Early Intervention Strategies for Self-Harm Prevention Using AI-Driven Behavior Tracking in Teenagers
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Digital Mental Health,
Machine Learning
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Abstract
Background. Self-harm among teenagers has become an increasing public health concern, often linked to emotional distress, social pressure, and undiagnosed mental health issues. Traditional intervention strategies often detect these behaviors after they occur. The emergence of artificial intelligence (AI) in mental health opens new possibilities for earlier detection and proactive intervention, especially through behavior tracking technologies.
Purpose. This study aimed to explore early intervention strategies for self-harm prevention by utilizing AI-driven behavior tracking tools among teenagers. The research also examined the potential effectiveness of AI in identifying early warning signs based on digital behavior patterns.
Method. This mixed-methods study involved 150 teenagers aged 13–18 across three urban schools. AI-based applications were installed on participants’ devices with consent to monitor digital activity patterns (e.g., sleep irregularities, social withdrawal, online search behavior). Psychological assessments and structured interviews were also conducted. Data were analyzed using a combination of statistical trend analysis and qualitative content analysis.
Results. Findings indicate that AI algorithms successfully detected behavioral anomalies correlated with self-harm risk, such as significant decreases in social interaction, increased usage of depressive language, and disrupted sleep patterns. The AI tool enabled counselors to initiate timely interventions before self-harm behaviors escalated. Participants reported greater emotional safety and support when interventions occurred early.
Conclusion. AI-driven behavior tracking shows promise as an early intervention tool for preventing self-harm in teenagers. Integrating such technology with school counseling programs could enhance mental health support systems. However, ethical concerns regarding privacy and data sensitivity must be addressed to ensure responsible implementation.
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