Adoption Intention of an IoT Based Healthcare Technologies in Rehabilitation Process

References

• Acosta, C. O., Palacio, R. R., Borrego, G., García, R., & Rodríguez, M. J. (2022). Design guidelines and usability for cognitive stimulation through technology in Mexican older adults. Informatics for Health & Social Care, 47(1), 103–119. https://doi.org/10.1080/17538157.2021.1941973
   PubMed Web of Science ®Google Scholar
• Aggarwal, D., Zhang, W., Hoang, T., Ploderer, B., Vetere, F., & Bradford, M. (2017). SoPhy: A wearable technology for lower limb assessment in video consultations of physiotherapy. CHI 2017 (pp. 3916–3928). Denver.
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x
   Web of Science ®Google Scholar
• Alexandre, R., & Postolache, O. (2018). Wearable and IoT Technologies Application for Physical Rehabilitation. 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI). IEEE. https://doi.org/10.1109/ISSI.2018.8538058
   Google Scholar
• Al-Jaroodi, J., Mohamed, N., & Abukhousa, E. (2020). Health 4.0: On the way to realizing the healthcare of the future. IEEE access, 8, 211189–211210. https://doi.org/10.1109/ACCESS.2020.3038858
   PubMed Web of Science ®Google Scholar
• Al-Qudah, A. A., Al-Emran, M., & Shaalan, K. (2021). Technology acceptance in healthcare: A systematic review. Applied Sciences, 11(22), 10537. https://doi.org/10.3390/app112210537
   Google Scholar
• Al-Rawashdeh, M., Keikhosrokiani, P., Belaton, B., Alawida, M., & Zwiri, A. (2022). IoT adoption and application for smart healthcare: A systematic review. Sensors, 22(14), 5377. https://doi.org/10.3390/s22145377
   PubMed Web of Science ®Google Scholar
• Ansari, A. A., Mishra, B., Gera, P., Khan, M. K., Chakraborty, C., & Mishra, D. (2022). Privacy-enabling framework for cloud-assisted digital healthcare industry. IEEE Transactions on Industrial Informatics, 18(11), 8316–8325. https://doi.org/10.1109/TII.2022.3170148
   Web of Science ®Google Scholar
• Asadi, S., Abdullah, R., Safaei, M., & Nazir, S. (2019). An integrated SEM-neural network approach for predicting determinants of adoption of wearable healthcare devices. Mobile Information Systems, 2019, e9. https://doi.org/10.1155/2019/8026042
   Web of Science ®Google Scholar
• Bagaria, N., Laamarti, F., Badawi, H., Albraikan, A., Martinez Velazquez, R., & El Saddik, A. (2020). Connected health in Smart Cities. In A. El Saddik, M. Hossain, & B. Kantarci (Eds.), Health 4.0: Digital twins for health and well-being (pp. 143–152). Springer. https://doi.org/10.1007/978-3-030-27844-1_7
   Google Scholar
• Bagot, K., Moloczij, N., Arthurson, L., Hair, C., Hancock, S., Bladin, C. F., & Cadilhac, D. A. (2020). Nurses’ role in implementing and sustaining acute telemedicine: A mixed-methods, pre-post design using an extended technology acceptance model. Journal of Nursing Scholarship, 52(1), 34–46. https://doi.org/10.1111/jnu.12509
   PubMed Web of Science ®Google Scholar
• Baudier, P., Ammi, C., & Lecouteux, A. (2019). Employees’ acceptance of the healthcare internet of things: A source of innovation in corporate human resource policies. Journal of Innovation Economics & Management, 3(30), 89–111. https://doi.org/10.3917/jie.pr1.051.URL
   Google Scholar
• Ben, W., Ben, I., Kondrateva, G., & Hkkerova, L. (2021). Technological forecasting & social change the role of trust in intention to use the IoT in eHealth. Technological Forecasting and Social Change, 167(2), 120688. https://doi.org/10.1016/j.techfore.2021.120688
   Google Scholar
• Bijalwan, V., Semwal, V. B., Singh, G., & Crespo, R. G. (2022). Heterogeneous computing model for post-injury walking pattern restoration and postural stability rehabilitation exercise recognition. Expert Systems, 39(6), e12706. https://doi.org/10.1111/exsy.12706
   Web of Science ®Google Scholar
• Binyamin, S. S., & Zafar, B. A. (2021). Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis. Health informatics Journal, 27(1), 1–27. https://doi.org/10.1177/1460458220976737
   Web of Science ®Google Scholar
• Blumenthal, J., Wilkinson, A., & Chignell, M. (2018). Physiotherapists’ and physiotherapy students’ perspectives on the use of mobile or wearable. Physiotherapy Canada, 70(3), 251–261. https://doi.org/10.3138/ptc.2016-100.e
   PubMed Web of Science ®Google Scholar
• Carnevale, A., Longo, U. G., Schena, E., Massaroni, C., Lo Presti, D., Berton, A., Candela, V., & Denaro, V. (2019). Wearable systems for shoulder kinematics assessment: A systematic review. BMC Musculoskeletal Disorders, 20(1), 546. https://doi.org/10.1186/s12891-019-2930-4
   PubMed Web of Science ®Google Scholar
• Chakraborty, C., & Kishor, A. (2022). Real-time cloud-based patient-centric monitoring using computational health systems. IEEE Transactions on Computational Social Systems, 9(6), 1613–1623. https://doi.org/10.1109/TCSS.2022.3170375
   Web of Science ®Google Scholar
• Chau, K. Y., Lam, M. H. S., Cheung, M. L., Tso, E. K. H., Flint, S. W., Broom, D. R., Tse, G., & Lee, K. Y. (2019). Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology. Health Psychology Research, 7(1), 8099. https://doi.org/10.4081/hpr.2019.8099
   PubMed Web of Science ®Google Scholar
• Cheung, M. L., Chau, K. Y., Lam, M. H. S., Tse, G., Ho, K. Y., Flint, S. W., Broom, D. R., Tso, E. K. H., & Lee, Y. K. (2019). Examining consumers’ adoption of wearable healthcare technology: The role of health attributes. Environmental Research and Public Health, 16, 2257. https://doi.org/10.3390/ijerph16132257
   PubMed Web of Science ®Google Scholar
• Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/bf02310555
   Web of Science ®Google Scholar
• Dadhich, M., Poddar, S., & Hiran, K. K. (2022). Antecedents and consequences of patients’ adoption of the IoT 4.0 for e-health management system: A novel PLS-SEM approach. Smart Health, 25(2022), 100300. https://doi.org/10.1016/j.smhl.2022.100300
   Google Scholar
• Davis, D., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. http://home.business.utah.edu/actme/7410/DavisBagozzi.pdf
   Web of Science ®Google Scholar
• Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Deloitte Eight IoT Barriers for Connected Medical Devices…and How to Overcome Them. (2018). August 14. (The wall street journal) Retrieved 2022, from https://deloitte.wsj.com/articles/eight-iot-barriers-for-connected-medical-devicesand-how-to-overcome-them-1534274330
   Google Scholar
• Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: An Indian perspective. International Journal of Medical Informatics, 141(4), 104164. https://doi.org/10.1016/j.ijmedinf.2020.104164
   PubMed Web of Science ®Google Scholar
• Di Paolo, S., Lopomo, N. F., Della Villa, F., Paolini, G., Figari, G., Bragonzoni, L., Grassi, A., & Zaffagnini, S. (2021). Rehabilitation and return to sport assessment after anterior cruciate ligament injury: quantifying joint kinematics during complex high-speed tasks through wearable sensors. Sensors, 21(7), 2331. https://doi.org/10.3390/s21072331
   PubMed Web of Science ®Google Scholar
• Gao, Y., Li, H., & Luo, Y. (2015). An Empirical Study of Wearable Technology Acceptance in Healthcare. Industrial Management & Data Systems, 115(9), 1704–1723. https://doi.org/10.1108/IMDS-03-2015-0087
   Web of Science ®Google Scholar
• Gradim, L., Archanjo, M., da Cruz, D. C., & de Deus Lopes, R. (2020). IoT Services and Applications in Rehabilitation: An Interdisciplinary and Meta-Analysis Review. IEEE transactions on Neural Systems and Rehabilitation Engineering, 28(9), 2043–2052. https://doi.org/10.1109/TNSRE.2020.3005616
   PubMed Web of Science ®Google Scholar
• Grewal, R., Cote, J. A., & Baumgartne, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23(4), 519–529. Retrieved from https://www.jstor.org/stable/30036686
   Web of Science ®Google Scholar
• Guillén-Gámez, F. D., & Mayorga-Fernández, M. J. (2019). Empirical study based on the perceptions of patients and relatives about the acceptance of wearable devices to improve their health and prevent possible diseases. Mobile Information Systems, 2019(5), 1–12. https://doi.org/10.1155/2019/4731048
   Web of Science ®Google Scholar
• Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–151. https://doi.org/10.2753/MTP1069-6679190202
   Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 1–21. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002
   PubMed Web of Science ®Google Scholar
• Hu, L. T., & Bentler, M. P. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
   Web of Science ®Google Scholar
• Jansen-Kosterink, S., Dekker-van Weering, M., & van Velsen, L. (2019). Patient acceptance of a telemedicine service for rehabilitation care: A focus group study. International Journal of Medical Informatics, 125, 22–29. https://doi.org/10.1016/j.ijmedinf.2019.01.011
   PubMed Web of Science ®Google Scholar
• Jiang, Y. (2020). Combination of wearable sensors and internet of things and its application in sports rehabilitation. Computer Communications, 150(2020), 167–176. https://doi.org/10.1016/j.comcom.2019.11.021
   Web of Science ®Google Scholar
• Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60(2020), 101212. https://doi.org/10.1016/j.techsoc.2019.101212
   Web of Science ®Google Scholar
• Kim, G. J., Parnandi, A., Eva, S., & Schambra, H. (2022). The use of wearable sensors to assess and treat the upper extremity after stroke: A scoping review. Disability and Rehabilitation, 44(20), 6119–6138. https://doi.org/10.1080/09638288.2021.1957027
   PubMed Web of Science ®Google Scholar
• King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
   Web of Science ®Google Scholar
• Klaic, M., Fong, J., Crocher, V., Davies, K., Brock, K., Sutton, E., Oetomo, D., Tan, Y., & Galea, M. (2020). Application of the extended technology acceptance model to explore clinician likelihood to use robotics in rehabilitation of the neurologically impaired upper limb. Authorea, 1–15. https://doi.org/10.22541/au.159309959.95489483
   Google Scholar
• Labus, A., Radenković, B., Rodić, B., Barać, D., & Malešević, A. (2021). Enhancing smart healthcare in dentistry: An approach to managing patients’ stress. Informatics for Health & Social Care, 46(3), 306–319. https://doi.org/10.1080/17538157.2021.1893322
   PubMed Web of Science ®Google Scholar
• Lang, C. E., Barth, J., Holleran, C. L., Konrad, J. D., & Bland, M. D. (2020). Implementation ofWearable sensing technology for movement: Pushing forward into the routine physical rehabilitation care field. Sensors, 20(20), 5744. https://doi.org/10.3390/s20205744
   PubMed Web of Science ®Google Scholar
• Li, J., Ma, Q., Chan, A. H., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75(2019), 162–169. https://doi.org/10.1016/j.apergo.2018.10.006
   PubMed Web of Science ®Google Scholar
• Lin, W.-Y., Chou, W.-C., Tsai, T.-H., Lin, C.-C., & Lee, M.-Y. (2016). Development of a wearable instrumented vest for posture monitoring and system usability verification based on the technology acceptance model. Sensors, 16(12), 2172. https://doi.org/10.3390/s16122172
   PubMedGoogle Scholar
• Litwin, M. (1995). The Survey Kit: How to measure survey reliability and validity, The survey kit. SAGE Publications. https://doi.org/10.4135/9781483348957
   Google Scholar
• Lobo, M. A., Hall, M. L., Greenspan, B., Rohloff, P., Prosser, L. A., & Smith, B. A. (2019). Wearables for pediatric rehabilitation: How to optimally design and use products to meet the needs of users. Physical Therapy, 99(6), 647–657. https://doi.org/10.1093/ptj/pzz024
   PubMed Web of Science ®Google Scholar
• Louie, D. R., Bird, M.-L., Menon, C., & Eng, J. J. (2020). Perspectives on the prospective development of stroke-specific lower extremity wearable monitoring technology: A qualitative focus group study with physical therapists and individuals with stroke. Journal of NeuroEngineering and Rehabilitation, 17(1), 31. https://doi.org/10.1186/s12984-020-00666-6
   PubMed Web of Science ®Google Scholar
• Maceira-Elvira, P., Popa, T., Schmid, A.-C., & Hummel, F. C. (2019). Wearable technology in stroke rehabilitation: Towards improved diagnosis and treatment of upper-limb motor impairment. Journal of Neuroengineering and Rehabilitation, 16(1), 142. https://doi.org/10.1186/s12984-019-0612-y
   PubMed Web of Science ®Google Scholar
• Mansour, R. F., Amraoui, A. E., Nouaouri, I., Díaz, V. G., Gupta, D., & Kumar, S. (2021). Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems. IEEE Access. 9, 45137–45146. https://doi.org/10.1109/ACCESS.2021.3066365
   Google Scholar
• Milosevic, B., Leardini, A., & Farella, E. (2020). Kinect and wearable inertial sensors for motor rehabilitation programs at home: State of the art and an experimental comparison. Biomedical Engineering Online, 19(1), 25–25. ( https://doi.org/10.1186/s12938-020-00762-7
   PubMed Web of Science ®Google Scholar
• Nadri, H., Rahimi, B., Afshar, H. L., Samadbeik, M., & Garavand, A. (2018). Factors affecting acceptance of hospital information systems based on extended technology acceptance model: A case study in three paraclinical departments. Applied Clinical Informatics, 9(2), 238–247. https://doi.org/10.1055/s-0038-1641595
   PubMed Web of Science ®Google Scholar
• Nair, R., Ragab, M., Mujallid, O. A., Mohammad, K. A., Mansour, R. F., & Viju, G. K. (2022). Impact of wireless sensor data mining with hybrid deep learning for human activity recognition. Wireless Communications and Mobile Computing, 2022, 1–8. https://doi.org/10.1155/2022/9457536
   Web of Science ®Google Scholar
• Nasralla, M. M. (2021). Sustainable virtual reality patient rehabilitation systems with IoT sensors using virtual smart cities. Sustainability, 13(9), 4716. https://doi.org/10.3390/su13094716
   Web of Science ®Google Scholar
• Navaz, A. N., Serhani, M. A., El Kassabi, H. T., Al-Qirim, N., & Ismail, H. (2021). Trends, technologies, and key challenges in smart and connected healthcare. IEEE Access, 9, 74044–74067. https://doi.org/10.1109/ACCESS.2021.3079217
   PubMed Web of Science ®Google Scholar
• Olasina, G., & Popoola, T. (2014). Predictors of the use of e-medicine by medical doctors. Journal of Health & Medical Informatics, 5(4), 166. https://doi.org/10.4172/2157-7420.1000166
   Google Scholar
• Palmcrantz, S., Borg, J., Sommerfeld, D., Plantin, J., Wall, A., Ehn, M., Sjölinder, M., & Boman, I.-L. (2017). An interactive distance solution for stroke rehabilitation in the home setting – A feasibility study. Informatics for Health & Social Care, 42(3), 303–320. https://doi.org/10.1080/17538157.2016.1253015
   PubMed Web of Science ®Google Scholar
• Palumbo, A., Vizza, P., Calabrese, B., & Ielpo, N. (2021). Biopotential signal monitoring systems in rehabilitation: A review. Sensors, 21(21), 7172. https://doi.org/10.3390/s21217172
   PubMed Web of Science ®Google Scholar
• Porciuncula, F., Roto, A., Kumar, D., Davis, I., Roy, S., Walsh, C., & Awad, L. (2018). Wearable movement sensors for rehabilitation: A focused review of technological and clinical advances. The Journal of Injury, Function, and Rehabilitation, 10(9 Suppl 2), S220–S232. https://doi.org/10.1016/j.pmrj.2018.06.013
   PubMedGoogle Scholar
• Prasad, A., Tyagi, A. K., Althobaiti, M. M., Almulihi, A., Mansour, R. F., & Mahmoud, A. M. (2021). Human activity recognition using cell phone-based accelerometer and convolutional neural network. Applied Sciences, 11(24), 12099. https://doi.org/10.3390/app112412099
   Google Scholar
• Rodgers, M. M., Alon, G., Pai, V. M., & Conroy, R. S. (2019). Wearable technologies for active living and rehabilitation: Current research challenges and future opportunities. Journal of Rehabilitation and Assistive Technologies Engineering, 6(2019), 607. https://doi.org/10.1177/2055668319839607
   Web of Science ®Google Scholar
• Rodic, B., Stevanović, V., Kljajić, D., Trajkov, M., Pecarski, D., Filipović, M. (2021). Exploring the antecedents of adoption intention of smart healthcare technologies in rehabilitation. E-Business Technologies Conference Proceedings, 1, pp. 142–145.
   Google Scholar
• Rodić-Trmčić, B., Labus, A., Barać, D., Popović, S., & Radenković, B. (2017). Designing a course for smart healthcare engineering education. Computer Applications in Engineering Education, 26(3), 484–499.
   Web of Science ®Google Scholar
• Sarstedt, M., Ringle, C. M., Smith, D., & Reams, R. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115. https://doi.org/10.1016/j.jfbs.2014.01.002
   Web of Science ®Google Scholar
• Schomakers, E.-M., Lidynia, C., & Ziefle, M. (2022). The role of privacy in the acceptance of smart technologies: Applying the privacy calculus to technology acceptance. International Journal of Human–Computer Interaction, 38(13), 1276–1289. https://doi.org/10.1080/10447318.2021.1994211
   Web of Science ®Google Scholar
• Seshadri, D. R., Thom, M. L., Harlow, E. R., Gabbett, T. J., Geletka, B. J., Hsu, J. J., Drummond, C. K., Phelan, D. M., & Voos, J. E. (2021). Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden. Frontiers in Sports and Active Living, 2, 630576. https://doi.org/10.3389/fspor.2020.630576
   PubMedGoogle Scholar
• Seth, A., Coffie, A. J., Richard, A., & Stephen, S.A.-Y. (2019). Hospital administration management technology adoption: A theoretical test of technology acceptance model and theory of planned behavior on HAMT adoption. American Journal of Public Health Research, 7(1), 21–26. https://doi.org/10.12691/ajphr-7-1-4
   Google Scholar
• Sethi, A., Ting, J., Allen, M., Clark, W., & Weber, D. (2020). Advances in motion and electromyography based wearable technology for upper extremity function rehabilitation: A review. Journal of Hand Therapy, 33(2), 180–187. https://doi.org/10.1016/j.jht.2019.12.021
   PubMed Web of Science ®Google Scholar
• Sivathanu, B. (2018). Adoption of internet of things (IOT) based wearables for healthcare of older adults – A behavioural reasoning theory (BRT) approach. Journal of Enabling Technologies, 12(4), 169–185. https://doi.org/10.1108/JET-12-2017-0048
   Web of Science ®Google Scholar
• Stone, B. M. (2021). The ethical use of fit indices in structural equation modeling: Recommendations for psychologists. Frontiers in Psychology, 12(2021), 783226. https://doi.org/10.3389/fpsyg.2021.783226
   PubMed Web of Science ®Google Scholar
• Sun, S.-L., Hwang, H.-G., Dutta, B., & Peng, M.-H. (2019). Exploring critical factors influencing nurses’ intention to use tablet PC in Patients’ care using an integrated theoretical model. The Libyan Journal of Medicine, 14(1), 1648963. https://doi.org/10.1080/19932820.2019.1648963
   PubMed Web of Science ®Google Scholar
• Sun, X., Ding, J., Dong, Y., Ma, X., Wang, R., Jin, K., Zhang, H., & Zhang, Y. (2022). A survey of technologies facilitating home and community-based stroke rehabilitation. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2022.2050545
   Web of Science ®Google Scholar
• Thuemmler, C., & Bai, C. (2017). Health 4.0: How virtualization and big data are revolutionizing healthcare (C. Thuemmler, & C. Bai, Eds.). Springer International Publishing.
   Google Scholar
• Tian, S., Yang, W., LeGrange, J. M., Wang, P., Huang, W., & Ye, Z. (2019). Smart healthcare: Making medical care more intelligent. Global Health Journal, 3(3), 62–65. https://doi.org/10.1016/j.glohj.2019.07.001
   Google Scholar
• Tubaishat, A. (2018). Perceived usefulness and perceived ease of use of electronic health records among nurses: Application of Technology Acceptance Model. Informatics for Health & Social Care, 43(4), 379–389. https://doi.org/10.1080/17538157.2017.1363761
   PubMed Web of Science ®Google Scholar
• Turan, A., Tunç, A., & Zehir, C. (2015). A theoretical model proposal: Personal innovativeness and user involvement as antecedents of unified theory of acceptance and use of technology. Procedia – Social and Behavioral Sciences, 210(2015), 43–51. https://doi.org/10.1016/j.sbspro.2015.11.327
   Google Scholar
• Vallati, C., Virdis, A., Gesi, M., Carbonaro, N., & Tognetti, A. (2018). ePhysio: A wearables-enabled platform for the remote management of musculoskeletal diseases. Sensors, 19(2), 01–18.
   PubMedGoogle Scholar
• van der Ham, I. J., van der Vaart, R., Miedema, A., Visser-Meily, J. M., & van der Kuil, M. N. (2020). Healthcare professionals’ acceptance of digital cognitive rehabilitation. Frontiers in Psychology, 11(2020), 617886. https://doi.org/10.3389/fpsyg.2020.617886
   PubMed Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Vukićević, S., Đorđević, M., Glumbić, N., Bogdanović, Z., & Đurić Jovičić, M. (2019). A demonstration project for the utility of kinect-based educational games to benefit motor skills of children with ASD. Perceptual and Motor Skills, 126(6), 1117–1144. https://doi.org/10.1177/0031512519867521
   PubMed Web of Science ®Google Scholar
• Vukićević, S., Stamenković, Z., Murugesan, S., Bogdanović, Z., & Radenković, B. (2016). A new Telerehabilitation system based on Internet of things. Facta Universitatis, Series, 29(3), 395–407.
   Google Scholar
• Wu, I.-L., & Chen, J.-L. (2005). An extension of Trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study. International Journal of Human-Computer Studies, 62(6), 784–808. https://doi.org/10.1016/j.ijhcs.2005.03.003
   Web of Science ®Google Scholar
• Wu, I.-L., Li, J.-Y., & Fu, C.-Y. (2011). The adoption of mobile healthcare by hospital’s professionals: An integrative perspective. Decision Support Systems, 51(3), 587–596. https://doi.org/10.1016/j.dss.2011.03.003
   Web of Science ®Google Scholar
• Xu, Z. (2019). An empirical study of patients’ privacy concerns for health informatics as a service. Technological Forecasting and Social Change, 143(2019), 297–306. https://doi.org/10.1016/j.techfore.2019.01.018
   Web of Science ®Google Scholar
• Yildirim, H., & Ali-Eldin, A. M. (2019). A model for predicting user intention to use wearable IoT devices at the workplace. Journal of King Saud University, 31(4), 497–505. https://doi.org/10.1016/j.jksuci.2018.03.001
   Web of Science ®Google Scholar
• Yun, H., Lee, G., & Kim, D. J. (2019). A chronological review of empirical research on personal information privacy concerns: An analysis of contexts and research constructs. Information & Management, 56(4), 570–601. https://doi.org/10.1016/j.im.2018.10.001
   Web of Science ®Google Scholar