Examining the Multi-Faceted Determinants influencing the Adoption of Diabetes Mobile Apps: Content Analysis and Regression Analysis

This research paper presents a comprehensive analysis of the factors influencing the adoption and user satisfaction of diabetes mobile health apps. This work evaluates six (6) machine learning regressors and employs ordinary least squares (OLS) multiple regression, including a polynomial regression extension, for hypothesis testing. It is important to note that this research is novel in its use of various machine learning regression models to explore the determinants influencing the adoption of diabetes mobile apps while also considering the user experience journey.By employing machine learning algorithms, particularly a stacked model with ridge regression, the study identifies developer reputation, usability, update frequency, and cost as significant determinants of app downloads, a proxy for adoption rates. The stacked model’s superior predictive accuracy is evidenced by its result of achieving the lowest RMSE (0.4212) and highest adjusted R2 (0.9586), outperforming other models such as Random Forest and XG-Boost. Additionally, user feedback analysis sheds light on the varying levels of user dissatisfaction across different UX stages, with the highest discontent observed during the Churn stage, despite fewer reported pain points.The study’s findings are supported by permutation feature importance analysis, F-statistics, and p-values. Key insights reveal that while update frequency may not greatly influence downloads, ease of use and developer reputation significantly impact user adoption rates. Furthermore, the research delves into business models, revealing that free and premium models are particularly effective in the app market, while regional factors, such as those about Taiwan, also play a crucial role in adoption.Recommendations from the study stress the importance of addressing technical glitches, enhancing connectivity and integration with health devices, providing educational content, and focusing on user-centric design. Finally, the paper underlines the need for such a complex approach in app design that puts users' requirements first and proposes to improve the predictive modelling for real-time solutions.