ABSTRACT
Personalization is a key aspect when developing applications targeting health behaviour change. However, the use of personalized mobile interventions for lifestyle behaviour is still in its infancy. Based on our former research on mobile applications to support cardiac patients in health behaviour change, we identified four key motivations to enhance the personalization offered in applications targeting health behaviour change. In this paper, we propose a mixed-methods approach, using both qualitative and quantitative data collected in prior studies, to apply personalization in the design of health applications. Our approach consists of five steps: 1) collecting data for personalization, 2) detecting patient profiles using clustering methods, 3) understanding patient profiles using a graphical representation, 4) describing patient profiles using personas, and 5) personalizing a health application according to patient profiles. One of the major strengths of our approach is that it combines established HCI techniques such as personas and data visualization techniques with methods from big data analytics and artificial intelligence to identify ways to personalize health applications. We conclude by presenting future directions to apply personalization in the domain of health technologies.
- Bushra Alsaadi and Dimah Alahmadi. 2021. The Use of Persona Towards Human-Centered Design in Health Field: Review of Types and Technologies. In 2021 International Conference on e-Health and Bioengineering (EHB). 1–4.Google Scholar
- Marco Ambrosetti, Ana Abreu, Ugo Corrà, Constantinos H Davos, Dominique Hansen, Ines Frederix, Marie C Iliou, Roberto FE Pedretti, Jean-Paul Schmid, Carlo Vigorito, 2021. Secondary prevention through comprehensive cardiovascular rehabilitation: From knowledge to implementation. 2020 update. A position paper from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology. European journal of preventive cardiology 28, 5 (2021), 460–495.Google Scholar
- John M Carroll. 2003. Making use: scenario-based design of human-computer interactions. MIT press.Google ScholarDigital Library
- Haiyan Fan and Marshall Scott Poole. 2006. What is personalization? Perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce 16, 3-4(2006), 179–202.Google ScholarCross Ref
- Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (Dec. 2016), 19 pages.Google ScholarDigital Library
- Mieke Haesen. 2011. User-Centered Process Framework and Techniques to Support the Realization of Interactive Systems by Multi-Disciplinary Teams.Google Scholar
- Richard J. Holden, Anand Kulanthaivel, Saptarshi Purkayastha, Kathryn M. Goggins, and Sunil Kripalani. 2017. Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure. International Journal of Medical Informatics 108 (2017), 158–167.Google ScholarCross Ref
- Paulus Kirchhof, ESC CRT R&D, and European Affairs Work Shop on Personalized Medicine et al.2014. The continuum of personalized cardiovascular medicine: a position paper of the European Society of Cardiology. European Heart Journal 35, 46 (08 2014), 3250–3257.Google ScholarCross Ref
- Predrag Klasnja, Shawna Smith, Nicholas J Seewald, Andy Lee, Kelly Hall, Brook Luers, Eric B Hekler, and Susan A Murphy. 2019. Efficacy of contextually tailored suggestions for physical activity: a micro-randomized optimization trial of HeartSteps. Annals of Behavioral Medicine 53, 6 (2019), 573–582.Google ScholarCross Ref
- James MacQueen 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1. Oakland, CA, USA, 281–297.Google Scholar
- Steve Mulder and Ziv Yaar. 2006. The user is always right: A practical guide to creating and using personas for the web. New Riders.Google Scholar
- John Pruitt and Tamara Adlin. 2006. The Persona Lifecycle : Keeping People in Mind Throughout Product Design. Morgan Kaufmann.Google Scholar
- John Pruitt and Jonathan Grudin. 2003. Personas: practice and theory. In Proceedings of the 2003 conference on Designing for user experiences. 1–15.Google ScholarDigital Library
- Joni Salminen, Kathleen Guan, Soon-gyo Jung, Shammur A Chowdhury, and Bernard J Jansen. 2020. A literature review of quantitative persona creation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–14.Google ScholarDigital Library
- Joni Salminen, Kathleen Guan, Lene Nielsen, Soon-gyo Jung, and Bernard J Jansen. 2020. A Template for Data-Driven Personas: Analyzing 31 Quantitatively Oriented Persona Profiles. In International Conference on Human-Computer Interaction. Springer, 125–144.Google ScholarDigital Library
- Supraja Sankaran, Cindel Bonneux, Paul Dendale, and Karin Coninx. 2018. Bridging patients’ needs and caregivers’ perspectives to tailor information provisioning during cardiac rehabilitation. In Proceedings of the 32nd International BCS Human Computer Interaction Conference 32. 1–11.Google ScholarDigital Library
- Supraja Sankaran, Paul Dendale, and Karin Coninx. 2019. Evaluating the impact of the HeartHab app on motivation, physical activity, quality of life, and risk factors of coronary artery disease patients: multidisciplinary crossover study. JMIR mHealth and uHealth 7, 4 (2019), e10874.Google Scholar
- Supraja Sankaran, Ines Frederix, Mieke Haesen, Paul Dendale, Kris Luyten, and Karin Coninx. 2016. A grounded approach for applying behavior change techniques in mobile cardiac tele-rehabilitation. In Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. 1–8.Google ScholarDigital Library
- Huong Ly Tong, Juan C. Quiroz, A. Baki Kocaballi, Sandrine Chan Moi Fat, Kim Phuong Dao, Holly Gehringer, Clara K. Chow, and Liliana Laranjo. 2021. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Preventive Medicine 148(2021), 106532.Google ScholarCross Ref
- Christos Troussas, Akrivi Krouska, and Cleo Sgouropoulou. 2020. Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education 144 (2020), 103698.Google ScholarDigital Library
- S. Vosbergen, J.M.R. Mulder-Wiggers, J.P. Lacroix, H.M.C. Kemps, R.A. Kraaijenhagen, M.W.M. Jaspers, and N. Peek. 2015. Using personas to tailor educational messages to the preferences of coronary heart disease patients. Journal of Biomedical Informatics 53 (2015), 100–112.Google ScholarDigital Library
- Bernhard Wöckl, Ulcay Yildizoglu, Isabella Buber, Belinda Aparicio Diaz, Ernst Kruijff, and Manfred Tscheligi. 2012. Basic senior personas: a representative design tool covering the spectrum of European older adults. In Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility. 25–32.Google ScholarDigital Library
- World Health Organization. 2021. Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) Accessed on 09/12/2021.Google Scholar
- Lucy Yardley, Bonnie Spring, Leanne Morrison, David Crane, Kristina Curtis, Gina Merchant, Felix Naughton, and Ann Blandford. 2016. Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. American Journal of Preventive Medicine 51 (11 2016), 833–842.Google Scholar
- Lucia Yu, Ethan Benjamin, Congzhe Su, Yinlin Fu, Jon Eskreis-Winkler, Xiaoting Zhao, and Diane Hu. 2021. Personalization in E-commerce Product Search by User-Centric Ranking. (2021).Google Scholar
- Xiang Zhang, Hans-Frederick Brown, and Anil Shankar. 2016. Data-driven personas: Constructing archetypal users with clickstreams and user telemetry. In Proceedings of the 2016 CHI conference on human factors in computing systems. 5350–5359.Google ScholarDigital Library
- Haining Zhu, Hongjian Wang, and John M Carroll. 2019. Creating Persona Skeletons from Imbalanced Datasets-A Case Study using US Older Adults’ Health Data. In Proceedings of the 2019 on Designing Interactive Systems Conference. 61–70.Google ScholarDigital Library
Index Terms
- Investigating Motivations and Patient Profiles for Personalization of Health Applications for Behaviour Change
Recommendations
Generating semantically enriched user profiles for Web personalization
Traditional collaborative filtering generates recommendations for the active user based solely on ratings of items by other users. However, most businesses today have item ontologies that provide a useful source of content descriptors that can be used ...
Tutorial on Personalization for Behaviour Change
IUI '15: Proceedings of the 20th International Conference on Intelligent User InterfacesDigital behaviour interventions aim to encourage and support people to change their behaviour, for their own or communal benefits. Personalization plays an important role in this, as the most effective persuasive and motivational strategies are likely ...
Theory-Informed Design Guidelines for Shared Decision Making Tools for Health Behaviour Change
Persuasive TechnologyAbstractRecently, the design and development of persuasive applications to support behaviour change in healthcare have gained interest. However, achieving sustained behaviour change remains challenging. Shared decision making (SDM) is increasingly ...
Comments