Advertisement

Influence of Age on Trade-Offs Between Benefits and Barriers of AAL Technology Usage

  • Julia Offermann-van HeekEmail author
  • Susanne Gohr
  • Simon Himmel
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11592)

Abstract

An aging population due to demographic change along with rising care needs lead to higher efforts in concepts and developments of ambient assisted living (AAL) technologies aiming at a longer staying at home and more independency for older people. Although research on technology acceptance and user diversity gains in importance, real-life decisions with trade-offs between potential benefits and barriers of AAL technology usage have not been investigated so far. Therefore, the current study (n = 140) represents a conjoint analysis approach focusing on younger and older people’s decisions between benefits (safety and relief) and barriers (data handling and data access) to use an assisting system in the home environment of a family member in need of care. The results revealed differences in the decision patterns of both groups indicating that data-relevant aspects were most relevant for the younger adults, while safety represented a more relevant criterion for the older participants. In addition, contradicting evaluations of both groups were found within the aspects data access and safety. The results contribute to a deeper understanding of real-life decisions regarding the use of assisting technologies focusing on age as relevant user factor.

Keywords

Ambient assisted living (AAL) Technology acceptance Benefit and barrier perception Trade-off Age 

Notes

Acknowledgements

The authors thank all participants for their openness to participate in the study and to share their opinions on relevant decisions regarding usage of AAL technologies. This work has been funded partly by the German Federal Ministry of Education and Research projects Whistle (16SV7530) and PAAL (6SV7955).

References

  1. 1.
    Pickard, L.: A growing care gap? The supply of unpaid care for older people by their adult children in England to 2032. Ageing Soc. 35(1), 96–123 (2015)CrossRefGoogle Scholar
  2. 2.
    WHO, World Health Organization: Aging and Health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health. Accessed 23 Jan 2018
  3. 3.
    Shaw, J.E., Sicree, R.A., Zimmet, P.Z.: Global estimates of the prevalence of diabetes for 2010 and 2030. Diab. Res. Clin. Pract. 87(1), 4–14 (2010)CrossRefGoogle Scholar
  4. 4.
    Roger, V.L., Go, A.S., Lloyd-Jones, D.M., Adams, R.J., Berry, J.D., Brown, T.M., et al., American Heart Association Statistics Committee and Stroke Statistics Subcommittee: Heart disease and stroke statistics–2011 update: a report from the American Heart Association. Circulation, 123(4), e18–e209 (2011)Google Scholar
  5. 5.
    Blackman, S., et al.: Ambient assisted living technologies for aging well: a scoping review. J. Intell. Syst. (2015).  https://doi.org/10.1515/jisys-2014-0136
  6. 6.
    Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013)CrossRefGoogle Scholar
  7. 7.
    Peek, S.T.M., Wouters, E.J.M., van Hoof, J., Luijkx, K.G., Boeije, H.R., Vrijhoef, H.J.M.: Factors influencing acceptance of technology for aging in place: a systematic review. Int. J. Med. Inform. 83(4), 235–248 (2014)CrossRefGoogle Scholar
  8. 8.
    Buckley, K., Tran, B., Prandoni, C.: Receptiveness, use and acceptance of telehealth by caregivers of stroke patients in the home. Online J. Issues Nurs. 9(3), 9 (2004)Google Scholar
  9. 9.
    Larizza, M.F., et al.: In-home monitoring of older adults with vision impairment: exploring patients’, caregivers’ and professionals’ views. J. Am. Med. Inform. Assoc. 21(1), 56–63 (2014)CrossRefGoogle Scholar
  10. 10.
    König, A., Francis, L.E., Joshi, J., Robillard, J.M., Hoey, J.: Qualitative study of affective identities in dementia patients for the design of cognitive assistive technologies. J. Rehab. Assistive Technol. Eng. 4, 1–15 (2017)CrossRefGoogle Scholar
  11. 11.
    van Heek, J., Himmel, S., Ziefle, M.: Caregivers’ perspectives on ambient assisted living technologies in professional care contexts. In: Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health – Volume 1: ICT4AWE, pp. 37–48 (2018).  https://doi.org/10.5220/0006691400370048
  12. 12.
    Offermann-van Heek, J., Ziefle, M.: They don’t care about us! Care personnel’s perspectives on ambient assisted living technology usage: scenario-based survey study. JMIR Rehab. Assistive Technol. 5(2), e10424 (2018)Google Scholar
  13. 13.
    Beringer, R., Sixsmith, A., Campo, M., Brown, J., McCloskey, R.: The “acceptance” of ambient assisted living: developing an alternate methodology to this limited research lens. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) ICOST 2011. LNCS, vol. 6719, pp. 161–167. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21535-3_21CrossRefGoogle Scholar
  14. 14.
    Abtoy, A., Touhafi, A., Tahiri, A.: Ambient assisted living system’s models and architectures: a survey of the state of the art. J. King Saud Univ.-Comput. Inf. Sci. (2018).  https://doi.org/10.1016/j.jksuci.2018.04.009
  15. 15.
    Philips Lifeline: Senior Living Communities (2019). https://philipsseniorliving.com
  16. 16.
    Burstein, A.A., DaDalt, O., Kramer, B., D’Ambrosio, L.A., Coughlin, J.F.: Dementia caregivers and technology acceptance: interest outstrips awareness. Gerontechnology 14, 45–56 (2015)CrossRefGoogle Scholar
  17. 17.
    Demiris, G., et al.: Older adults’ attitudes towards and perceptions of “smart home” technologies: a pilot study. Med. Inform. Internet 29(2), 87–94 (2004)CrossRefGoogle Scholar
  18. 18.
    Schomakers, E.-M., Offermann-van Heek, J., Ziefle, M.: Attitudes towards aging and the acceptance of ICT for aging in place. In: Zhou, J., Salvendy, G. (eds.) ITAP 2018. LNCS, vol. 10926, pp. 149–169. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92034-4_12CrossRefGoogle Scholar
  19. 19.
    Himmel, S., Ziefle, M., Lidynia, C., Holzinger, A.: Older users’ wish list for technology attributes - a comparison of household and medical technologies. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 16–27. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40511-2_2CrossRefGoogle Scholar
  20. 20.
    Lai, C.K., Chung, J.C., Leung, N.K., Wong, J.C., Mak, D.P.: A survey of older Hong Kong people’s perceptions of telecommunication technologies and telecare devices. J. Telemed. Telecare 16(8), 441–446 (2010)CrossRefGoogle Scholar
  21. 21.
    Chappell, N.L., Zimmer, Z.: Receptivity to new technology among older adults. Disabil. Rehabil. 21(5–6), 222–230 (1999)CrossRefGoogle Scholar
  22. 22.
    Arning, K., Ziefle, M.: Different perspectives on technology acceptance: the role of technology type and age. In: Holzinger, A., Miesenberger, K. (eds.) USAB 2009. LNCS, vol. 5889, pp. 20–41. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-10308-7_2CrossRefGoogle Scholar
  23. 23.
    Luce, R.D., Tukey, J.W.: Simultaneous conjoint measurement: a new type of fundamental measurement. J. Math. Psychol. 1(1), 1–27 (1964)zbMATHCrossRefGoogle Scholar
  24. 24.
    Orme, B.: Interpreting the Results of Conjoint Analysis, Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research, pp. 77–89. Research Publications LLC, Madison (2010)Google Scholar
  25. 25.
    Green, P.E., Srinivasan, V.: Conjoint analysis in consumer research: issues and outlook. J. Consum. Res. 5(2), 103–123 (1978)CrossRefGoogle Scholar
  26. 26.
    Baier, D., Brusch, M.: Erfassung von Kundenpräferenzen für Produkte und Dienstleistungen. [Collection of customer preferences for products and services]. In: Baier, D., Brusch, M. (eds.) Conjoint Analysis. Methods, Applications, practical examples, pp. 3–18. Springer, Berlin (2009).  https://doi.org/10.1007/978-3-642-00754-5_1CrossRefGoogle Scholar
  27. 27.
    Arning, K.: Conjoint measurement. In: Matthes, J., Davis, C.S., Potter, R.F. (eds.) International Encyclopedia of Communication Research Methods, pp. 1–10. Wiley, Hoboken (2017).  https://doi.org/10.1002/9781118901731CrossRefGoogle Scholar
  28. 28.
    Alriksson, S., Öberg, T.: Conjoint analysis for environmental evaluation. Environ. Sci. Pollut. Res. 15(3), 244–257 (2008)CrossRefGoogle Scholar
  29. 29.
    Phillips, K.A., Maddala, T., Johnson, F.R.: Measuring preferences for health care interventions using conjoint analysis: an application to HIV testing. Health Serv. Res. 37(6), 1681–1705 (2002)CrossRefGoogle Scholar
  30. 30.
    Orme, B.: Formulating attributes and levels in conjoint analysis. Sawtooth Software research paper, pp. 1–4 (2002)Google Scholar
  31. 31.
    Steggell, C.D., Hooker, K., Bowman, S., Choun, S., Kim, S.J.: The role of technology for healthy aging among Korean and Hispanic women in the United States: a pilot study. Gerontechnology 9(4), 433–449 (2010)CrossRefGoogle Scholar
  32. 32.
    Wilkowska, W., Ziefle, M., Himmel, S.: Perceptions of personal privacy in smart home technologies: do user assessments vary depending on the research method? In: Tryfonas, T., Askoxylakis, I. (eds.) HAS 2015. LNCS, vol. 9190, pp. 592–603. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20376-8_53CrossRefGoogle Scholar
  33. 33.
    Joe, J., Chaudhuri, S., Chung, J., Thompson, H., Demiris, G.: Older adults’ attitudes and preferences regarding a multifunctional wellness tool: a pilot study. Inform. Health Soc. Care 41(2), 143–158 (2016)Google Scholar
  34. 34.
    Wild, K., Boise, L., Lundell, J., Foucek, A.: Unobtrusive in-home monitoring of cognitive and physical health: reactions and perceptions of older adults. J. Appl. Gerontol. 27(2), 181–200 (2008)CrossRefGoogle Scholar
  35. 35.
    Lorenzen-Huber, L., Boutain, M., Camp, L.J., Shankar, K., Connelly, K.H.: Privacy, technology, and aging: a proposed framework. Ageing Int. 36(2), 232–252 (2011)CrossRefGoogle Scholar
  36. 36.
    Himmel, S., Zaunbrecher, B.S., Wilkowska, W., Ziefle, M.: The youth of today designing the smart city of tomorrow. In: Kurosu, M. (ed.) HCI 2014. LNCS, vol. 8512, pp. 389–400. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07227-2_37CrossRefGoogle Scholar
  37. 37.
    Xu, H., Dinev, T., Smith, H.J., Hart, P.: Examining the formation of individual’s privacy concerns: toward an integrative view. In: ICIS 2008 Proceedings, p. 6 (2008)Google Scholar
  38. 38.
    Morton, A.: Measuring inherent privacy concern and desire for privacy - a pilot survey study of an instrument to measure dispositional privacy concern. In: International Conference on Social Computing (SocialCom), pp. 468–477. IEEE (2013)Google Scholar
  39. 39.
    Sawtooth Software: Testing the CBC Design. Technical Paper Series. Sawtooth Software (Version 9.6.1): [Software for the conceptual design and analysis of the online conjoint questionnaire]. Software Software Inc., Sequim (2018) http://www.sawtoothsoftware.com/help/lighthouse-studio/manual/index.html?hid_web_cbc_designs_6.html
  40. 40.
    Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs (1980)Google Scholar
  41. 41.
    Green, P.E., Krieger, A.M., Agarwal, M.K.: Adaptive conjoint analysis: some caveats and suggestions. J. Mark. Res. 28, 215–222 (1991)CrossRefGoogle Scholar
  42. 42.
    DeSarbo, W.S., Wedel, M., Vriens, M., Ramaswamy, V.: Latent class metric conjoint analysis. Mark. Lett. 3(3), 273–288 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Julia Offermann-van Heek
    • 1
    Email author
  • Susanne Gohr
    • 1
  • Simon Himmel
    • 1
  • Martina Ziefle
    • 1
  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

Personalised recommendations