Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults

Autoři: Tsai-Hsuan Tsai aff001;  Wen-Yen Lin aff005;  Yung-Sheng Chang aff007;  Po-Cheng Chang aff008;  Ming-Yih Lee aff006
Působiště autorů: Department of Industrial Design, College of Management, Chang Gung University, Taoyuan, Taiwan aff001;  AI Innovation Research Center, Chang Gung University, Taoyuan, Taiwan aff002;  Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan aff003;  Department of Visual Communication Design, Ming Chi University, New Taipei City, Taiwan aff004;  Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan aff005;  Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan aff006;  School of Information, The University of Texas at Austin, Austin, Texas, United States of America aff007;  Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan aff008;  Graduate Institute of Medical Mechatronics, Center for Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan aff009
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227270


With advances in technology, wireless and sensor technologies represent a method for continuously recording people’s biomedical signals, which may enhance the diagnosis and treatment of users’ everyday health conditions. These technologies mostly target older adults. In this study, we examine a smart clothing system targeting clinically high-risk patients, including older adults with cardiovascular disease (31 outpatients) and older adults in general (81 participants), to obtain an understanding of the patients’ perception of using wearable healthcare technologies. Given that technology anxiety has been shown to affect users’ resistance to using new technology and that perceived ubiquity is considered a characteristic of wearable devices and other mobile wireless technologies, we included three external variables: i.e., technology anxiety, perceived ubiquity, and resistance to change, in addition to the traditional components of the technology acceptance model (TAM). The results of the hypothesized model showed that among older adults in general, technology anxiety had a negative effect on the perceived ease of use and perceived ubiquity. The perceived ubiquity construct affects both user groups’ perceived ease of use and perceived usefulness of wearing smart clothes. Most relationships among the original constructs of the TAM were validated in older adults in general. Interestingly, we found that perceived usefulness had an indirect effect on behavioral intention through attitude. These results further confirm the validity of the extended TAM in determining older users’ technology acceptance behavior.

Klíčová slova:

Anxiety – Cardiovascular diseases – Computers – Elderly – Geriatrics – Heart – Schools – Taiwan


1. Jin K, Simpkins JW, Ji X, Leis M, Stambler I. The critical need to promote research of aging and aging-related diseases to improve health and longevity of the elderly population. Aging and disease. 2015;6(1):1. doi: 10.14336/AD.2014.1210 25657847

2. Arai H, Ouchi Y, Toba K, Endo T, Shimokado K, Tsubota K, et al. Japan as the front‐runner of super‐aged societies: Perspectives from medicine and medical care in Japan. Geriatrics & gerontology international. 2015;15(6):673–87.

3. Lin Y-Y, Huang C-S. Aging in Taiwan: Building a society for active aging and aging in place. The Gerontologist. 2015;56(2):176–83. doi: 10.1093/geront/gnv107 26589450

4. National Institute of Health. World’s older population grows dramatically 2016 [cited 2018]. Available from:

5. World Health Organization. The top 10 causes of death: World Health Organization; 2017 [cited 2017 October 24]. Available from:

6. CDC/ National Center for Health Statistics. Leading Causes of Death Center of Disease Control and Prevention: Center of Disease Control and Prevention; March 17, 2017 [cited 2017 October 24]. Available from:

7. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. Jama. 2001;285(18):2370–5. doi: 10.1001/jama.285.18.2370 11343485

8. Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, Ernst S, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). European heart journal. 2010;31(19):2369–429. doi: 10.1093/eurheartj/ehq278 20802247

9. Hung K, Zhang Y-T, Tai B, editors. Wearable medical devices for tele-home healthcare. Engineering in Medicine and Biology Society, 2004 IEMBS'04 26th Annual International Conference of the IEEE; 2004: IEEE.

10. Phan D, Siong LY, Pathirana PN, Seneviratne A, editors. Smartwatch: Performance evaluation for long-term heart rate monitoring. Bioelectronics and Bioinformatics (ISBB), 2015 International Symposium on; 2015: IEEE.

11. Oresko JJ, Jin Z, Cheng J, Huang S, Sun Y, Duschl H, et al. A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine. 2010;14(3):734–40. doi: 10.1109/TITB.2010.2047865 20388600

12. Athilingam P, Labrador MA, Remo EFJ, Mack L, San Juan AB, Elliott AF. Features and usability assessment of a patient-centered mobile application (HeartMapp) for self-management of heart failure. Applied Nursing Research. 2016;32:156–63. doi: 10.1016/j.apnr.2016.07.001 27969021

13. Lmberis A, Dittmar A. Advanced wearable health systems and applications-research and development efforts in the European union. IEEE Engineering in Medicine and Biology Magazine. 2007;26(3):29–33. doi: 10.1109/memb.2007.364926 17549917

14. Wavelet Health. WRISTBAND: Wavelet Health; 2017 [cited 2017 October 24]. Available from:

15. iRhythm Technologies. Zio XT: iRhythm Technologies; 2017 [cited 2017 October 24]. Available from:

16. Qardio I,. QardioCore: Qardio, Inc.; 2017 [cited 2017 October 24]. Available from:

17. Lin W-Y, Chou W-C, Tsai T-H, Lin C-C, Lee M-Y. Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model. Sensors. 2016;16(12):2172.

18. Cárdenas AF, Pon RK, Cameron RB, editors. Management of Streaming Body Sensor Data for Medical Information Systems. METMBS; 2003.

19. Paradiso R, Loriga G, Taccini N. A wearable health care system based on knitted integrated sensors. IEEE transactions on Information Technology in biomedicine. 2005;9(3):337–44. doi: 10.1109/titb.2005.854512 16167687

20. Habetha J, editor The MyHeart project-fighting cardiovascular diseases by prevention and early diagnosis. Conf Proc IEEE Eng Med Biol Soc; 2006.

21. Guo X, Sun Y, Wang N, Peng Z, Yan Z. The dark side of elderly acceptance of preventive mobile health services in China. Electronic Markets. 2013;23(1):49–61.

22. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: An introduction to theory and research. 1977.

23. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly. 1989:319–40.

24. Legris P, Ingham J, Collerette P. Why do people use information technology? A critical review of the technology acceptance model. Information & management. 2003;40(3):191–204.

25. Abdullah F, Ward R, Ahmed E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior. 2016;63:75–90.

26. Abdullah F, Ward R. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior. 2016;56:238–56.

27. Marakhimov A, Joo J. Consumer adaptation and infusion of wearable devices for healthcare. Computers in Human Behavior. 2017;76:135–48.

28. Chuah SH-W, Rauschnabel PA, Krey N, Nguyen B, Ramayah T, Lade S. Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior. 2016;65:276–84.

29. Lunney A, Cunningham NR, Eastin MS. Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior. 2016;65:114–20.

30. Kim KJ, Shin D-H. An acceptance model for smart watches: implications for the adoption of future wearable technology. Internet Research. 2015;25(4):527–41.

31. Schaar AK, Ziefle M, editors. Smart clothing: Perceived benefits vs. perceived fears. Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on; 2011: IEEE.

32. Spagnolli A, Guardigli E, Orso V, Varotto A, Gamberini L, editors. Measuring user acceptance of wearable symbiotic devices: validation study across application scenarios. International Workshop on Symbiotic Interaction; 2014: Springer.

33. Choi J, Kim S. Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior. 2016;63:777–86.

34. Heinssen RK, Glass CR, Knight LA. Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Computers in human behavior. 1987;3(1):49–59.

35. Dyck JL, Smither JA-A. Age differences in computer anxiety: The role of computer experience, gender and education. Journal of educational computing research. 1994;10(3):239–48.

36. Parayitam S, Desai KJ, Desai MS, Eason MK. Computer attitude as a moderator in the relationship between computer anxiety, satisfaction, and stress. Computers in Human Behavior. 2010;26(3):345–52.

37. Saadé RG, Kira D. The emotional state of technology acceptance. Issues in informing science and information technology. 2006;3:529–39.

38. Igbaria M, Parasuraman S, Baroudi JJ. A motivational model of microcomputer usage. Journal of management information systems. 1996;13(1):127–43.

39. Or CK, Karsh B-T. A systematic review of patient acceptance of consumer health information technology. Journal of the American Medical Informatics Association. 2009;16(4):550–60. doi: 10.1197/jamia.M2888 19390112

40. Chang SJ, Im E-O. A path analysis of Internet health information seeking behaviors among older adults. Geriatric Nursing. 2014;35(2):137–41. doi: 10.1016/j.gerinurse.2013.11.005 24332965

41. Bhattacherjee A, Hikmet N. Physicians' resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems. 2007;16(6):725–37.

42. Durndell A, Haag Z. Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in human behavior. 2002;18(5):521–35.

43. Kim S, Garrison G. Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers. 2009;11(3):323–33.

44. Hsiao C-H, Tang K-Y. Examining a model of mobile healthcare technology acceptance by the elderly in Taiwan. Journal of Global Information Technology Management. 2015;18(4):292–311.

45. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research. 1975;12(3):189–98. doi: 10.1016/0022-3956(75)90026-6 1202204

46. Halín N, Junnila M, Loula P, Aarnio P. The LifeShirt system for wireless patient monitoring in the operating room. Journal of telemedicine and telecare. 2005;11(2_suppl):41–3.

47. Poon CC, Liu Q, Gao H, Lin W-H, Zhang Y-T. Wearable intelligent systems for e-health. Journal of Computing Science and Engineering. 2011;5(3):246–56.

48. Ajzen I. The theory of planned behavior. Organizational behavior and human decision processes. 1991;50(2):179–211.

49. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS quarterly. 2003:425–78.

50. Marcoulides GA, Saunders C. Editor's comments: PLS: a silver bullet? MIS quarterly. 2006:iii–ix.

51. Hair JF, Sarstedt M, Ringle CM, Mena JA. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science. 2012;40(3):414–33.

52. Ringle C, Sarstedt M, Straub D. A critical look at the use of PLS-SEM in MIS quarterly. Manag. Inf Syst Q. 2012;36.

53. Martínez-Torres MR, Toral Marín S, Garcia FB, Vazquez SG, Oliva MA, Torres T. A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area. Behaviour & Information Technology. 2008;27(6):495–505.

54. Nunnally JC, Bernstein IH, Berge JMt. Psychometric theory: McGraw-hill New York; 1967.

55. Lee MK, Cheung CM, Chen Z. Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management. 2005;42(8):1095–104.

56. Fornell C, Bookstein FL. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing research. 1982:440–52.

57. Wu W-S, Lin W-Y, Lee M-Y, editors. Forward-flexed posture detection for the early Parkinson'S disease symptom. Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on; 2014: IEEE.

58. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis: Prentice hall Upper Saddle River, NJ; 1998.

59. Chin WW, Peterson RA, Brown SP. Structural equation modeling in marketing: Some practical reminders. Journal of marketing theory and practice. 2008;16(4):287–98.

60. Cohen J. A power primer. Psychological bulletin. 1992;112(1):155. doi: 10.1037//0033-2909.112.1.155 19565683

61. Henseler J, Dijkstra TK, Sarstedt M, Ringle CM, Diamantopoulos A, Straub DW, et al. Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods. 2014;17(2):182–209.

62. Zarmpou T, Saprikis V, Markos A, Vlachopoulou M. Modeling users’ acceptance of mobile services. Electronic Commerce Research. 2012;12(2):225–48.

63. Borsci S, Buckle P, Walne S, Salanitri D, editors. Trust and Human Factors in the Design of Healthcare Technology. Congress of the International Ergonomics Association; 2018: Springer.

64. Rossiter JR, Braithwaite B. C-OAR-SE-based single-item measures for the two-stage Technology Acceptance Model. Australasian Marketing Journal (AMJ). 2013;21(1):30–5.

65. Norman DA, Draper SW. User centered system design: New perspectives on human-computer interaction: CRC Press; 1986.

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