App-Assisted Day Reconstruction to Reduce Treatment Burden and Logistic Toxicity in Cancer Patients - Aim 3

Each year, more than 1.7 million new cancer patients in the U.S. undergo intense, multimodal treatments that that create numerous logistical challenges in managing treatment and everyday life priorities. In the current cancer care system, "logistic toxicity"-the toxic effects imposed by the logistical burden of carrying out cancer treatment-related tasks on patient well-being-has been largely unmeasured and unaddressed. Current methods for measuring logistic toxicity generate retrospective assessments intended for researchers. They do not offer timely information that empower patients to solicit assistance from care providers, employers, family, and friends. Nor do they empower providers to explore the increasingly available treatment options for patient- centered cancer care. This proposal aims to apply a new method-app-assisted day reconstruction-to develop the first digital health tool to enable remote patient monitoring of logistic toxicity, which is the necessary first step towards developing effective care interventions for addressing it. Our product is both conceptually and technically innovative. Conceptually, the investigators apply the day reconstruction method-a method initially created by well-being researchers for collecting more accurate data on daily life experiences-to collect activity engagement and well-being information related to cancer treatment tasks. Technically, the investigators leverage the existing patented technology and new machine learning techniques to enable novel integration of objective mobile sensing with subjective patient input. Mobile sensing and machine learning will constitute the "assist" that the app provides for day reconstruction in relation to logistic toxicity, significantly reducing recall errors and the need for manual input. The "assist" will also prompt patients to provide information such as subjective well-being ratings that are not detectable by mobile sensing or machine learning, generating more accurate and comprehensive measures of logistic toxicity than existing methods. The project has three specific aims, including (1) an initial system design based upon input from cancer patients and cancer care stakeholders, (2) prototype development and initial tests, and (3) field tests of the app among 60 diverse patients undergoing treatment for cancer. In Aim 3, patients will rate the quality of the app using the Mobile App Rating Scale (MARS) and their satisfaction with the three key app features: 1) the app's ability to capture out-of-home treatment-related activities and trips, 2) the ease of the interface for inputting home-based treatment-related activities and well-being ratings, and 3) the usefulness of the logistic toxicity summary report. The outcome of this project will be a final prototype app with 70% of patients indicating an overall MARS score of 4.0 or more and satisfaction with the three features. In Phase II, the team will test the efficacy of the app-both separately and in conjunction with care coordination, telemedicine, and home-based treatments-in reducing logistic toxicity and improving treatment outcomes in a randomized controlled trial.