A Framework for Personalize Rehabilitation Program Development in Post-Stroke Patients

Thesis Abstract

Stroke remains a leading cause of long-term disability worldwide, with heterogeneous functional impairments necessitating individualized rehabilitation approaches to optimize recovery outcomes. Despite significant advances in stroke rehabilitation, existing programs often adopt standardized protocols that may not adequately address the unique needs, preferences, and neuroplastic potential of diverse patients. This study aims to develop a comprehensive framework for personalized rehabilitation program development tailored specifically to post-stroke patients, thereby enhancing functional recovery, patient engagement, and quality of life. The primary objectives are to identify patient-specific factors influencing rehabilitation outcomes, synthesize relevant theoretical models including the Biopsychosocial Model and the Motor Learning Theory, and formulate an integrative framework that guides clinicians in designing and implementing personalized rehabilitation interventions. To achieve this, a mixed-methods research design was adopted, comprising qualitative and quantitative phases. The qualitative phase involved semi-structured interviews with 35 stroke rehabilitation specialists from hospitals across diverse urban and rural settings, aimed at eliciting expert insights into critical determinants of personalized rehabilitation. Thematic analysis, utilizing NVivo software, was employed to identify recurrent themes related to patient assessment, goal setting, intervention customization, and outcome evaluation. Concurrently, the quantitative phase included a longitudinal cohort study involving 120 post-stroke patients aged 40-75 years, recruited through stratified random sampling from rehabilitation centers. Data collection instruments encompassed the Functional Independence Measure (FIM), the Stroke Impact Scale (SIS), and a Patient Preference and Satisfaction Questionnaire. Baseline assessments were conducted at admission, with follow-ups at three and six months post-intervention. Statistical analyses utilized multiple regression models to identify predictors of functional outcomes, and hierarchical linear modeling examined changes over time. Key anticipated findings include the identification of core patient characteristics—such as cognitive status, motivation, psychosocial support, and comorbidities—that significantly influence rehabilitation trajectories. The thematic analysis is expected to reveal existing gaps in clinician decision-making processes regarding personalization, such as inadequate assessment tools for individual preferences and factors affecting adherence. Quantitative analyses are projected to demonstrate that personalized interventions, structured around identified patient factors, lead to statistically significant improvements in functional independence and quality of life compared to conventional standardized approaches. Hierarchical models are likely to show sustained benefits at follow-up assessments. The study’s contribution to knowledge lies in establishing an empirically validated, theoretically grounded framework that integrates clinical assessment, patient-centered goal setting, and adaptable intervention strategies, aligned with established models such as the International Classification of Functioning, Disability and Health (ICF) and the Motor Learning Theory. This framework advances personalized rehabilitation practice, providing clinicians with a structured approach to tailor interventions based on individual patient profiles and preferences. The main conclusion highlights that effective personalization significantly enhances functional recovery outcomes in post-stroke rehabilitation. Based on these findings, the study recommends implementing the proposed framework in clinical settings, training rehabilitation practitioners in its application, and further testing its adaptability across various healthcare contexts. Future research should explore the integration of digital health technologies and decision support systems to facilitate real-time personalization. Overall, this research underscores the importance of individualized care in stroke rehabilitation and provides a strategic model to support evidence-based, patient-centered practice.