THE IMPACT OF MOBILE APPLICATIONS ON HEALTHY LIFESTYLES AND CHRONIC DISEASE MANAGEMENT
Abstract
Introduction: Mobile applications (mHealth) are playing an increasingly important role in promoting healthy lifestyles and supporting patients with chronic diseases. Evidence from evidence-based medicine (EBM) studies indicates that they can support behavioral change, improve clinical parameter control and facilitate contact with the healthcare system.
Objective: The aim of this study was to present current scientific evidence on the effectiveness of mobile applications in the field of health, with particular emphasis on behavioral and social effects.
Methods: Systematic reviews, meta-analyses and randomized trials on the use of mobile applications for physical activity, weight loss, diabetes control, hypertension and other chronic diseases, as well as for improving adherence to pharmacotherapy recommendations, were included. Reports on data security, digital inequalities and WHO recommendations on mHealth were also analyzed.
Results: According to meta-analyses, diabetes management apps lead to a significant reduction in HbA1c (by ~0.5% on average), especially when they are part of integrated clinical care. Apps that remind users to take their medication improve health behaviors related to pharmacotherapy, while those that promote physical activity and diet promote moderate, short-term weight loss and increased physical activity. Interventions using behavior change techniques such as self-monitoring, feedback and reminders are most effective. Positive social effects include increased motivation through support networks and facilitation of telemonitoring, but there are challenges related to data security, digital inequalities and lack of standardization in app quality.
Conclusions: Mobile applications have potential as a tool to support the prevention and management of chronic diseases, but their effectiveness is moderate and variable. Further long-term randomized trials and the implementation of quality and safety standards are needed to fully exploit their clinical and social potential.
References
WHO guideline Recommendations on Digital Interventions for Health System Strengthening. (2019). World Health Organization.
Iribarren, S. J., Akande, T. O., Kamp, K. J., Barry, D., Kader, Y. G., & Suelzer, E. (2021). Effectiveness of Mobile Apps to Promote Health and Manage Disease: Systematic Review and Meta-analysis of Randomized Controlled Trials. JMIR mHealth and uHealth, 9(1), e21563. https://doi.org/10.2196/21563
Deniz-Garcia, A., Fabelo, H., Rodriguez-Almeida, A. J., Zamora-Zamorano, G., Castro-Fernandez, M., Alberiche Ruano, M. D. P., Solvoll, T., Granja, C., Schopf, T. R., Callico, G. M., Soguero-Ruiz, C., Wägner, A. M., & WARIFA Consortium (2023). Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. Journal of medical Internet research, 25, e44030. https://doi.org/10.2196/44030
Armitage, L. C., Kassavou, A., & Sutton, S. (2020). Do mobile device apps designed to support medication adherence demonstrate efficacy? A systematic review of randomised controlled trials, with meta-analysis. BMJ open, 10(1), e032045. https://doi.org/10.1136/bmjopen-2019-032045
Bond, Z., Scanlon, T., & Judah, G. (2021). Systematic Review of RCTs Assessing the Effectiveness of mHealth Interventions to Improve Statin Medication Adherence: Using the Behaviour-Change Technique Taxonomy to Identify the Techniques That Improve Adherence. Healthcare (Basel, Switzerland), 9(10), 1282. https://doi.org/10.3390/healthcare9101282
Li, A., Del Olmo, M. G., Fong, M., Sim, K., Lymer, S. J., Cunich, M., & Caterson, I. (2021). Effect of a smartphone application (Perx) on medication adherence and clinical outcomes: a 12-month randomised controlled trial. BMJ open, 11(8), e047041. https://doi.org/10.1136/bmjopen-2020-047041
Paneerselvam, G. S., Lua, P. L., Chooi, W. H., Rehman, I. U., Goh, K. W., & Ming, L. C. (2025). Effectiveness of Mobile Apps in Improving Medication Adherence Among Chronic Kidney Disease Patients: Systematic Review. Journal of medical Internet research, 27, e53144. https://doi.org/10.2196/53144
Gomez-Garcia, C., Maher, C. A., Sañudo, B., & Jurado-Castro, J. M. (2025). Mobile Health Interventions for Individuals with Type 2 Diabetes and Overweight or Obesity-A Systematic Review and Meta-Analysis. Journal of functional morphology and kinesiology, 10(3), 292. https://doi.org/10.3390/jfmk10030292
Maida, E., Caruso, P., Bonavita, S., Abbadessa, G., Miele, G., Longo, M., Scappaticcio, L., Ruocco, E., Trojsi, F., Esposito, K., Lavorgna, L., & Maiorino, M. I. (2025). Digital Health in Diabetes Care: A Narrative Review from Monitoring to the Management of Systemic and Neurologic Complications. Journal of clinical medicine, 14(12), 4240. https://doi.org/10.3390/jcm14124240
Bretschneider, M. P., Roth, L., & Schwarz, P. E. H. (2023). Effectiveness of a Digital Health Application for the Treatment of Diabetes Type II-A Pilot Study. Journal of clinical medicine, 12(19), 6317. https://doi.org/10.3390/jcm12196317
Lee, J. L., & Kim, Y. (2024). Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review. Healthcare (Basel, Switzerland), 12(3), 368. https://doi.org/10.3390/healthcare12030368
Kassim, P. S. J., Muhammad, N. A., Rahman, N. F. A., Sidik, S. M., Essau, C. A., & Shah, S. A. (2022). Digital behaviour change interventions to promote physical activity in overweight and obese adolescents: a systematic review protocol. Systematic reviews, 11(1), 188. https://doi.org/10.1186/s13643-022-02060-w
Spinean, A., Mladin, A., Carniciu, S., Stănescu, A. M. A., & Serafinceanu, C. (2025). Emerging Methods for Integrative Management of Chronic Diseases: Utilizing mHealth Apps for Lifestyle Interventions. Nutrients, 17(9), 1506. https://doi.org/10.3390/nu17091506
Ghose, Anindya & Guo, Xitong & Li, Beibei & Dang, Yuanyuan. (2021). Empowering Patients Using Smart Mobile Health Platforms: Evidence From A Randomized Field Experiment. 10.48550/arXiv.2102.05506.
Hochberg, Irit & Feraru, Guy & Kozdoba, Mark & Mannor, Shie & Tennenholtz, Moshe & Yom-Tov, Elad. (2016). A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients. 10.48550/arXiv.1605.04070.
Bashi, N., Fatehi, F., Mosadeghi-Nik, M., Askari, M. S., & Karunanithi, M. (2020). Digital health interventions for chronic diseases: a scoping review of evaluation frameworks. BMJ health & care informatics, 27(1), e100066. https://doi.org/10.1136/bmjhci-2019-100066
Skalidis, I., Maurizi, N., Salihu, A., Fournier, S., Cook, S., Iglesias, J. F., Laforgia, P., D'Angelo, L., Garot, P., Hovasse, T., Neylon, A., Unterseeh, T., Champagne, S., Amabile, N., Sayah, N., Sanguineti, F., Akodad, M., Lu, H., & Antiochos, P. (2025). Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care. Medicina (Kaunas, Lithuania), 61(9), 1597. https://doi.org/10.3390/medicina61091597
Putra, R. P.., Rochmawati, I. D.., & Prima, D. A.. (2022). IAI SPECIAL EDITION: The impact of mobile application: "Friends of Heart" in knowledge and compliance of patients with coronary heart disease. Pharmacy Education, 22(2), p. 36–40. https://doi.org/10.46542/pe.2022.222.3640
Compernolle, S., DeSmet, A., Poppe, L., Crombez, G., De Bourdeaudhuij, I., Cardon, G., van der Ploeg, H. P., & Van Dyck, D. (2019). Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis. The international journal of behavioral nutrition and physical activity, 16(1), 63. https://doi.org/10.1186/s12966-019-0824-3
Aguiar, M., Trujillo, M., Chaves, D., Álvarez, R., & Epelde, G. (2022). mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review. JMIR mHealth and uHealth, 10(9), e33247. https://doi.org/10.2196/33247
MacPherson, M. M., Merry, K. J., Locke, S. R., & Jung, M. E. (2019). Effects of Mobile Health Prompts on Self-Monitoring and Exercise Behaviors Following a Diabetes Prevention Program: Secondary Analysis From a Randomized Controlled Trial. JMIR mHealth and uHealth, 7(9), e12956. https://doi.org/10.2196/12956
DeSmet, A., De Bourdeaudhuij, I., Chastin, S., Crombez, G., Maddison, R., & Cardon, G. (2019). Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study. JMIR mHealth and uHealth, 7(12), e15707. https://doi.org/10.2196/15707
Abdelrazek, Mohamed & Alshehhi, Yasmeen & Bonti, Alessio. (2022). Analysis of Personal Data Visualization Reviews On mHealth Apps (short paper). 10.48550/arXiv.2202.10620.
Krukowski, R. A., Denton, A. H., & König, L. M. (2024). Impact of feedback generation and presentation on self-monitoring behaviors, dietary intake, physical activity, and weight: a systematic review and meta-analysis. The international journal of behavioral nutrition and physical activity, 21(1), 3. https://doi.org/10.1186/s12966-023-01555-6
Gosetto, L., Falquet, G., & Ehrler, F. (2025). Personalizing mobile applications for health based on user profiles: A preference matrix from a scoping review. PLOS digital health, 4(8), e0000978. https://doi.org/10.1371/journal.pdig.0000978
Pirolli, P., Mohan, S., Venkatakrishnan, A., Nelson, L., Silva, M., & Springer, A. (2017). Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model. Journal of medical Internet research, 19(11), e397. https://doi.org/10.2196/jmir.8217
Dugas, M., Gao, G. G., & Agarwal, R. (2020). Unpacking mHealth interventions: A systematic review of behavior change techniques used in randomized controlled trials assessing mHealth effectiveness. Digital health, 6, 2055207620905411. https://doi.org/10.1177/2055207620905411
Thornton, L., Gardner, L. A., Osman, B., Green, O., Champion, K. E., Bryant, Z., Teesson, M., Kay-Lambkin, F., Chapman, C., & Health4Life Team (2021). A Multiple Health Behavior Change, Self-Monitoring Mobile App for Adolescents: Development and Usability Study of the Health4Life App. JMIR formative research, 5(4), e25513. https://doi.org/10.2196/25513
Valentine, Lee & Hinton, Jordan & Bajaj, Kriti & Boyd, Larissa & O’Sullivan, Shaunagh & Sorenson, Rory & Bell, Imogen & Sobredo, Miguel & Liu, Virginia & Peters, Wilma & Mangelsdorf, Shaminka & Wren, Thomas & Moller, Carl & Cross, Shane & McEnery, Carla & Bendall, Sarah & Nicholas, Jennifer & Alvarez-Jimenez, Mario. (2024). Systematic Review and Meta-Analyses of Mental Health App RCTs: Persuasive Design, Engagement, and Efficacy. 10.21203/rs.3.rs-5302304/v1.
Chaudhry, S. I., Barton, B., Mattera, J., Spertus, J., & Krumholz, H. M. (2007). Randomized trial of Telemonitoring to Improve Heart Failure Outcomes (Tele-HF): study design. Journal of cardiac failure, 13(9), 709–714. https://doi.org/10.1016/j.cardfail.2007.06.720
Takahashi, P. Y., Pecina, J. L., Upatising, B., Chaudhry, R., Shah, N. D., Van Houten, H., Cha, S., Croghan, I., Naessens, J. M., & Hanson, G. J. (2012). A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Archives of internal medicine, 172(10), 773–779. https://doi.org/10.1001/archinternmed.2012.256
Badr, J., Motulsky, A., & Denis, J. L. (2024). Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations. Health policy (Amsterdam, Netherlands), 146, 105122. https://doi.org/10.1016/j.healthpol.2024.105122
Benjumea, J., Ropero, J., Rivera-Romero, O., Dorronzoro-Zubiete, E., & Carrasco, A. (2020). Privacy Assessment in Mobile Health Apps: Scoping Review. JMIR mHealth and uHealth, 8(7), e18868. https://doi.org/10.2196/18868
Martínez-Pérez, B., de la Torre-Díez, I., & López-Coronado, M. (2015). Privacy and security in mobile health apps: a review and recommendations. Journal of medical systems, 39(1), 181. https://doi.org/10.1007/s10916-014-0181-3
Aljedaani, B., & Babar, M. A. (2021). Challenges With Developing Secure Mobile Health Applications: Systematic Review. JMIR mHealth and uHealth, 9(6), e15654. https://doi.org/10.2196/15654
Iwaya, L. H., Babar, M. A., Rashid, A., & Wijayarathna, C. (2023). On the privacy of mental health apps: An empirical investigation and its implications for app development. Empirical software engineering, 28(1), 2. https://doi.org/10.1007/s10664-022-10236-0
Hakiem, N., Afrizal, S. H., Setiadi, Y., Albab, H. S., Riasetiawan, M., & Zulhuda, S. (2024). Security and Privacy Policy Assessment in Mobile Health Applications: A Literature Review. Journal of System and Management Sciences, 14(2), 355-371.
Arora, S., Yttri, J., & Nilse, W. (2014). Privacy and Security in Mobile Health (mHealth) Research. Alcohol research: current reviews, 36(1), 143–152.
Forsberg, Albin & Iwaya, Leonardo. (2024). Security Analysis of Top-Ranked mHealth Fitness Apps: An Empirical Study.
Alhammad, N., Alajlani, M., Abd-Alrazaq, A., Epiphaniou, G., & Arvanitis, T. (2024). Patients' Perspectives on the Data Confidentiality, Privacy, and Security of mHealth Apps: Systematic Review. Journal of medical Internet research, 26, e50715. https://doi.org/10.2196/50715
Showail, Ahmad & Shambour, Moh'D Khaled & Jaradat, Hosam & Abu-Hashem, Muhannad & Aldhubaib, Hani. (2025). Assessing the Compliance of Mobile Applications with Personal Data Privacy Regulations: An Analytical Study. Engineering, Technology & Applied Science Research. 15. 24746-24751. 10.48084/etasr.11214.
Aljedaani, Bakheet & Ahmad, Aakash & Zahedi, Mansooreh & Ali Babar, Muhammad. (2022). An Empirical Study on Secure Usage of Mobile Health Apps: The Attack Simulation Approach. 10.48550/arXiv.2211.07585.
Ni, Y. X., Liu, X. H., He, L., Wen, Y., & You, G. Y. (2024). Mobile Application-Based Interventions for People with Heart Failure: A Systematic Review and Meta-Analysis. Journal of nursing management, 2024, 6859795. https://doi.org/10.1155/2024/6859795
Ghani, Zartashia & Jarl, Johan & Berglund, Johan & Andersson, Martin & Anderberg, Peter. (2020). The Cost-Effectiveness of Mobile Health (mHealth) Interventions for Older Adults: Systematic Review. International journal of environmental research and public health. 17.
Schirmann, F., Kanehl, P., & Jones, L. (2022). What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients. Nutrients, 14(14), 2999. https://doi.org/10.3390/nu14142999
Hurkmans, E., Matthys, C., Bogaerts, A., Scheys, L., Devloo, K., & Seghers, J. (2018). Face-to-Face Versus Mobile Versus Blended Weight Loss Program: Randomized Clinical Trial. JMIR mHealth and uHealth, 6(1), e14. https://doi.org/10.2196/mhealth.7713
Zimmermann, M., Greenberg, L., & Breland, J. Y. (2024). Engagement and Use of a Blended mHealth Intervention for Health Behavior Change. International journal of behavioral medicine, 31(2), 284–291. https://doi.org/10.1007/s12529-023-10182-1
Ju, H., Kang, E., Kim, Y., Ko, H., & Cho, B. (2022). The Effectiveness of a Mobile Health Care App and Human Coaching Program in Primary Care Clinics: Pilot Multicenter Real-World Study. JMIR mHealth and uHealth, 10(5), e34531. https://doi.org/10.2196/34531
Giebel, G. D., Speckemeier, C., Schrader, N. F., Abels, C., Plescher, F., Hillerich, V., Wiedemann, D., Börchers, K., Wasem, J., Blase, N., & Neusser, S. (2024). Quality assessment of mHealth apps: a scoping review. Frontiers in health services, 4, 1372871. https://doi.org/10.3389/frhs.2024.1372871
Alfawzan, N., Christen, M., Spitale, G., & Biller-Andorno, N. (2022). Privacy, Data Sharing, and Data Security Policies of Women's mHealth Apps: Scoping Review and Content Analysis. JMIR mHealth and uHealth, 10(5), e33735. https://doi.org/10.2196/33735
Tangari, G., Ikram, M., Ijaz, K., Kaafar, M. A., & Berkovsky, S. (2021). Mobile health and privacy: cross sectional study. BMJ (Clinical research ed.), 373, n1248. https://doi.org/10.1136/bmj.n1248
Shojaei, Parisasadat & Vlahu-Gjorgievska, Elena & Chow, Yang-Wai. (2025). Enhancing privacy in mHealth Applications: A User-Centric model identifying key factors influencing Privacy-Related behaviours. International Journal of Medical Informatics. 199. 105907. 10.1016/j.ijmedinf.2025.105907.
European Medicines Agency (2024). mHealth data for real world evidence in regulatory decision-making: expert report for HMA-EMA Big Data Steering Group.
Lehmann, M., Jones, L., & Schirmann, F. (2024). App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data. Journal of medical Internet research, 26, e45469. https://doi.org/10.2196/45469
Torous, J., Linardon, J., Goldberg, S. B., Sun, S., Bell, I., Nicholas, J., Hassan, L., Hua, Y., Milton, A., & Firth, J. (2025). The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality. World psychiatry: official journal of the World Psychiatric Association (WPA), 24(2), 156–174. https://doi.org/10.1002/wps.21299
Hartch, C. E., Dietrich, M. S., & Stolldorf, D. P. (2023). Effect of a Medication Adherence Mobile Phone App on Medically Underserved Patients with Chronic Illness: Preliminary Efficacy Study. JMIR formative research, 7, e50579. https://doi.org/10.2196/50579
Kanai, M., Miki, T., Sakoda, T., & Hagiwara, Y. (2025). The Effect of Combining mHealth and Health Professional-Led Intervention for Improving Health-Related Outcomes in Chronic Diseases: Systematic Review and Meta-Analysis. Interactive journal of medical research, 14, e55835. https://doi.org/10.2196/55835
Machado, B., Quimbaya, P., Bustos, R. H., Jaimes, D., Cortes, K., Vargas, D., & Perdomo, L. (2024). Assessment of Medication Adherence Using Mobile Applications in Chronic Obstructive Pulmonary Disease: A Scoping Review. International journal of environmental research and public health, 21(10), 1265. https://doi.org/10.3390/ijerph21101265
Copyright (c) 2025 Joanna Barwacz, Dagmara Gładysz, Magdalena Adamik, Layla Settaf-Cherif, Katarzyna Malinowska, Elhatra Settaf-Cherif
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
