Development of a Mobile App for Remote Monitoring of Post-Stroke Motor Rehabilitation Progress

Thesis Abstract

Stroke remains a leading cause of long-term disability globally, with motor impairments significantly affecting patients' independence and quality of life. Despite advances in neurorehabilitation, monitoring post-stroke motor recovery predominantly relies on periodic clinical assessments, which are limited by resource constraints, accessibility issues, and subjective evaluation biases. This study aims to develop and evaluate a mobile application designed to facilitate remote, continuous monitoring of motor rehabilitation progress for post-stroke patients, thereby enhancing intervention responsiveness and patient engagement. The specific objectives include designing a user-friendly mobile app integrated with motion-tracking sensors, establishing its validity and reliability in capturing motor performance data, evaluating its usability and acceptability among patients and clinicians, and exploring its impact on rehabilitation outcomes. Employing a mixed-methods research design, the study first involves the development of the mobile app through an iterative user-centered design process informed by the Technology Acceptance Model (TAM) and the Self-Determination Theory, emphasizing usability and motivation. The app integrates inertial measurement units (IMUs) attached to affected limbs to record kinematic data during prescribed exercises. An initial pilot study with 50 post-stroke patients from a tertiary rehabilitation center will assess the app’s data accuracy, comparing automated assessments with traditional clinical scales such as the Fugl-Meyer Motor Assessment (FMA) and the Motor Activity Log (MAL). Quantitative data will be analyzed using Bland-Altman plots to evaluate agreement, and test-retest reliability will be examined through intraclass correlation coefficients (ICCs). Qualitative data obtained from semi-structured interviews with 20 participants and 10 clinicians will be analyzed thematically to gauge usability, satisfaction, and perceived benefits, following Braun and Clarke’s thematic analysis approach. Subsequently, a longitudinal study involving 100 stroke survivors will be conducted over a 12-week period, with participants using the app during their routine therapy. Rehabilitation progress data will be analyzed via repeated-measures ANOVA to identify significant improvements over time, and multiple regression analysis will determine predictors of rehabilitation success. The study anticipates that the mobile app will demonstrate high validity and reliability in tracking motor performance, with demographic factors such as age, initial impairment severity, and technological literacy influencing user engagement and adherence. The integration of sensor data with clinical assessments is expected to provide nuanced insights into recovery trajectories, potentially enabling early intervention adjustments. This research contributes to the existing body of knowledge by providing empirical evidence on the feasibility and effectiveness of ICT-driven tools for remote stroke rehabilitation monitoring. It advances the understanding of how mobile health solutions can augment traditional therapeutic approaches, foster patient-centered care, and improve rehabilitation outcomes, particularly in resource-limited settings. The study also offers a validated prototype framework for similar applications targeting other neurological and musculoskeletal disorders. The main conclusion underscores the potential of mobile health technology in transforming post-stroke rehabilitation paradigms, emphasizing the importance of user-centered design, data validity, and integrating behavioral theories to enhance engagement. Recommendations include scaling the app for broader clinical deployment, incorporating machine learning algorithms for personalized feedback, and conducting larger multicenter trials to confirm generalizability. Future research should explore integrating physiologic data such as electromyography (EMG) and expanding functionalities to include cognitive and psychosocial monitoring, thereby fostering holistic recovery management.