Impact of Physical Health and Exercise Activity on Online User Experience: Elderly People and High Risk for Diabetes

  • Harri Oinas-KukkonenEmail author
  • Li Zhao
  • Heidi Enwald
  • Maija-Leena Huotari
  • Riikka Ahola
  • Timo Jämsä
  • Sirkka Keinänen-Kiukaanniemi
  • Juhani Leppäluoto
  • Karl-Heinz Herzig
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 32)


This article studies how an individual’s physical wellbeing contributes to one’s online user experience. The study subjects were elderly people at high risk for type 2 diabetes. The results suggest that the web usage experience of these pre-diabetic individuals is related to their physical health status and level of physical activity. Those with a better physical health status were more likely to feel ease of orientation in their web usage, and those with more frequent regular physical activity were more likely to perceive pleasure in navigating the web. In practice, variation in physical health and activity levels between individuals could, and should. be addressed in designing systems and services. In more general, studying user experience on par with biochemical measurements provides an exciting combination of research methods and paves the way for new design practices.


User experience Flow Webflow Physical health Physical exercise Type 2 diabetes 


  1. 1.
    Danaei, G., Finucane, M. M., Lu, Y., Singh, G. M., Cowan, M. J., Paciorek, C. J., et al. (2011). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: Systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet, 378(9785), 31–40.CrossRefGoogle Scholar
  2. 2.
    WHO. (1999). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus (WHO/NCD/NCS/99.2). Geneva, Switzerland: World Health Organization.Google Scholar
  3. 3.
    Uusitupa, M., Tuomilehto, J., & Puska, P. (2011). Are we really active in the prevention of obesity and type 2 diabetes at the community level? Nutrition, Metabolism and Cardiovascular Diseases, 21(5), 380–389.CrossRefGoogle Scholar
  4. 4.
    Farmer, A. J., Levy, J. C., & Turner, R. C. (1999). Knowledge of risk of developing diabetes mellitus among siblings of type 2 diabetes patients. Diabetic Medicine, 16(3), 233–237.CrossRefGoogle Scholar
  5. 5.
    Tuomilehto, J., Lindström, J., Eriksson, J. G., Valle, T. T., Hämäläinen, H., Ilanne-Parikka, P., et al. (2001). Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. The New England Journal of Medicine, 344, 1343–1350.CrossRefGoogle Scholar
  6. 6.
    van Esch, S. C., Cornel, M. C., & Snoek, F. J. (2006). Type 2 diabetes and inheritance: What information do diabetes organizations provide on the Internet? Diabetic Medicine, 23(11), 1233–1238.CrossRefGoogle Scholar
  7. 7.
    Satterfield, D., Jenkins, C., Bodnar, B., Constance, A., & Sisson, E. (2008). Diabetes education and public health. Diabetes Educator, 34(1), 45–48.CrossRefGoogle Scholar
  8. 8.
    Lustria, M. L., Cortese, J., Noar, S. M., & Glueckauf, R. L. (2009). Computer-tailored health interventions delivered over the web: Review and analysis of key components. Patient Education and Counseling, 74(2), 156–173.CrossRefGoogle Scholar
  9. 9.
    Yap, T. L., & Davis, L. S. (2008). Physical activity: The science of health promotion through tailored messages. Rehabilitation Nursing, 33(2), 55–62.CrossRefGoogle Scholar
  10. 10.
    Hawkins, R. P., Kreuter, M., Resnicow, K., Fishbein, M., & Dijkstra, A. (2008). Understanding tailoring in communicating about health. Health Education Research, 23(3), 454–466.CrossRefGoogle Scholar
  11. 11.
    Rimer, B. K., & Kreuter, M. W. (2006). Advancing tailored health communication: A persuasion and message effects perspective. Journal of Communication, 56, 184–201.CrossRefGoogle Scholar
  12. 12.
    Enwald, H. P. K., & Huotari, M. L. A. (2010). Preventing the obesity epidemic by second generation tailored health communication: An interdisciplinary review. Journal of Medical Internet Research, 12(2), e24.CrossRefGoogle Scholar
  13. 13.
    Enwald, H., Niemelä, R., Keinänen-Kiukaanniemi, S., Leppäluoto, J., Jämsä, T., Herzig, K. H., et al. (2012). Human information behaviour and physiological measurements as a basis to tailor health information. An explorative study in a physical activity intervention among prediabetic individuals in Northern Finland. Health Information and Libraries Journal, 29(2), 131–140.CrossRefGoogle Scholar
  14. 14.
    Enwald, H., Kortelainen, T., Leppäluoto, J., Keinänen-Kiukaanniemi, S., Jämsä, T., Oinas-Kukkonen, H., et al. (2013). Perceptions of fear appeal and preferences for feedback in tailored health communication. An explorative study among prediabetic individuals. Information Research: An International Electronic Journal, 18(3), 584.Google Scholar
  15. 15.
    Oinas-Kukkonen, H. (2013). A foundation for the study of behavior change support systems. Personal and Ubiquitous Computing, 17(6), 1223–1235.CrossRefGoogle Scholar
  16. 16.
    Kraft, P., Drozd, F., & Olsen, E. (2009). ePsychology: Designing theory-based health promotion interventions. Communications of the Association for Information Systems, 24(24).Google Scholar
  17. 17.
    Hoffman, D., & Novak, T. (1997). A new marketing paradigm for electronic commerce. The Information Society, 13, 43–54.CrossRefGoogle Scholar
  18. 18.
    Finneran, C. M., & Zhang, P. (2003). A person-artifact-task (PAT) model of flow antecedents in computer-mediated environments. International Journal of Human-Computer Studies, 59, 475–496.CrossRefGoogle Scholar
  19. 19.
    Kamis, A., Koufaris, M., & Stern, T. (2008). Using an attribute-based DSS for user-customized products online: An experimental investigation. MIS Quarterly, 32(1), 159–177.CrossRefGoogle Scholar
  20. 20.
    Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model, and system features. Communications of The Association For Information Systems, 24(28), 485–500.Google Scholar
  21. 21.
    Oinas-Kukkonen, H. (2000). Balancing the vendor and consumer requirements for electronic shopping systems. Information Technology and Management, 1(1&2), 73–84.CrossRefGoogle Scholar
  22. 22.
    Case, D. O. (2012). Looking for information. A survey of research on information seeking, needs, and behavior (3rd ed.). Bingley, UK: Emerald.Google Scholar
  23. 23.
    Csikszentmihalyi, M. (1977). Beyond boredom and anxiety. San Francisco, CA: Jossey-Bass.Google Scholar
  24. 24.
    Choi, D. H., Kim, J., & Kim, S. H. (2007). ERP training with a web-based electronic learning system: The flow theory perspective. International Journal of Human-Computer Studies, 65, 223–243.CrossRefGoogle Scholar
  25. 25.
    Lu, Y., Zhou, T., & Wan, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39.CrossRefGoogle Scholar
  26. 26.
    Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123–129.CrossRefGoogle Scholar
  27. 27.
    Hoffman, D., & Novak, T. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, (July), 50–68.Google Scholar
  28. 28.
    Oinas-Kukkonen, H., Räisänen, T., Leiviskä, K., Seppänen, M., & Kallio, M. (2009). Physicians’ user experiences of mobile pharmacopoeias and evidence-based medical guidelines. International Journal of Healthcare Information Systems and Informatics, 4(2), 57–68.CrossRefGoogle Scholar
  29. 29.
    Parvinen, P., Oinas-Kukkonen, H., & Kaptein, M. (2015). E-selling: A new avenue of research for service design and online engagement. Electronic Commerce Research and Applications.Google Scholar
  30. 30.
    Adamo, K. B., Prince, S. A., Tricco, A. C., Connor-Gorder, S., & Tremblay, M. (2009). A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: A systematic review. International Journal of Pediatric Obesity, 4(1), 2–27.CrossRefGoogle Scholar
  31. 31.
    De Cocker, K., Spittaels, H., Cardon, G., De Bourdeaudhuij, I., & Vandelanotte, C. (2012). Web-based, computer-tailored, pedometer-based physical activity advice: Development, dissemination through general practice, acceptability, and preliminary efficacy in a randomized controlled trial. Journal of Medical Internet Research, 14(2), e53.CrossRefGoogle Scholar
  32. 32.
    Plotnikoff, R. C., & Karunamuni, N. (2011). Steps towards permanently increasing physical activity in the population. Current Opinion in Psychiatry, 24, 162–167.CrossRefGoogle Scholar
  33. 33.
    Short, C. E., James, E. L., Plotnikoff, R. C., & Girgis, A. (2011). Efficacy of tailored-print interventions to promote physical activity: A systematic review of randomized trials. International Journal of Behavioral Nutrition and Physical Activity, 8, 113.CrossRefGoogle Scholar
  34. 34.
    Broekhuizen, K., Kroeze, W., van Poppel, M. N. M., Oenema, A., & Brug, J. (2012). A systematic review of randomized controlled trials on the effectiveness of computer-tailored physical activity and dietary behavior promotion programs: An update. Annals of Behavioral Medicine, 44, 259–286.CrossRefGoogle Scholar
  35. 35.
    Kroeze, W., Werkman, A., & Brug, J. (2006). A systematic review of randomized trials on the effectiveness of computer-tailored education on physical activity and dietary behaviors. Annals of Behavioral Medicine, 31(3), 205–223.CrossRefGoogle Scholar
  36. 36.
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.CrossRefGoogle Scholar
  37. 37.
    Knowler, W. C., Barrett-Connor, E., Fowler, S. E., Hamman, R. F., Lachlin, J. M., Walker, E. A., et al. (Diabetes Prevention Program Research Group). (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. The New England Journal of Medicine, 346, 393–403.Google Scholar
  38. 38.
    Blair, S. N., & Morris, J. N. (2009). Healthy hearts—and the universal benefits of being physically active: Physical activity and health. Annals of Epidemiology, 19, 253–256.CrossRefGoogle Scholar
  39. 39.
    Vainionpää, A., Korpelainen, R., Kaikkonen, H., Knip, M., Leppäluoto, J., & Jämsä, T. (2007). Effect of impact exercise on physical performance and cardiovascular risk factors. Medicine and Science in Sports and Exercise, 39, 756–763.CrossRefGoogle Scholar
  40. 40.
    Herzig, K. H., Ahola, R., Leppäluoto, J., Jokelainen, J., Jämsä, T., Keinänen-Kiukaanniemi, S. (2013). Light physical activity determined by a motion sensor decreases insulin resistance, improves lipid homeostasis and reduces visceral fat in high risk subjects: PreDiabEx study RCT. International Journal of Obesity, November 28. Scholar
  41. 41.
    Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS Version 2.0 M2.
  42. 42.
    Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G.A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahway, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
  43. 43.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 383–388.CrossRefGoogle Scholar
  44. 44.
    Werts, C. E., Linn, R. L., & Jöreskög, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34(1), 25–33.CrossRefGoogle Scholar
  45. 45.
    Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling techniques and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–79.Google Scholar
  46. 46.
    Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.CrossRefGoogle Scholar
  47. 47.
    Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.CrossRefGoogle Scholar
  48. 48.
    Wild, S., Roglic, G., Green, A., Sicree, R., & King, H. (2004). Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care, 27(5), 1047–1053.CrossRefGoogle Scholar
  49. 49.
    Bech-Larsen, T., & Scholderer, J. (2010). Functional foods in Europe: Consumer research, market experiences and regulatory aspects. Trends in Food Science & Technology, 18, 231–234.CrossRefGoogle Scholar
  50. 50.
    Assaf, A. R., Parker, D., Lapane, K. L., Coccio, E., Evangelou, E., & Carleton, R. A. (2003). Does the Y chromosome make a difference? Gender differences in attempts to change cardiovascular disease risk factors. Journal of Women’s Health, 12(4), 321–330.CrossRefGoogle Scholar
  51. 51.
    Goh, J.M,. & Agarwal, R. (2008). Taking charge of your health: The drivers of enrollment and continued participation in online health intervention programs. In Proceedings of the 41st Hawaii International Conference on System Sciences 2008.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Harri Oinas-Kukkonen
    • 1
    Email author
  • Li Zhao
    • 1
  • Heidi Enwald
    • 2
  • Maija-Leena Huotari
    • 2
  • Riikka Ahola
    • 3
  • Timo Jämsä
    • 3
  • Sirkka Keinänen-Kiukaanniemi
    • 4
  • Juhani Leppäluoto
    • 5
  • Karl-Heinz Herzig
    • 5
  1. 1.Faculty of Information Technology and Electrical Engineering, Oulu Advanced Research on Service and Information SystemsUniversity of OuluOuluFinland
  2. 2.Faculty of Humanities, Information StudiesUniversity of OuluOuluFinland
  3. 3.Medical Research Center, Research Unit of Medical Imaging, Physics and TechnologyOulu University Hospital and University of OuluOuluFinland
  4. 4.Institute of Health SciencesOulu University Hospital and University of OuluOuluFinland
  5. 5.Medical Research Center, Institute of Biomedicine and Biocenter Oulu, PhysiologyOulu University Hospital and University of OuluOuluFinland

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