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BackgroundCatheter-associated urinary tract infections (CAUTIs) are the main cause of health care–associated infections, and they increase the disease burden, antibiotic usage, and hospital stay. Inappropriate placement and unnecessarily prolonged usage of a catheter lead to an elevated and preventable risk of infection. The smartphone app Participatient has been developed to involve hospitalized patients in communication and decision-making related to catheter use and to control unnecessary (long-term) catheter use to prevent CAUTIs. Sustained behavioral changes for infection prevention can be promoted by empowering patients through Participatient. ObjectiveThe primary aim of our multicenter prospective interrupted time-series analysis is to reduce inappropriate catheter usage by 15%. We will evaluate the efficacy of Participatient in this quality improvement study in clinical wards. Our secondary endpoints are to reduce CAUTIs and to increase patient satisfaction, involvement, and trust with health care services. MethodsWe will conduct a multicenter interrupted time-series analysis—a strong study design when randomization is not feasible—consisting of a pre- and postintervention point-prevalence survey distributed among participating wards to investigate the efficacy of Participatient in reducing the inappropriate usage of catheters. After customizing Participatient to the wards’ requirements, it will be implemented with a catheter indication checklist among clinical wards in 4 large hospitals in the Netherlands. We will collect clinical data every 2 weeks for 6 months in the pre- and postintervention periods. Simultaneously, we will assess the impact of Participatient on patient satisfaction with health care services and providers and the patients’ perceived involvement in health care through questionnaires, and the barriers and facilitators of eHealth implementation through interviews with health care workers. ResultsTo reduce the inappropriate use of approximately 40% of catheters (currently in use) by 15%, we aim to collect 9-12 data points from 70-100 patients per survey date per hospital. Thereafter, we will conduct an interrupted time-series analysis and present the difference between the unadjusted and adjusted rate ratios with a corresponding 95% CI. Differences will be considered significant when P