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Identifying critical success factors for wearable medical devices: a comprehensive exploration

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Abstract

For healthy living, the successful use of wearable medical devices such as smartwatches, smart clothes, smart glasses, sports/activity trackers, and various sensors placed on a body is getting more important as benefits of these devices become apparent. Yet, the existing knowledge about the critical success factors for wearable medical devices needs to evolve and develop further. The main objective of this research is to distill salient constructs to enhance the successful use of wearable medical devices. Specifically, the study aims to identify factors, associated items, and interactions of the relevant factors. A questionnaire has been developed and deployed. The data were collected from 1057 people specifically chosen to represent a wide range of the population. Comprehensive and meaningful inferences have been drawn. Principally, as a fusion of factor analysis and path analysis, a partial least squares structural equation modeling approach consisting of exploratory and confirmatory factor analyses has been applied. In order to assess internal generalization and to precisely identify additional constructs, quasi-statistics have been used. The analyses of data collected revealed 11 salient constructs with 39 items and 18 statistically significant relationships among these constructs. Consequently, composed of distilled constructs and their associations, a novel model with an explanatory power of 73.884% has been approved. Moreover, 13 additional factors were identified as a result of the applied quasi-statistics. This research is the first of its kind on account of its sample characteristics with applied comprehensive methodology and distilled results. This research contributes to the pertinent body of knowledge concerning the critical success factors for wearable medical devices with distilled results. These contributions notably advance the relevant understanding and will be beneficial for researchers and for developers in the field of wearable medical devices.

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References

  1. Bates, D.W., Cresswell, K.M., Wright, A., Sheikh, A.: The future of medical informatics. In: Key Advances in Clinical Informatics (pp. 293–300). Elsevier, (2017). https://doi.org/10.1016/B978-0-12-809523-2.00020-0

  2. Page, T.: A forecast of the adoption of wearable technology. Int. J. Technol. Diffus. 6(2), 12–29 (2015). https://doi.org/10.4018/IJTD.2015040102

    Article  Google Scholar 

  3. Wright, R., Keith, L.: Wearable technology: if the tech fits, wear it. J Electr. Resour. Med. Libr. 11(4), 204–216 (2014). https://doi.org/10.1080/15424065.2014.969051

    Article  Google Scholar 

  4. Reeder, B., David, A.: Health at hand: a systematic review of smart watch uses for health and wellness. J. Biomed. Inform. 63, 269–276 (2016). https://doi.org/10.1016/j.jbi.2016.09.001

    Article  Google Scholar 

  5. Hemapriya, D., Viswanath, P., Mithra, V. M., Nagalakshmi, S., Umarani, G.: Wearable medical devices —design challenges and issues. In: 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT) (pp. 1–6). IEEE, (2017).https://doi.org/10.1109/IGEHT.2017.8094096

  6. Fang, Y.-M., Chang, C.-C.: Users’ psychological perception and perceived readability of wearable devices for elderly people. Behav. Inf. Technol. 35(3), 225–232 (2016). https://doi.org/10.1080/0144929X.2015.1114145

    Article  Google Scholar 

  7. Jung, Y., Kim, S., Choi, B.: Consumer valuation of the wearables: the case of smartwatches. Comput. Hum. Behav. 63(2016), 899–905 (2016). https://doi.org/10.1016/j.chb.2016.06.040

    Article  Google Scholar 

  8. 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. Hum. Behav. 65, 276–284 (2016). https://doi.org/10.1016/j.chb.2016.07.047

    Article  Google Scholar 

  9. Dehghani, M.: Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behav. Inf. Technol. 37(2), 145–158 (2018). https://doi.org/10.1080/0144929X.2018.1424246

    Article  MathSciNet  Google Scholar 

  10. Araujo de Carvalho, I., Epping-Jordan, J., Pot, A.M., Kelley, E., Toro, N., Thiyagarajan, J.A., Beard, J.R.: Organizing integrated health-care services to meet older people’s needs. Bull. World Health Organ. 95(11), 756–763 (2017). https://doi.org/10.2471/BLT.16.187617

    Article  Google Scholar 

  11. Do, H.M., Pham, M., Sheng, W., Yang, D., Liu, M.: RiSH: a robot-integrated smart home for elderly care. Robot. Auton. Syst. 101, 74–92 (2018). https://doi.org/10.1016/j.robot.2017.12.008

    Article  Google Scholar 

  12. World Health Organization.: World report on ageing and health. Luxembourg (2015)

  13. Steven Kohn, M.: Wearable devices and personalized healthcare. Trends Cardiovasc. Med. 28(2), 151–152 (2018). https://doi.org/10.1016/j.tcm.2017.09.001

    Article  Google Scholar 

  14. Seshadri, D.R., Rowbottom, J.R., Drummond, C., Voos, J.E., Craker, J.: A review of wearable technology: moving beyond the hype: from need through sensor implementation. In: 2016 8th Cairo International Biomedical Engineering Conference (CIBEC) (pp. 52–55). IEEE, (2016). https://doi.org/10.1109/CIBEC.2016.7836118

  15. Erdmier, C., Hatcher, J., Lee, M.: Wearable device implications in the healthcare industry. J. Med. Eng. Technol. 40(4), 141–148 (2016). https://doi.org/10.3109/03091902.2016.1153738

    Article  Google Scholar 

  16. Lee, J., Kim, D., Ryoo, H.-Y., Shin, B.-S.: Sustainable wearables: wearable technology for enhancing the quality of human life. Sustainability 8(5), 466 (2016). https://doi.org/10.3390/su8050466

    Article  Google Scholar 

  17. Wu, J., Li, H., Cheng, S., Lin, Z.: The promising future of healthcare services: when big data analytics meets wearable technology. Inf. Manag. 53(8), 1020–1033 (2016). https://doi.org/10.1016/j.im.2016.07.003

    Article  Google Scholar 

  18. Hentschel, M.A., Haaksma, M.L., van de Belt, T.H.: Wearable technology for the elderly: underutilized solutions. Eur. Geriatr. Med. 7(5), 399–401 (2016). https://doi.org/10.1016/j.eurger.2016.07.008

    Article  Google Scholar 

  19. Zheng, J., Shen, Y., Zhang, Z., Wu, T., Zhang, G., Lu, H.: Emerging wearable medical devices towards personalized healthcare. In: Proceedings of the 8th International Conference on Body Area Networks (pp. 427–431). ACM, (2013). https://doi.org/10.4108/icst.bodynets.2013.253725

  20. Haghi, M., Thurow, K., Stoll, R.: Wearable devices in medical internet of things: scientific research and commercially available devices. Healthc. Inform. Res. 23(1), 4 (2017). https://doi.org/10.4258/hir.2017.23.1.4

    Article  Google Scholar 

  21. Aileni, R.M., Valderrama, A.C., Strungaru, R.: Wearable electronics for elderly health monitoring and active living. In: Ambient Assisted Living and Enhanced Living Environments (1st ed., pp. 247–269). Elsevier, (2017). https://doi.org/10.1016/B978-0-12-805195-5.00010-7

  22. Godfrey, A., Hetherington, V., Shum, H., Bonato, P., Lovell, N.H., Stuart, S.: From A to Z: wearable technology explained. Maturitas 113(April), 40–47 (2018). https://doi.org/10.1016/j.maturitas.2018.04.012

    Article  Google Scholar 

  23. 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(555), 8–17 (2016). https://doi.org/10.1016/j.ijmedinf.2015.12.010

    Article  Google Scholar 

  24. Cella, M., Okruszek, Ł., Lawrence, M., Zarlenga, V., He, Z., Wykes, T.: Using wearable technology to detect the autonomic signature of illness severity in schizophrenia. Schizophr. Res. 195, 537–542 (2018). https://doi.org/10.1016/j.schres.2017.09.028

    Article  Google Scholar 

  25. Kumari, P., Mathew, L., Syal, P.: Increasing trend of wearables and multimodal interface for human activity monitoring: a review. Biosens. Bioelectr. 90, 298–307 (2017). https://doi.org/10.1016/j.bios.2016.12.001

    Article  Google Scholar 

  26. Dehghani, M., Dangelico, R.M.: Smart wearable technologies: current status and market orientation through a patent analysis. In: 2017 IEEE International Conference on Industrial Technology (ICIT) (pp. 1570–1575). IEEE, (2017). https://doi.org/10.1109/ICIT.2017.7915602

  27. Kim, Y.K., Wang, H., Mahmud, M.S.: Wearable body sensor network for health care applications. In: Smart Textiles and Their Applications (pp. 161–184). Elsevier, (2016). https://doi.org/10.1016/B978-0-08-100574-3.00009-6

  28. Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A., Rios Rincon, A.: Smart homes and home health monitoring technologies for older adults: a systematic review. Int. J. Med. Inform. 91, 44–59 (2016). https://doi.org/10.1016/j.ijmedinf.2016.04.007

    Article  Google Scholar 

  29. Srizongkhram, S., Shirahada, K., Chiadamrong, N.: Critical factors for adoption of wearable technology for the elderly: case study of Thailand. In: 2018 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1–9). IEEE, (2018). https://doi.org/10.23919/PICMET.2018.8481990

  30. Park, S., Chung, K., Jayaraman, S.: Wearables. In Wearable Sensors (pp. 1–23). Elsevier, (2014). https://doi.org/10.1016/B978-0-12-418662-0.00001-5

  31. Khosravi, P., Ghapanchi, A.H.: Investigating the effectiveness of technologies applied to assist seniors: a systematic literature review. Int. J. Med. Inform. 85(1), 17–26 (2016). https://doi.org/10.1016/j.ijmedinf.2015.05.014

    Article  Google Scholar 

  32. Qi, J., Yang, P., Min, G., Amft, O., Dong, F., Xu, L.: Advanced internet of things for personalised healthcare systems: a survey. Pervasive Mob. Comput. 41, 132–149 (2017). https://doi.org/10.1016/j.pmcj.2017.06.018

    Article  Google Scholar 

  33. Beard, J.R., Officer, A., de Carvalho, I.A., Sadana, R., Pot, A.M., Michel, J.-P., Chatterji, S.: The world report on ageing and health: a policy framework for healthy ageing. Lancet 387(10033), 2145–2154 (2016). https://doi.org/10.1016/S0140-6736(15)00516-4

    Article  Google Scholar 

  34. World Health Organization. (2019). Ageing and life-course - What is Healthy Ageing? Retrieved September 13, 2019, from https://www.who.int/ageing/healthy-ageing/en/

  35. Beard, J.R., Officer, A.M., Cassels, A.K.: The world report on ageing and health. Gerontologist 56(Suppl 2), S163–S166 (2016). https://doi.org/10.1093/geront/gnw037

    Article  Google Scholar 

  36. Amado, C.A.F., São José, J.M.S., Santos, S.P.: Measuring active ageing: a data envelopment analysis approach. Eur. J. Oper. Res. 255(1), 207–223 (2016). https://doi.org/10.1016/j.ejor.2016.04.048

    Article  Google Scholar 

  37. Robbins, T.D., Keung, S.N.L.C., Arvanitis, T.N.: E-health for active ageing; a systematic review. Maturitas 114, 34–40 (2018). https://doi.org/10.1016/j.maturitas.2018.05.008

    Article  Google Scholar 

  38. Sullivan, A.N., Lachman, M.E.: Behavior change with fitness technology in sedentary adults: a review of the evidence for increasing physical activity. Frontiers in Public Health, 4(Jan), (2017). https://doi.org/10.3389/fpubh.2016.00289

  39. Muellmann, S., Forberger, S., Möllers, T., Bröring, E., Zeeb, H., Pischke, C.R.: Effectiveness of eHealth interventions for the promotion of physical activity in older adults: a systematic review. Prev. Med. 108, 93–110 (2018). https://doi.org/10.1016/j.ypmed.2017.12.026

    Article  Google Scholar 

  40. Thompson, W.G., Kuhle, C.L., Koepp, G.A., McCrady-Spitzer, S.K., Levine, J.A.: “Go4Life” exercise counseling, accelerometer feedback, and activity levels in older people. Arch. Gerontol. Geriatr. 58(3), 314–319 (2014). https://doi.org/10.1016/j.archger.2014.01.004

    Article  Google Scholar 

  41. Jonkman, N.H., van Schooten, K.S., Maier, A.B., Pijnappels, M.: eHealth interventions to promote objectively measured physical activity in community-dwelling older people. Maturitas 113(March), 32–39 (2018). https://doi.org/10.1016/j.maturitas.2018.04.010

    Article  Google Scholar 

  42. Cooper, C., Gross, A., Brinkman, C., Pope, R., Allen, K., Hastings, S., Goode, A.P.: The impact of wearable motion sensing technology on physical activity in older adults. Exp. Gerontol. 112, 9–19 (2018). https://doi.org/10.1016/j.exger.2018.08.002

    Article  Google Scholar 

  43. Tocci, F.L., Morey, M.C., Caves, K.M., Deberry, J., Leahy, G.D., Hall, K.: Are older adults ready for wireless physical activity tracking devices? a comparison of commonly used tracking devices. J. Am. Geriatr. Soc. 64(1), 226–228 (2016). https://doi.org/10.1111/jgs.13895

    Article  Google Scholar 

  44. Kim, K., Gollamudi, S.S., Steinhubl, S.: Digital technology to enable aging in place. Exp. Gerontol. 88, 25–31 (2017). https://doi.org/10.1016/j.exger.2016.11.013

    Article  Google Scholar 

  45. World Health Organization: Integrated Care for Older People. World Health Organization, Geneva (2017)

    Google Scholar 

  46. World Health Organization. (2011). A handbook on how to implement mAgeing. Geneva: World Health Organization and International Telecommunication Union. Retrieved from https://www.who.int/ageing/health-systems/mAgeing/mAgeing-handbook-April2018.PDF?ua=1

  47. World Health Organization. (2017). Global strategy and action plan on ageing and health. Geneva: World Health Organization. Retrieved from https://www.who.int/ageing/WHO-GSAP-2017.pdf?ua=1%0Ahttp://www.who.int/ageing/WHO-GSAP-2017.pdf?ua=1

  48. Beard, J.R., Araujo de Carvalho, I., Sumi, Y., Officer, A., Thiyagarajan, J.A.: Healthy ageing: moving forward. Bull. World Health Organ. 95(11), 730–730A (2017). https://doi.org/10.2471/BLT.17.203745

    Article  Google Scholar 

  49. 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 

  50. 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). https://doi.org/10.1016/j.tele.2017.04.011

    Article  Google Scholar 

  51. Kalantari, M.: Consumers’ adoption of wearable technologies: literature review, synthesis, and future research agenda. Int. J. Technol. Mark. 12(3), 274–306 (2017). https://doi.org/10.1504/IJTMKT.2017.089665

    Article  Google Scholar 

  52. Pal, D., Funilkul, S., Charoenkitkarn, N., Kanthamanon, P.: Internet-of-things and smart homes for elderly healthcare: an end user perspective. IEEE Access 6, 10483–10496 (2018). https://doi.org/10.1109/ACCESS.2018.2808472

    Article  Google Scholar 

  53. Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C., Campo, E.: Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137–156 (2012). https://doi.org/10.1016/j.artmed.2012.09.003

    Article  Google Scholar 

  54. Mostaghel, R.: Innovation and technology for the elderly: systematic literature review. J. Bus. Res. 69(11), 4896–4900 (2016). https://doi.org/10.1016/j.jbusres.2016.04.049

    Article  Google Scholar 

  55. Ward, R.: The application of technology acceptance and diffusion of innovation models in healthcare informatics. Health Policy Technol. 2(4), 222–228 (2013). https://doi.org/10.1016/j.hlpt.2013.07.002

    Article  Google Scholar 

  56. Renaud, K., van Biljon, J.: Predicting technology acceptance and adoption by the elderly. In: Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries riding the wave of technology-SAICSIT ’08 (vol. 338, pp. 210–219). New York, USA: ACM Press, (2008). https://doi.org/10.1145/1456659.1456684

  57. 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). https://doi.org/10.1016/j.jbi.2009.07.002

    Article  Google Scholar 

  58. Baig, M.M., GholamHosseini, H., Moqeem, A.A., Mirza, F., Lindén, M.: A systematic review of wearable patient monitoring systems—current challenges and opportunities for clinical adoption. J. Med. Syst. 41(7), 115 (2017). https://doi.org/10.1007/s10916-017-0760-1

    Article  Google Scholar 

  59. 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 

  60. Iqbal, M.H., Aydin, A., Brunckhorst, O., Dasgupta, P., Ahmed, K.: A review of wearable technology in medicine. J. R. Soc. Med. 109(10), 372–380 (2016). https://doi.org/10.1177/0141076816663560

    Article  Google Scholar 

  61. Gücin, N.Ö., Berk, Ö.S.: Technology acceptance in health care: An integrative review of predictive factors and intervention programs. Proc. Soc. Behav. Sci. 195, 1698–1704 (2015). https://doi.org/10.1016/j.sbspro.2015.06.263

    Article  Google Scholar 

  62. Or, C.K.L., Karsh, B.-T.: A systematic review of patient acceptance of consumer health information technology. J. Am. Med. Inform. Assoc. 16(4), 550–560 (2009). https://doi.org/10.1197/jamia.M2888

    Article  Google Scholar 

  63. Legris, P., Ingham, J., Collerette, P.: Why do people use information technology? a critical review of the technology acceptance model. Inf. Manag. 40(3), 191–204 (2003). https://doi.org/10.1016/S0378-7206(01)00143-4

    Article  Google Scholar 

  64. Gao, Y., Li, H., Luo, Y.: An empirical study of wearable technology acceptance in healthcare. Ind. Manag. Data Syst. 115(9), 1704–1723 (2015). https://doi.org/10.1108/IMDS-03-2015-0087

    Article  Google Scholar 

  65. 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. Chang. 129(February), 154–163 (2018). https://doi.org/10.1016/j.techfore.2018.01.002

    Article  Google Scholar 

  66. Park, E., Kim, K.J., Kwon, S.J.: Understanding the emergence of wearable devices as next-generation tools for health communication. Inf. Technol. People 29(4), 717–732 (2016). https://doi.org/10.1108/ITP-04-2015-0096

    Article  Google Scholar 

  67. Zhang, M., Luo, M., Nie, R., Zhang, Y.: Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. Int. J. Med. Inform. 108(August), 97–109 (2017). https://doi.org/10.1016/j.ijmedinf.2017.09.016

    Article  Google Scholar 

  68. Marakhimov, A., Joo, J.: Consumer adaptation and infusion of wearable devices for healthcare. Comput. Hum. Behav. 76, 135–148 (2017). https://doi.org/10.1016/j.chb.2017.07.016

    Article  Google Scholar 

  69. Deranek, K., Hewitt, B., Gudi, A., McLeod, A.: The impact of exercise motives on adolescents’ sustained use of wearable technology. Behav. Inf. Technol. (2020). https://doi.org/10.1080/0144929X.2020.1720295

    Article  Google Scholar 

  70. Kim, K.J., Shin, D.-H.: An acceptance model for smart watches. Internet Res. 25(4), 527–541 (2015). https://doi.org/10.1108/IntR-05-2014-0126

    Article  Google Scholar 

  71. Adapa, A., Nah, F.F.-H., Hall, R.H., Siau, K., Smith, S.N.: Factors influencing the adoption of smart wearable devices. Int. J. Human. Comput. Interact. 34(5), 399–409 (2018). https://doi.org/10.1080/10447318.2017.1357902

    Article  Google Scholar 

  72. 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). https://doi.org/10.1016/j.tele.2015.08.007

    Article  Google Scholar 

  73. Nelson, E.C., Verhagen, T., Noordzij, M.L.: Health empowerment through activity trackers: an empirical smart wristband study. Comput. Hum. Behav. 62, 364–374 (2016). https://doi.org/10.1016/j.chb.2016.03.065

    Article  Google Scholar 

  74. 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). https://doi.org/10.1080/0267257X.2016.1234505

    Article  Google Scholar 

  75. Li, J., Ma, Q., Chan, A.H., Man, S.S.: Health monitoring through wearable technologies for older adults: smart wearables acceptance model. Appl. Ergon. 75, 162–169 (2019). https://doi.org/10.1016/j.apergo.2018.10.006

    Article  Google Scholar 

  76. Koo, S.H., Fallon, K.: Explorations of wearable technology for tracking self and others. Fash. Text. 5(1), 8 (2018). https://doi.org/10.1186/s40691-017-0123-z

    Article  Google Scholar 

  77. Pfeiffer, J., Von Entress-Fuersteneck, M., Urbach, N., Buchwald, A.: Quantify-me: consumer acceptance of wearable self-tracking devices. In: Proceedings of the 24th European Conference on Information Systems (p. 99), (2016). https://aisel.aisnet.org/ecis2016_rp/99

  78. Cimperman, M., Makovec Brenčič, M., Trkman, P.: Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. Int. J. Med. Inform. 90, 22–31 (2016). https://doi.org/10.1016/j.ijmedinf.2016.03.002

    Article  Google Scholar 

  79. Karahoca, A., Karahoca, D., Aksöz, M.: Examining intention to adopt to internet of things in healthcare technology products. Kybernetes 47(4), 742–770 (2018). https://doi.org/10.1108/K-02-2017-0045

    Article  Google Scholar 

  80. Mattila, AAE., Kaipainen, K., Miikka Ermes, PV.: Oiva—a mobile phone intervention for psychological flexibility and wellbeing. In: Designing for Wellness and Behavior Change. Association for Computing Machinery, (2012)

  81. Mattila, E., Parkka, J., Hermersdorf, M., Kaasinen, J., Vainio, J., Samposalo, K., Korhonen, I.: Mobile diary for wellness management—results on usage and usability in two user studies. IEEE Trans. Inf. Technol. Biomed. 12(4), 501–512 (2008). https://doi.org/10.1109/TITB.2007.908237

    Article  Google Scholar 

  82. Salvi, D., Ottaviano, M., Muuraiskangas, S., Martínez-Romero, A., Vera-Muñoz, C., Triantafyllidis, A., Deighan, C.: An m-Health system for education and motivation in cardiac rehabilitation: the experience of heartcycle guided exercise. J. Telemed. Telec. 24(4), 303–316 (2018). https://doi.org/10.1177/1357633X17697501

    Article  Google Scholar 

  83. Holzinger, A., Searle, G., Kleinberger, T., Seffah, A., Javahery, H.: Investigating usability metrics for the design and development of applications for the elderly. In: K. Miesenberger, J. Klaus, W. Zagler, & A. Karshmer (Eds.), Computers Helping People with Special Needs (vol. ICCHP 2008, pp. 98–105). Berlin, Heidelberg, (2008). https://doi.org/10.1007/978-3-540-70540-6_13

  84. 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(3), 245–260 (2011). https://doi.org/10.1007/s10209-010-0212-x

    Article  Google Scholar 

  85. Wen, D., Zhang, X., Lei, J.: Consumers’ perceived attitudes to wearable devices in health monitoring in China: a survey study. Comput. Methods Progr. Biomed. 140, 131–137 (2017). https://doi.org/10.1016/j.cmpb.2016.12.009

    Article  Google Scholar 

  86. Wu, J.-H., Wang, S.-C., Lin, L.-M.: Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int. J. Med. Informatics 76(1), 66–77 (2007). https://doi.org/10.1016/j.ijmedinf.2006.06.006

    Article  Google Scholar 

  87. Nascimento, B., Oliveira, T., Tam, C.: Wearable technology: What explains continuance intention in smartwatches? J. Retail. Consum. Serv. 43, 157–169 (2018). https://doi.org/10.1016/j.jretconser.2018.03.017

    Article  Google Scholar 

  88. Pal, D., Funilkul, S., Vanijja, V.: The future of smartwatches: assessing the end-users’ continuous usage using an extended expectation-confirmation model. Univ. Access Inf. Soc. (2018). https://doi.org/10.1007/s10209-018-0639-z

    Article  Google Scholar 

  89. 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). https://doi.org/10.1016/j.tele.2018.01.007

    Article  Google Scholar 

  90. 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). https://doi.org/10.1016/j.tele.2017.10.002

    Article  Google Scholar 

  91. Wu, L.-H., Wu, L.-C., Chang, S.-C.: Exploring consumers’ intention to accept smartwatch. Comput. Hum. Behav. 64, 383–392 (2016). https://doi.org/10.1016/j.chb.2016.07.005

    Article  Google Scholar 

  92. Choi, J., Kim, S.: Is the smartwatch an IT product or a fashion product? a study on factors affecting the intention to use smartwatches. Comput. Hum. Behav. 63, 777–786 (2016). https://doi.org/10.1016/j.chb.2016.06.007

    Article  Google Scholar 

  93. Puri, A., Kim, B., Nguyen, O., Stolee, P., Tung, J., Lee, J.: User acceptance of wrist-worn activity trackers among community-dwelling older adults: mixed method study. JMIR mHealth uHealth 5(11), e173 (2017). https://doi.org/10.2196/mhealth.8211

    Article  Google Scholar 

  94. Preusse, K.C., Mitzner, T.L., Fausset, C.B., Rogers, W.A.: Older adults’ acceptance of activity trackers. J. Appl. Gerontol. 36(2), 127–155 (2017). https://doi.org/10.1177/0733464815624151

    Article  Google Scholar 

  95. Heart, T., Kalderon, E.: Older adults: are they ready to adopt health-related ICT? Int. J. Med. Inform. 82(11), e209–e231 (2013). https://doi.org/10.1016/j.ijmedinf.2011.03.002

    Article  Google Scholar 

  96. Golant, S.M.: A theoretical model to explain the smart technology adoption behaviors of elder consumers (Elderadopt). J. Aging Stud. 42, 56–73 (2017). https://doi.org/10.1016/j.jaging.2017.07.003

    Article  Google Scholar 

  97. Massa, P., Mazzali, A., Zampini, J., Zancanaro, M.: Quantify yourself: Are older adults ready? In: Lecture Notes in Electrical Engineering (vol. 426, pp. 377–389), (2017). https://doi.org/10.1007/978-3-319-54283-6_27

  98. Jeong, S.C., Kim, S.-H., Park, J.Y., Choi, B.: Domain-specific innovativeness and new product adoption: a case of wearable devices. Telemat. Inform. 34(5), 399–412 (2017). https://doi.org/10.1016/j.tele.2016.09.001

    Article  Google Scholar 

  99. Davis, F.D.: 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 

  100. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46(2), 186–204 (2000). https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  101. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003). https://doi.org/10.2307/30036540

    Article  Google Scholar 

  102. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Dec. Sci. 39(2), 273–315 (2008). https://doi.org/10.1111/j.1540-5915.2008.00192.x

    Article  Google Scholar 

  103. Hung, M.-C., Jen, W.-Y.: The adoption of mobile health management services: an empirical study. J. Med. Syst. 36(3), 1381–1388 (2012). https://doi.org/10.1007/s10916-010-9600-2

    Article  Google Scholar 

  104. Macedo, I.M.: Predicting the acceptance and use of information and communication technology by older adults: an empirical examination of the revised UTAUT2. Comput. Hum. Behav. 75, 935–948 (2017). https://doi.org/10.1016/j.chb.2017.06.013

    Article  Google Scholar 

  105. 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). https://doi.org/10.1016/j.ijmedinf.2017.02.002

    Article  Google Scholar 

  106. Ziefle, M.Z., Röcker, C., Holzinger, A.: Perceived usefulness of assistive technologies and electronic services for ambient assisted living. In: Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare (pp. 585–592). IEEE, (2011). https://doi.org/10.4108/icst.pervasivehealth.2011.246044

  107. Ziefle, M., Klack, L., Wilkowska, W., Holzinger, A.: Acceptance of telemedical treatments – a medical professional point of view. In: S. Yamamoto (Ed.), Human Interface and the Management of Information. Information and Interaction for Health, Safety, Mobility and Complex Environments (vol. LNCS 8017, pp. 325–334). Berlin, Heidelberg, (2013). https://doi.org/10.1007/978-3-642-39215-3_39

  108. Holzinger, A., Searle, G., Pruckner, S., Steinbach-Nordmann, S., Kleinberger, T., Hirt, E., Temnitzer, J.: Perceived usefulness among elderly people: experiences and lessons learned during the evaluation of a wrist device. In: Proceedings of the 4th International ICST Conference on Pervasive Computing Technologies for Healthcare. IEEE, (2010). https://doi.org/10.4108/ICST.PERVASIVEHEALTH2010.8912

  109. Sezgin, E., Özkan-Yildirim, S., Yildirim, S.: Investigation of physicians’ awareness and use of mHealth apps: a mixed method study. Health Policy Technol. 6(3), 251–267 (2017). https://doi.org/10.1016/j.hlpt.2017.07.007

    Article  Google Scholar 

  110. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991). https://doi.org/10.1016/0749-5978(91)90020-T

    Article  Google Scholar 

  111. Peek, S.T.M., Luijkx, K.G., Rijnaard, M.D., Nieboer, M.E., van der Voort, C.S., Aarts, S., Wouters, E.J.M.: Older adults’ reasons for using technology while aging in place. Gerontology 62(2), 226–237 (2016). https://doi.org/10.1159/000430949

    Article  Google Scholar 

  112. Hussein, Z., Oon, S.W., Fikry, A.: Consumer attitude: does it influencing the intention to use mHealth? Proc. Comput. Sci. 105, 340–344 (2017). https://doi.org/10.1016/j.procs.2017.01.231

    Article  Google Scholar 

  113. van Hoof, J., Kort, H.S.M., Rutten, P.G.S., Duijnstee, M.S.H.: Ageing-in-place with the use of ambient intelligence technology: perspectives of older users. Int. J. Med. Inform. 80(5), 310–331 (2011). https://doi.org/10.1016/j.ijmedinf.2011.02.010

    Article  Google Scholar 

  114. Motti, V.G., Caine, K.: Users’ privacy concerns about wearables. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (vol. 8976, pp. 231–244), (2015). https://doi.org/10.1007/978-3-662-48051-9_17

  115. Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Seneviratne, A.: A Survey of wearable devices and challenges. IEEE Commun. Surv. Tutor. 19(4), 2573–2620 (2017). https://doi.org/10.1109/COMST.2017.2731979

    Article  Google Scholar 

  116. Al-Janabi, S., Al-Shourbaji, I., Shojafar, M., Shamshirband, S.: Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications. Egypt. Inform. J. 18(2), 113–122 (2017). https://doi.org/10.1016/j.eij.2016.11.001

    Article  Google Scholar 

  117. Zhang, Y., Rau, P.-L.P.: Playing with multiple wearable devices: Exploring the influence of display, motion and gender. Comput. Hum. Behav. 50, 148–158 (2015). https://doi.org/10.1016/j.chb.2015.04.004

    Article  Google Scholar 

  118. Chen, K., Chan, A.H.S.: Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5), 635–652 (2014). https://doi.org/10.1080/00140139.2014.895855

    Article  Google Scholar 

  119. Luijkx, K., Peek, S., Wouters, E.: “Grandma, you should do it—it’s cool” older sdults and the role of family members in their acceptance of technology. Int. J. Environ. Res. Public Health 12(12), 15470–15485 (2015). https://doi.org/10.3390/ijerph121214999

    Article  Google Scholar 

  120. Stragier, J., Vanden Abeele, M., Mechant, P., De Marez, L.: Understanding persistence in the use of online fitness communities: comparing novice and experienced users. Comput. Hum. Behav. 64, 34–42 (2016). https://doi.org/10.1016/j.chb.2016.06.013

    Article  Google Scholar 

  121. Beaudry, A., Pinsonneaul, A.: The other side of acceptance: studying the direct and indirect effects of emotions on information technology use. MIS Q. 34(4), 689 (2010). https://doi.org/10.2307/25750701

    Article  Google Scholar 

  122. Vincent, C.J., Li, Y., Blandford, A.: Integration of human factors and ergonomics during medical device design and development: it’s all about communication. Appl. Ergon. 45(3), 413–419 (2014). https://doi.org/10.1016/j.apergo.2013.05.009

    Article  Google Scholar 

  123. Patel, A.D., Moss, R., Rust, S.W., Patterson, J., Strouse, R., Gedela, S., Lin, S.M.: Patient-centered design criteria for wearable seizure detection devices. Epilepsy. Behav. 64, 116–121 (2016). https://doi.org/10.1016/j.yebeh.2016.09.012

    Article  Google Scholar 

  124. Holden, R.J., Kulanthaivel, A., Purkayastha, S., Goggins, K.M., Kripalani, S.: Know thy eHealth user: development of biopsychosocial personas from a study of older adults with heart failure. Int. J. Med. Inform. 108, 158–167 (2017). https://doi.org/10.1016/j.ijmedinf.2017.10.006

    Article  Google Scholar 

  125. Kim, K.J.: Shape and size matter for smartwatches: effects of screen shape, screen size, and presentation mode in wearable communication. J. Comput. Med. Commun. 22(3), 124–140 (2017). https://doi.org/10.1111/jcc4.12186

    Article  Google Scholar 

  126. Shieh, M.-D., Hsiao, H.-C., Lin, Y.-H., Lin, J.-Y.: A study of the elderly people’s perception of wearable device forms. J. Interdiscip. Math. 20(3), 789–804 (2017). https://doi.org/10.1080/09720502.2016.1258839

    Article  Google Scholar 

  127. Privitera, M.B., Evans, M., Southee, D.: Human factors in the design of medical devices —approaches to meeting international standards in the European Union and USA. Appl. Ergon. 59, 251–263 (2017). https://doi.org/10.1016/j.apergo.2016.08.034

    Article  Google Scholar 

  128. Hagedorn, T.J., Krishnamurty, S., Grosse, I.R.: An information model to support user-centered design of medical devices. J. Biomed. Inform. 62, 181–194 (2016). https://doi.org/10.1016/j.jbi.2016.07.010

    Article  Google Scholar 

  129. Ogbanufe, O., Gerhart, N.: Watch it! factors driving continued feature use of the smartwatch. Int. J. Human Comput. Interact. 34(11), 999–1014 (2018). https://doi.org/10.1080/10447318.2017.1404779

    Article  Google Scholar 

  130. Kimberlin, C.L., Winterstein, A.G.: Validity and reliability of measurement instruments used in research. Am. J. Health Syst. Pharm. 65(23), 2276–2284 (2008). https://doi.org/10.2146/ajhp070364

    Article  Google Scholar 

  131. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis. Pearson Education Limited, Essex (2014)

    Google Scholar 

  132. Hox, J.J., Bechger, T.M.: An introduction to structural equation modeling. Family Sci. Rev. 11, 354–373 (1998)

    Google Scholar 

  133. Weston, R., Gore, P.A.: A brief guide to structural equation modeling. Couns. Psychol. 34(5), 719–751 (2006). https://doi.org/10.1177/0011000006286345

    Article  Google Scholar 

  134. Chin, W.W., Peterson, R.A., Brown, S.P.: Structural equation modeling in marketing: some practical reminders. J. Mark. Theory. Pract. 16(4), 287–298 (2008). https://doi.org/10.2753/MTP1069-6679160402

    Article  Google Scholar 

  135. Wong, K.K.-K.: Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark. Bull. 24(1), 1–32 (2013)

    Google Scholar 

  136. Oliver, G., Liehr-gobbers, K., Krafft, M.: Evaluation of structural equation models using the partial least squares (PLS) approach. In: V. Esposito Vinzi, W.W. Chin, J. Henseler, H. Wang (eds.), Handbook of Partial Least Squares (pp. 691–711) Springer, Berlin, (2010). https://doi.org/10.1007/978-3-540-32827-8

  137. Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19(2), 139–152 (2011). https://doi.org/10.2753/MTP1069-6679190202

    Article  Google Scholar 

  138. Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A.: An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40(3), 414–433 (2012). https://doi.org/10.1007/s11747-011-0261-6

    Article  Google Scholar 

  139. Sarstedt, M., Ringle, C.M., Hair, J.F.: PLS-SEM: looking back and moving forward. Long Range Plan. 47(3), 132–137 (2014). https://doi.org/10.1016/j.lrp.2014.02.008

    Article  Google Scholar 

  140. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd edn.). Thousand Oaks, CA: SAGE, (2017)

  141. Lowry, P.B., Gaskin, J.: Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Trans. Prof. Commun. 57(2), 123–146 (2014). https://doi.org/10.1109/TPC.2014.2312452

    Article  Google Scholar 

  142. Gefen, D., Rigdon, E.E., Straub, D.: An update and extension to SEM guidelines for administrative and social science research. MIS Q. (2011). https://doi.org/10.2307/23044042

    Article  Google Scholar 

  143. Matsunaga, M.: How to factor-analyze your data right: do’s, don’ts, and how-to’s. Int. J. Psychol. Res. 3(1), 97–110 (2010)

    Article  Google Scholar 

  144. Fabrigar, L.R., Wegener, D.T., MacCallum, R.C., Strahan, E.J.: Evaluating the use of exploratory factor analysis in psychological research. Psychol. Methods 4(3), 272–299 (1999). https://doi.org/10.1037/1082-989X.4.3.272

    Article  Google Scholar 

  145. Myers, N.D., Ahn, S., Jin, Y.: Sample size and power estimates for a confirmatory factor analytic model in exercise and sport. Res. Q. Exerc. Sport 82(3), 412–423 (2011). https://doi.org/10.1080/02701367.2011.10599773

    Article  Google Scholar 

  146. Pett, M.A., Lackey, N.R., Sullivan, J.: Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research. SAGE, Thousand Oaks, CA (2003)

    Book  Google Scholar 

  147. Tabachnick, B.G., Fidell, L.S.: Using Multivariate Statistics (4th edn.). Boston: Allyn and Bacon, (2001)

  148. Yong, A.G., Pearce, S.: A Beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutor. Quant. Methods Psychol. 9(2), 79–94 (2013). https://doi.org/10.20982/tqmp.09.2.p079

    Article  Google Scholar 

  149. Brown, J.D.: Choosing the right type of rotation in PCA and EFA. JALT Test Eval. SIG Newsl. 13(3), 20–25 (2009)

    Google Scholar 

  150. Beavers, A.S., Lounsbury, J.W., Richards, J.K., Huck, S.W., Skolits, G.J., Esquivel, S.L.: Practical considerations for using exploratory factor analysis in educational research. Pract. Assess Res. Eval. 18, 6 (2013)

    Google Scholar 

  151. O’Brien, R.M.: Identification of simple measurement models with multiple latent variables and correlated errors. Sociol. Methodol. 24(1994), 137–170 (1994). https://doi.org/10.2307/270981

    Article  Google Scholar 

  152. Bollen, K.A.: Latent variables in psychology and the social sciences. Annu. Rev. Psychol. 53(1), 605–634 (2002). https://doi.org/10.1146/annurev.psych.53.100901.135239

    Article  Google Scholar 

  153. Ringle, C.M., Wende, S., Becker, J.-M.: SmartPLS. SmartPLS. Retrieved from www.smartpls.com, Bönningstedt (2015)

    Google Scholar 

  154. Hulland, J.: Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strateg. Manag. J. 20(2), 195–204 (1999). https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195:AID-SMJ13>3.0.CO;2-7

    Article  Google Scholar 

  155. Bagozzi, R.P., Yi, Y.: On the evaluation of structural equation models. J. Acad. Mark. Sci. 16(1), 74–94 (1988). https://doi.org/10.1007/BF02723327

    Article  Google Scholar 

  156. Costello, A.B., Osborne, J.W.: Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract. Assess Res. Eval. 10(7), 1–9 (2005)

    Google Scholar 

  157. Gefen, D., Straub, D., Boudreau, M.-C.: Structural equation modeling and regression: guidelines for research practice. Commun. Assoc. Inf. Syst. (2000). https://doi.org/10.17705/1cais.00407

    Article  Google Scholar 

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  1. Graduate School of Informatics, Middle East Technical University, 06800, Cankaya, Ankara, Turkey

    Mustafa Degerli & Sevgi Ozkan Yildirim

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  1. Mustafa Degerli
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MD was involved in conceptualization, methodology, validation, formal analysis, investigation, data curation, writing—original draft, and project administration, and S-OY analyzed conceptualization, methodology, writing—review & editing, and project administration.

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Correspondence to Mustafa Degerli.

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Appendix A. The final form of the questionnaire in English: The final English form of the questionnaire developed and deployed is given in Appendix A. (DOCX 84 kb)

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Appendix B. The raw data in English: The raw data collected by means of the questionnaire in this research is given in Appendix B (XLSX 232 kb)

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Appendix C. Additional tables for analysis results: Additional tables for analysis results are given in Appendix C (DOCX 53 kb)

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Degerli, M., Ozkan Yildirim, S. Identifying critical success factors for wearable medical devices: a comprehensive exploration. Univ Access Inf Soc 21, 121–143 (2022). https://doi.org/10.1007/s10209-020-00763-2

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