Developing a Mobile App for Remote Musculoskeletal Rehabilitation Adherence Monitoring

Thesis Abstract

Musculoskeletal disorders (MSDs) constitute a significant proportion of global disability, with effective rehabilitation crucial for improving functional outcomes and reducing healthcare costs. Despite the proven efficacy of prescribed physiotherapy regimens, patient adherence remains a persistent challenge, often leading to suboptimal recovery and increased risk of relapse. The advent of mobile health (mHealth) technologies presents an opportunity to enhance adherence through remote monitoring and personalized feedback; however, existing solutions lack integration with physiotherapy protocols tailored specifically for musculoskeletal rehabilitation, limiting their effectiveness and acceptability. This study aims to develop, implement, and evaluate a mobile application designed to monitor and enhance adherence to remote musculoskeletal rehabilitation exercises. The specific objectives are to (1) identify key features necessary for an effective adherence monitoring mobile app grounded in behavioral change theories; (2) develop a user-centered mobile app prototype incorporating these features, guided by the Health Belief Model and Self-Determination Theory; (3) assess the usability and acceptability of the app among physiotherapists and patients through qualitative and quantitative methods; (4) evaluate the impact of app-guided adherence on functional recovery outcomes; and (5) analyze the relationship between app engagement levels and clinical improvements. Employing a mixed-methods research design, the study begins with a qualitative exploratory phase involving focus group discussions with 15 physiotherapists and individual interviews with 20 patients to identify user needs and preferences. The findings inform the development of a prototype, created using an iterative design process employing agile methodologies. Following development, a quantitative quasi-experimental study involving a sample of 120 patients undergoing musculoskeletal rehabilitation is conducted, with participants randomly assigned to the intervention group (app-guided adherence) and control group (standard care). Data collection instruments include validated questionnaires such as the System Usability Scale (SUS), adapted adherence logs generated automatically by the app, and functional outcome measures like range of motion and pain scores assessed at baseline, 4 weeks, and 8 weeks. Data analysis involves descriptive statistics for usability and engagement metrics, independent t-tests and chi-square tests for group comparisons on adherence rates and clinical outcomes, and multiple linear regression analysis to examine predictors of recovery. Thematic analysis of qualitative interview transcripts adds depth to the usability and engagement data, fostering triangulation of findings. Statistical significance is determined at p<0.05, and effect sizes are calculated to quantify intervention impact. Expected key findings include high usability scores and positive user acceptability, alongside statistically significant improvements in adherence rates and functional recovery metrics in the app-assisted group compared to controls. It is anticipated that higher engagement levels with the app correlate strongly with better clinical outcomes, supporting the theoretical frameworks that underpin the app design. This research contributes to knowledge by providing a validated, theoretically grounded mobile application tailored for remote musculoskeletal rehabilitation, filling existing gaps in digital health interventions targeting adherence. It offers evidence on the effectiveness of mHealth solutions in enhancing compliance, which can guide future development of integrated rehabilitation platforms. The study concludes with recommendations for integrating such apps into routine physiotherapy practice, emphasizing the importance of user-centered approaches and ongoing behavioral engagement strategies. Future research directions include longitudinal studies for long-term adherence, scalability assessments, and the incorporation of emerging technologies such as artificial intelligence for personalized feedback.