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

Abstract

BACKGROUND Prevention of the risk factors of 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, Unified Technology of the Acceptance and use of Technology 2, and perceived risk. We conducted an online 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 influencing the habitual use of wearable devices (β=.537, P<.001) and intention to continue the use of wearable devices (β=.848, P<.001). Habitual use was positively influenced by intention to continue the use of wearable devices (β=.439, P<.001). The indirect effects of performance expectancy were partially mediated by the intention to continue the use of wearable devices (β=.372, P=.03). Performance expectancy was influenced by health motivation (β=0.497, P<.001) and effort expectancy (β=.558, P<.001). Perceived vulnerability contributed more to health motivation (β=.562, P<.001) than perceived severity (β=.243, P=.008). CONCLUSIONS The habitual use of wearable devices was influenced by users’ expectation that the device productivity exceeded its required effort. Additionally, health motivation positively influenced the performance expectancy. The healthcare needs of middle-aged individuals with MetS risk factors should be explored to enhance the performance expectancy of managing health with wearable health devices and to increase their habitual use.