# SCH: Personalized AI-Driven Models for Supporting User Engagement and Adherence in Health Interventions: Validation in Cognitive Behavioral Therapy for Anxiety

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2024 · $299,999

## Abstract

Untreated anxiety undermines long-term physical and emotional wellbeing, especially among college
 students, with rates worsening since the onset of the COVID-19 pandemic. Cognitive Behavioral Therapy
 (CBT) is the leading evidence-based intervention for anxiety, but many students fail to complete exercises
 between CBT sessions, reducing its effectiveness. Socially assistive robots (SARs) help promote
adherence to home-based practice in the context of elder care, social skill learning, and physical therapy,
 but it is unknown how SARs can enhance CBT. The specific objective of this research is to develop
personalized CBT SARs that can support CBT compliance for college students with anxiety. To meet the
goals of the proposed work, we will conduct eight collaborative design sessions and three user studies
 and data collections and evaluations: Specifically, studies will determine how SAR personalization based
on implicit and explicit feedback can help promote greater CBT compliance and anxiety reduction
 outcomes for students. Specific Aim 1 will develop machine learning models to personalize a CBT SAR
 with implicit personalization–using only visual and auditory cues and no user input. Specific Aim 2 will
develop machine learning models to enhance SAR engagement based on explicit user feedback–using
direct input from the user to change the SAR behaviors. Specific Aim 3a will test the efficacy of
 personalized CBT SARs on key outcomes of a 6-week CBT for anxiety intervention: robot-student
 alliance, CBT engagement, CBT adherence, and anxiety symptom reduction. In Study 3a, n=60 students
with anxiety will be randomly assigned to either a CBT SAR that performs implicit personalization (n=30)
or a CBT SAR with no personalization (control, n=30). In Aim 3b, a separate sample of n=60 students will
 be randomly assigned to either complete a 6-week CBT SAR intervention that performs explicit
personalization (n=30) or a CBT SAR with no personalization (control, n=30). We predict that implicit and
 explicit CBT SAR personalization will enhance pre- versus post-intervention SAR-user alliance,
 engagement in CBT, and lower anxiety outcomes over the course of a 6-week daily CBT home-based
 intervention for anxiety compared to the non-personalized control CBT SAR.
RELEVANCE (See instructions):
 The proposed research is relevant to public health, as it will assess whether personalized SARs impact
 engagement and outcomes in CBT exercises for anxiety, which is key to developing effective, scalable
 treatments for mood disorders such as anxiety. This research aligns with the NIMH mission of leveraging
 novel methods to intuitively and intelligently collect, sense, connect, analyze and interpret data from
 individuals, devices and systems to enable discovery and optimize health.

## Key facts

- **NIH application ID:** 11064932
- **Project number:** 1R01MH139134-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Maja J Mataric
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $299,999
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11064932

## Citation

> US National Institutes of Health, RePORTER application 11064932, SCH: Personalized AI-Driven Models for Supporting User Engagement and Adherence in Health Interventions: Validation in Cognitive Behavioral Therapy for Anxiety (1R01MH139134-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11064932. Licensed CC0.

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