The Problem

Mental illness is a significant global concern among adolescents, with three-quarters of all mental health conditions beginning by the age 24 and most common ones including depression, anxiety disorders, and substance use disorders. Around 31.9% of adolescents ages 13-18 experience a mental disorder annually, with 22.2% experiencing a severe mental disorder that significantly impacts their daily functioning (NIMH). These statistics highlight the importance of addressing mental health concerns in adolescents and the need for early risk identification, effective prevention, early intervention, and equitable access to mental health services to support their well-being. Sustained stress and maladaptive stress coping trigger a complex physiological response that affects various systems in the body and contribute to the onset and severity of mental and physical illnesses. Chronic stress contributes to an increased risk of developing mental health disorders (McEwen BS., 2017) by affecting brain chemistry and disrupting the balance of neurotransmitters. Stress can also affect behavior, leading to unhealthy coping mechanisms such as overeating, increased alcohol or substance use, and reduced physical activity, which can have negative impacts on overall health (Sin et al., 2016). Additionally, stress can disrupt sleep patterns, leading to sleep disturbances and insomnia, which can further impact physical and mental health.

To date, the gold standard in mental health care is to evaluate stressors and the well-being of individuals in a clinician’s office or through telemedicine using surveys and self-reports that assess their current and past experiences retrospectively and subjectively. Furthermore, interventions rely on recommendations for monitoring and self-regulation approaches that need to be deployed in the real world often in temporal and physical contexts that are highly divergent from the clinician’s office. Hence, we lack (a) data on an individual’s momentary thoughts, feelings, and behaviors in their natural environment to assess the unique internal and external context and events that trigger the onset or deterioration of psychological and physical distress and associated mental health symptoms, and (b) interventions delivered in real-time within the context associated with negative experiences. Wearable health monitoring devices and ecological momentary assessments (EMAs) have emerged as valuable research and wellness monitoring methodologies that capture the dynamic fluctuations of mental health symptoms in real-time through measures of individuals’ activity, thoughts, emotions, behaviors, and environmental factors, that interactively impact an individual’s daily functioning.

The Gap and Specific Aims

The current wearable health movement lacks a Digital Health Platform (DHP) that integrates real-time biological signals with contemporaneous assessments of psychosocial and environmental signals to enhance our understanding of the unique factors that contribute to psychological well-being. A gap also exists in our ability to deliver real-time effective and targeted interventions that enhance adolescent self-regulation tailored to their individual momentary needs. A DHP can further bridge the disparities minoritized populations are facing, magnified by unique stressors and lack of access to patient-oriented health services. Finally, the energy requirements of current devices also pose limitations to the development, scaling, and widespread adoption of wearable health devices.

The TRACS funding will support 2 aims: AIM 1 (At NCSTATE)- To develop a DHP – a bidirectional integrated physiological sensor network to communicate with mobile EMA technology for real-time stress signal sensing and internet-delivered, hybrid instructor-administered first aid intervention: We will establish the feasibility of collecting stress physiology markers using a wrist sensor ( e.g. electrodermal activity (EDA), heart rate, heart rate variability, activity, ambient environmental measures) and triggering EMA app in a lab setting, validated against standardized autonomic and HPA axis activation during the same stress tests. The EMA app will also deliver surveys to assess participants’ moods as well as app-based interventions. AIM 2 (At UNC)- To pilot the DHP with a lab-based stress task in n=24 participants to establish reliability of the bidirectional communication between the sensors and the mobile EMA app in controlled lab settings during a lab-based validated stress task. (Open for recruitment end of November).