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	Analysis and control of user engagement in personalized mobile assisting software for chronic disease patients</div>

Maxim A. Kabyshev, Kovalchuk Sergey V.

2023 , VOLUME 23, NUMBER 2 ( March-April )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2023-23-2-331-339

Analysis and control of user engagement in personalized mobile assisting software for chronic disease patients

M. A. Kabyshev, S. V. Kovalchuk

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Kabyshev M.V., Kovalchuk S.V. Analysis and control of user engagement in personalized mobile assisting software for chronic disease patients. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 331–339. doi: 10.17586/2226-1494-2023-23-2-331-339

Abstract

Existing solutions for patients support in mobile apps do not allow customization of the user interface to the needs of a particular user. It reduces the involvement of patients in the process of using the system. The lack of information leads to a decrease in the quality of treatment and the emergence of potential complications. The paper proposes a variant of a new interactive mobile patient support system. This technology allows patients to enter data about their health into a mobile application and track the dynamics in time, and doctors can monitor the course of treatment remotely. Models for tracking user engagement, such as the Cox proportional hazards model and the random effects model, are considered and demonstrated. The use of A/B testing to improve user experience is analyzed. The architecture of the mobile application, web application, and their interaction was developed and implemented. Risk assessment models for patients with chronic diseases have been built. The work of interactive user support technology within a single interactive system is shown. The developed approaches can be used to build a wide range of telemedicine solutions that support interaction with both medical specialists and patients within the framework of the 4P approach in medicine.

Keywords: mobile health application, remote assisting, mobile application, predictive models, user engagement, user experience, chronic disease patients

Acknowledgements. This work was supported by the Ministry of Science and Higher Education of the Russian Federation, Goszadanie (State Assignment) No. 2019-1339.

References

Reynolds R., Dennis S., Hasan I., Slewa J., Chen W., Tian D., Bobba S., Zwar N. A systematic review of chronic disease management interventions in primary care. BMC Family Practice, 2018, vol. 19, no. 1, pp. 11. https://doi.org/10.1186/s12875-017-0692-3
Davenport T., Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal, 2019, vol. 6, no. 2, pp. 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Gurupur V.P., Wan T.T.H. Challenges in implementing mHealth interventions: a technical perspective. mHealth, 2017, vol. 3, pp. 32. https://doi.org/10.21037/mhealth.2017.07.05
Devi B.R., Syed-Abdul S., Kumar A., Iqbal U., Nguyen P.-A., Li Y.-C., Jian W.-S. MHealth: An updated systematic review with a focus on HIV/AIDS and tuberculosis long term management using mobile phones. Computer Methods and Programs in Biomedicine, 2015, vol. 122, no. 2. pp. 257–265. https://doi.org/10.1016/j.cmpb.2015.08.003
Kan Y.-C., Chen K.-H., Lin H.-C. Developing a ubiquitous health management system with healthy diet control for metabolic syndrome healthcare in Taiwan. Computer Methods and Programs in Biomedicine, 2017, vol. 144, pp. 37–48. https://doi.org/10.1016/j.cmpb.2017.02.027
Luna-Perejon F., Malwade S., Styliadis C., Civit J., Cascado-Caballero D., Konstantinidis E., Abdul S.S., Bamidis P.D., Civit A., Li Y.-C. Evaluation of user satisfaction and usability of a mobile app for smoking cessation. Computer Methods and Programs in Biomedicine, 2019, vol. 182, pp. 105042. https://doi.org/10.1016/j.cmpb.2019.105042
Georgsson M., Staggers N. Quantifying usability: An evaluation of a diabetes mHealth system on effectiveness, efficiency, and satisfaction metrics with associated user characteristics. Journal of the American Medical Informatics Association, 2016, vol. 23, no. 1, pp. 5–11. https://doi.org/10.1093/jamia/ocv099
Kabyshev M.V., Kovalchuk S.V. Development of personalized mobile assistant for chronic disease patients: diabetes mellitus case study. Procedia Computer Science, 2019, vol. 156, pp. 123–133. https://doi.org/10.1016/j.procs.2019.08.187
Camm A.J., Kirchhof P., Lip G.Y.H. et al. Guidelines for the management of atrial fibrillation: The task force for the management of atrial fibrillation of the European Society of Cardiology (ESC). European Heart Journal, 2010, vol. 31, no. 19, pp. 2369–429. https://doi.org/10.1093/eurheartj/ehq278
Bin Azhar F.A., Dhillon J.S. A systematic review of factors influencing the effective use of mHealth apps for self-care. Proc. of the 3^rd International Conference on Computer and Information Sciences (ICCOINS), 2016, pp. 191–196. https://doi.org/10.1109/iccoins.2016.7783213
Bourgeois F.T., Simons W.W., Olson K., Brownstein J.S., Mandl K.D. Evaluation of influenza prevention in the workplace using a personally controlled health record: Randomized controlled trial. Journal of Medical Internet Research, 2008, vol. 10, no. 1, pp. e5. https://doi.org/10.2196/jmir.984
Harden J.J., Kropko J. Simulating duration data for the cox model. Political Science Research and Methods, 2019, vol. 7, no. 4, pp. 921–928. https://doi.org/10.1017/psrm.2018.19
Lee K., Kwon H., Lee B., Lee G., Lee J.H., Park Y.R., Shin S.-Y. Effect of self-monitoring on long-term patient engagement with mobile health applications. PLoS One, 2018, vol. 13, no. 7, pp. e0201166. https://doi.org/10.1371/journal.pone.0201166

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