Document text
Principal Investigator: Maja J Mataric
Organization: UNIVERSITY OF SOUTHERN CALIFORNIA
Fiscal Year: 2024
Award: $299,999
Funding agency: National Institute of Mental Health
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.
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