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The Development and Systematic Evaluation of an AI-Assisted Just-in-Time-Adaptive-Intervention for Improving Child Mental Health

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R42MH123368-01
Agency Tracking Number: R42MH123368
Amount: $859,319.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 104
Solicitation Number: PA18-579
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-07-08
Award End Date (Contract End Date): 2022-06-30
Small Business Information
Miami, FL 33131-3546
United States
DUNS: 116975402
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (305) 348-7580
Business Contact
Phone: (785) 691-5023
Research Institution
MIAMI, FL 33199-2516
United States

 Nonprofit College or University

Early childhood mental health problems constitute a significant public health concern with wide-ranging impacts
on functioning both concurrently and later in life. Although childhood mental health is influenced by a variety of
factors, the quality of relationships with caregivers plays a critical role. Critical, coercive, and conflictual parent-
child interactions have been consistently linked with increased risk of externalizing and internalizing symptoms,
whereas supportive and nurturing relationships have been shown to confer protective effects. Early intervention
of maladaptive family relationships is thus crucial for preventing or offsetting negative developmental trajectories
in at-risk children. A variety of therapeutic methods have been developed and employed to foster positive parent-
child relationships and improve child mental health, including parent training/education, in-person therapy, home
visiting, school curriculums, and web programs. However, systematic obstacles interfere with the accessibility,
generalizability, and acceptability of these traditional appointment- and module-based approaches. Furthermore,
limitations in the family-centered flexibility, individual responsiveness, and broad availability of these services
render them inadequate to address the unique needs of at-risk populations who would benefit from more readily
accessible and inexpensive 24-hour support that is provided in real time and real life—when and where support
is needed most. Not surprisingly, research finds that roughly half of the families who do participate in traditional
appointment- and module-based mental health services fail to show sufficient symptom improvement. Just-in-
time adaptive interventions (JITAIs), in contrast, utilize smartphones, wearables, and artificial intelligence (AI) to
identify and respond to psychological and behavioral processes and contextual events as they unfold in everyday
life. Although JITAIs have the potential to transform the way people receive mental health support, barriers to
their successful, wide-scale implementation remain. Using pilot data collected from smartphones and wearables,
our interdisciplinary team of psychologists and engineers used AI to build machine learning algorithms to detect
psychological states and contextual events, such as ongoing moods and relationship conflict, in couples. In the
current project, we propose developing and testing a JITAI to provide opportune supports to families in dynamic
response to contextual events and shifting psychological states to amplify attachment bonds, regulate emotion,
and intervene in maladaptive parent-child interactional patterns. Building on our prior research, we will (1) build
software to unobtrusively capture real-time data from commercially-available mobile devices, (2) use machine
learning to develop algorithms to automatically monitor psychological and behavioral processes relevant to child
mental health, (3) launch a JITAI to provide as-needed intervention, and (4) carry out a micro-randomized clinical
trial to test the efficacy, acceptability, and safety of our JITAI for decreasing child internalizing and externalizing
symptoms. Our project will contribute to the development of technology ecosystems and service delivery models
with the power to meaningfully transform the accessibility and dynamic responsiveness of mental health care.PROJECT NARRATIVE
Mental health problems in childhood are highly prevalent and impairing, placing an immense social and economic
burden on society. Parent-child relationships in the early years of life play a critical role in promoting child mental
health, with critical and coercive relationships increasing the risk of mental health problems across the lifespan.
Our project includes the creation and systematic evaluation of an innovative technology-based therapy system
that employs artificial intelligence to monitor and responsively administer dynamic (i.e., as needed) supports to
families in real-world settings in order to promote positive family relationships and improve child mental health.

* Information listed above is at the time of submission. *

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