Improving Long-Term Adherence to Endocrine Therapy Among Breast Cancer Survivors: Development of a Multiscale Modeling and Intervention System

Background: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of long-term adherence to endocrine therapy (ET) remain low among certain breast cancer survivors. Using embedded sensors in smartphones and wearables, ecological momentary assessment data and health behavior theory may facilitate a richer understanding of the real-world context of medication-taking behaviors, which can aid in the development of personalized interventions. Objective: The objective of this paper is to describe the development of a multiscale modeling intervention (MMI) system to facilitate adherence to daily oral ET for breast cancer survivors. This represents the first phase of a larger project that aims to use machine learning to predict when breast cancer survivors are most likely to miss their ET medications in order to deploy personalized interventions. The purpose of this paper was (1) to determine the acceptability of the proposed MMI system, (2) to identify modifiable predictors of ET medication adherence among breast cancer survivors, and (3) to select surveys or items measuring constructs associated with ET adherence among breast cancer survivors for inclusion in the MMI system. Methods: Study 1 consisted of usability interviews with a cohort of breast cancer survivors (n=25) prescribed ET. For study 1, all qualitative usability interviews were conducted using a semistructured interview guide and assessed whether breast cancer survivors were willing to use various components of the MMI system. Study 2 consisted of (1) a secondary data analysis of ET adherence data from 32 breast cancer survivors using a social cognitive theory framework and (2) a review of research literature of constructs and surveys measuring constructs associated with ET adherence among breast cancer survivors using a social cognitive theory framework. The secondary data analysis included the use of randomized neural network analysis to predict factors strongly associated with medication adherence. Results: In study 1, usability interview findings suggested that participants were willing to use an ecological momentary assessment smartphone app, a smartwatch and associated smartphone app, a smart pill bottle or smart pill box and associated smartphone app, and the entire MMI system for a 6-month study period. In study 2, the randomized neural network analysis identified 104 survey items with significant contributions to 4-week medication adherence using a threshold of the 70th percentile for feature importance. After a review of peer-reviewed studies, we abstracted modifiable constructs significantly associated with adherence to adjuvant ET and identified 42 surveys used to measure these constructs. When these findings were combined, the final survey for the MMI system consisted of 32 surveys and demographic items. Conclusions: Our research highlights the use of social cognitive theory, data-driven models, and participant feedback to inform the development of a medication adherence monitoring system. Data from studies 1 and 2 were used to develop a prototype MMI system that will be deployed in a future longitudinal study with 20 breast cancer survivors over 6 months.