Citation

Sander LB, Spangenberg L, La Sala L, van Ballegooijen W. Front. Digit. Health 2023; 5: e1148356.

Copyright

(Copyright © 2023, Frontiers Media)

DOI

10.3389/fdgth.2023.1148356

PMID

36937249

PMCID

PMC10020690

Abstract

Suicide is a major public health concern and a leading cause of death in young people around the world, with a global mortality rate of over 700,000 people per year (1). Despite advances in mental health care and suicide prevention efforts, suicide rates continue to be high (1). In addition, the ability to predict suicides has not progressed in the last five decades of research, also because studies repeatedly investigated the same set of potential risk factors in most trials and used assessments, which do not adequately take the complexity of suicidality into account (2). Suicidal ideation is a complex condition that can manifest both within and outside of various mental health and physical health disorders (3-6). Furthermore, the fluctuation of suicidal ideation and associated risk factors within hours or days necessitate more precise and detailed assessments (7, 8). Thus, to make progress in suicide research, multiple underlying processes or mechanisms may need to be investigated.

The digital era has brought a range of opportunities and methodological advances for comprehending, predicting, and averting suicidal behaviour. Ecological Momentary Assessment (EMA) allows for the timely identification of suicidal ideation and potentially related risk factors in a naturalistic setting (9). The utilization of machine learning techniques presents a unique approach for the analysis of intricate datasets (10). Furthermore, digital technologies can provide access to support and information, allow for early identification and intervention, and facilitate data-driven approaches to suicide prevention...

Language: en

Keywords

prevention; suicide; prediction; technology; ecological momentary assessment (EMA)