Development of a Mobile App for Personalized Post-Stroke Rehabilitation Monitoring

Thesis Abstract

Stroke remains a leading cause of long-term disability globally, with post-stroke rehabilitation critical to enhancing functional recovery and improving quality of life. Despite the proliferation of rehabilitation programs, many stroke survivors face challenges in adhering to prescribed therapy regimens due to limited access to healthcare facilities, lack of personalized training plans, and insufficient monitoring of progress outside clinical settings. This study aims to develop and evaluate a mobile application designed to facilitate personalized post-stroke rehabilitation monitoring, thereby promoting adherence, enabling remote assessment, and supporting tailored therapeutic interventions. The primary objectives include designing an intuitive, evidence-based mobile app that incorporates rehabilitation exercises aligned with individual patient profiles, integrating real-time progress tracking features, and supporting communication between patients and healthcare providers. The study adopts a mixed-methods research design, combining qualitative usability assessments with quantitative validation of the app's effectiveness. The theoretical framework underpinning this development is based on the Health Belief Model and the Technology Acceptance Model, which guide understanding of user engagement and behavioral adherence to rehabilitation protocols. The population comprises 150 post-stroke patients, aged 40 to 75 years, recruited from outpatient neurology clinics across three urban hospitals. A stratified random sampling technique ensures representation across different stroke severity levels, with an estimated sample size calculated using Cochran’s formula to achieve 95% confidence and a 5% margin of error. Data collection instruments include validated questionnaires measuring usability (System Usability Scale), adherence (Revised Stroke Rehabilitation Adherence Scale), and user satisfaction, complemented by semi-structured interviews for qualitative insights. The app’s functionality will be tested over an eight-week trial period, with pre- and post-intervention assessments. Quantitative data will be analyzed using descriptive statistics, paired t-tests to evaluate changes in adherence, and multiple regression analysis to identify predictors of engagement. Thematic analysis will process qualitative interview data to explore user experiences and perceived barriers. Additionally, usage data from the app (e.g., frequency of exercise completion, interaction logs) will be subjected to logistic regression to assess correlations with rehabilitation outcomes. Expected findings include significant improvements in adherence to rehabilitation exercises, increased patient engagement, and positive user evaluations regarding the app’s usability and relevance. The integration of personalized exercise modules and remote monitoring features is anticipated to foster sustained participation and functional gains. The study is expected to demonstrate that a tailored mobile health solution can effectively augment traditional rehabilitation methods, especially in settings with limited access to in-person therapy. This research contributes to existing knowledge by empirically validating a theoretical model of user acceptance in the context of post-stroke rehabilitation technology and providing a prototype that can be scaled or adapted for broader clinical applications. It advances understanding of how mobile health interventions influence behavioral change and rehabilitation outcomes among stroke survivors. The study concludes that personalized mobile apps represent a viable strategy to enhance post-stroke recovery, with implications for health policy, telehealth integration, and patient-centered care. Recommendations include further refinement of the app based on user feedback, larger-scale trials across diverse populations, and integration with electronic health records to facilitate comprehensive rehabilitative management. Future research should explore long-term adherence and the app’s impact on functional independence, extending beyond initial recovery phases to support lifelong stroke management.