Consumer Acceptance of mHealth Services: A Comparison of Behavioral Intention Models

Recent appropriation of mobile devices to deliver health services is transforming the health care landscape, offering reduced costs and increased access for service providers and consumers. This article examines factors influencing consumers' decisions to adopt mobile health (mHealth) services through a comparison of three behavioral intention models. A national web-based survey of 482 French adults indicates that the model of goal-directed behavior (MGB) more fully, though less parsimoniously, explains consumers' acceptance of mHealth services. This research provides insight into the usefulness of the MGB in improving understanding of the determinants of behavior situated at the intersection of health, service, and technology.     

Socio-economic impact of the mHealth adoption in managing diabetes

The increasing prevalence of prediabetes and diabetes has become a serious problem in Korea. This study aims to compare the effects of various policy options for mHealth proliferation for managing and preventing diabetes. To this end, we simulate the plausible possibility of mHealth using system dynamics modelling. There are several important findings of this study that are helpful to policy makers’ decisions. First, innovative healthcare delivery through mHealth has a positive influence on health to significantly reduce prediabetes and diabetes. Moreover, the gap between the healthcare system with and without mHealth increases over time. Second, the effectiveness of mHealth adoption depends on the timing of implementation of institutional reforms. Finally, mHealth adoption can stimulate national economic growth as the demand for a new healthcare system rises.     

Privacy and the quantified self: A review of U.S. health information policy limitations related to wearable technologies

As Americans increasingly integrate quantified self-health and fitness tracking (QSHFT) technologies into their lives, the data collected by these devices offer to not only help users to live healthier lives, but also present opportunities for interested parties to identify and target them based on their health-related behaviors. Clinicians, employers, health insurers, data brokers, marketers, and litigators have all expressed interest in accessing individuals' QSHFT data for a variety of purposes. Existing policies related to the collection, aggregation, and use of these data do not consistently address and protect individual health privacy concerns. Indeed, U.S. lawmakers recently proposed two separate bills designed to correct this deficiency. The purpose of this review is to examine current motivations, practices, policies, and regulations related to QSHFT data, identify areas where individuals' health information privacy is currently being compromised, and propose specific solutions to address this escalating area of privacy concern.     

Building user engagement to mhealth apps from a learning perspective: Relationships among functional,emotional and social drivers of user value

Smartphone applications for health-oriented purposes, or mHealth apps (MHAs), represent a growing opportunity to improve efficiency and sustainability in both national health systems and individual citizens' self-management of their health condition. However, little is known about how to build user engagement with MHAs to avoid user dropout and encourage their long-term loyalty and advocacy of MHAs to other users. This paper analyzes how to build user engagement from a learning perspective, strengthening the effort to learn about personal healthcare. Specifically, we investigate how the determinants of MHAs' functional value (technology effort, technology performance, and brand trust) drive the user's hedonic (enjoyment) and social (networking, social image) value. Data is obtained from a sample of 400 current users of MHAs. Our findings show that technology performance mainly enhances the user's networking experience, while technology effort contributes mostly to enjoyment and brand trust is critical to the user's social image. The user's hedonic and social experience benefit user engagement, which ultimately fosters user loyalty and advocacy, thus bolstering the relevance of developing user-centric MHAs. Robustness analysis does not reveal gender, age, income level, or type of MHA impact, although user education moderates the strength of some of these relationships.