based 'Living Life to the Full for Farming Communities' (LLTTF-F; www.llttf.com ), and (2) a holistic social and emotional support service delivered by the Royal Scottish Agricultural Benevolent Institution (RSABI). The feasibility was supplemented by process evaluation.

METHODS

This feasibility study aimed to recruit 40 individuals from the farming community who were experiencing a common health problem defined as a score of >  = 8 on PHQ-9. A snowball approach was used to recruit interested individuals who had an association with farming. An initial telephone call screened for eligibility and obtained consent to randomisation to the two specified interventions, or to a thirdly group receiving a combination of both LLTTF-F and 'Social and emotional support'. Participants were permitted to override the randomised option if they expressed a strong preference before the interventions began.

RESULTS

Thirty-two participants provided baseline and three-month data. All three interventions showed positive improvements on PHQ-9 scores as follows: the 'combined intervention' mean baseline score was 18.1 compared to 12.0 at 3-month follow-up (mean change 6.1). 'Social and emotional support' mean baseline score was 11.3 compared to 6.7 at 3-month follow-up (mean change 4.6). 'LLTTF-F CBT-based intervention only' mean baseline score was 11.8 compared to 4.5 at 3-month follow-up (mean change 7.3). The retention rate was 81% at three months. In a sub-group of the LLTTF-F CBT-based intervention online materials were supplemented by telephone guided support. This approach received very positive feedback.

CONCLUSIONS

Recruitment from the farming community required intense effort, and good engagement can then be retained for at least three months. There is evidence that the interventions used were feasible, and tentative evidence that they had a demonstrable effect on mental wellbeing, with the LLTTFF providing the largest effect on PHQ-9 scores. Trial Registration Number ISRCTN27173711, submitted 25/08/2023, confirmed 22/092023.

Evaluation of the Use of Digital Mental Health Platforms and Interventions: Scoping Review

BACKGROUND

The increasing use of digital mental health (DMH) platforms and digital mental health interventions (DMHIs) is hindered by uncertainty over effectiveness, quality and usability. There is a need to identify the types of available evidence in this domain.

AIM

This study is a scoping review identifying evaluation of the (1) DMH platform/s used; and (2) DMHI/s applied on the DMH platform/s.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guided the review process. Empirical studies that focused on evaluation of the use and application of DMH platforms were included from journal articles (published 2012-2022). A literature search was conducted using four electronic databases (Scopus, ScienceDirect, Sage and ACM Digital Library) and two search engines (PubMed and Google Scholar).

RESULTS

A total of 6874 nonduplicate records were identified, of which 144 were analyzed and 22 met the inclusion criteria. The review included general/unspecified mental health and/or suicidality indications (n = 9, 40.9%), followed by depression (n = 5, 22.7%), psychosis (n = 3, 13.6%), anxiety and depression (n = 2, 9.1%), as well as anxiety, depression and suicidality (n = 1, 4.5%), loneliness (n = 1, 4.5%), and addiction (n = 1, 4.5%). There were 11 qualitative studies (50%), 8 quantitative studies (36.4%), and 3 mixed-methods studies (n = 3, 13.6%). The results contained 11 studies that evaluated the DMH platform/s and 11 studies that evaluated the DMHI/s. The studies focused on feasibility, usability, engagement, acceptability and effectiveness. There was a small amount of significant evidence (1 in each 11), notably the (cost-)effectiveness of a DMHI with significant long-term impact on anxiety and depression in adults.

CONCLUSION

The empirical research demonstrates the feasibility of DMH platforms and DMHIs. To date, there is mostly heterogeneous, preliminary evidence for their effectiveness, quality and usability. However, a scalable DMHI reported effectiveness in treating adults' anxiety and depression. The scope of effectiveness may be widened through targeted strategies, for example by engaging independent young people.

Linking music streaming platform advertisements with a digital mental health assessment and interventions

Accessibility issues and low rates of help-seeking hinder engagement with mental health resources and treatment. Pragmatic, (cost-)effective solutions are required to increase engagement with efficacious digital mental health interventions (DMHIs) including for hard-to-reach individuals. As an example, music-based interventions have been positively used in health care to reduce stress, anxiety and depression through music medicine, music therapy and recreational use. Although, enhanced mental health awareness from music listening has yet to be converted into engagement with a DMH assessment (DMHA) and DMHIs. Therefore, a new study is proposed to place linked advertisements on Spotify, the most used music streaming platform. MindSpot's vetted DMHA is suitable to use as an example for linking unto because it measures depression, anxiety, general mental well-being problems and psychological distress in Australian adults and provides access to DMHIs. The primary aim is to provide a convenient, robust and scalable consumer pathway to reduce engagement barriers and maximize facilitation to a vetted DMHA and DMHIs. The proposed study is important because it addresses notorious help-seeking difficulties in the adult population (e.g., young people and men). It also expands outreach to the underserved and the unserved and streamlines the integration of digital solutions with mental health services.

Human-Computer Interaction in Digital Mental Health

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for "pragmatic AI-augmentation" that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice-automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.