Development of a Mobile App for Personalized Medication Adherence Monitoring

Thesis Abstract

Inadequate medication adherence remains a significant challenge in managing chronic diseases, often resulting in suboptimal health outcomes, increased healthcare costs, and higher rates of hospitalization. Traditional methods for monitoring adherence, such as patient self-reporting and periodic clinical assessments, exhibit considerable limitations, including recall bias, delayed feedback, and lack of real-time engagement. This study aims to develop a personalized mobile application designed to enhance medication adherence through real-time monitoring, tailored reminders, and user-centric feedback features. The specific objectives include designing and implementing the app based on behavioral change theories such as the Health Belief Model and the Self-Determination Theory, evaluating its usability and acceptability among a target population, and assessing its impact on adherence rates over a three-month period. A mixed-method research design was adopted, integrating quantitative and qualitative data collection approaches to provide a comprehensive evaluation of the application’s effectiveness and user experience. The study population consisted of 250 adults aged 30 to 65 years diagnosed with hypertension or type 2 diabetes mellitus and currently on prescribed medication regimens. A stratified random sampling technique was employed to ensure representation across gender, age groups, and socioeconomic status. Data were collected through structured questionnaires assessing baseline medication adherence, usability surveys such as the System Usability Scale (SUS), and semi-structured interviews exploring user perceptions. The app’s adherence data was collected digitally and integrated with cloud-based analytics. Quantitative data were analyzed using descriptive statistics to characterize the sample and adherence levels at baseline and post-intervention. Inferential statistics, including paired t-tests and multivariate regression analysis, were applied to determine the statistical significance of changes in adherence and identify predictors of improved compliance. Qualitative data from interviews were subjected to thematic analysis to uncover key themes related to user engagement, perceived benefits, and barriers to app usage. The analysis employed NVivo software to systematically code and interpret interview transcripts. It is anticipated that the mobile app will significantly improve medication adherence rates, with quantitative results demonstrating at least a 20% increase in adherence scores and statistically significant improvements (p < 0.05) compared to baseline. The thematic analysis is expected to reveal that personalized reminders, real-time feedback, and ease of use are critical factors influencing user engagement. The study hypothesizes that integrating behavioral theories into app design will positively influence user motivation and compliance. This research contributes novel insights into the efficacy of ICT-driven interventions for medication management, extending existing literature by providing empirical evidence on the application of behavioral change theories within mobile health solutions tailored for chronic disease management. The findings offer a practical framework for developing scalable, user-centric mobile apps that foster sustained medication adherence and support health system efforts to reduce disease burden. In conclusion, the study affirms that a personalized mobile app can serve as an effective tool for improving medication adherence among adults managing chronic conditions. The recommendations include the integration of such digital interventions into routine clinical practice, further enhancements based on user feedback, and exploration of long-term adherence outcomes. Future research should examine the applicability of the developed app across diverse populations and healthcare settings, as well as longitudinal impacts on health outcomes and quality of life.