Augmenting Health Self-Regulation across the Cancer Survivorship Continuum by Digital Phenotyping

Time Frame


Total Funding to Date



  • Jessie Chin

Breast cancer (BC) is a chronic illness. BC survivors often deal with the lifelong needs of self-management, such as controlling the symptoms, taking diet or nutrition plans, or adopting a physical activity program across their cancer survivorship continuum. Our team is eager to develop a novel technique to predict the intentions of lifestyle behavior by tracking ones' information behavior and patient-reported health outcomes. The proposed study aims at bridging natural language processing, Health Action Process Approach, digital phenotyping, and active machine learning (AML) to build a model, which predicts the states of behavioral intentions (BI) using information search (IS) behavior for the people with breast cancer across their cancer survivorship continuum. Our study is the first ever attempt to develop a model to estimate the states of BI using digital phenotyping, which will be the foundation of precision self-management.

head with questions


  • Co-PIs include Chung-Yi Chiu, Kinesiology and Community Health, and Chengxiang Zhai, Computer Sciences

Funding Agencies

  • Campus Research Board, 2022 – $30,000.00