Rewarding Fitness Tracking—The Communication and Promotion of Health Insurers’ Bonus Programs and the Use of Self-tracking Data

  • Maria HenkelEmail author
  • Tamara Heck
  • Julia Göretz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10914)


This paper analyzes German and Australian health insurer programs that offer self-tracking options for customers. We considered aspects of program promotion, program goals, and data privacy issues. Results are based on scanning current information available online via insurer websites. Seven Australian and six German insurers apply self-tracking. Programs in both countries vary, whereas most Australian insurers build their programs on third-party providers, and German insurers offer direct financial rewards. Those differences may be reasonable due to diverse health systems in both countries. Commonalities regarding the programs’ intentions are obvious. Furthermore, concerns about data policies arise across countries. The reward systems and intended program goals vary. The outcomes give insights into the status quo of self-tracking health insurer programs and contribute to a better understanding of the use of self-tracking data by providers. Moreover, further questions arise about the benefits of those programs and the protection of sensitive self-tracking data.


Fitness tracker Activity tracking Wearables Health insurance Self-tracking Data privacy 


  1. 1.
    Van der Meulen, R., Forni, A.A.: Gartner says worldwide wearable device sales to grow 17 percent in 2017 (2017). Accessed 15 Dec 2017
  2. 2.
    Ubrani, J., Llamas, R., Shirer, M.: Wearables aren’t dead, they’re just shifting focus as the marketgGrows 16.9% in the fourth quarter, according to IDC (2017). Accessed 15 Dec 2017
  3. 3.
    Liew, R., Binsted, T.: Your insurer wants to know everything about you (2015). Accessed 15 Dec 2017
  4. 4.
    Mihm, A.: Erste Krankenkasse zahlt für Apple Watch (2015). Accessed 15 Dec 2017
  5. 5.
    Boyd, A.: Could your Fitbit data be used to deny you health insurance? (2017). Accessed 15 Dec 2017
  6. 6.
    Yang, R., Shin, E., Newman, M.W., et al.: When fitness trackers don’t ‘fit’. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), pp. 623–634. ACM, New York (2015).
  7. 7.
    Shih, P.C., Han, K., Poole, E.S., et al.: Use and adoption challenges of wearable activity trackers. In: Proceedings of iConference. iSchools (2015). Accessed 15 Dec 2017
  8. 8.
    McMurdo, M.E.T., Sugden, J., Argo, I., et al.: Do pedometers increase physical activity in sedentary older women? A randomized controlled trial. J. Am. Geriatr. Soc. 58(11), 2099–2106 (2010). Scholar
  9. 9.
    Tedesco, S., Barton, J., O’Flynn, B.: A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry. Sensors (Basel) 17(6) (2017).
  10. 10.
    Gough, D., Oliver, S., Thomas, J. (eds.): An Introduction to Systematic Reviews, 2nd edn. Sage, London (2017)Google Scholar
  11. 11.
    Arksey, H., O’Malley, L.: Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8(1), 19–32 (2005). Scholar
  12. 12.
    Australian Bureau of Statistics: Health Service Usage and Health Related Actions, Australia, 2014–15: Private Health Insurance (2017). Accessed 20 Feb 2018
  13. 13.
    Australian Bureau of Statistics: Australian Demographic Statistics, June 2017 (2017). Accessed 20 Feb 2018
  14. 14.
    Bundesministerium für Gesundheit: Gesetzliche Krankenversicherung - Mitglieder, mitversicherte Angehörige und Krankenstand (2018). Accessed 20 Feb 2018
  15. 15.
    GKV-Spitzenverband: Anzahl der Krankenkassen im Zeitverlauf (2018). Accessed 20 Feb 2018
  16. 16.
    Wikipedia: Private Krankenversicherung (2018). Accessed 20 Feb 2018
  17. 17.
    Henkel, M., Heck, T., Göretz, J.: Dataset of ‘Rewarding fitness tracking – the communication and promotion of health insurers’ bonus programs and the usage of self-tracking data’ (2018).
  18. 18.
    Lupton, D.: Beyond techno-utopia: critical approaches to digital health technologies. Societies 4(4), 706–711 (2014). Scholar
  19. 19.
    Lupton, D.: Health promotion in the digital era: a critical commentary. Health Promot. Int. 30(1), 174–183 (2015). Scholar
  20. 20.
    Lupton, D.: Self-tracking cultures. In: Robertson, T. (ed.) Designing Futures: The Future of Design. Proceedings of the 26th Australian Computer-Human Interaction Conference (OzCHI 2014), pp. 77–86. ACM, New York (2014).
  21. 21.
    Lupton, D.: The Quantified Self. Polity Press, Cambridge, Malden (2016)Google Scholar
  22. 22.
    Rooksby, J., Rost, M., Morrison, A., et al.: Personal tracking as lived informatics. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1163–1172. ACM, New York (2014).
  23. 23.
    Asimakopoulos, S., Asimakopoulos, G., Spillers, F.: Motivation and user engagement in fitness tracking: heuristics for mobile healthcare wearables. Informatics 4(1), 5 (2017). Scholar
  24. 24.
    Donnachie, C., Wyke, S., Mutrie, N., et al.: ‘It’s like a personal motivator that you carried around wi’ you’: utilising self-determination theory to understand men’s experiences of using pedometers to increase physical activity in a weight management programme. Int. J. Behav. Nutr. Phys. Act. 14(1), 61 (2017). Scholar
  25. 25.
    Rowe-Roberts, D., Cercos, R., Mueller, F.: Preliminary results from a study of the impact of digital activity trackers on health risk status. Stud. Health Technol. Inf. 204, 143–148 (2014). Scholar
  26. 26.
    Lin, J.J., Mamykina, L., Lindtner, S., Delajoux, G., Strub, H.B.: Fish‘n’Steps: encouraging physical activity with an interactive computer game. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 261–278. Springer, Heidelberg (2006). Scholar
  27. 27.
    Brodin, N., Eurenius, E., Jensen, I., et al.: Coaching patients with early rheumatoid arthritis to healthy physical activity: a multicenter, randomized, controlled study. Arthritis Rheum. 59(3), 325–331 (2008). Scholar
  28. 28.
    Toscos, T., Faber, A., An, S., et al.: Chick clique. In: Extended Abstracts on Human Factors in Computing Systems (CHI06), pp. 1873–1878. ACM, New York (2006).
  29. 29.
    Glance, D.G., Ooi, E., Berman, Y., et al.: Impact of a digital activity tracker-based workplace activity program on health and wellbeing. In: Kostkova, P., Grasso, F., Castillo, C. (eds.) Proceedings of the 2016 Digital Health Conference (DH 2016), pp. 37–41. ACM, New York (2016).
  30. 30.
    Purpura, S., Schwanda, V., Williams, K., et al.: Fit4life. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 423–432. ACM, New York (2011).
  31. 31.
    Rosenbaum, M.S., Ramírez, G.C., Edwards, K., et al.: The digitization of health care retailing. J. Res. Interact. Mark. 11(4), 432–446 (2017). Scholar
  32. 32.
    Torre, I., Sanchez, O.R., Koceva, F., et al.: Supporting users to take informed decisions on privacy settings of personal devices. Pers. Ubiquit. Comput. 12(2), 1 (2017). Scholar
  33. 33.
    Tudor-Locke, C., Bassett, D.R.: How many steps/day are enough? Sports Med. 34(1), 1–8 (2004). Scholar
  34. 34.
    Australian Government: Private Health Insurance Act 2007 (2007). Accessed 20 Feb 2018
  35. 35.
    IKK Südwest: Digitale Diät: Alle reden von Digital Health – und kaum jemand nutzt es (2017). Accessed 20 Feb 2018
  36. 36.
    Zhang, J., Brackbill, D., Yang, S., et al.: Support or competition? How online social networks increase physical activity: a randomized controlled trial. Prev. Med. Rep. 4, 453–458 (2016). Scholar
  37. 37.
    Lehto, M., Lehto, M.: Health information privacy of activity trackers. In: Proceedings of the European Conference on Cyber Warfare and Security (ECCWS), pp. 243–251 (2017). Accessed 20 Feb 2018
  38. 38.
    Lidynia, C., Brauner, P., Ziefle, M.: A step in the right direction – understanding privacy concerns and perceived sensitivity of fitness trackers. In: Ahram, T., Falcão, C. (eds.) AHFE 2017. AISC, vol. 608, pp. 42–53. Springer, Cham (2018). Scholar
  39. 39.
    Voas, J., Kshetri, N.: Human tagging. Computer 50(10), 78–85 (2017). Scholar
  40. 40.
    Till, C.: Exercise as labour: quantified self and the transformation of exercise into labour. Societies 4(4), 446–462 (2014). Scholar
  41. 41.
    Kaye, K.: FTC: Fitness Apps Can Help You Shed Calories – and Privacy (2014). Accessed 20 Feb 2018
  42. 42.
    Peppet, S.R.: Regulating the Internet of Things: first steps toward managing discrimination, privacy, security and consent. Tex. Law Rev. 78 p. (2014). Accessed 20 Feb 2018
  43. 43.
    Kawamoto, K., Tanaka, T., Kuriyama, H.: Your activity tracker knows when you quit smoking. In: Proceedings of the 2014 ACM International Symposium on Wearable Computers (ISWC), pp. 107–110. ACM, New York (2014).
  44. 44.
    Ertin, E., Stohs, N., Kumar, S., et al.: AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys), pp. 274–287. ACM, New York (2011).
  45. 45.
    Yan, T., Lu, Y., Zhang, N.: Privacy disclosure from wearable devices. In: Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing (PAMCO), pp. 13–18. ACM, New York (2015).

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Heinrich Heine UniversityDüsseldorfGermany
  2. 2.University of Southern QueenslandToowoombaAustralia

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