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Grant Details

Grant Number: 5R01CA239246-04 Interpret this number
Primary Investigator: Barnes, Laura
Organization: University Of Virginia
Project Title: SCH:INT: Collaborative Research: Multiscale Modeling and Intervention for Improving Long-Term Medication
Fiscal Year: 2022


By 2022, there will be nearly 4 million breast cancer survivors in the USA. Adherence to long-term endocrine therapy is crucial for survivors of hormone receptor-positive breast cancer prescribed these daily medications to prevent cancer recurrence. Despite the life-saving benefits of these medications, rates of adherence are low. Medication-taking behavior is simultaneously influenced by multiscale factors, including personal and environmental factors, and a patient's other behavioral patterns. Despite advances in smart and connected health, there have been few attempts to develop and deliver personalized interventions for medication adherence. Existing technology-based interventions focus on cognitive reasons for non-adherence to medications experienced by some people (e.g., forgetting), but fail to account for interactions between cognitive factors and other types of factors (i.e., environmental, behavioral) that contribute to adherence. Furthermore, few technology-driven interventions have been assessed for efficacy in supporting medication adherence. Tools to understand interactions between multiscale factors and the effect that personalized interventions have on these factors would ultimately improve medication adherence. This 3-phase project will overcome these fundamental scientific barriers by developing and employing a Multiscale Modeling and Intervention (MMI) system. First, a system consisting of sensor-rich smartphones, wireless medication event monitoring systems (MEMS), wireless beacons, and wearable sensors that collect in situ data on medication adherence, will be developed to provide continuous, noninvasive adherence assessment and multiscale monitoring of factors. Second, the MMI system will be deployed to breast cancer survivors to model relationships between adherence and multiscale factors, identify patterns associated with medication-taking behavior, and develop interventions. Third, a proof-of-concept for MMI will be demonstrated through a human subjects study, with subjects receiving multiscale interventions. The proposed research has significant public health implications as it is expected to increase our understanding of medication adherence in breast cancer survivors, thus providing a general framework that will be applicable to oral chemotherapy use across multiple cancers.


Mobile Sensing in the COVID-19 Era: A Review.
Authors: Wang Z. , Xiong H. , Tang M. , Boukhechba M. , Flickinger T.E. , Barnes L.E. .
Source: Health data science, 2022; 2022, p. 9830476.
EPub date: 2022-08-08.
PMID: 36408201
Related Citations

Theory-Guided Randomized Neural Networks for Decoding Medication-Taking Behavior.
Authors: Kaur N. , Gonzales M. , Garcia Alcaraz C. , Barnes L.E. , Wells K.J. , Gong J. .
Source: ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, 2021 Jul; 2021, .
EPub date: 2021-08-10.
PMID: 34505062
Related Citations

Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence.
Authors: Baglione A.N. , Gong J. , Boukhechba M. , Wells K.J. , Barnes L.E. .
Source: IEEE pervasive computing, 2020 Jul-Sep; 19(3), p. 24-36.
EPub date: 2020-06-26.
PMID: 33510585
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