Multiscale modeling intervention development and perspectives from early-stage breast cancer survivors on technology to improve long-term adherence to endocrine therapy.

Abstract

442 Background: Despite the important role of endocrine therapy (ET) in preventing cancer recurrence, rates of long-term adherence are poor among certain breast cancer survivors (BCS). Traditional medication adherence (MA) interventions that have primarily incorporated medication-taking reminders mainly focused on a “one size fits all” approach, which may explain why many interventions have proven unsuccessful. However, when combined with other context, sensors (i.e., wearable sensors, smartphone sensors) can facilitate a better understanding of medication-taking behaviors leading to individualized interventions that are time and context appropriate. The project team is developing a multiscale modeling and intervention (MMI) system designed to improve adherence to ET among BCS. This study describes MMI development. Methods: MMI development has included: 1) usability testing; 2) review of research literature regarding factors associated with ET MA; and 3) a neural network analysis of previously collected ET MA data. In usability testing, 20 BCS were recruited via social media posts to participate in semi-structured usability interviews. Interviews were conducted via videoconferencing and assessed perceptions of and willingness to use an ecological momentary assessment (EMA) smartphone app, smartwatch, smart pill bottle, and smart pill box. The literature review examined multiple systematic reviews to identify constructs associated with ET MA. Randomized neural network analysis with 32 early stage BCS taking ET was used to determine important features of ET MA 4 weeks following completion of a 346-item survey. Four-week medication adherence was measured daily with a medication event monitoring system (MEMS). Results: Usability testing participants were accepting of each technology and willing to use each technology at various frequencies. Forty-two surveys were reviewed as predictors of MA in systematic reviews. Randomized neural network analysis found 104 survey items had absolute weights at the 70th percentile, indicating a strong influence on week 4 ET MA, and 11 surveys were determined theoretically relevant. Conclusions: BCS are willing to use 4 components of the MMI system. The MMI system will soon be deployed for 6 months of data collection. If shown to be effective, the MMI framework can be used by oncologists and researchers to develop personalized interventions focused on understanding and increasing ET MA.