Abstract
Mobile healthcare applications can empower users to self-monitor their health conditions without the need to visit any medical centre. However, the lack of attention on engagement aspects of mobile healthcare applications often result in users choosing to uninstall the application after the first usage experience. This results in failure of effective prolonged personalised healthcare, especially for users with chronic disease related to weather conditions such as asthma and eczema which require long-term monitoring and self-care. Therefore, this paper aims to identify the pattern of application user engagement with a weather-based mobile healthcare application through cohort retention analysis. Enhancement features for improving the engagement of personalised healthcare can provide meaningful insight. The proposed application allows the patient to conduct disease control tests to check the severity of their condition on a daily basis. To measure the application engagement, we distribute the mobile application designed for primary testing over a period of ten days. Based on the primary testing, data related to retention rate and the number of control test reported were collected via Firebase Analytic to determine the application engagement. Subsequently, we apply cohort analysis using a machine learning clustering technique implemented in Python to identify the pattern of the engagement by application users. Finally, useful insights were analysed and implemented as enhancement features within the application for improving the personalised weather-based mobile healthcare. The findings in this paper can assist machine learning facilitators design effective use policies for weather-based mobile healthcare with fundamental knowledge enhanced with personalisation and user engagement.
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References
Bertalan, M., Zsofia, D., Éva, B., Bence, G., Zsuzsa, G.: Digital health is a cultural transformation of traditional healthcare. mHealth 3(9), 38 (2017). https://doi.org/10.21037/mhealth.2017.08.07
Fatin, A., Nobuaki, M.: Smartphone-based healthcare technology adoption in Malaysian public healthcare services. Int. J. Jpn. Assoc. Manage. Syst. 10(1), 95–104 (2018). https://doi.org/10.14790/ijams.10.95
Hammer, R.: 30 amazing mobile health technology statistics for today’s physicians (2015). https://getreferralmd.com/2015/08/mobile-healthcare-technology-statistics
Abdullah, N.: The uberisation of healthcare in Malaysia (2019). https://www.theedgemarkets.com/article/uberisation-healthcare-malaysia
Enberg, J.: What makes smartphone owners download (2016). https://www.emarketer.com/Article/What-Makes-Smartphone-Owners-Download-App/2
Tom, S.: Tactics for combining UX and CX to get clinicians and IT working together (2017). https://www.healthcareitnews.com/news/tactics-combining-ux-and-cx-get-clinicians-and-it-working-together
Varabyova, Y., Blankart, C., Greer, A., Schreyögg, J.: The determinants of medical technology adoption in different decisional systems: a systematic literature review. Health Policy 121(3), 230–242 (2017). https://doi.org/10.1016/j.healthpol.2017.01.005
Haenssgen, M., Ariana, P.: The social implications of technology diffusion: uncovering the unintended consequences of people’s health-related mobile phone use in rural India and China. World Dev. 94, 286–304 (2017). https://doi.org/10.1016/j.worlddev.2017.01.014
Zarka, N., Hinnawi, M., Dardari, A., Tayyan, M.: Patient keeper medical application on mobile phone. In: Proceedings of the 2004 International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA), Damascus, Syria, pp. 19–23. IEEE (2004). https://doi.org/10.1109/ICTTA.2004.1307599
Mielnik, P., Tokarz, K., Mrozek, D., Czekalski, P., Fojcik, M., Hjelle, A.M., Milik, M.: Monitoring of chronic arthritis patients with wearables - a report from the concept phase. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11684, pp. 229–238. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28374-2_20
Mosa, J., Yoo, I., Sheets, L.: A systematic review of healthcare applications for smartphones. BMC Med. Inform. Decis. Mak. 12, 67 (2012). https://doi.org/10.1186/1472-6947-12-67
Chekati, A., Riahi, M., Moussa, F.: Framework for self-adaptation and decision-making of smart objects. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11684, pp. 297–308. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28374-2_26
Diakou, C.M., Kokkinaki, A.I., Kleanthous, S.: A methodological approach towards crisis simulations: qualifying ci-enabled information systems. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 569–578. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67074-4_55
Guisado-Clavero, M., Roso-Llorach, A., López-Jimenez, T.: Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis. BMC Geriatr. 18, 16 (2018). https://doi.org/10.1186/s12877-018-0705-7
Gueye, M.L.: Modeling a knowledge-based system for cyber-physical systems: applications in the context of learning analytics. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11684, pp. 568–580. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28374-2_49
Zhang, Y., et al.: Effects of meteorological factors on daily hospital admissions for asthma in adults: a time-series analysis. PLoS ONE 9(7), e102475 (2014). https://doi.org/10.1371/journal.pone.0102475
Engebretsen, K.A., Johansen, J.D., Kezic, S., Linneberg, A., Thyssen, J.P.: The effect of environmental humidity and temperature on skin barrier function and dermatitis. J. Eur. Acad. Dermatol. Venereol. 30(2), 223–249 (2016)
Pernice, K.: F-shaped pattern of reading on the web: misunderstood, but still relevant (even on mobile) (2017). https://www.nngroup.com/articles/f-shaped-pattern-reading-web-content
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The authors would like to thank the financial support given by the Malaysian Fundamental Research Grant Scheme, FRGS/1/2019/SS06/MMU/02/4.
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Ho, SB. et al. (2020). Integrating Mobile Devices with Cohort Analysis into Personalised Weather-Based Healthcare. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_47
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