Feasibility of a Smartphone Application for Education and Symptom Management of Patients With Renal Cell Carcinoma on Combined Tyrosine Kinase and Immune Checkpoint Inhibitors

Abstract

PURPOSE Patients with advanced renal cell carcinoma (RCC) face significant challenges, stemming both from the complexities of the disease itself and the adverse effects of treatments. This study evaluated the feasibility and acceptability of a mobile health (mHealth) application tailored for education and symptom management of patients with advanced RCC receiving combined immune checkpoint inhibitor and tyrosine kinase inhibitor (ICI-TKI) therapy. METHODS The primary end points were acceptability and feasibility. Acceptability was defined as the proportion of patients approached who consented to participate, setting a benchmark of at least 50% for this metric. Feasibility was gauged by the completion rate of the intervention among the participants; it required at least 50% of participants to fully complete the intervention and at least 70% to finish half of the administered questionnaires. The secondary end points included knowledge assessment and patient-reported outcomes (PROs). PROs were evaluated using validated instruments. To discern the changes between pre- and post-educational module quiz scores, we used the Wilcoxon signed-rank test. Time-course data of PROs were visualized using line plots and then compared using paired t-tests. RESULTS From November 2022 to July 2023, 20 of 22 (90%) patients approached for the study consented and enrolled. Of the enrolled patients, 60% completed all questionnaires and knowledge assessments at every time point and 75% completed at least half of the surveys and questionnaires. Significant pre/post differences were noted in two of six quizzes in the knowledge assessment. This study population did not experience a significant change in PRO scores after starting therapy. CONCLUSION The mHealth application designed for education and symptom management in patients with advanced RCC undergoing combination ICI-TKI has proven to be both acceptable and feasible, meeting previous research benchmarks.