Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study

Jaeyoung Ha; Jungmi Park; Sangyi Lee; Jeong Lee; Jin-Young Choi; Junhyoung Kim; Sung-Il Cho; Gyeong-Suk Jeon

doi:10.2196/42087

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study

JMIR Form Res. 2023 Apr 6:7:e42087. doi: 10.2196/42087.

Authors

Jaeyoung Ha¹, Jungmi Park¹, Sangyi Lee¹, Jeong Lee², Jin-Young Choi¹, Junhyoung Kim¹, Sung-Il Cho^{1

3}, Gyeong-Suk Jeon⁴

Affiliations

¹ Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
² Department of Nursing, Chodang University, Muan, Republic of Korea.
³ Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.
⁴ Department of Nursing, Mokpo National University, Muan, Republic of Korea.

PMID: 37023419
PMCID: PMC10131994
DOI: 10.2196/42087

Abstract

Background: Prevention of the risk factors for metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear.

Objective: We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS.

Methods: We proposed a combined theoretical model based on the health belief model, the Unified Technology of Acceptance and Use of Technology 2, and perceived risk. We conducted a web-based survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling.

Results: The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable explaining the habitual use of wearable devices. The direct effect of the performance expectancy on habitual use of wearable devices was greater (β=.537, P<.001) than that of intention to continue use (β=.439, P<.001), and the total effect estimate of the performance expectancy was 0.909 (P<.001), including the indirect effect (β=.372, P=.03) on habitual use of wearable devices via intention to continue use. Furthermore, performance expectancy was influenced by health motivation (β=.497, P<.001), effort expectancy (β=.558, P<.001), and risk perception (β=.137, P=.02). Perceived vulnerability (β=.562, P<.001) and perceived severity (β=.243, P=.008) contributed to health motivation.

Conclusions: The results suggest the importance of the users' performance expectations for wearable health devices for the intention of continued use for self-health management and habituation. Based on our results, developers and health care practitioners should find better ways to meet the performance expectations of middle-aged individuals with MetS risk factors. They also should generate device use easier and find a way to encourage users' health motivation, thereby reducing users' effort expectancy and resulting in a reasonable performance expectancy of the wearable health device, to induce users' habitual use behaviors.

Keywords: HBM; UTAUT2; acceptability; digital intervention; habitual use; health promotion; metabolic syndrome; predictors of use; wearable health devices; wearables.

©Jaeyoung Ha, Jungmi Park, Sangyi Lee, Jeong Lee, Jin-Young Choi, Junhyoung Kim, Sung-il Cho, Gyeong-Suk Jeon. Originally published in JMIR Formative Research (https://formative.jmir.org), 06.04.2023.