Implementation strategies for monitoring adherence in real time (iSMART): A pilot randomized trial.
Authors: Rendle Katharine A. , Wainwright Jocelyn , Pucci Donna , Yarosh Deirdre , Cambareri Christine , Jacobs Linda A. , Blauch Abigail , Mandel Abigail , Johnston Caleb , Colbert James , Mooney Bethany , Bekelman Justin E. , Kimmel Stephen E. , Rosin Roy , Shulman Lawrence N. , Wileyto E. Paul , Cohen Sarah , Aggarwal Charu , Langer Corey J. , Takvorian Samuel U 
Published: June 2023

9115 Background: Helping patients to manage symptoms and adhere to oral anticancer agents (OACAs) is a major challenge in oncology. OACAs cause side effects that can lead to suboptimal adherence if not optimally managed, contributing to decreased effectiveness. Low-cost, text messaging approaches have shown promise, but have not been robustly studied in oncology. Guided by principles from implementation and behavioral science, we developed and tested the effect of an augmented intelligence chatbot on OACA adherence and symptom burden in patients with advanced lung cancer. Methods: We conducted a two-arm pilot randomized trial (NCT04347161) to evaluate the effect of the chatbot on OACA adherence and symptom burden compared to usual care. The chatbot engages patients via text messaging and applies natural language processing and machine learning to learn from interactions. Core functionalities include: 1) motivational daily adherence reminders, 2) longitudinal symptom monitoring with self-management support, and 3) bidirectional communication with clinical teams. Participants included English-speaking patients with advanced lung cancer treated with any of 9 OACAs targeting EGFR, ALK, or ROS-1. The primary outcome was 12-week adherence, measured using the microelectronic monitoring system (MEMS) and defined dichotomously if the patient achieved ≥95% adherent days or not. Secondary outcomes were assessed at baseline and 12 weeks using validated survey instruments, including symptom burden using the Edmonton Symptom Assessment System Total Distress Score (range 0-90), health-related quality of life (HRQOL) using EQ-5D-3L (range 0-100), and usability using Health-ITUES (range 1-5). We used multivariable logistic regression adjusting for stratification variables to test the chatbot’s effect on adherence (intent-to-treat analysis) and evaluated mean differences (by arm) in secondary outcomes using Fisher’s exact test. Results: From February 2021 to August 2022, 75 patients across 4 sites enrolled (median age 65 years, 64% female, 88% White, 21.3% high school education or lower); 50.7% (n=38) were randomized to intervention. Compared to usual care, we observed no significant differences in adherence in the intervention arm (78.8% vs 81.8%; aOR=1.7 95% CI: 0.3-9.4). However, in contrast to those in usual care, participants in the intervention arm had significantly greater decreases in symptom burden (mean difference: -2.7 vs 2.6; p<0.05) and increases in HRQOL (mean difference: 4.1 vs -4.8; p<0.05) from baseline to 12 weeks. Overall chatbot usability was high (mean score=3.9). Conclusions: In this pilot randomized trial, an augmented intelligence chatbot successfully reduced symptom burden and improved HRQOL but did not significantly alter OACA adherence. Chatbots are a potentially scalable strategy for improving symptom management that warrant study in larger randomized trials. Clinical trial information: NCT04347161 .