JMIR Publications

ABSTRACT

Background:

Suicide is a major global public health issue becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach.

Objective:

We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized via real-time smartphone monitoring in a cohort of suicidal patients

Methods:

We recruited 225 patients between February 2018 and March 2020 with a history of suicidal behavior and/or ideation, as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on suicide risk events. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide risk events were tested.

Results:

During follow-up, 8% of participants attempted suicide and 6.2% presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of one week with an area under the curve of 0.78, indicating good accuracy.

Conclusions:

We describe an innovative method to identify suicide risk based on passively collected information from patients’ smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crisis.