Abstract
Samsung Health, Samsung’s primary health-related app, is now one of the most comprehensive wellness applications, as it offers a personalized health coach, tracks daily activities according to users’ goals, and charts users’ activity to promote regular exercise. This study aimed to examine Korean consumers’ responses to healthcare apps, specifically Samsung Health, to enhance consumer satisfaction with such apps. From April 21, 2015 to January 3, 2020, 11,361 user reviews of the application were collected from the Google Play website. The final tidy dataset was composed of 7,407 reviews. We conducted hierarchical clustering, CONCOR analysis, and semantic network analysis with R.3.5.3 and Ucinet6. First, the hierarchical clustering analysis resulted in 39 clusters. Second, the CONCOR analysis revealed four clusters: benefits, costs, health care, and system error. Third, the betweenness centrality of each node was reviewed to identify the importance of terms in the semantic network. Consumers appreciated the benefits of being able to achieve their goals through the measurement of their physical activities and competition with others. However, consumers pointed out inaccurate measurements, synchronization errors, and a lack of information as points for improvement for Samsung Health. Health is an essential factor in improving consumers’ quality of life, and while Samsung Health provides useful functions, the improvement of basic errors in the application could enhance consumer satisfaction.
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References
Statista Homepage. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide. Accessed 10 Mar 2020
Siddiqui, S.A., Zhang, Y., Feng, Z.Q., Kos, A.: A pulse rate estimation algorithm using PPG and smartphone camera. J. Med. Syst. 40(5), 126 (2016)
Tayfur, İ., Afacan, M.A.: Reliability of smartphone measurements of vital parameters: a prospective study using a reference method. Am. J. Emerg. Med. 37(8), 1527–1530 (2019)
Ding, X., Nassehi, D., Larson, E.C.: Measuring oxygen saturation with smartphone cameras using convolutional neural networks. IEEE J. Biomed. Health Inf. 23(6), 2603–2610 (2018)
Junior, A.D., Murali, S., Rincon, F., Atienza, D.: Methods for reliable estimation of pulse transit time and blood pressure variations using smartphone sensors. Microprocess. Microsyst. 46, 84–95 (2016)
Gesche, H., Grosskurth, D., Küchler, G., Patzak, A.: Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. Eur. J. Appl. Physiol. 112(1), 309–315 (2012)
Gaurav, A., Maheedhar, M., Tiwari, V.N., Narayanan, R.: Cuff-less PPG based continuous blood pressure monitoring—A smartphone based approach. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 607–610. IEEE (2016)
Gu, F., Khoshelham, K., Shang, J., Yu, F., Wei, Z.: Robust and accurate smartphone-based step counting for indoor localization. IEEE Sens. J. 17(11), 3453–3460 (2017)
Menhour, I., Fergani, B., Abidine, M.B.: E-health human activity recognition scheme using smartphone’s data. In: Hajji, B., Tina, G.M., Ghoumid, K., Rabhi, A., Mellit, A. (eds.) ICEERE 2018. LNEE, vol. 519, pp. 128–134. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1405-6_17
Nemcova, A., et al.: Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone. Biomed. Signal Process. Control 59, 101928 (2020)
Farshchian, Babak A., Vilarinho, T.: Which mobile health toolkit should a service provider choose? A comparative evaluation of Apple HealthKit, Google fit, and Samsung digital health platform. In: Braun, A., Wichert, R., Maña, A. (eds.) AmI 2017. LNCS, vol. 10217, pp. 152–158. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56997-0_12
Gavalas, D., Economou, D.: Development platforms for mobile applications: Status and trends. IEEE Softw. 28(1), 77–86 (2011)
Anvaari, M., Jansen, S.: Evaluating architectural openness in mobile software platforms. In: Proceedings of the Fourth European Conference on Software Architecture: Companion, pp. 85–92 (2010)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Stanley, W., Faust, K.: Social Network Analysis: Methods and Applications. Cambrigdge University, Cambridge (1994)
Raad, E., Chbeir, R.: Socio-graph representations, concepts, data, and analysis. In: Alhajj, P.R., Rokne, P.J. (eds.) Encyclopedia of Social Network Analysis and Mining, pp. 1936–1946. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-7131-2_402
Heymann, S.: Gephi. In: Alhajj, P.R., Rokne, P.J. (eds.) Encyclopedia of Social Network Analysis and Mining, pp. 612–625. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-6170-8_299
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Ha, H.R., Suk, J., Deng, Y., Huang, Y., Lee, S. (2020). How Consumers Utilize Healthcare Apps? – Focusing on Samsung Health. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1224. Springer, Cham. https://doi.org/10.1007/978-3-030-50726-8_56
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