A Model for Optimizing Telerehabilitation Outcomes in Post-Stroke Care

Thesis Abstract

This study addresses the escalating need for effective delivery of post-stroke rehabilitation through telerehabilitation by examining how a structured model can optimize outcomes across physical, cognitive, and psychosocial domains. The aim is to develop and validate an integrative telerehabilitation model that enhances functional recovery, adherence, and patient satisfaction while reducing care gaps in remote settings. Specific objectives include (i) identifying determinants of telerehabilitation engagement and adherence among stroke survivors and caregivers; (ii) examining the relationships between tele-delivered therapy dose, modality (synchronous vs. asynchronous), and functional outcomes; (iii) evaluating the moderating roles of digital literacy, caregiver support, and home environment on rehabilitation efficacy; (iv) synthesizing theoretical constructs from Self-Efficacy Theory and the Technology Acceptance Model to formulate a diagnostic framework for telerehabilitation readiness; and (v) validating a comprehensive model through predictive analytics and stakeholder feedback. A mixed-methods, multi-site study will be conducted over 24 months, employing a parallel convergent design. The population comprises adult stroke survivors (n=320) within six months post-event, enrolled from three urban rehabilitation networks and two rural satellite clinics. A stratified random sample of 240 participants will be allocated to a 12-week telerehabilitation program and 80 to standard in-person care as a comparator. Data collection will include standardized instruments the Fugl-Meyer Assessment (upper and lower extremity), Barthel Index, Montreal Cognitive Assessment, Stroke Self-Efficacy Scale, and System Usability Scale, administered at baseline, mid-intervention (6 weeks), post-intervention (12 weeks), and 6-month follow-up. Qualitative data will be gathered via semi-structured interviews with 40 participants and 20 caregivers to elicit experiences, perceived barriers, and facilitator mechanisms. Data collection instruments will be evaluated for validity and reliability, including pilot testing (n=20) and Cronbach’s alpha assessments for internal consistency. Quantitative analyses will employ multiple regression to determine predictors of functional improvement, repeated-measures ANOVA to assess changes over time, and mediation analyses to test pathways from engagement and dose to outcomes. Structural equation modeling will be used to test the proposed telerehabilitation readiness framework, integrating Self-Efficacy Theory and the Technology Acceptance Model as latent constructs. Subgroup analyses will explore differential effects by age, sex, stroke severity, and digital literacy levels. Qualitative data will be analyzed using reflexive thematic analysis to identify core themes related to acceptability, usability, and perceived value of telerehabilitation. Integration of quantitative and qualitative findings will be achieved through side-by-side comparison and joint display techniques to refine the conceptual model. Expected findings include higher functional gains in the telerehabilitation group, with effect sizes surpassing minimal clinically important differences on the Barthel Index and Fugl-Meyer scales; adherence and engagement will mediate the relationship between dose and outcomes; digital literacy and caregiver support will significantly moderate program effectiveness; and the integrated model will demonstrate good fit indices (CFI > .95, RMSEA < .06) in SEM analyses. The study will contribute to knowledge by presenting a theoretically grounded, empirically validated model that synthesizes neurorehabilitation principles with health technology acceptance, offering a pragmatic framework for clinicians, policymakers, and technology developers to optimize telerehabilitation delivery. The main conclusion will posit that a tailored, readiness-informed telerehabilitation model can achieve equivalent or superior functional outcomes compared with conventional in-person care, particularly for individuals with greater access barriers. Recommendations include coupling telerehabilitation with caregiver training programs, investing in user-centered platform design to enhance usability, and implementing policy guidelines that support standardized dosing protocols and data-driven personalization. Implications for future research include longitudinal validation across diverse populations, exploration of AI-driven personalization algorithms, and cost-effectiveness analyses to inform scaling and integration into standard post-stroke rehabilitation pathways.