Advertisement

Exploring the Acceptance of mHealth Applications - Do Acceptance Patterns Vary Depending on Context?

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 795)

Abstract

In the present study, we investigate influencing factors on the acceptance of mHealth smartphone apps, using an extended UTAUT model. N = 165 participants evaluated use intention, performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), as well as privacy concerns for a fitness app (lifestyle context) and a diabetes app (medical context). Structural equation modeling is used to assess the relevance of influences on adoption intention in these contexts. Results show that acceptance factors indeed differ strongly between lifestyle and medical contexts. For the latter, only PE and SI determine intention to use, although privacy concerns are higher than in the lifestyle context. In contrast, intention to use the fitness app is predicted by PE, SI, FA, and privacy concerns. The extended UTAUT model showed very good predictive relevance for use intention in both contexts. These findings reveal that technology acceptance needs to be examined depending on context.

Keywords

Technology acceptance mHealth Life-logging UTAUT Privacy concerns Human factors 

Notes

Acknowledgements

The authors thank all participants for sharing their thoughts and opinions and Niklas Kunstleben for research support. This research was funded by the German Ministry of Education and Research (BMBF) under the project MyneData (KIS1DSD045).

References

  1. 1.
    Whiting, D.R., Guariguata, L., Weil, C., Shaw, J.: IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diab. Res. Clin. Pract. 94, 311–321 (2011)CrossRefGoogle Scholar
  2. 2.
    Statistisches Bundesamt: Statistisches Jahrbuch 2017. Statistisches Bundesamt (Destatis), Wiesbaden (2017)Google Scholar
  3. 3.
    Cowan, L.T., Van Wagenen, S.A., Brown, B.A., Hedin, R.J., Seino-Stephan, Y., Hall, P.C., West, J.H.: Apps of steel: are exercise apps providing consumers with realistic expectations?: a content analysis of exercise apps for presence of behavior change theory. Health Educ. Behav. 40, 133–139 (2013)CrossRefGoogle Scholar
  4. 4.
    Sieverdes, J.C., Treiber, F., Jenkins, C.: Improving diabetes management with mobile health technology. Am. J. Med. Sci. 345, 289–295 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Xue, H., Huang, Y., Huang, L., Zhang, D.: A systematic review of application and effectiveness of mHealth interventions for obesity and diabetes treatment and self-management. Adv. Nutr. Int. Rev. J. 8, 449–462 (2017)CrossRefGoogle Scholar
  6. 6.
    IMS Health: Distribution of disease specific apps available worldwide in 2013 and 2015, by category https://www.statista.com/statistics/623981/healthcare-apps-worldwide-by-disease-category/
  7. 7.
    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, 4–14 (2010)CrossRefGoogle Scholar
  8. 8.
    Hamine, S., Gerth-Guyette, E., Faulx, D., Green, B.B., Ginsburg, A.S.: Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J. Med. Internet Res. 17, e52 (2015)CrossRefGoogle Scholar
  9. 9.
    Krug, S., Jordan, S., Mensink, G.B.M., Müters, S., Finger, J., Lampert, T.: Physical activity results of the german health interview and examination survey for adults (DEGS1). Bundesgesundheitsblatt - Gesundheitsforsch. - Gesundheitsschutz 56, 765–771 (2013)Google Scholar
  10. 10.
    World Health Organization: Global Recommendations on Physical Activity for Health. WHO Press, Geneva, Switzerland (2010)Google Scholar
  11. 11.
    Rasche, P., Wille, M., Bröhl, C., Theis, S., Schäfer, K., Knobe, M., Mertens, A., Medic, R.: Prevalence of health app use among older adults in Germany: national survey. JMIR mHealth uHealth 6, e26 (2018)CrossRefGoogle Scholar
  12. 12.
    Li, H., Wu, J., Gao, Y., Shi, Y.: Examining individuals’ adoption of healthcare wearable devices: an empirical study from privacy calculus perspective. Int. J. Med. Inform. 88, 8–17 (2016)CrossRefGoogle Scholar
  13. 13.
    Baruh, L., Secinti, E., Cemalcilar, Z.: Online privacy concerns and privacy management: a meta-analytical review. J. Commun. 67, 26–53 (2017)CrossRefGoogle Scholar
  14. 14.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)CrossRefGoogle Scholar
  15. 15.
    Davis, F., Bagozzi, R., Warshaw, P.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35, 982 (1989)CrossRefGoogle Scholar
  16. 16.
    King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manag. 43, 740–755 (2006)CrossRefGoogle Scholar
  17. 17.
    Klein, R.: Internet-based patient-physician electronic communication applications: patient acceptance and trust. e-Serv. J. 5, 27–52 (2007)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Sun, Y., Wang, N., Guo, X., Peng, Z.: Understanding the acceptance of mobile health services: a comparison and integration of alternative models. J. Electron. Commer. Res. 14, 183–200 (2013)Google Scholar
  20. 20.
    Williams, M.D., Rana, N.P., Dwivedi, Y.K.: The unified theory of acceptance and use of technology (UTAUT): a literature review (2015)CrossRefGoogle Scholar
  21. 21.
    Attuquayefio, S., Addo, H.: Review of studies with UTAUT as conceptual framework. Eur. Sci. J. 10, 1857–7881 (2014)Google Scholar
  22. 22.
    Hoque, R., Sorwar, G.: Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int. J. Med. Inform. 101, 75–84 (2017)CrossRefGoogle Scholar
  23. 23.
    Yuan, S., Ma, W., Kanthawala, S., Peng, W.: Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model. Telemed. e-Health 21, 735–741 (2015)CrossRefGoogle Scholar
  24. 24.
    Westin, A.F.: Privacy and Freedom. Am. Sociol. Rev. 33, 173 (1967)Google Scholar
  25. 25.
    Burgoon, J.: Privacy and communication. Ann. Int. Commun. Assoc. 6, 206–249 (1982)CrossRefGoogle Scholar
  26. 26.
    Koops, B., Newell, B.C., Timan, T., Skorvanek, I., Chokrevski, T., Galic, M.: A typology of privacy. Univ. Pennsylvanica J. Int. Law. 38, 1–93 (2017)Google Scholar
  27. 27.
    European Commission: Special Eurobarometer 431 - Data Protection, Cologne (2015)Google Scholar
  28. 28.
    Mothersbaugh, D.L., Foxx, W.K., Beatty, S.E., Wang, S.: Disclosure antecedents in an online service context: the role of sensitivity of information. J. Serv. Res. 15, 76–98 (2012)CrossRefGoogle Scholar
  29. 29.
    Anderson, C.L., Agarwal, R.: The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Inf. Syst. Res. 22, 469–490 (2011)CrossRefGoogle Scholar
  30. 30.
    Rohm, A.J., Milne, G.R.: Just what the doctor ordered: the role of information sensitivity and trust in reducing medical information privacy concern. J. Bus. Res. 57, 1000–1011 (2004)CrossRefGoogle Scholar
  31. 31.
    Xu, H., Dinev, T., Smith, J., Hart, P.: Information privacy concerns: linking individual perceptions with institutional privacy assurances. J. Assoc. Inf. Syst. 12, 798–824 (2011)Google Scholar
  32. 32.
    Bansal, G., Zahedi, F.M., Gefen, D.: Do context and personality matter? Trust and privacy concerns in disclosing private information online. Inf. Manag. 53, 1–12 (2016)CrossRefGoogle Scholar
  33. 33.
    Huckvale, K., Prieto, J.T., Tilney, M., Benghozi, P.-J., Car, J.: Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment. BMC Med. 13, 214 (2015)CrossRefGoogle Scholar
  34. 34.
    Or, C.K.L., Karsh, B.T.: A systematic review of patient acceptance of consumer health information technology. J. Am. Med. Informat. Assoc. 16, 550–560 (2009)CrossRefGoogle Scholar
  35. 35.
    Lidynia, C., Brauner, P., Ziefle, M.: A step in the right direction – understanding privacy concerns and perceived sensitivity of fitness trackers. In: AHFE 2017: Advances in Human Factors in Wearable Technologies and Game Design, pp. 42–53 (2018)Google Scholar
  36. 36.
    Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., Papasratorn, B.: Factors influencing the Thai elderly intention to use smartphone for e-Health services. In: 2012 IEEE Symposium on Humanities, Science and Engineering Research, SHUSER 2012, pp. 479–483 (2012)Google Scholar
  37. 37.
    Parker, S.J., Jessel, S., Richardson, J.E., Reid, M.C.: Older adults are mobile too! Identifying the barriers and facilitators to older adults’ use of mHealth for pain management. BMC Geriatr. 13, 43 (2013)CrossRefGoogle Scholar
  38. 38.
    Guo, X., Sun, Y., Yan, Z., Wang, N.: Privacy-personalization paradox in adoption of mobile health service: the mediating role of trust. In: Proceedings PACIS 2012 Paper 27 (2012)Google Scholar
  39. 39.
    Ringle, C., Wende, S., Becker, J.-M.: SmartPLS 3. Bönningstedt, SmartPLS (2015)Google Scholar
  40. 40.
    Hair Jr., J.F., Hult, G., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications (2011)Google Scholar
  41. 41.
    Ziefle, M., Wilkowska, W.: Technology acceptability for medical assistance. In: 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2010)Google Scholar
  42. 42.
    Kokolakis, S.: Privacy attitudes and privacy behaviour: a review of current research on the privacy paradox phenomenon. Comput. Secur. 2011, 1–29 (2015)Google Scholar
  43. 43.
    Dinev, T., Hart, P.: An extended privacy calculus model for e-commerce transactions. Inf. Syst. Res. 17, 61–80 (2006)CrossRefGoogle Scholar
  44. 44.
    Fittkau & Maaß Consulting: Share of Smartphone Users that Used Fitness APS in Germany in May 2015, by Age Group https://www.statista.com/statistics/452454/fitness-app-usage-among-smartphone-users-in-germany-by-age/
  45. 45.
    Guariguata, L., Whiting, D.R., Hambleton, I., Beagley, J., Linnenkamp, U., Shaw, J.E.: Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract. 103, 137–149 (2014)CrossRefGoogle Scholar
  46. 46.
    Czaja, S.J., Charness, N., Fisk, A.D., Hertzog, C., Nair, S.N., Rogers, W.A., Sharit, J.: Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (CREATE). Psyhol Aging. 21, 333–352 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

Personalised recommendations