Mobile applications for non-communicable disease Management : A systematic review of development methods and effectiveness

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Aim: To evaluate the most effective development methods for mobile applications that support self-management of non-communicable diseases and to determine the features that enhance their effectiveness and user adoption. Methods: The design was a systematic review of research papers published in the period 2019–2024. The review included randomized and quasi-experimental clinical trials. The search was performed in six databases (PubMed, Scopus, Scielo, CINAHL, Web of Science, and Clinical Trials). Bias and methodological quality were assessed using the RoB2 and MINORS tools. Results: The review included six studies involving 2421 patients across four countries. The applications demonstrated improvements in treatment adherence, self-efficacy, and control of clinical variables such as glycemia and blood pressure. The most effective applications incorporated therapeutic education, monitoring, and reminders. However, limitations were noted, including insufficient user involvement in early development stages, which could affect relevance and usability. The heterogeneity of study designs and populations, coupled with the lack of large-scale clinical trials, limits the generalizability of the findings. Additionally, variability in technological platforms and the absence of standardized evaluation metrics complicate outcome comparisons. Conclusion: Mobile applications for chronic disease self-management are most effective when developed with a user-centered approach and continuous validation. Despite these findings, further research is necessary to generalize the results and optimize the integration of these applications into healthcare systems. Prospero registration number: CRD42024571644.

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Camino Ortega, E, Gil de Gómez, A B, González Gamarra, A, Cuevas-Budhart, M A, García Klepzig, J L & Gómez del Pulgar García-Madrid, M 2025, 'Mobile applications for non-communicable disease Management : A systematic review of development methods and effectiveness', Computers in Biology and Medicine, vol. 193, 110411. https://doi.org/10.1016/j.compbiomed.2025.110411