A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

• Published in: Npj mental health research Vol. 3; no. 1; pp. 42 - 10
• Main Authors: Büscher, Rebekka, Winkler, Tanita, Mocellin, Jacopo, Homan, Stephanie, Josifovski, Natasha, Ciharova, Marketa, van Breda, Ward, Kwon, Sam, Larsen, Mark E, Torous, John, Firth, Joseph, Sander, Lasse B
• Format: Journal Article
• Language: English
• Published: England Springer Nature B.V 23.09.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Adults; Emergency medical care; Feasibility; Machine learning; Risk factors; Sensors; Sleep; Smartphones; Social networks; Statistical analysis; Suicidal behavior; Suicidal ideation; Suicides & suicide attempts; Systematic review
• ISSN: 2731-4251; 2731-4251
• DOI: 10.1038/s44184-024-00089-4

Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.