Influence of User Factors on the Acceptance of Ambient Assisted Living Technologies in Professional Care Contexts

  • Julia Offermann-van HeekEmail author
  • Martina Ziefle
  • Simon Himmel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 982)


Demographic change in conjunction with increasing amounts of older people and people in need of care pose high burdens and challenges for the care sectors of today’s society. In the last decades, it is tried to face these challenges by developing technical solutions in the area of Ambient Assisted Living (AAL). Besides technical functions and opportunities, the acceptance of future users is decisive for a successful implementation of those technologies in everyday life situations. As AAL technologies have an enormous potential to support care staff as well as caretakers in professional care situations, it is questionable to what extent professional care staff accepts and adopts to use assisting technologies in their professional everyday life. In more detail, it is of major interest to analyze how care professionals perceive potential benefits and barriers of AAL technology usage, specific characteristics of data gathering and storage as well as if individual user factors of care professionals influence the perception and acceptance of AAL technologies.


Ambient assisted living technologies Technology acceptance Professional caregivers User factors 



This work was partly funded by the German Federal Ministry of Education and Research projects Whistle (16SV7530) and PAAL (6SV7955).


  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.
    Walker, A., Maltby, T.: Active ageing: a strategic policy solution to demographic ageing in the European Union. Int. J. Soc. Welf. 21, 117–130 (2012)CrossRefGoogle Scholar
  3. 3.
    Bloom, D.E., Canning, D.: Global Demographic Change: Dimensions and Economic Significance National Bureau of Economic Research. Working Paper No. 10817 (2004)Google Scholar
  4. 4.
    Siewert, U., Fendrich, K., Doblhammer-Reiter, G., Scholz, R.D., Schuff-Werner, P., Hoffmann, W.: Health care consequences of demographic changes in Mecklenburg-West Pomerania: projected case numbers for age-related diseases up to the year 2020, based on the study of health in Pomerania (SHIP). Deutsches Ärzteblatt Int. 107(18), 328 (2010)Google Scholar
  5. 5.
    Shaw, J.E., Sicree, R.A., Zimmet, P.Z.: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res. Clin. Pract. 87(1), 4–14 (2010)CrossRefGoogle Scholar
  6. 6.
    Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–1053 (2004)CrossRefGoogle Scholar
  7. 7.
    Roger, V.L., Go, A.S., Lloyd-Jones, D.M., Adams, R.J., Berry, J.D., Brown, T.M., 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 (2004)Google Scholar
  8. 8.
    Poore, C.: Disability in Twentieth-Century German Culture. University of Michigan Press, Ann Arbor (2007)CrossRefGoogle Scholar
  9. 9.
    World Health Organization (WHO): World Health Day 2012: ageing and health: toolkit for event organizers (2012).
  10. 10.
    Memon, M., Wagner, S.R., Pedersen, C.F., Beevi, F.H.A., Hansen, F.O.: Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 14(3), 4312–4341 (2014)CrossRefGoogle Scholar
  11. 11.
    Frank, S., Labonnote, N.: Monitoring technologies for buildings equipped with ambient assisted living: current status and where next. In: SAI Intelligent Systems Conference (IntelliSys), pp. 431–438. IEEE (2015)Google Scholar
  12. 12.
    Cheng, J., Chen, X., Shen, M.: A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J. Biomed. Health Inform. 17(1), 38–45 (2013)CrossRefGoogle Scholar
  13. 13.
    Baig, M.M., Gholamhosseini, H.: Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 1–14 (2013)CrossRefGoogle Scholar
  14. 14.
    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. LNCS, vol. 4555, pp. 103–112. Springer, Heidelberg (2007). Scholar
  15. 15.
    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
  16. 16.
    Wichert, R., Furfari, F., Kung, A., Tazari, M.R.: How to overcome the market entrance barrier and achieve the market breakthrough in AAL. In: Wichert, R., Eberhardt, B. (eds.) Ambient Assisted Living. ATSC, pp. 349–358. Springer, Heidelberg (2012). Scholar
  17. 17.
    Georgieff, P.: Ambient assisted living. Marktpotenziale IT-unterstützter Pflege für ein selbstbestimmtes Altern. [Market potential of IT-supported care for self-determined aging]. FAZIT Forschungsbericht 17, 9–10 (2008)Google Scholar
  18. 18.
    Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., Schreier, G.: The Internet of Things for ambient assisted living. In: 2010 Seventh International Conference on Information Technology: New Generations (ITNG), pp. 804–809. IEEE (2010)Google Scholar
  19. 19.
    Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the Microsoft Kinect. IEEE J. Biomed. Health Inform. 19(1), 290–301 (2015)CrossRefGoogle Scholar
  20. 20.
    Ni, B., Nguyen, C.D., Moulin, P.: RGBD-camera based get-up event detection for hospital fall prevention. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1405–1408. IEEE (2012)Google Scholar
  21. 21.
    Costa, R., Novais, P., Costa, Â., Neves, J.: Memory support in ambient assisted living. In: Camarinha-Matos, L.M., Paraskakis, I., Afsarmanesh, H. (eds.) PRO-VE 2009. IAICT, vol. 307, pp. 745–752. Springer, Heidelberg (2009). Scholar
  22. 22.
    Hristova, A., Bernardos, A.M., Casar, J.R.: Context-aware services for ambient assisted living: a case-study. In: IEEE Applied Sciences on Biomedical and Communication Technologies, pp. 1–5 (2008)Google Scholar
  23. 23.
    Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9(1), 21 (2012)CrossRefGoogle Scholar
  24. 24.
    Essence Homepage: Smart Care - Care@ Home Product Suite (2018).
  25. 25.
    Tunstall Homepage: Tunstall - Solutions for Healthcare Professionals (2018).
  26. 26.
    Gövercin, M., Meyer, S., Schellenbach, M., Steinhagen-Thiessen, E., Weiss, B., Haesner, M.: SmartSenior@home: acceptance of an integrated ambient assisted living system. Results of a clinical field trial in 35 households. Inform. Health Soc. Care 41, 1–18 (2016)CrossRefGoogle Scholar
  27. 27.
    Isern, D., Sánchez, D., Moreno, A.: Agents applied in health care: a review. Int. J. Med. Inform. 79(3), 145–166 (2010)CrossRefGoogle Scholar
  28. 28.
    van Heek, J., Himmel, S., Ziefle, M.: Helpful but spooky? Acceptance of AAL-systems contrasting user groups with focus on disabilities and care needs’. In: Proceedings of the International Conference on ICT for Aging Well (ICT4AWE 2017), pp. 78–90. SCITEPRESS – Science and Technology Publications (2017)Google Scholar
  29. 29.
    Himmel, S., Ziefle, M.: Smart home medical technologies: users’ requirements for conditional acceptance. I-Com 15(1), 39–50 (2016)CrossRefGoogle Scholar
  30. 30.
    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). Scholar
  31. 31.
    Sun, H., De Florio, V., Gui, N., Blondia, C.: The missing ones: key ingredients towards effective ambient assisted living systems. J. Ambient Intell. Smart Environ. 2(2), 109–120 (2010)Google Scholar
  32. 32.
    Wilkowska, W., Ziefle, M.: Privacy and data security in e-health: requirements from users’ perspective. Health Inform. J. 18(3), 191–201 (2012)CrossRefGoogle Scholar
  33. 33.
    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). Scholar
  34. 34.
    van Heek, J., Himmel, S., Ziefle, M.: Privacy, data security, and the acceptance of AAL-systems – a user-specific perspective. In: Zhou, J., Salvendy, G. (eds.) ITAP 2017. LNCS, vol. 10297, pp. 38–56. Springer, Cham (2017). Scholar
  35. 35.
    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
  36. 36.
    Ziefle, M., Himmel, S., Wilkowska, W.: When your living space knows what you do: acceptance of medical home monitoring by different technologies. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 607–624. Springer, Heidelberg (2011). Scholar
  37. 37.
    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
  38. 38.
    López, S.A., Corno, F., Russis, L.D.: Supporting caregivers in assisted living facilities for persons with disabilities: a user study. Univ. Access Inf. Soc. 14(1), 133–144 (2015)CrossRefGoogle Scholar
  39. 39.
    Mortenson, W.B., Demers, L., Fuhrer, M.J., Jutai, J.W., Lenker, J., DeRuyter, F.: Effects of an assistive technology intervention on older adults with disabilities and their informal caregivers: an exploratory randomized controlled trial. Am. J. Phys. Med. Rehabil./Assoc. Acad. Physiatr. 92(4), 297–306 (2013)CrossRefGoogle Scholar
  40. 40.
    Ziefle, M., Jakobs, E.M.: New challenges in human computer interaction: strategic directions and interdisciplinary trends. In: 4th International Conference on Competitive Manufacturing Technologies, COMA, pp. 389–398 (2010)Google Scholar
  41. 41.
    Beier, G.: Kontrollüberzeugungen im Umgang mit Technik, [Control beliefs in dealing with technology]. Rep. Psychol. 9, 684–693 (1999)Google Scholar
  42. 42.
    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
  43. 43.
    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
  44. 44.
    McKnight, D.H., Choudhury, V., Kacmar, C.: Developing and validating trust measures for e-commerce: an integrative typology. Inf. Syst. Res. 13(3), 334–359 (2002)CrossRefGoogle Scholar
  45. 45.
    Van Heek, J., Ziefle, M., Himmel, S.: 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 (ICT4AWE 2018), pp. 37–48 (2018)Google Scholar
  46. 46.
    Ziefle, M., Schaar, A.K.: Technical expertise and its influence on the acceptance of future medical technologies: what is influencing what to which extent? In: Leitner, G., Hitz, M., Holzinger, A. (eds.) USAB 2010. LNCS, vol. 6389, pp. 513–529. Springer, Heidelberg (2010). Scholar
  47. 47.
    Simonazzi, A.: Care regimes and national employment models. Camb. J. Econ. 33(2), 211–232 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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