Grant Number:	4R37CA248434-05 Interpret this number
Primary Investigator:	Chow, Philip
Organization:	University Of Virginia
Project Title:	Evaluating Digital Micro-Interventions to Reduce Distress and Increase Wellbeing in Breast Cancer Survivors
Fiscal Year:	2025

Project Summary/Abstract Nearly half of all breast cancer survivors will experience clinically significant symptoms of depression and/or anxiety in the first five years after their cancer diagnosis. However, the impact of mental health needs extends beyond those with clinically significant symptoms. Interventions, therefore, should target the broad range of affective, cognitive, and behavioral mechanisms that are critical to the development and maintenance of depression, anxiety, and general psychological well-being. Our research team is currently testing a modular, app-delivered intervention, IntelliCare, for reducing symptoms of depression/anxiety in breast cancer survivors. IntelliCare is composed of five digital micro-interventions (DMIs)—brief, targeted, and technology-enabled interventions that can be seamlessly integrated into an individual's daily life. In exit interviews with survivors that used IntelliCare (n=210), there was a clear desire for the addition of DMIs that promote well-being (e.g., increasing social interaction, positive emotions). Pursuing DMIs that not only reduce symptoms but also promote general well-being will allow us to reach a broader population of end-users and potentially enhance future outcomes. In addition, although survivors generally reported that the program was easy to use, intuitive, and accessible, many reported that built-in guidance specifically related to timing of use of the DMIs might improve their overall impact. The constructive feedback from our current R01 trial leads us to the next logical step of expanding the number and scope of empirically-informed DMIs for breast cancer survivors that can ultimately be used in a future proposal that adapts the delivery of DMIs in real-time to match individual survivors’ needs. Following the Obesity-Related Behavioral Intervention Trials (ORBIT) for developing empirically-supported behavioral interventions, the goals of this project are: (1) to design new DMIs for breast cancer survivors, and (2) to evaluate the feasibility of DMIs in a pre-post field trial. First, we will adopt a user- centered design approach to identify the preferences, needs, and perceived obstacles to using DMIs among women diagnosed with breast cancer within the last 5 years (N=20). We will initially develop up to 10 DMIs based on feedback from our R01 trial participants and evidence-based psychological therapy components (Aim 1). Iterative feedback will be collected from survivors who will engage with prototypes of DMIs in a controlled setting. This formative data will help us identify the most promising DMIs to be developed and evaluated in a single-arm, 8-week pre-post study among 40 breast cancer survivors (Aim 2). The primary goal of this aim is to evaluate the feasibility of including DMIs for a future proposal. A DMI will be considered feasible if it meets minimum engagement metrics defined in prior studies. We will also conduct exit interviews with survivors to inform future tailoring and personalization of the DMIs. A secondary goal is to obtain preliminary estimates of the proximal and longer term impact of the DMIs on psychological mechanistic variables for a future proposal to construct a just-in-time adaptive intervention that personalizes the delivery of DMIs to individual survivors.




Evaluating the impact of patients' psychological and physical problems on their interest in participating in research at a cancer center with a rural catchment area.
Authors: Chow P.I. , Sheffield C. , Cohn W.F. .
Source: Contemporary Clinical Trials, 2023 Aug; 131, p. 107245.
EPub date: 2023-05-29 00:00:00.0.
PMID: 37257725
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Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study.
Authors: Baglione A.N. , Cai L. , Bahrini A. , Posey I. , Boukhechba M. , Chow P.I. .
Source: Jmir Medical Informatics, 2022-06-02 00:00:00.0; 10(6), p. e30712.
EPub date: 2022-06-02 00:00:00.0.
PMID: 35653183
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