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Collecting electronic patient-reported outcomes via smartphone text messages in a bladder cancer RCT: A feasibility
Lene Munk1, Vanaja Kumarasegaram1, Josephine Maria Hyldgaard2
1Department of Urology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Aarhus University, Denmark.
None:
Patient-reported outcome measures are increasingly used to assess symptoms, treatment tolerance, and quality of life (QoL) in oncology. Electronic patient-reported outcome measures (ePROM) offer advantages over paper questionnaires, but large-scale evidence using smartphone text messaging in clinical trials is limited. To evaluate the feasibility of collecting ePROM via smartphone text messages in a large-scale, multinational randomized controlled trial of patients receiving Bacillus Calmette-Guérin therapy for non-muscle-invasive bladder cancer. This methodological study was embedded in the North-REG Dwell Time study. During each instillation week, participants received a daily text message linking to a questionnaire assessing side effects. Four additional QoL questionnaires were distributed during the 1-year study period. Non-responders received 2 automated reminders. Patients were randomized into an intervention and control group that differed only in dwell time. All responses were transferred directly into a trial database. Feasibility was assessed by calculating overall response rates and separate response rates for daily side-effect and QoL questionnaires. Between February 2021 and November 2024, 242 participants were enrolled and 225 received at least one instillation. A total of 24,286 text messages were sent, corresponding to 15,485 initially activated questionnaires. The overall response rate was 95%. Response rates were 95% for daily side-effect questionnaires and 87% for QoL questionnaires. No significant differences were found between the intervention and control groups, regarding response rates. Smartphone text messaging is highly feasible for collecting ePROM in a multinational bladder cancer trial, yielding excellent response rates and enabling near real-time symptom monitoring with minimal recall bias.
