ArticleJMIR formative research2023
Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Acceptance of Machine Learning for Medication Selection in Epilepsy to Inform Clinical Trial Design: Co-Design Survey Study.JMIR AI · 2026Article
- Factors influencing the continuance intention of the women's health WeChat public account: an integrated model of UTAUT2 and HBM.Frontiers in public health · 2024Article
- Identifying Clusters of Health Behaviors in a Japanese Working Population at Risk for Non-Communicable Diseases: A Latent Class Analysis of 12,168 Individuals.SSM - population health · 2023Article
- Adoption of Wearable Insulin Biosensors for Diabetes Management: A Cross-Sectional Study.Cureus · 2023Article
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Authors and funding
8 authors.
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Abstract
backgroundPrevention 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.
objectiveWe investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS.
methodsWe 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.
resultsThe 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.
conclusionsThe 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.
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