Abstract
The growing interest in wearable devices has stimulated the development of mHealth applications: users can be monitored at different levels of granularity and their data can be exploited for recommendations about different aspects of their conditions, i.e., physical, psychological, and social. To this aim, recommendation systems should be able to profile patients to suggest them the most proper actions to promote effective behavior changes. This paper presents a solution to this challenging research topic implemented in an Android app, based on the adoption of fuzzy logic to cluster users according to quantitative and qualitative variables about their physical and psychological well-being. Four classes have been obtained from the two models developed, in accordance with previous experiments. The final aim of user profiling is promoting group physical activity among users characterized by similar behaviors.
A prior version of this paper has been published in the ISD2021 Proceedings (http://aisel.aisnet.org/isd2014/proceedings2021).
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References
Armstrong, T., Bonita, R.: Capacity building for an integrated non communicable disease risk factor surveillance system in developing countries. Ethn. Dis. 13(2 Suppl 2), S13–S18 (2002)
Association, A.D., et al.: Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Supplement 1), S81–S90 (2014)
Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191 (1977)
Baretta, D., Sartori, F., Greco, A., D’Addario, M., Melen, R., Steca, P.: Improving physical activity mhealth interventions: development of a computational model of self-efficacy theory to define adaptive goals for exercise promotion. Adv. Human-Comput. Interact. 2019 (2019)
Baretta, D., et al.: Wearable devices and AI techniques integration to promote physical activity. In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, pp. 1105–1108. ACM (2016)
Bauer, A.M., et al.: Acceptability of mhealth augmentation of collaborative care: a mixed methods pilot study. Gen. Hosp. Psychiatr. 51, 22–29 (2018)
Bendixen, R.M., et al.: A user centered approach: understanding client and caregiver needs and preferences in the development of mhealth apps for self-management. JMIR mHealth uHealth 5(9), e141 (2017)
Cappon, G., et al.: Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics 6(3), 65 (2017)
Chuah, M., Jakes, G., Qin, Z.: Wifi treasure hunt: a mobile social application for staying active physically. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 631–632 (2012)
Das, S., Ghosh, P.K., Kar, S.: Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7. IEEE (2013)
Desbiens, C., Filion, M., Brien, M.C., Hogue, J.C., Laflamme, C., Lemieux, J.: Impact of physical activity in group versus individual physical activity on fatigue in patients with breast cancer: a pilot study. The Breast 35, 8–13 (2017)
Dijkstra, A.: The psychology of tailoring-ingredients in computer-tailored persuasion. Soc. Pers. Psychol. Compass 2(2), 765–784 (2008)
Farao, J., Malila, B., Conrad, N., Mutsvangwa, T., Rangaka, M.X., Douglas, T.S.: A user-centered design framework for mhealth. PLOS ONE 15(8), 1–18 (08 2020)
Fenicio, A., Calvary, G., Laurillau, Y., Vanderdonckt, J.: Prosper: modeling the change, driving the persuasion. In: Actes de la 28ième conference francophone sur l’Interaction Homme-Machine, pp. 59–69 (2016)
Floch, J., et al.: User needs in the development of a health app ecosystem for self-management of cystic fibrosis: user-centered development approach. JMIR mHealth uHealth 6(5), e113 (2018)
Gomes, E., et al.: Effects of a group physical activity program on physical fitness and quality of life in individuals with schizophrenia. Ment. Health Phys. Act. 7(3), 155–162 (2014)
Gosetto, L., Ehrler, F., Falquet, G.: Personalization dimensions for mhealth to improve behavior change: a scoping review. Stud. Health Technol. Inf. 275, 77–81 (2020)
Kadhim, M.A., Alam, M.A., Kaur, H.: Design and implementation of fuzzy expert system for back pain diagnosis. Int. J. Innov. Technol. Creat. Eng. 1(9), 16–22 (2011)
Khansa, L., et al.: Health information technologies for patients with diabetes. Technol. Soc. 44, 1–9 (2016)
Khosravi, P., Ghapanchi, A.H.: Investigating the effectiveness of technologies applied to assist seniors: a systematic literature review. Int. J. Med. Inf. 85(1), 17–26 (2016)
Khozeimeh, F., et al.: An expert system for selecting wart treatment method. Comput. Biol. Med. 81, 167–175 (2017)
Liew, M.S., Zhang, J., See, J., Ong, Y.L.: Usability challenges for health and wellness mobile apps: mixed-methods study among mhealth experts and consumers. JMIR mHealth uHealth 7(1), e12160 (2019)
Maillot, P., Perrot, A., Hartley, A.: Effects of interactive physical-activity videogame training on physical and cognitive function in older adults. Psychol. Aging 27(3), 589 (2012)
Mauseth, R., et al.: Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. J. Diabetes Sci. Technol. 4(4), 913–922 (2010)
Michie, S., et al.: The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann. Behav. Med. 46(1), 81–95 (2013)
Milani, R.V., Bober, R.M., Lavie, C.J.: The role of technology in chronic disease care. Prog. Cardiovasc. Dis. 58(6), 579–583 (2016)
Milani, R.V., Lavie, C.J.: Health care 2020: reengineering health care delivery to combat chronic disease. Am. J. Med. 128(4), 337–343 (2015)
Ojokoh, B., Omisore, M., Samuel, O., Ogunniyi, T.: A fuzzy logic based personalized recommender system. Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS) 2(5), 1008–1015 (2012)
Rodriguez, N.D., et al.: Couch potato or gym addict? Semantic lifestyle profiling with wearables and knowledge graphs. In: 6th Workshop on Automated Knowledge Base Construction, AKBC@NIPS 2017, Long Beach, California, USA, December 8, 2017. OpenReview.net (2017)
Sartori, F., et al.: Virtual round table knights for the treatment of chronic diseases. J. Reliab. Intell. Env. 5(3), 131–143 (2019)
Sartori, F., Melen, R., Pinardi, S.: Cultivating virtual communities of practice in kafka. Data Technol. Appl. 52(1), 34–57 (2018)
Schutzer, K.A., Graves, B.S.: Barriers and motivations to exercise in older adults. Prev. Med. 39(5), 1056–1061 (2004)
Tikka, P., Woldemicael, B., Oinas-Kukkonen, H.: Building an app for behavior change: case rightontime. In: BCSS@ PERSUASIVE, pp. 3–14 (2016)
Troussas, C., Krouska, A., Sgouropoulou, C.: Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Comput. Educ. 144, 103698 (2020)
Vo, V., Auroy, L., Sarradon-Eck, A.: Patients’ perceptions of mhealth apps: Meta ethnographic review of qualitative studies. JMIR Mhealth Uhealth 7(7), e13817 (Jul 2019)
Woldaregay, A.Z., et al.: Motivational factors for user engagement with mhealth apps. In: pHealth, pp. 151–157 (2018)
Yusif, S., Soar, J., Hafeez-Baig, A.: Older people, assistive technologies, and the barriers to adoption: a systematic review. Int. J. Med. Inf. 94, 112–116 (2016)
Zhang, D., Adipat, B.: Challenges, methodologies, and issues in the usability testing of mobile applications. Int. J. Human-Comput. Interact. 18(3), 293–308 (2005)
Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G., Lu, J.: A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf. Sci. 235, 117–129 (2013)
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Sartori, F., Tonelli, L.L. (2022). Fuzzy Personalization of Mobile Apps: A Case Study from mHealth Domain. In: Insfran, E., et al. Advances in Information Systems Development. Lecture Notes in Information Systems and Organisation, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-030-95354-6_6
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DOI: https://doi.org/10.1007/978-3-030-95354-6_6
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