Assessing Youth Suicidality Trends Through Digital Phenotyping and Sensor-Based Risk Identification Systems

Abstract

Youth suicidality remains a critical global mental health challenge, necessitating innovative and data-driven approaches to early detection and intervention. This study examines the emerging role of digital phenotyping and sensor-based risk identification systems in assessing suicidality trends among young populations. By leveraging data from smartphones, wearable devices, and online behavioral patterns, digital phenotyping enables continuous, real-time monitoring of psychological states, including mood variability, social withdrawal, and sleep disturbances.

Sensor-based systems further enhance predictive capacity through the integration of machine learning algorithms capable of identifying subtle behavioral anomalies associated with suicidal ideation.

The research adopts a multidisciplinary framework, combining insights from computational psychiatry, behavioral science, and artificial intelligence to evaluate the effectiveness, limitations, and ethical implications of these technologies. While findings suggest significant potential for early risk detection and personalized intervention, concerns regarding data privacy, algorithmic bias, and informed consent remain paramount. The study concludes by highlighting the need for ethically grounded, clinically integrated, and policy-supported implementations to ensure responsible deployment in youth mental health contexts.