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BACKGROUND Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STB) is key to prevent attempts. Passive sensing of digital and behavioral markers is discussed to enhance detection and prediction of STB. OBJECTIVE The aim of this systematic review is to summarize the existing research on passive sensing of STB and evaluate whether the prediction of STB can be improved using passive sensing compared to prior prediction models. METHODS A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Web of Science, PsycInfo and EMBASE. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STB. The predictive value of passive sensing will be the primary outcome. Practical implication and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Extracted data will be described narratively and displayed in tables. If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STB. RESULTS The review process will take place in Summer 2022 with expected results in December 2022. CONCLUSIONS Despite intensive research efforts, the ability to predict STB is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve prediction of STB. Future research will be stimulated as gaps in the current literature will be identified and promising next steps towards clinical implementation will be outlined. |
