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

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.

Understanding and Predicting the Adoption of Fitness Mobile Apps: Evidence from China

Increasing global interest in diet and fitness mobile applications (apps) has prompted the question: What are the factors affecting users' adoption and usage behaviors on a specific fitness app? By combining the unified theory of acceptance and use of technology (UTAUT) with the health belief model (HBM), and including risk perception of information technology with the farsighted planner and myopic doer from the theory of self-control, we explore the understanding of this academic question. We analyzed data from 8,840 users of Boohee, a diet and fitness app (ranked first in the weight-loss category on the App Store in China). Structural equation modeling revealed that self-efficacy as well as the perceived benefits, barriers, and threats of weight loss significantly influence a fitness app's performance expectancy, which, in turn, predicts users' intention to adopt it. Furthermore, actual usage behavior (i.e., diet, exercise, weight, and login records within 30 days after respondents completed the questionnaire) is positively affected by weight-loss intention and behavioral intention to use the app and negatively affected by users' risk perception. The main findings of this research could help healthcare practitioners and app developers find better ways to encourage people to adopt health apps for various reasons. App developers should attach more importance to users' actual continuous use behavior than to their intention to use an app. They should provide sufficient introductory information about their apps, thereby reducing users' risk perception and generating reasonable performance expectancy of the app, so as to improve users' actual continuous use behavior.

New product launch actions and firm performance

Purpose

This paper aims to examine the effect of new product launch actions and firm reputation on firm performance in the Chinese auto industry.

Design/methodology/approach

This analysis adopts empirical data from 66 auto firms in China’s auto market from 2007 to 2012 to explore how new product launch actions undertaken by a firm can contribute to achieving superior performance and to investigate the relationships between new product launch actions and firm performance. Moreover, how firm reputation interacts with new product launch actions to affect firm performance is also investigated. Fixed effects regression model following the Hausman specification test was used to quantitatively examine the relationship.

Findings

It was concluded that the focal firm’s new product launch actions, including new product launch breadth, complexity and heterogeneity of its new repertoire of product launch actions, and firm reputation can impact its performance. Firm reputation can impact the signaling process and the capability of firms to enhance their performance via new product launch movements.

Originality/value

This research contributes to new product launch research by providing a more comprehensive view of competitive dynamic actions by which a firm’s performance is strengthened by examining the effects of two factors that affect performance. These factors are as follows: the characteristics in terms of breadth, complexity, and heterogeneity of new product launch actions undertaken by a firm and the characteristic of firm reputation.