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

Abstract

Suicidal men feel the need to be self-reliant and that they cannot find another way out of relationship or socioeconomic issues. Suicide prevention is of crucial importance worldwide. The much higher rate of suicide in men engenders action. The prelude is a subjective experience that can be very isolating and severely distressing. Men may not realize a change in their thinking and behaviors, which makes it more difficult to seek and get help, thereby interrupting a “downward spiral”. Stoicism often prevents men from admitting to their personal struggle. The lack of “quality” connections and “non-tailored” therapies has led to a high number of men “walking out” on traditional clinical approaches. But there are complicated relationships in motivations and formative behaviors of suicide with regards to emotional state, psychiatric disorders, interpersonal life events and suicidal behavior method selection. Middle-aged and older men have alternated as the most at-risk of suicide. There is no one solution that applies to all men, but digital tools may be of assistance (e.g., video conferences, social networks, telephone calls, and emails). Digital interventions require higher levels of effectiveness for distress and suicidality but self-guided approaches may be the most suitable for men especially where linked with an integrated online suicide prevention platform (e.g., quick response with online chats, phone calls, and emails). Furthermore, technology-enabled models of care offer promise to advance appropriate linking to mental health services through better and faster understanding of the specific needs of individuals (e.g., socio-cultural) and the type and level of suicidality experienced. Long-term evidence for suicidality and its evaluation may benefit from progressing human computer-interaction and providing impetus for an eminent integrated digital platform.

Codesign of digital health tools for suicide prevention: A scoping review

“…Table 3 provides an overview of the study characteristics. There were several study types from the 52 published articles including four randomized control trials [17][18][19][20], one meta-analysis of randomised control trials [21], one clinical review [22], four systematic review and meta-analysis [4,7,23,24], nine systematic reviews [3,5,6,[25][26][27][28][29]63], one mixed methods [30], five literature or scoping reviews [31][32][33][34][35], two integrative reviews [36][37], thirteen pilots or trials [38][39][40][41][42][43][44][45][46][47][48][49][50], seven qualitative studies [51][52][53][54][55][56]…”

Human-Computer Interaction in Digital Mental Health

“…Although digital interventions via common digital tools (e.g., video conferences, social networks, telephone calls, and emails) are effective at the population level for common disorders, mental health care practitioners are grappling with using and overcoming difficulties in connecting with other digital tools (e.g., web-based screening and intervention, AI, smartphone, immersive and wearable technologies, the Internet of Things (IoT) and digital phenotyping) [40]. Specifications are needed for subpopulations [145,146] and the psychological disorders and symptoms for which different types of digital tools are effective and readily useful [147].…”

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

“…Developers can use a human-in-the-loop approach to data collection and model creation that enlists MH practitioners in all stages of development and use ( 61 ). There also needs to be more separation between studies that measure degree of use of AI technologies, and those that evaluate the efficacy of these technologies; in order to gain the trust and understanding of clinicians, developers need to demonstrate that these technologies work better than existing service delivery ( 17 , 62 ). For example, in the case of stroke risk prediction and prevention, this means that novel ML-based approaches need to compete against established models to win clinicians' and patients' trust ( 53 ).…”