Serious Games for Cognitive Training in Ambient Assisted Living Environments – A Technology Acceptance Perspective

  • Jan Wittland
  • Philipp Brauner
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9296)


Two technology trends address the rising costs of healthcare systems in aging societies: Serious Games for Healthcare and Ambient Assisted Living Environments. Surprisingly, these concepts are rarely combined and the users’ perception and use of Serious Games in Ambient Assisted Living environments is insufficiently understood. We present the evaluation of a serious game for stimulating cognitive abilities for elderly with regard to technology acceptance (based on the UTAUT2 model), performance and preference for an interaction device (tablet, table, wall). The results suggest that acceptance of serious games is independent of gender, technical expertise, gaming habits, and only weakly influenced by age. Determinants for acceptance are perceived fun and the feeling that the users can make playing the game a habit. Performance within the game is explained by age and previous gaming experience. All investigated interaction devices were rated as useful and easy to learn, although the wall-sized display had lower approval levels. The article concludes with guidelines for successfully introducing serious games for healthcare to residents in ambient assisted living environments.


Serious games for healthcare Ubiquitous computing Ambient assisted living Technology acceptance Design for elderly 



We thank all subjects for their willingness to participate and to share their thoughts about the game with us. Furthermore, we thank Markus Gottfried and Dirk Nettelnbreker for support during experiment and evaluation. We also thank RWTH Aachen University’s eHealth team for collaboratively building the lab in which the study was carried out and especially Felix Heidrich and Kai Kasugai for crafting the input devices used in this study. The precious comments of the anonymous reviewers are highly acknowledged.


  1. 1.
    Giannakouris, K.: Ageing characterises the demographic perspectives of the European societies (2008)Google Scholar
  2. 2.
    Ho, K.K.L., Pinsky, J.L., Kannel, W.B., Levy, D.: The epidemiology of heart failure: the framingham study. J. Am. Coll. Cardiol. 22, A6–A13 (1993)CrossRefGoogle Scholar
  3. 3.
    Health at a Glance: Europe 2012. OECD Publishing (2012)Google Scholar
  4. 4.
    Weiser, M.: The computer for the 21st century. Sci. Am. 265, 94–104 (1991)CrossRefGoogle Scholar
  5. 5.
    Kleinberger, T., Becker, M., Ras, E., Holzinger, A., Müller, P.: Ambient intelligence in assisted living: enable elderly people to handle future interfaces. In: Stephanidis, C., (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 103–112. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Mukasa, K.S., Holzinger, A., Karshmer, A.: Intelligent User Interfaces for Ambient Assisted Living. Fraunhofer IRB Verlag, Stuttgart (2008)CrossRefGoogle Scholar
  7. 7.
    Raisinghani, M.S., Benoit, A., Ding, J., Gomez, M., Gupta, K., Gusila, V., Power, D., Schmedding, O.: Ambient intelligence: changing forms of human-computer interaction and their social implications. J. Digit. Inf. 5(4) (2004)Google Scholar
  8. 8.
    Röcker, C., Ziefle, M.: Current approaches to ambient assisted living. In: International Conference on Future Information Technology and Management Science & Engineering, pp. 6–14 (2012)Google Scholar
  9. 9.
    Lahlou, S.: Identity, social status, privacy and face-keeping in digital society. Soc. Sci. Inf. 47, 299–330 (2008)CrossRefGoogle Scholar
  10. 10.
    Wilkowska, W., Ziefle, M.: Privacy and data security in e-health: requirements from the user’s perspective. Health Inf. J. 18, 191–201 (2012)CrossRefGoogle Scholar
  11. 11.
    Mynatt, E.D., Rogers, W.A.: Developing technology to support the functional independence of older adults. Ageing Int. 27, 24–41 (2001)CrossRefGoogle Scholar
  12. 12.
    Meyer, S., Mollenkopf, H.: Home technology, smart homes, and the aging user. In: Schaie, K.W., Wahl, H.-W., Mollenkopf, H., Oswald, F. (eds.) Aging Independently: Living Arrangements and Mobility, pp. 148–161. Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Ziefle, M., Röcker, C., Wilkowska, W., Kasugai, K., Klack, L., Möllering, C., Beul, S.: A multi-disciplinary approach to ambient assisted living. In: Röcker, C., Ziefle, M. (eds.) E-Health, Assistive Technologies and Applications for Assisted Living: Challenges and Solutions, pp. 76–93. Hershey, IGI Global (2011)CrossRefGoogle Scholar
  14. 14.
    Abt, C.C.: Serious Games. Reprint. University Press of America, Lanham MD (1987)Google Scholar
  15. 15.
    Michael, D., Chen, S.: Serious Games: Games That Educate, Train, and Inform. Thomson Course Technology, Boston (2006)Google Scholar
  16. 16.
    Premack, D.: Toward empirical behavior laws: I. Positive reinforcement. Psychol. Rev. 66, 219–233 (1959)CrossRefGoogle Scholar
  17. 17.
    Huizinga, J.: Homo Ludens: A Study of the Play-Element in Culture. Pantheon, New York (1939)Google Scholar
  18. 18.
    Lewis, G.N., Rosie, J.A.: Virtual reality games for movement rehabilitation in neurological conditions: how do we meet the needs and expectations of the users? Disabil. Rehabil. 34, 1880–1886 (2012)CrossRefGoogle Scholar
  19. 19.
    Macvean, A., Robertson, J.: Understanding exergame users’ physical activity, motivation and behavior over time. In: CHI 2013 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1251–1260 (2013)Google Scholar
  20. 20.
    Tanaka, K., Parker, J., Baradoy, G., Sheehan, D., Holash, J.R., Katz, L.: A comparison of exergaming interfaces for use in rehabilitation programs and research. Loading. 6, 69–81 (2012)Google Scholar
  21. 21.
    Ball, K.K., Wadley, V.G., Vance, D.E., Edwards, J.D.: Cognitive skills: training, maintenance, and daily usage. Encycl. Appl. Psychol. 1, 387–392 (2004)CrossRefGoogle Scholar
  22. 22.
    Heyn, P., Abreu, B.C., Ottenbacher, K.J.: The effects of exercise training on elderly persons with cognitive impairment and dementia: a meta-analysis. Arch. Phys. Med. Rehabil. 85, 1694–1704 (2004)CrossRefGoogle Scholar
  23. 23.
    Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley Publishing Company Inc., Reading (1975)Google Scholar
  24. 24.
    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211 (1991)CrossRefGoogle Scholar
  25. 25.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319 (1989)CrossRefGoogle Scholar
  26. 26.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46, 186–204 (2000)CrossRefzbMATHGoogle Scholar
  27. 27.
    Brauner, P., Holzinger, A., Ziefle, M.: Ubiquitous computing at its best: serious exercise games for older adults in ambient assisted living environments – a technology acceptance perspective. EAI Endorsed Trans. Serious Games 15, 1–12 (2015). doi: 10.4108/sg.1.4.e3 Google Scholar
  28. 28.
    Arning, K., Ziefle, M.: Different perspectives on technology acceptance : the role of technology type and age. HCI Usability Inclusion 5889, 20–41 (2009)CrossRefGoogle Scholar
  29. 29.
    Rogers, Y.: The changing face of human-computer interaction in the age of ubiquitous computing. In: Holzinger, A., Miesenberger, K. (eds.) USAB 2009. LNCS, vol. 5889, pp. 1–19. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Ziefle, M., Rocker, C., Holzinger, A.: Medical technology in smart homes: exploring the user’s perspective on privacy, intimacy and trust. In: IEEE (2011)Google Scholar
  31. 31.
    Fisk, A.D., Rogers, W.A.: Handbook of Human Factors and the Older Adult. Academic Press, San Diego (1997)Google Scholar
  32. 32.
    Kliegel, M., McDaniel, M.A., Einstein, G.O.: Plan formation, retention, and execution in prospective memory: a new approach and age-related effects. Mem. Cogn. 28, 1041–1049 (2000)CrossRefGoogle Scholar
  33. 33.
    Craik, F.I., Bialystok, E.: Planning and task management in older adults: cooking breakfast. Mem. Cogn. 34, 1236–1249 (2006)CrossRefGoogle Scholar
  34. 34.
    d’Ydewalle, G., Bouckaert, D., Brunfaut, E.: Age-related differences and complexity of ongoing activities in time- and event-based prospective memory. Am. J. Psychol. 114, 411–423 (2001)CrossRefGoogle Scholar
  35. 35.
    Arning, K., Ziefle, M.: Effects of age, cognitive, and personal factors on PDA menu navigation performance. Behav. Inf. Technol. 28, 251–268 (2009)CrossRefGoogle Scholar
  36. 36.
    Goodman, J., Gray, P., Khammampad, K., Brewster, S.: Using landmarks to support older people in navigation. Mob. Hum. Comput. Interact. - MobileHCI 2004, 38–48 (2004)Google Scholar
  37. 37.
    Marquié, J.C., Jourdan-Boddaert, L., Huet, N.: Do older adults underestimate their actual computer knowledge? Behav. Inf. Technol. 21, 273–280 (2002)CrossRefGoogle Scholar
  38. 38.
    Tuomainen, K., Haapanen, S.: Needs of the active elderly for mobile phones. In: Stephanidis, C. (ed.) Universal Access in HCI: Inclusive Design in the Information Society, pp. 494–498. Lawrence Erlbaum Associates Inc., Mahwah (2003)Google Scholar
  39. 39.
    De Schutter, B., Vandenabeele, V.: Meaningful play in elderly life. In: 58th Annual Conference of the International Communication Association “Communicating for Social Impact” (2008)Google Scholar
  40. 40.
    Nielsen, J.: Usability Engineering. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  41. 41.
    Snyder, C.: Paper Prototyping: The Fast and Easy Way to Design and Refine User Interfaces. Morgan Kaufmann, Elsevier Science, San Francisco (2003)Google Scholar
  42. 42.
    Kieras, D., Polson, P.G.: An approach to the formal analysis of user complexity. Int. J. Man Mach. Stud. 22, 365–394 (1985)CrossRefGoogle Scholar
  43. 43.
    Beier, G.: Kontrollüberzeugungen im Umgang mit Technik [Locus of control when interacting with technology]. Rep. Psychol. 24, 684–693 (1999)Google Scholar
  44. 44.
    Blais, A.-R., Weber, E.U.: A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations. Judgment Deci. Making J. 1, 33–47 (2006)Google Scholar
  45. 45.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Q. 27, 425–478 (2003)Google Scholar
  46. 46.
    Arning, K., Ziefle, M.: Understanding age differences in PDA acceptance and performance. Comput. Hum. Behav. 23, 2904–2927 (2007)CrossRefGoogle Scholar
  47. 47.
    Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84, 191–215 (1977)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.Human-Computer Interaction Center (HCIC) Chair of Communication ScienceRWTH Aachen UniversityAachenGermany

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