Skip to main content
SpringerLink
Log in

Understanding Wearable Device Adoption: Review on Adoption Factors and Directions for Further Research in Smart Healthcare

  • Conference paper
  • First Online:
Advances on Intelligent Informatics and Computing (IRICT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 127))

  • 1006 Accesses

Abstract

This paper analyses prior literature that identify adoption model for smart wearable healthcare devices. This assessment aims to contribute and identify factors that enable users to adopt wearable devices in the Internet of Things (IoT) based healthcare to monitor blood glucose measuring. This study has set off in quest of research in IoT smart healthcare focusing on blood glucose monitoring based on previous studies on wearable devices for smart healthcare. The key aim of this paper is to provide a summary of published articles and to find the current factors leading to the adoption of wearable devices for smart healthcare. The authors guided a systematic review of wearable devices in smart healthcare to explore the factors of adopting smart healthcare devices. 55 studies were analyzed where 21 studies directly address wearable devices, adoption models, and also IoT systems. Most of the studies covered a few factors; namely Interpersonal Influence, Self-efficiency, Individual Innovativeness, Attitude toward wearable devices, Self-interest, Perceived Expensiveness, and Perceived Usefulness in a wearable fitness tracker or monitoring. Findings show that the effect of trustworthiness has a very extensive potential to be explored to improve the model prediction to measure the adoption of IoT wearable devices in smart healthcare as well as blood glucose monitoring.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fotiadis, D.I., Glaros, C., Likas, A.: Wearable medical devices. Wiley Encycl. Biomed. Eng. (2006)

    Google Scholar 

  2. Kim, S., Kim, S.: User preference for an IoT healthcare application for lifestyle disease management. Telecomm. Policy 42(4), 304–314 (2018)

    Article  Google Scholar 

  3. Casselman, J., Onopa, N., Khansa, L.: Wearable healthcare: lessons from the past and a peek into the future. Telemat. Inform. 34(7), 1011–1023 (2017)

    Article  Google Scholar 

  4. Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A.P.: Machine learning for internet of things data analysis: a survey. Digit. Commun. Netw. 4(3), 161–175 (2018)

    Article  Google Scholar 

  5. Hsiao, K.L., Chen, C.C.: What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telemat. Inform. 35(1), 103–113 (2018)

    Article  Google Scholar 

  6. Aslani, P., et al.: Consumer opinions on adverse events associated with medical devices. Res. Soc. Adm. Pharm. 15(5), 568–574 (2019)

    Article  MathSciNet  Google Scholar 

  7. Yang, H., Yu, J., Zo, H., Choi, M.: User acceptance of wearable devices: an extended perspective of perceived value. Telemat. Inform. 33(2), 256–269 (2016)

    Article  Google Scholar 

  8. Yachana, N.K., Sood, S.K.: A trustworthy system for secure access to patient centric sensitive information. Telemat. Inform. 35(4), 790–800 (2018)

    Google Scholar 

  9. Buenaflor, C., Kim, H.C.: Six human factors to acceptability of wearable computers. Int. J. Multimed. Ubiquitous Eng. 8(3), 103–114 (2013)

    Google Scholar 

  10. Hossain, M.I., Yusof, A.F., Hussin, A.R.C., lahad, N.A., Sadiq, A.S.: Factors influencing adoption model of continuous glucose monitoring devices for internet of things healthcare. Internet Things 15, 100353 (2021)

    Google Scholar 

  11. Lin, D., Lee, C.K.M., Tai, W.C.: Application of interpretive structural modelling for analyzing the factors of IoT adoption on supply chains in the Chinese agricultural industry. In: IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2017-Decem, pp. 1347–1351 (2018)

    Google Scholar 

  12. Gajanayake, R., Iannella, R., Sahama, T.: An insight into the adoption of Accountable-eHealth systems – an empirical research model based on the Australian context. IRBM 37(4), 219–231 (2016)

    Article  Google Scholar 

  13. Lee, S.Y.: Examining the factors that influence early adopters’ smartphone adoption: the case of college students. Telemat. Inform. 31(2), 308–318 (2014)

    Article  Google Scholar 

  14. Moon, L.A.: Factors influencing health data sharing preferences of consumers: a critical review. Heal. Policy Technol. 6(2), 169–187 (2017)

    Article  Google Scholar 

  15. Haque, M.M., Ahlan, A.R., Mohamed Razi, M.J.: Factors affecting knowledge sharing on innovation in the higher education institutions (HEis). ARPN J. Eng. Appl. Sci. 10(23), 18200–18210 (2015)

    Google Scholar 

  16. Gücin, N.Ö., Berk, Ö.S.: Technology acceptance in health care: an integrative review of predictive factors and intervention programs. Procedia Soc. Behav. Sci. 195, 1698–1704 (2015)

    Article  Google Scholar 

  17. Canhoto, A.I., Arp, S.: Exploring the factors that support adoption and sustained use of health and fitness wearables. J. Mark. Manag. 33(1–2), 32–60 (2017)

    Article  Google Scholar 

  18. Yildirim, H., Ali-Eldin, A.M.T.: A model for predicting user intention to use wearable IoT devices at the workplace. J. King Saud Univ. Comput. Inf. Sci. 1–9 (2018)

    Google Scholar 

  19. Lunney, A., Cunningham, N.R., Eastin, M.S.: Wearable fitness technology: a structural investigation into acceptance and perceived fitness outcomes. Comput. Human Behav. 65, 114–120 (2016)

    Article  Google Scholar 

  20. Rupp, M.A., Michaelis, J.R., McConnell, D.S., Smither, J.A.: The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Appl. Ergon. 70(April 2017), 77–87 (2018)

    Google Scholar 

  21. Lee, S.Y., Lee, K.: Factors that influence an individual’s intention to adopt a wearable healthcare device: the case of a wearable fitness tracker. Technol. Forecast. Soc. Change 129(December 2017), 154–163 (2018)

    Google Scholar 

  22. Dehghani, M., Kim, K.J., Dangelico, R.M.: Will smartwatches last? Factors contributing to intention to keep using smart wearable technology. Telemat. Inform. 35(2), 480–490 (2018)

    Article  Google Scholar 

  23. Martínez-Caro, E., Cegarra-Navarro, J.G., García-Pérez, A., Fait, M.: Healthcare service evolution towards the internet of things: an end-user perspective. Technol. Forecast. Soc. Change 136(March), 268–276 (2018)

    Article  Google Scholar 

  24. Keikhosrokiani, P., Mustaffa, N., Zakaria, N.: Success factors in developing iHeart as a patient-centric healthcare system: a multi-group analysis. Telemat. Inform. 35(4), 753–775 (2018)

    Article  Google Scholar 

  25. Dehghani, M., Joon, K., Maria, R.: Telematics and informatics will smartwatches last? Factors contributing to intention to keep using smart wearable technology. Telemat. Inform. 35(2), 480–490 (2020)

    Article  Google Scholar 

  26. Chuah, S.H.W., Rauschnabel, P.A., Krey, N., Nguyen, B., Ramayah, T., Lade, S.: Wearable technologies: the role of usefulness and visibility in smartwatch adoption. Comput. Human Behav. 65, 276–284 (2016)

    Article  Google Scholar 

  27. Marakhimov, A., Joo, J.: Consumer adaptation and infusion of wearable devices for healthcare. Comput. Human Behav. 76, 135–148 (2017)

    Article  Google Scholar 

  28. Hatz, M.H.M., Sonnenschein, T., Blankart, C.R.: The PMA scale: a measure of physicians’ motivation to adopt medical devices. Value Heal. 20(4), 533–541 (2017)

    Article  Google Scholar 

  29. Li, J., Zhang, C., Li, X., Zhang, C.: Patients’ emotional bonding with MHealth apps: an attachment perspective on patients’ use of MHealth applications. Int. J. Inf. Manag. (March), 102054 (2019)

    Google Scholar 

  30. Nasir, S., Yurder, Y.: Consumers’ and physicians’ perceptions about high tech wearable health products. Procedia Soc. Behav. Sci. 195, 1261–1267 (2015)

    Article  Google Scholar 

  31. Anwar, M., Joshi, J., Tan, J.: Anytime, anywhere access to secure, privacy-aware healthcare services: Issues, approaches and challenges. Heal. Policy Technol. 4(4), 299–311 (2015)

    Article  Google Scholar 

  32. Wu, T., Deng, Z., Zhang, D., Buchanan, P.R., Zha, D., Wang, R.: International journal of medical informatics seeking and using intention of health information from doctors in social media : the effect of doctor-consumer interaction. Int. J. Med. Inform. 115(April), 106–113 (2018)

    Article  Google Scholar 

  33. Yee-Loong Chong, A., Liu, M.J., Luo, J., Keng-Boon, O.: Predicting RFID adoption in healthcare supply chain from the perspectives of users. Int. J. Prod. Econ. 159, 66–75 (2015)

    Google Scholar 

  34. Holden, R.J., Karsh, B.-T.: The technology acceptance model: its past and its future in health care. J. Biomed. Inform. 43(1), 159–172 (2010)

    Article  Google Scholar 

  35. Asrar-ul-Haq, M., Anwar, S.: A systematic review of knowledge management and knowledge sharing: trends, issues, and challenges. Cogent Bus. Manag. 3(1), 1–17 (2016)

    Article  Google Scholar 

  36. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  37. Jebaseeli, T.J., Durai, C.A.D., Peter, J.D.: IOT based sustainable diabetic retinopathy diagnosis system. Sustain. Comput. Inform. Syst. (2018). Elsevier Inc.

    Google Scholar 

  38. Byrne, J.R., O’Sullivan, K., Sullivan, K.: An IoT and wearable technology Hackathon for promoting careers in computer science. IEEE Trans. Educ. 60(1), 50–58 (2017)

    Article  Google Scholar 

  39. Ahmad, N.A., Drus, S.M., Kasim, H., Othman, M.M.: Assessing content validity of enterprise architecture adoption questionnaire (EAAQ) among content experts. In: 2019 IEEE 9th Symposium on Computer Applications and Industrial Electronics, pp. 160–165 (2019)

    Google Scholar 

  40. Balapour, A., Reychav, I., Sabherwal, R., Azuri, J.: Mobile technology identity and self-efficacy: implications for the adoption of clinically supported mobile health apps. Int. J. Inf. Manag. 49(October 2018), 58–68 (2019)

    Google Scholar 

  41. Haron, N., Jaafar, J., Aziz, I.A., Hassan, M.H., Shapiai, M.I.: Data trustworthiness in internet of things: a taxonomy and future directions. In: 2017 IEEE Conference on Big Data Analysis, ICBDA 2017, vol. 2018-Janua, pp. 25–30 (2018)

    Google Scholar 

  42. Hennemann, S., Beutel, M.E., Zwerenz, R.: Ready for eHealth? Health professionals’ acceptance and adoption of eHealth interventions in inpatient routine care. J. Health Commun. 22(3), 274–284 (2017)

    Article  Google Scholar 

  43. Lassar, W.M., Manolis, C., Lassar, S.S.: The relationship between consumer innovativeness, personal characteristics, and online banking adoption, 23(2) (2005)

    Google Scholar 

  44. Ayeh, J.K., Au, N., Law, R.: Predicting the intention to use consumer-generated media for travel planning. Tour. Manag. 35, 132–143 (2013)

    Article  Google Scholar 

  45. Zhang, T., Tao, D., Qu, X., Zhang, X., Lin, R., Zhang, W.: The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transp. Res. Part C Emerg. Technol. 98(June 2018), 207–220 (2019)

    Google Scholar 

  46. Konuk, F.A.: The role of store image, perceived quality, trust and perceived value in predicting consumers’ purchase intentions towards organic private label food. J. Retail. Consum. Serv. 43(March), 304–310 (2018)

    Article  Google Scholar 

  47. Liu, X., Li, Q., Lai, I.K.W.: A trust model for the adoption of cloud-based supply chain management systems: a conceptual framework. In: ICEMSI 2013 - 2013 International Conference on Engineering, Management Science and Innovation, pp. 1–4 (2013)

    Google Scholar 

  48. Rupp, M.A., Michaelis, J.R., McConnell, D.S., Smither, J.A.: The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Appl. Ergon. 70, 77–87 (2018)

    Article  Google Scholar 

  49. Hong, I.B.: Understanding the consumer’s online merchant selection process: the roles of product involvement, perceived risk, and trust expectation. Int. J. Inf. Manag. 35(3), 322–336 (2015)

    Article  Google Scholar 

  50. Wright, E.B., Holcombe, C., Salmon, P.: Doctors’ communication of trust, care, and respect in breast cancer: qualitative study. Br. Med. J. 328(7444), 864–867 (2004)

    Article  Google Scholar 

  51. Ho, S.M., Ocasio-Velázquez, M., Booth, C.: Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption. Comput. Secur. 70, 581–595 (2017)

    Article  Google Scholar 

  52. Wu, T., Deng, Z., Zhang, D., Buchanan, P.R., Zha, D., Wang, R.: Seeking and using intention of health information from doctors in social media: the effect of doctor-consumer interaction. Int. J. Med. Inform. 115(April), 106–113 (2018)

    Article  Google Scholar 

  53. Zhang, B.M., Raghunathan, A., Jha, N.K.: Trustworthiness of medical devices and body area networks, 102(8) (2014)

    Google Scholar 

  54. Olczuk, D., Priefer, R.: A history of continuous glucose monitors (CGMs) in self-monitoring of diabetes mellitus. Diabetes Metab. Syndr. Clin. Res. Rev. 12(2), 181–187 (2018)

    Article  Google Scholar 

  55. Hossain, M.I., Yusof, A.F., Hussin, A.R.C., Billah, M., Shanmugam, M.: The content and construct development of CGMs device adoption model. In: Siconian 2019, vol. 172 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

    Md Ismail Hossain & Ahmad Fadhil Yusof

  2. Department of Informatics, College of Computing and Informatics, Universiti Tenaga Nasional, Selangor, Malaysia

    Mohana Shanmugam

Authors
  1. Md Ismail Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmad Fadhil Yusof
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohana Shanmugam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Fadhil Yusof .

Editor information

Editors and Affiliations

  1. Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah, Malaysia

    Fathey Mohammed

  3. Department of Computer Science, School of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Fuad Ghaleb

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hossain, M.I., Yusof, A.F., Shanmugam, M. (2022). Understanding Wearable Device Adoption: Review on Adoption Factors and Directions for Further Research in Smart Healthcare. In: Saeed, F., Mohammed, F., Ghaleb, F. (eds) Advances on Intelligent Informatics and Computing. IRICT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-98741-1_54

Download citation

Publish with us

Policies and ethics

Search

Navigation