Skip to main content

Exploring factors affecting consumers' adoption of wearable devices to track health data

Abstract

Mobile health is a rapidly emerging topic due to technological advances, especially in mobile computing and communication technologies. Increased capabilities of mobile devices, including smartphones, smart bands, and other wearables provide vast opportunities to collect health data easily. Health professionals can use this data in order to support medical diagnosis and treatment. In addition to health professionals, consumers can also benefit from the data collected by these devices to assist self-motivation to adopt and track healthier daily life practices. In this research, the factors affecting the adoption of wearable devices to track health information are investigated. We used the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model as a basis for our study as it is focusing on the acceptance of technology from consumers' perspectives. We enhanced the model with the concept of technology use categorization. The original Use construct of the UTAUT2 model addresses technology use only in terms of use frequency. We believe that this is not sufficient to analyze wearable devices that lend themselves to varying degrees of passive and active use. We propose that wearable device usage should be analyzed according to three types of use: Type 1 Use: Users wear the device primarily out of habit with no significant focus on the data; Type 2 Use: Users check the collected data; Type 3 Use: Users take actions based on the collected data. Our quantitative analysis showed that different factors with remarkably different intensities influence these three types of usage. Furthermore, we proposed three new constructs, namely goal clarity, technology stack compatibility, and perceived risk to improve the explanatory power of the UTAUT2 model. A strong relation is found between goal clarity and behavioral intention for type 3 use. Additionally, for all three types of use, it is seen that the Technology Stack Compatibility construct is a strong determinant of behavioral intention to use wearable devices for health tracking purposes.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Abbreviations

AVE:

Average variance extracted

BI:

Behavioral intention to use

CA:

Cronbach's alpha

DOI:

Diffusion of Innovation Theory

EE:

Effort expectancy

FC:

Facilitating conditions

GC:

Goal Clarity

HM:

Hedonic motivation

MM:

Motivational model

MPCU:

Model of PC Utilization

PDA:

Personal digital assistant

PE:

Performance expectancy

PR:

Perceived risk

SCT:

Social cognitive theory

SI:

Social influence

TAM:

Technology acceptance model

TPB:

Theory of planned behavior

TRA:

Theory of reasoned action

TSC:

Technology stack compatibility

UTAUT:

Unified Theory Of Acceptance And Use Of Technology

UTAUT2:

Extended Unified Theory Of Acceptance And Use Of Technology

VIF:

Variance inflation factor

References

  1. 1.

    de Moraes, J.L.C., Souza, W.L., Pires, L.F., Prado, A.F.: A methodology based on openEHR archetypes and software agents for developing e-health applications reusing legacy systems. Comput. Methods Programs Biomed. 134, 267–287 (2016)

    Article  Google Scholar 

  2. 2.

    Holzinger, A., Dorner, S., Födinger, M., Valdez, A.C., Ziefle, M.: Chances of Increasing Youth Health Awareness through Mobile Wellness Applications. In: Leitner G., Hitz M., Holzinger A. (eds) HCI in Work and Learning, Life and Leisure. USAB 2010. Lecture Notes in Computer Science, vol 6389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16607-5_5(2010).

  3. 3.

    Holzinger, A., Schaupp, K., Eder-Halbedl, W. (2008) An investigation on acceptance of ubiquitous devices for the elderly in a geriatric hospital environment: using the example of person tracking. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds) Computers Helping People with Special Needs. ICCHP 2008. Lecture Notes in Computer Science, vol 5105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70540-6_3

  4. 4.

    Najafi, B., Horn, D., Marclay, S., Crews, R.T., Wu, S., Wrobel, J.S.: Assessing postural control and postural control strategy in diabetes patients using innovative and wearable technology. J. Diabetes Sci. Technol. 4(4), 780–791 (2010). https://doi.org/10.1177/193229681000400403

    Article  Google Scholar 

  5. 5.

    Runkle, J., Sugg, M., Boase, D., Galvin, S.L., Coulson, C., C.: Use of wearable sensors for pregnancy health and environmental monitoring: Descriptive findings from the perspective of patients and providers. Dig. Health 5, 2055207619828220 (2019). https://doi.org/10.1177/2055207619828220

    Article  Google Scholar 

  6. 6.

    Holzinger, A., Searle, G., Wernbacher, M.: The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. Univ. Access Inf. Soc. 10, 245–260 (2011). https://doi.org/10.1007/s10209-010-0212-x

    Article  Google Scholar 

  7. 7.

    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000)

    Article  Google Scholar 

  8. 8.

    Davis, F.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989). https://doi.org/10.2307/249008

    Article  Google Scholar 

  9. 9.

    Roy, A., Zalzala, A.M., Kumar, A.: Disruption of things: a model to facilitate adoption of IoT-based innovations by the urban poor. Proc. Eng. 159, 199–209 (2016). https://doi.org/10.1016/j.proeng.2016.08.159

    Article  Google Scholar 

  10. 10.

    Park, Y.T.: Emerging new era of mobile health technologies. Healthcare Inf. Res. 22(4), 253–254 (2016). https://doi.org/10.4258/hir.2016.22.4.253

    Article  Google Scholar 

  11. 11.

    Patel, M.S., Asch, D.A., Volpp, K.G.: Wearable devices as facilitators, not drivers, of health behavior change. JAMA 313(5), 459–460 (2015). https://doi.org/10.1001/jama.2014.14781

    Article  Google Scholar 

  12. 12.

    Nasir, S., Yurder, Y.: Consumers’ and Physicians’ Perceptions about High Tech Wearable Health Products. Proc. Soc. Behav. Sci. 195, 1261–1267 (2015). https://doi.org/10.1016/j.sbspro.2015.06.279

    Article  Google Scholar 

  13. 13.

    Lunney, A., Cunningham, N.R., Eastin, M.S.: Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Comput. Hum. Behav. 65, 114–120 (2016). https://doi.org/10.1016/j.chb.2016.08.007

    Article  Google Scholar 

  14. 14.

    McMaster, T., Wastell, D.: Diffusion – or delusion? Challenging an IS research tradition. Inf. Technol. People 18(4), 383–404 (2005). https://doi.org/10.1108/09593840510633851

    Article  Google Scholar 

  15. 15.

    Eysenbach, G.: What is e-health? J. Med. Internet Res. 3(2), e20 (2001)

    Article  Google Scholar 

  16. 16.

    Venkatesh, V., Thong, J., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)

    Article  Google Scholar 

  17. 17.

    Alazzam, M.B., Al-Sharo, Y.M., Al-azzam, M.K.: Developing (UTAUT 2) model of adoption mobile health application in Jordan E-government. J. Theor. Appl. Inf. Technol. 96(12), 3846–3860 (2018)

    Google Scholar 

  18. 18.

    Hew, J.-J., Lee, V.-H., Ooi, K.-B., Wei, J.: What catalyses mobile apps usage intention: an empirical analysis. Ind. Manag. Data Syst. 115(7), 1269–1291 (2015). https://doi.org/10.1108/IMDS-01-2015-0028

    Article  Google Scholar 

  19. 19.

    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. J. E Health. 21(9), 735–741 (2015). https://doi.org/10.1089/tmj.2014.0148

    Article  Google Scholar 

  20. 20.

    Phaik, K.B., Yuvaraj, G., Mohammad, I., Behzad, F.: Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behav. Inf. Technol. (2019). https://doi.org/10.1080/0144929X.2019.1685597

    Article  Google Scholar 

  21. 21.

    Albugami, M., Bellaaj, M. The continued use of Internet banking: Combining UTAUT2 theory and service quality model. J. Global Manag. Res. (2014).

  22. 22.

    Mhina, J.R.A., Johar, M.G.M.: Investigating Tanzania government employees’ acceptance and use of social media: An empirical validation and extension of UTAUT. Int. J. Manag. Inf. Technol. JMIT 10, 2 (2018)

    Google Scholar 

  23. 23.

    Venkatesh, V., Thong, J.Y.L., Xu, X.: Unified theory of acceptance and use of technology: a synthesis and the road ahead. J. Assoc. Inf. Syst. (2016). https://doi.org/10.17705/1jais.00428

    Article  Google Scholar 

  24. 24.

    SmartPLS: Ringle, C. M., Wende, S., and Becker, J.-M.: "SmartPLS 3." Boenningstedt: SmartPLS GmbH, http://www.smartpls.com. (2015)

  25. 25.

    Nunnally, J.: Psychometric theory / Jum C. Nunnally. McGraw-Hill, New York (1978)

    Google Scholar 

  26. 26.

    Chin, W. W. The partial least squares approach for structural equation modeling. In: G. A. Marcoulides (Ed.), Methodology for business and management. Modern methods for business research (p. 295–336). Lawrence Erlbaum Associates Publishers (1998).

  27. 27.

    Höck, M., Ringle, C. M. Strategic networks in the software industry: An empirical analysis of the value continuum. IFSAM 8th World Congress, Berlin (2006).

  28. 28.

    Henseler, J., Ringle, C.M., Sarstedt, M.: Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In: Okzaki, S. (ed.) Handbook of partial least squares: Concepts, methods and applications in marketing and related fields, pp. 252–276. Springer, Berlin (2012)

    Google Scholar 

  29. 29.

    Segars, A.H.: Assessing the unidimensionality of measurement: a paradigm and illustration within the context of information systems research. Omega 25(1), 107–121 (1997). https://doi.org/10.1016/S0305-0483(96)00051-5

    Article  Google Scholar 

  30. 30.

    Fornell, C., Larcker, D.: Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (1981).

  31. 31.

    Garson, G. D.: (2016). Partial least squares. Regression and structural equation models.

  32. 32.

    Grewal, R., Cote, J.A., Baumgartner, H.: Multicollinearity and measurement error in structural equation models: implications for theory testing. Mark. Sci. 23(4), 519–529 (2004). https://doi.org/10.1287/mksc.1040.0070

    Article  Google Scholar 

  33. 33.

    Rasoolimanesh, S.M., Roldan, J.L., Jaafar, M., Ramayah, T. Factors influencing residents’ perceptions toward tourism development: Differences across rural and urban world heritage sites. J. Travel Res., 56, 760–775, (2017). https://doi.org/10.1177/0047287516662354.

  34. 34.

    Henseler, J., Ringle, C., Sinkovics, R.: The use of partial least squares path modeling in international marketing. Adv. Int. Market. AIM 20, 277–320 (2009). https://doi.org/10.1108/S1474-7979(2009)0000020014

    Article  Google Scholar 

  35. 35.

    Bagozzi, R.: The legacy of the technology acceptance model and a proposal for a paradigm shift. J J. Assoc. Inf. Syst. (2017). https://doi.org/10.17705/1jais.00122.

  36. 36.

    Wang, Y., Rajan, P., Sankar, C., Raju, P.: Relationships between Goal Clarity, Concentration and Learning Effectiveness when Playing Serious Games. 20th Americas Conference on Information Systems, AMCIS (2014).

  37. 37.

    Sweetser, P., Wyeth, P.: GameFlow: a model for evaluating player enjoyment in games. Comput. Entertain. 3, 3 (2005). https://doi.org/10.1145/1077246.1077253

    Article  Google Scholar 

  38. 38.

    Erez, M., Kanfer, F.: The role of goal acceptance in goal setting and task performance. The Acad. Manag. Rev. 8(3), 454–463 (1983)

    Article  Google Scholar 

  39. 39.

    Anderson, D., Stritch, J.: Goal clarity, task significance, and performance: evidence from a laboratory experiment. J. Public Administr. Res. Theory 26(2), 211–225 (2016). https://doi.org/10.1093/jopart/muv019

    Article  Google Scholar 

  40. 40.

    Locke, E.A., Latham, G.P.: A theory of goal setting & task performance. Prentice-Hall Inc, Englewood Cliffs, NJ (1990)

    Google Scholar 

  41. 41.

    Cullen, K.W., Baranowski, T., Smith, S.P.: Using goal setting as a strategy for dietary behavior change. J. Am. Diet. Assoc. 101, 562–566 (2001). https://doi.org/10.1016/S0002-8223(01)00140-7.

    Article  Google Scholar 

  42. 42.

    Sharon, M. N., Thom, J. M., Jones, I. R., Hindle, J. V., Clare, L.: Goal-setting to promote a healthier lifestyle in later life: Qualitative evaluation of the agewell trial. Clin. Gerontol. 1–11 (2017). https://doi.org/10.1080/07317115.2017.1416509.

  43. 43.

    Lee, L., Egelman, S., Lee, J.H., Wagner, D.: Risk Perceptions for Wearable Devices. (2015). ArXiv, abs/1504.05694.

  44. 44.

    Chellappa, R.K., Sin, R.G.: Personalization versus privacy: an empirical examination of the online consumer’s Dilemma. Inf. Technol. Manag. 6, 181–202 (2005). https://doi.org/10.1007/s10799-005-5879-y.

    Article  Google Scholar 

  45. 45.

    Hong, Z., Yi, L.: Research on the influence of perceived risk in consumer on-line purchasing decision. Phys. Proc., 24, part B, 1304–1310, (2012). https://doi.org/10.1016/j.phpro.2012.02.195(2012).

  46. 46.

    Degerli, M., Ozkan Yildirim, S.: Identifying critical success factors for wearable medical devices: a comprehensive exploration. Univ. Access Inf. Soc. (2020). https://doi.org/10.1007/s10209-020-00763-2

    Article  Google Scholar 

  47. 47.

    Wu, J., Li, H., Lin, Z., et al.: Competition in wearable device market: the effect of network externality and product compatibility. Electron Commer. Res 17, 335–359 (2017). https://doi.org/10.1007/s10660-016-9227-6

    Article  Google Scholar 

  48. 48.

    Ozkan Yildirim S. & Pancar T.: Smart Wearable Technology for Health Tracking: What are the factors that affect their use? In: Marques, G., Bhoi, A.K., Albuquerque, V.H.C. de, K.S., H. (Eds.) IoT in Healthcare and Ambient Assisted Living (2021, in press).

  49. 49.

    Neufeld, D.J., Dong, L.Y. and Higgins, C.: Charismatic leadership and user acceptance of information technology. Euro. J. Inf. Syst. 16(4), 494–510 (2007)

  50. 50.

    Williams, M. D., Rana, N., Dwivedi, Y. K.: The unified theory of acceptance and use of technology (UTAUT): A literature review. J Enter. Inf. Manage. 28(3), 443–488 (2015)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Tansu Pancar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest concerning this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 634 KB)

Supplementary file2 (PDF 587 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pancar, T., Ozkan Yildirim, S. Exploring factors affecting consumers' adoption of wearable devices to track health data. Univ Access Inf Soc (2021). https://doi.org/10.1007/s10209-021-00848-6

Download citation