Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions

Abstract

Recent advances in real-time monitoring technology make this an exciting time to study risk for suicidal thoughts and behaviors among youth. Although there is good reason to be excited about these methods, there is also reason for caution in adopting them without first understanding their limitations. In this article, we present several broad future directions for using real-time monitoring among youth at risk for suicide focused around three broad themes: novel research questions, novel analytic methods, and novel methodological approaches. We also highlight potential technical, logistical, and ethical challenges with these methodologies, as well as possible solutions to these challenges.

Future Directions in Single-Session Youth Mental Health Interventions

“…For example, risk of suicide skyrockets during the period following psychiatric hospitalization, with an estimated suicide rate more than 3 times the rate estimated among inpatients (Chung et al 2017;Walsh, Sara, Ryan, & Large, 2015). Recent research has used machine learning and real-time monitoring methods to identify other potential high-risk periods for SITBS (Kleiman, Glenn, & Liu, 2019;Kleiman & Nock, 2018). The SSI mode of treatment delivery has great potential for future combination with these risk monitoring techniques; after identifying those at elevated risk, SSIs could deliver intervention content precisely when it is needed.…”

Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk

“…For the purposes of this paper, underlying mechanisms are not crucial; our focus was on the utility of a potential marker. To estimate causality, researchers must use intensive longitudinal approaches with well-validated, multilevel measurement ( 87 , 88 ). We expect these designs will be even easier with passive monitoring.…”

“…Researchers should do as much as they can to reassure participants that they will not be “watched” in real time; passive monitoring data go through numerous cloud-based data checks prior to becoming visible to researchers (8; Figure 1 ). Although a detailed discussion of these issues is beyond the scope of this manuscript, several excellent resources exist already ( 2 , 87 , 92 , 93 ).…”

New Methods for Assessing Rapid Changes in Suicide Risk

“…Such studies will therefore need to have clear boundaries and expectations in the informed consent process about clinical monitoring of responses and when the participant should seek emergency care. Finally, Kleiman and colleagues raised concerns about data overfitting in EMA studies ( 48 ); in this context, previously-used machine learning models in suicide research may not be appropriate for real-time monitoring data ( 38 , 49 ).…”