NIH grant: UNDERSTAND: Uplifting the New generation through DBT Education and Resilience for Social Triggers, Anxiety, Negativity, and Depression (1R01MH140883-01)

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NIH grant 1R01MH140883-01: UNDERSTAND: Uplifting the New generation through DBT Education and Resilience for Social Triggers, Anxiety, Negativity, and Depression
Institute: {'code': 'MH', 'abbreviation': 'NIMH', 'name': 'National Institute of Mental Health', 'admin_org_id': '12500050', 'admin_funding_url': ''}. PI: Nadir  Weibel. Award: $2115893.
Start: 2026-08-01T00:00:00.
PROJECT SUMMARY
The UNDERSTAND project aims to develop and evaluate an innovative mobile health system designed to
support young adults dealing with comorbid Major Depressive Disorder (MDD), Social Anxiety Disorder (SAD),
Generalized Anxiety Disorder (GAD), and other anxiety-related conditions. These mental health conditions are
highly prevalent, frequently co-occur, and lead to significant functional impairment in social, academic, and
daily life settings. Traditional therapeutic approaches, such as Cognitive Behavioral Therapy (CBT) and
Dialectical Behavior Therapy (DBT), have demonstrated efficacy in clinical settings but often fail to provide
real-time, context-sensitive support during the daily interactions and triggers that exacerbate these symptoms.
The overarching goal of UNDERSTAND is to address this critical gap by leveraging mobile and wearable
sensors, machine learning models, and Large Language Models (LLMs) to deliver Just-In-Time Adaptive
Interventions (JITAI) based on DBT principles. Grounded in transdiagnostic theory, the system targets
shared mechanisms such as emotional dysregulation, cognitive distortions, and interpersonal challenges
across MDD, SAD, GAD, and related disorders. It will unobtrusively monitor physiological and socio-behavioral
indicators such as heart rate variability, speech patterns, and body posture, which are correlated with
symptoms across these emotional disorders. In moments of heightened emotional distress, the system will
deliver tailored interventions in real-time, helping individuals manage their anxiety, depressive
symptoms, and other triggers during challenging situations.
The project has three specific aims: (1) to develop and validate predictive models using sensor data to detect
emotional distress in real-time across a range of anxiety and depressive triggers, (2) to design and implement
personalized, LLM-driven interventions that provide DBT-based guidance through mobile and wearable
devices, and (3) to evaluate the feasibility and user experience of the system through iterative co-design with
young adults and DBT-trained clinical psychologists.
If successful, this project represents a paradigm shift in mental health care by moving from clinic-based,
reactive treatments to proactive, real-time interventions that can be seamlessly integrated into daily life. The
system has the potential to improve mental health outcomes, reduce the long-term burden of untreated
comorbid emotional disorders, and advance the field of mobile health technology by providing scalable,
technology-driven solutions for managing complex mental health conditions in naturalistic environments.

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