Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Factors and Design Features Influencing the Continued Use of Wearable Devices

  • Research Article
  • Published:
Journal of Healthcare Informatics Research Aims and scope Submit manuscript

Abstract

The initial healthy uptake of wearable devices is not necessarily accompanied by sustained or continued use. Accordingly, this study investigates the factors influencing the continuous use of wearable devices with a particular emphasis on design features. We complemented the expectation-confirmation model (ECM) theoretical foundation with various design features such as trust, readability, dialogue support, personalization, device battery, appeal, and social support. The study employs a simultaneous mixed method research design denoted as QUANT + qual. The quantitative analysis leverages partial least squares structural equation modeling (PLS-SEM) using survey data collected from wearable device users. The qualitative analysis complements the quantitative focus of the research by providing insights into the results obtained from the quantitative analysis. We found that subjects tend to use wearables daily (60%) or several times a week (33%), and 91% plan to use them even more. Subjects indicated multiple usages for wearables. Most subjects were using wearables for healthcare and wellness (61%) or sports and fitness (54%) and had smartwatches wearable type (74%). The model explains 24.1% (p < 0.01) of the variance of continued intention to use. As a theoretical contribution, the findings support using the ECM as a theoretical foundation for explaining the continued use of wearables. Partial least squares (PLS) and qualitative data analysis highlight the relative importance that wearable users place on perceived usefulness. Most notable are tracking functions and design features such as device battery, integration with other apps/devices, dialogue support, and appeal.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. MarketsandMarkets (2021) Global wearable technology market size, share trends analysis trends 2022-2026. https://www.marketsandmarkets.com/Market-Reports/wearable-electronics-market-983.html. Accessed 12 Jan 2023

  3. Precedence Research (2022) Wearable Technology market size, trends, growth, Report 2030. https://www.precedenceresearch.com/wearable-technology-market. Accessed 12 Jan 2023

  4. Motti VG, Caine K (2015) Users’ privacy concerns about wearables. In: Brenner M, Christin N, Johnson B, Rohloff K (eds) Financial cryptography and data security. Springer, Berlin Heidelberg, pp 231–244

    Chapter  Google Scholar 

  5. Mercer K, Li M, Giangregorio L, Burns C, Grindrod K (2016) Behavior change techniques present in wearable activity trackers: a critical analysis. JMIR Mhealth Uhealth 4:e40–e40. https://doi.org/10.2196/mhealth.4461

    Article  Google Scholar 

  6. Warraich MU (2016) Wellness routines with wearable activity trackers: a systematic review. In: Tenth Mediterranean Conference on Information Systems (MCIS), Paphos, Cyprus, p 14

  7. Hendker A, Jetzke M, Eils E, Voelcker-Rehage C (2020) The implication of wearables and the factors affecting their usage among recreationally active people. Int J Environ Res Public Health 17:8532. https://doi.org/10.3390/ijerph17228532

    Article  Google Scholar 

  8. Gribel L, Regier S, Stengel I (2016) Acceptance factors of wearable computing: an empirical investigation. In: Proceedings of the Eleventh International Network Conference (INC 2016). pp 62–72

  9. El-Gayar O, Nasralah T, Elnoshokaty A (2019) Wearable devices for health and wellbeing: design insights from Twitter. In: 52nd Hawaii International Conference on Systems Sciences (HICSS-52’19). IEEE Computer Society, Maui, HI

    Google Scholar 

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

    Article  Google Scholar 

  11. Ahmad A, Rasul T, Yousaf A, Zaman U (2020) Understanding factors influencing elderly diabetic patients’ continuance intention to use digital health wearables: extending the technology acceptance model (TAM). J Open Innov Technol Mark Complex 6:81. https://doi.org/10.3390/joitmc6030081

    Article  Google Scholar 

  12. Canhoto AI, Arp S (2017) Exploring the factors that support adoption and sustained use of health and fitness wearables. J Destin Mark Manag 33:32–60. https://doi.org/10.1080/0267257X.2016.1234505

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Shin G, Feng Y, Jarrahi MH, Gafinowitz N (2019) Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use. JAMIA Open 2:62–72. https://doi.org/10.1093/jamiaopen/ooy048

    Article  Google Scholar 

  15. Clawson J, Pater JA, Miller AD, Mynatt ED, Mamykina L (2015) No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15. ACM Press, Osaka, Japan, pp 647–658

    Chapter  Google Scholar 

  16. Epstein DA, Caraway M, Johnston C, Ping A, Fogarty J, Munson SA (2016) Beyond abandonment to next steps: understanding and designing for life after personal informatics tool use. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, San Jose California, USA, pp 1109–1113

    Chapter  Google Scholar 

  17. Jeong H, Kim H, Kim R, Lee U, Jeong Y (2017) Smartwatch wearing behavior analysis: a longitudinal study. Proc ACM Interact Mob Wearable Ubiquitous Technol 1:1–31. https://doi.org/10.1145/3131892

    Article  Google Scholar 

  18. Lazar A, Koehler C, Tanenbaum J, Nguyen DH (2015) Why we use and abandon smart devices. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15. ACM Press, Osaka, Japan, pp 635–646

    Chapter  Google Scholar 

  19. Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Q 25:351. https://doi.org/10.2307/3250921

    Article  Google Scholar 

  20. Thong J, Hong S-J, Tam KY (2006) The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. Int J Hum-Comput Stud 64:799–810. https://doi.org/10.1016/j.ijhcs.2006.05.001

    Article  Google Scholar 

  21. Lee AS (1999) Rigor and relevance in MIS research: beyond the approach of positivism alone. MIS Q 23:29–33. https://doi.org/10.2307/249407

    Article  Google Scholar 

  22. Mingers J (2001) Combining IS research methods: towards a pluralist methodology. Inf Syst Res 12:240–259. https://doi.org/10.1287/isre.12.3.240.9709

    Article  Google Scholar 

  23. Niknejad N, Hussin ARC, Ghani I, Ganjouei FA (2020) A confirmatory factor analysis of the behavioral intention to use smart wellness wearables in Malaysia. Univers Access Inf Soc 19:633–653. https://doi.org/10.1007/s10209-019-00663-0

    Article  Google Scholar 

  24. Rodríguez I, Cajamarca G, Herskovic V, Fuentes C, Campos M (2017) Helping elderly users report pain levels: a study of user experience with mobile and wearable interfaces. Mob Inf Syst 2017:1–12. https://doi.org/10.1155/2017/9302328

    Article  Google Scholar 

  25. Chau KY, Lam MHS, Cheung ML, Tso EKH, Flint SW, Broom DR, Tse G, Lee KY (2019) Smart technology for healthcare: exploring the antecedents of adoption intention of healthcare wearable technology. Health Psychol Res 7. https://doi.org/10.4081/hpr.2019.8099

  26. Cheung ML, Chau KY, Lam MHS, Tse G, Ho KY, Flint SW, Broom DR, Tso EKH, Lee KY (2019) Examining consumers’ adoption of wearable healthcare technology: the role of health attributes. Int J Environ Res Public Health 16. https://doi.org/10.3390/ijerph1613225

  27. Dai B, Larnyo E, Tetteh EA, Aboagye AK (2020) Musah, A.-A.I.: Factors affecting caregivers’ acceptance of the use of wearable devices by patients with dementia: an extension of the unified theory of acceptance and use of technology model. Am J Alzheimers Dis Dementias® 35:153331751988349. https://doi.org/10.1177/1533317519883493

    Article  Google Scholar 

  28. Shih PC, Han K, Poole ES, Rosson MB, Carroll JM (2015) Use and adoption challenges of wearable activity trackers. iConference. University of California, Irvine, p 12

  29. Adapa A, Nah FF-H, Hall RH, Siau K, Smith SN (2018) Factors influencing the adoption of smart wearable devices. Int J Human–Computer Interact 34:399–409. https://doi.org/10.1080/10447318.2017.1357902

    Article  Google Scholar 

  30. Epstein DA, Eslambolchilar P, Kay J, Meyer J, Munson SA (2021) Opportunities and challenges for long-term tracking. In: Karapanos E, Gerken J, Kjeldskov J, Skov MB (eds) Advances in Longitudinal HCI Research. Springer International Publishing, Cham, pp 177–206

    Chapter  Google Scholar 

  31. Pal D, Funilkul S, Vanijja V (2020) The future of smartwatches: assessing the end-users’ continuous usage using an extended expectation-confirmation model. Univers Access Inf Soc 19:261–281. https://doi.org/10.1007/s10209-018-0639-z

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Dehghani M, Kim KJ, Dangelico RM (2018) Will smartwatches last? Factors contributing to intention to keep using smart wearable technology. Telemat Inform 35:480–490. https://doi.org/10.1016/j.tele.2018.01.007

    Article  Google Scholar 

  34. Anderson E, Sullivan M (1993) The antecedents and consequences of customer satisfaction for firms. Mar Sci 12:125–143

    Article  Google Scholar 

  35. Oliver R (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Market Res 20:460–469

    Article  Google Scholar 

  36. Oliver RL (1993) Cognitive, affective, and attribute bases of the satisfaction response. J Consum Res 20:418–430. https://doi.org/10.1086/209358

    Article  Google Scholar 

  37. Kim DJ, Ferrin DL, Rao HR (2009) Trust and satisfaction, two stepping stones for successful e-commerce Relationships: a longitudinal exploration. Inf Syst Res 20:237–257. https://doi.org/10.1287/isre.1080.0188

    Article  Google Scholar 

  38. Chen L, Meservy T, Gillenson M (2012) Understanding information systems continuance for information-oriented mobile applications. Commun Assoc Inf Syst 30. https://doi.org/10.17705/1CAIS.03009

  39. Hsu C-L, Lin JC-C (2015) What drives purchase intention for paid mobile apps? – An expectation confirmation model with perceived value. Electron Commer Res Appl 14:46–57. https://doi.org/10.1016/j.elerap.2014.11.003

    Article  Google Scholar 

  40. Tam C, Santos D, Oliveira T (2020) Exploring the influential factors of continuance intention to use mobile apps: extending the expectation confirmation model. Inf Syst Front 22:243–257. https://doi.org/10.1007/s10796-018-9864-5

    Article  Google Scholar 

  41. Oghuma AP, Libaque-Saenz CF, Wong SF, Chang Y (2016) An expectation-confirmation model of continuance intention to use mobile instant messaging. Telemat Inform 33:34–47. https://doi.org/10.1016/j.tele.2015.05.006

    Article  Google Scholar 

  42. Susanto A, Chang Y, Ha Y (2016) Determinants of continuance intention to use the smartphone banking services: an extension to the expectation-confirmation model. Ind Manag Data Syst 116:508–525. https://doi.org/10.1108/IMDS-05-2015-0195

    Article  Google Scholar 

  43. Wairimu J, Sun J (2018) Is smartwatch really for me? An expectation-confirmation perspective. In: Twenty-fourth Americas Conference on Information Systems, New Orleans, p 10

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

    Article  Google Scholar 

  45. Parthasarathy M, Bhattacherjee A (1998) Understanding post-adoption behavior in the context of online services. Inf Syst Res 9:362–379

    Article  Google Scholar 

  46. Brown V (2005) Model of adoption of technology in households: a baseline model test and extension incorporating household life cycle. MIS Q 29:399. https://doi.org/10.2307/25148690

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Kim SC, Yoon D, Han EK (2016) Antecedents of mobile app usage among smartphone users. J Mark Commun 22:653–670. https://doi.org/10.1080/13527266.2014.951065

    Article  Google Scholar 

  49. Limayem H (2007) Cheung: How habit limits the predictive power of intention: the case of information systems continuance. MIS Q 31:705. https://doi.org/10.2307/25148817

    Article  Google Scholar 

  50. Vodanovich S, Sundaram D, Myers M (2010) Research commentary—digital natives and ubiquitous information systems. Inf Syst Res 21:711–723. https://doi.org/10.1287/isre.1100.0324

    Article  Google Scholar 

  51. Zhang P, Carey J, Te’eni, D., Tremaine, M. (2005) Integrating human-computer interaction development into the systems development life cycle: a methodology. Commun Assoc Inf Syst 15. https://doi.org/10.17705/1CAIS.01529

  52. Oinas-Kukkonen H, Harjumaa M (2009) Persuasive systems design: key issues, process model, and system features. Commun Assoc Inf Syst 24:485–500

    Google Scholar 

  53. Rahmati A, Qian A, Zhong L (2007) Understanding human-battery interaction on mobile phones. In: Proceedings of the 9th international conference on Human computer interaction with mobile devices and services - MobileHCI ’07. ACM Press, Singapore, pp 265–272

    Chapter  Google Scholar 

  54. Chattaraman V, Rudd NA (2006) Preferences for aesthetic attributes in clothing as a function of body image, body cathexis and body size. Cloth Text Res J 24:44–61

    Article  Google Scholar 

  55. Coorevits L, Coenen T (2016) The rise and fall of wearable fitness trackers. Acad Manag Proc 2016:17305. https://doi.org/10.5465/ambpp.2016.17305abstract

    Article  Google Scholar 

  56. Jeong SC, Byun JS, Jeong YJ (2016) The effect of user experience and perceived similarity of smartphone on acceptance intention for smartwatch. ICIC Express Lett 10:8

    Google Scholar 

  57. Page T (2015) Barriers to the adoption of wearable technology. -Manag. J Inf Technol 4(1–13). https://doi.org/10.26634/jit.4.3.3485

  58. Gan C, Li H (2018) Understanding the effects of gratifications on the continuance intention to use WeChat in China: a perspective on uses and gratifications. Comput Hum Behav 78:306–315. https://doi.org/10.1016/j.chb.2017.10.003

    Article  Google Scholar 

  59. Liu N, Yu R (2017) Identifying design feature factors critical to acceptance and usage behavior of smartphones. Comput Hum Behav 70:131–142. https://doi.org/10.1016/j.chb.2016.12.073

    Article  Google Scholar 

  60. Abdul-Rahman A, Hailes S (2000) Supporting trust in virtual communities. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. IEEE Comput. Soc, Maui, HI, USA, p 9. https://doi.org/10.1109/HICSS.2000.926814

  61. Harrison McKnight D, Choudhury V, Kacmar C (2002) The impact of initial consumer trust on intentions to transact with a web site: a trust building model. J Strateg Inf Syst 11:297–323. https://doi.org/10.1016/S0963-8687(02)00020-3

    Article  Google Scholar 

  62. Gefen K (2003) Straub: Trust and TAM in online shopping: an integrated model. MIS Q 27:51. https://doi.org/10.2307/30036519

    Article  Google Scholar 

  63. Gu Z, Wei J, Xu F (2016) An empirical study on factors influencing consumers’ initial trust in wearable commerce. J Comput Inf Syst 56:79–85

    Google Scholar 

  64. Gottlieb BH, Bergen AE (2010) Social support concepts and measures. J Psychosom Res 69:511–520. https://doi.org/10.1016/j.jpsychores.2009.10.001

    Article  Google Scholar 

  65. Liu Y, Su X, Du X, Cui F, Liu Y, Su X, Du X, Cui F (2019) How social support motivates trust and purchase intentions in mobile social commerce. Rev Bras Gest Neg 21:839–860. https://doi.org/10.7819/rbgn.v21i5.4025

    Article  Google Scholar 

  66. Al-Ramahi MA, Liu J, El-Gayar OF (2017) Discovering design principles for health behavioral change support systems. ACM Trans Manag Inf Syst 8:1–24. https://doi.org/10.1145/3055534

    Article  Google Scholar 

  67. McCallum C, Rooksby J, Gray CM (2018) Evaluating the impact of physical activity apps and wearables: interdisciplinary review. JMIR Mhealth Uhealth 6:e58. https://doi.org/10.2196/mhealth.9054

    Article  Google Scholar 

  68. Bhattacherjee A, Barfar A (2011) Information technology continuance research: current state and future directions. Asia Pac J Inf Syst 21:1–18

    Google Scholar 

  69. Lee S, Kim D-Y (2018) The effect of hedonic and utilitarian values on satisfaction and loyalty of Airbnb users. Int J Contemp Hosp Manag 30:1332–1351. https://doi.org/10.1108/IJCHM-09-2016-0504

    Article  Google Scholar 

  70. Chen S, Chen H, Chen M (2009) Determinants of satisfaction and continuance intention towards self-service technologies. Ind Manag Data Syst 109:1248–1263. https://doi.org/10.1108/02635570911002306

    Article  Google Scholar 

  71. Kim KJ, Shin D-H (2015) An acceptance model for smart watches: implications for the adoption of future wearable technology. Internet Res 25:527–541. https://doi.org/10.1108/IntR-05-2014-0126

    Article  Google Scholar 

  72. Sundar SS, Tamul DJ, Wu M (2014) Capturing “cool”: measures for assessing coolness of technological products. Int J Hum-Comput Stud 72:169–180. https://doi.org/10.1016/j.ijhcs.2013.09.008

    Article  Google Scholar 

  73. Hwang C, Chung T-L (2016) Sanders, E.A.: Attitudes and purchase intentions for smart clothing: examining U.S. consumers’ functional, expressive, and aesthetic needs for solar-powered clothing. Cloth Text Res J 34:207–222. https://doi.org/10.1177/0887302X16646447

    Article  Google Scholar 

  74. Cobos, L. (2017) Determinants of continuance intention and word of mouth for hotel branded mobile app users., https://stars.library.ucf.edu/etd/5719

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

    Article  Google Scholar 

  76. Rustam V (2012) Design-type research in information systems: findings and practices. IGI Global, Ukraine

    Google Scholar 

  77. Shchiglik C, Barnes SJ (2004) Evaluating website quality in the airline industry. J Comput Inf Syst 44:17–25. https://doi.org/10.1080/08874417.2004.11647578

    Article  Google Scholar 

  78. Baleghi-Zadeh S, Ayub A, Mahmud R, Daud S (2017) The influence of system interactivity and technical support on learning management system utilization. Knowl Manag E-Learn Int J:50–68. https://doi.org/10.34105/j.kmel.2017.09.004

  79. Creswell JW, Plano C, Hanson WE (2003) Advanced mixed methods research designs. In: Tashakkori A, Teddlie C, Gutmann ML (eds) Handbook of mixed methods in social and behavioral research. SAGE Publications, Inc, Thousand Oaks, CA, US

    Google Scholar 

  80. Morse JM (1991) Approaches to qualitative-quantitative methodological triangulation. Nurs Res 40:120–123

    Article  Google Scholar 

  81. Götz O, Liehr-Gobbers K, Krafft M (2010) Evaluation of structural equation models using the partial least squares (PLS) approach. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H (eds) Handbook of Partial Least Squares. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 691–711

    Chapter  Google Scholar 

  82. Urbach N, Ahlemann F (2010) Structural equation modeling in information systems research using partial least squares. J Inf Technol Theory Appl 11:36

    Google Scholar 

  83. Hair J, Hult T, Ringle C, Sarstedt M (2017) A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications, Inc, Thousand Oaks, CA, US

    MATH  Google Scholar 

  84. Cohen J (1992) A power primer. Psychol Bull 112:155–159. https://doi.org/10.1037/0033-2909.112.1.155

    Article  Google Scholar 

  85. Gefen D, Straub D (2005) A practical guide to factorial validity using PLS-GRAPH: tutorial and annotated example. Commun Assoc Inf Syst 16:91–109

    Google Scholar 

  86. Henseler J, Ringle CM, Sinkovics RR (2009) The use of partial least squares path modeling in international marketing. Advances in International Marketing 20:277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014

    Article  Google Scholar 

  87. Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Market Res 18:39. https://doi.org/10.2307/3151312

    Article  Google Scholar 

  88. Hair JF, Sarstedt M, Ringle CM, Mena JA (2012) An assessment of the use of partial least squares structural equation modeling in marketing research. J Acad Market Sci 40:414–433. https://doi.org/10.1007/s11747-011-0261-6

    Article  Google Scholar 

  89. Chiu C-M, Hsu M-H, Sun S-Y, Lin T-C, Sun P-C (2005) Usability, quality, value and e-learning continuance decisions. Comput Educ 45:399–416

    Article  Google Scholar 

  90. Grégoire Y, Fisher RJ (2006) The effects of relationship quality on customer retaliation. Mark Lett 17:31–46. https://doi.org/10.1007/s11002-006-3796-4

    Article  Google Scholar 

  91. Charmaz K (2006) Constructing grounded theory: a practical guide through qualitative analysis. SAGE Publications Ltd, London, Thousand Oaks, CA, US

    Google Scholar 

  92. Strauss AL, Corbin JM (1998) Basics of qualitative research: techniques and procedures for developing grounded theory. Sage Publications, Thousand Oaks, CA, US

    Google Scholar 

  93. Karapanos E, Gouveia R, Hassenzahl M, Forlizzi J (2016) Wellbeing in the making: peoples’ experiences with wearable activity trackers. Psychol Well-Being 6:4. https://doi.org/10.1186/s13612-016-0042-6

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dakota State University, 820 N Washington St., Madison, SD, 57042, USA

    Omar El-Gayar

  2. California State University, 5500 University Pkwy, San Bernardino, CA, 92407, USA

    Ahmed Elnoshokaty

Authors
  1. Omar El-Gayar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Elnoshokaty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omar El-Gayar.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Appendix

Appendix

See Table 4

Table 4 Survey items
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El-Gayar, O., Elnoshokaty, A. Factors and Design Features Influencing the Continued Use of Wearable Devices. J Healthc Inform Res 7, 359–385 (2023). https://doi.org/10.1007/s41666-023-00135-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41666-023-00135-4

Keywords

Search

Navigation