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Principal Investigator: Noelle E. Carlozzi
Organization: UNIVERSITY OF MICHIGAN AT ANN ARBOR
Fiscal Year: 2024
Award: $504,261
Funding agency: National Institute of Nursing Research
Abstract
Care partners of persons with moderate to severe traumatic brain injury (TBI) are often required to assume a
caregiver role, bearing primary responsibility for assisting the person with TBI in physical, mental, financial, and
leisure activities. As a result of these new caregiving responsibilities, it is common for these care partners to
report problems with physical, mental, and social health, as well as compromised health-related quality of life
(HRQOL). Given that care partners’ well-being and functioning affects both their own personal health
outcomes, as well as the functional and rehabilitation outcomes of the person with TBI, there is a critical need
to develop novel health management interventions to ensure better health outcomes for both members of the
TBI care partnership dyad. Therefore, our long-term goal is to improve the health and well-being of these care
partners, as well as their ability to provide care to their loved one. Thus, the proposed randomized control trial
will examine the efficacy of a just-in-time adaptive intervention (JITAI), or personalized self-management
intervention that uses objective mobile sensor data feedback to improve physical activity, sleep, and HRQOL in
240 care partners of persons with TBI. Our primary objectives are to: 1) assess the efficacy of this novel
personalized self-management intervention (i.e., JITAI) for preventing the development of adverse symptoms
(e.g., mobility, sleep, poor HRQOL); and 2) use data collected from wearables (i.e., the Fitbit Charge®) and
real-time assessments of HRQOL to identify care partners with the greatest risk for negative physical and
mental HRQOL outcomes, as well as the timing for when they are at greatest risk. We expect care partners
that are randomized to receive the JITAI will demonstrate improvements in HRQOL, physical activity and sleep
relative to controls. We also hypothesize is that objective, data-derived mobile phenotypes can predict risk for
adverse caregiver HRQOL. The scientific premise for the proposed research is based on our multidisciplinary
team with expertise in caregiver research, evaluation of HRQOL using patient-reported outcomes, clinical trials
implementation, expertise in real time assessment of HRQOL, as well as the statistical expertise in high-
dimensional statistics and machine learning. Furthermore, the innovation is grounded in the examination of a
real-time, real-world, personalized and scalable JITAI designed to improve HRQOL in care partners.
Ultimately, this study will allow us to identify care partners at the greatest risk for negative physical and mental
health outcomes, and will provide important efficacy data to support the clinical utility of a low cost, low burden
self-management intervention to improve HRQOL for care partners of persons with TBI.
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