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Factors Affecting the Acceptability of Technology in Health Care Among Older Korean Adults with Multiple Chronic Conditions: A Cross-Sectional Study Adopting the Senior Technology Acceptance Model

Authors Ha J, Park HK

Received 20 June 2020

Accepted for publication 7 September 2020

Published 2 October 2020 Volume 2020:15 Pages 1873—1881

DOI https://doi.org/10.2147/CIA.S268606

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Richard Walker


Jiyeon Ha,1 Hyeyoung K Park2

1College of Nursing, Konyang University, Daejeon, South Korea; 2College of Nursing, University of Massachusetts Amherst, Amherst, MA, USA

Correspondence: Hyeyoung K Park
College of Nursing, University of Massachusetts Amherst, 126 Skinner Hall, 651 North Pleasant Street, Amherst, MA 01003, USA
Tel +1 413 545 1343
Fax +1 413 545 0086
Email hyeyoungpark@umass.edu

Purpose: Older adults experience challenges employing technology in their health-care management due to changes in cognitive and physical functions. This study aimed to investigate the acceptance of technology among older Korean adults with multiple chronic health conditions and examine factors associated with technology acceptance, adopting the senior technology acceptance model (STAM).
Patients and Methods: In total, 226 community-dwelling older adults with more than two chronic conditions participated in this study. We conducted a survey that covered demographics, gerontechnology self-efficacy, gerontechnology anxiety, facilitating conditions, self-reported health conditions, cognitive ability, social relationships, attitude toward life and satisfaction, physical functioning, and technology acceptance.
Results: Older Korean adults with multiple chronic health conditions scored moderately high for technology acceptance (25.36± 5.28). There were significant differences in technology acceptance according to age (r=− 0.241), cognitive ability (r=0.225), gerontechnology self-efficacy (r=0.323), and facilitating conditions (r=0.288). Only age and education were significant factors predicting technology acceptance (Adjusted R2=0.151, p< 0.001).
Conclusion: Although older Korean adults with multiple chronic conditions displayed good technology acceptance, their age and education level predicted the level of acceptance. Given that some components of the STAM model have social and cultural relevance, it is necessary to conduct research across various cultures to better understand technology acceptance by older adults.

Keywords: gerontechnology, multiple chronic conditions, technology, acceptance, aged

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