Kleiman Evan M, Glenn Catherine R, Liu Richard T
Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Department of Psychology, Old Dominion University, Norfolk, VA, USA.
Nat Rev Psychol. 2023 Jun;2(6):347-359. doi: 10.1038/s44159-023-00175-y. Epub 2023 Apr 6.
In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.
在过去十年间，自杀研究领域出现了两个主题。其一，根据荟萃分析，预测和预防自杀念头及行为的能力，相较于该领域的规模而言，比预期的要弱。其二，综述和评论文章提出，技术和统计方法（如智能手机、可穿戴设备、数字表型分析和机器学习）可能成为解决这一问题的办法。在本综述中，我们旨在在这些荟萃分析所呈现的悲观图景与综述和评论文章所呈现的乐观图景之间取得平衡，后者认为先进的技术和统计方法有望提高理解、预测和预防自杀的能力。我们将讨论分为两大类。第一，我们讨论旨在评估的研究，目标是更好地理解或更准确地预测自杀念头及行为。第二，我们讨论专注于预防自杀念头及行为的文献。生态瞬时评估、可穿戴设备以及其他技术和统计进展在预测和预防自杀方面极具潜力，但仍有许多工作要做。
