Factors Influencing Usage Behavior on Mobile Health Applications

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This entry is adapted from the peer-reviewed paper 10.3390/healthcare10020208

As people pay more attention to health, mobile health applications (mHealth apps) are becoming popular. These apps offer health services that run on mobile devices to help improve users’ health behaviors. However, few studies explore what motivates users to continue to use these apps.

mHealth apps e-satisfaction continued usage intention continued usage behavior UTAUT2

1. Introduction

The speedy evolutions of information technology (IT) have encouraged mobile devices to become a broad part of daily life. The widespread mobile device usage contributes to the integration of health and mobile services ^[1]. mHealth apps have changed the way health information acquiring and improved the efficacy of services in the healthcare field ^[2]^[3]. Zion Market Research ^[4] proposes that the mHealth apps market will be more than USD 111 billion by 2025. However, people use mHealth apps unfrequently, likely because these apps are perceived as unreliable ^[5]. The development of mHealth apps depends on their attracting and retaining users. Understanding users’ continued usage behavior is essential for the success of mHealth apps. In this study, mHealth apps refer to applications installed on mobile devices that provide users with health information and have simple reminders and health data tracking functions to effectively promote users’ self-health behaviors and improve the efficiency of health services and the accessibility of health information.

Though mHealth apps thrive, people rarely continue to use such apps after the initial acceptance ^[5]. Among the empirical studies on users’ continued use of health technology, researchers have explored the impact of factors on the continued behavior of using health apps. For example, Cho ^[6] identified the effects of perceptual and emotional factors on motivating continuous using health apps relying on the post-acceptance model and the technology acceptance model; based on the social cognitive theory, Kim and Han ^[7] examined the effects of health technology self-efficacy, self-evaluative outcome expectations, self-regulation, and privacy risk on older users’ continued intention to use health apps. Recently, one previous study proposed a research model including context and contents values to explore the intention of users over the age of 40 in using health apps on mobile phones ^[8], another study applied the expectation-confirmation model and the investment model to explore the effects of perceived usefulness, satisfaction, and commitment on continuance intention to use health apps ^[9]. However, few studies explored the effects of factors on the intention and behavior of keeping to use mHealth apps based on the extended UTAUT2.

2. Factors Influencing Usage Behavior on Mobile Health Applications

This study aims to explore the influences of users’ continued behaviors of mHealth apps usage from the perspective of UTAUT2. The researchers results proved that performance expectancy, social influence, facilitating conditions, perceived reliability, price value, online reviews have significant positive impacts on users’ continued usage intention through the mediation role of e-satisfaction. Moreover, users’ continued intention positively impacts the usage behavior of keeping to use mHealth apps. It is further identified that habits of using mHealth apps enhance the positive effect of e-satisfaction on continued usage intention.

This study identifies that habit directly affects users’ continuous use of mHealth apps and users should be attracted to form the habit of mHealth apps usage, which is crucial to these apps’ successful development. Interestingly, the stronger the habit of using mHealth apps, the stronger the relationship between users’ e-satisfaction and their willingness to continue using mHealth apps. Generally, individuals who are satisfied with experience tend to repeat the behavior. Furthermore, individuals who develop habits towards emerging technologies are likely to continue to use the apps.

Two constructs (perceived reliability and online reviews) added to the research model have important effects on the continued intention of using mHealth apps. For example, users are significantly interested in the availability of online comments provided by others in mHealth apps. This suggests that users regard such comments as reliable, useful, and relevant health sources in these platforms when consulting professional physicians. Thus, extant reviews published by others can be easily accessed and conveniently through mHealth apps. In addition, continued behavior of mHealth apps usage also can facilitate users’ seeking health information, thereby saving their time and effort. In this study, the empirical results show that all hypotheses have been verified and the extended UTAUT2 is an important theoretical model for predicting the continued use of mHealth apps.

2.1. Theoretical Implications

Because mHealth apps are an emerging technology in China, a deeper understanding of the usage of these apps is required. The researchers purpose is to explore the effects of users’ e-satisfaction with mHealth apps and actual usage behavior from the perspective of the extended UTAUT2. There are some theoretical implications in the study.

First, this study pays attention to users’ continued acceptance of mHealth apps. Prior studies mainly examined the effect of users’ initial behavioral intention of emerging technologies while ignoring continuous usage behaviors. However, uses’ continuous intention and usage behavior are significantly important for the success of emerging technologies. This study extends the UTAUT2 in the field of continuous use of mHealth apps.

Second, this study contributes by validating the effects of perceived reliability and online reviews on e-satisfaction with mHealth apps. In the context of healthcare services, due to the life-threatening possibilities, perceived reliability is important for their selection. In the context of mobile apps, people believe that online reviewss are valuable sources of information when they are selecting services and products. Thus, in these two respects, this study integrates perceived reliability and online reviewss into the UTAUT2 model, which enriches the theoretical framework.

Third, the key strength of this study is revealing the moderating role of habits. The results reveal that when people are accustomed to using mobile apps, then the relationship between e-satisfaction and continued intention will be enhanced. Habit has the moderating effect between e-satisfaction and continued intention of using mHealth apps, and this finding enriches the research on mHealth apps and expands the theoretical scope of UTAUT2.

2.2. Practical Implications

The study has some practical implications regarding the understanding of designing and managing mHealth apps. First, managers of mHealth apps should organize promotional activities and pay attention to the roles of performance expectation and effort expectation. Users of mHealth apps should be encouraged to use mHealth apps due to requiring less time and effort compared to the traditional ways of waiting and visiting hospitals. In addition, the price value of health services on mHealth apps positively affects continued usage intention through the mediation role of e-satisfaction. In this study, the researchers suggest that managers should regulate the price value of health services on mHealth apps.

Second, managers should encourage users to publish rich online reviewss on the features and health services of mHealth apps. For example, the number of users who review their attitude of the experience in such apps should be a focus. To ensure that online reviews is relevant and credible to other users, mHealth apps should improve the information quality of the published online reviewss ^[10], which convinces users that online reviewss are valuable to acquire health services and information. In addition, this study found that users’ habits moderated the positive relationship between e-satisfaction and continued intention of using mHealth apps. Users’ habits can be enhanced by personalized notifications of using mHealth apps ^[11].

Third, managers of mHealth apps could focus on increasing users’ e-satisfaction with the functions of mobile platforms, thereby improving users’ intention of continues using mHealth apps to seek health information and services. In addition, if users receive high-quality and friendly personalized healthcare services, then they will be satisfied with their experience and the high value of using such apps. Regarding mHealth apps that have the ability to capture and record large amounts of patient data, physicians could implement and provide personalized health services through these mobile platforms. Moreover, users have autonomous control in the processes of consulting and can provide appropriate and relevant solutions to problems in mHealth apps.

References

Wallis, L.; Blessing, P.; Dalwai, M.; Shin, S.D. Integrating mHealth at point of care in low-and middle-income settings: The system perspective. Glob. Health Action 2017, 10, 1327686.
Sadegh, S.S.; Khakshour, P.S.; Sepehri, M.M.; Assadi, V. A framework for m-health service development and success evaluation. Int. J. Med. Inform. 2018, 112, 123–130.
Zhang, X.; Yan, X.; Cao, X.; Sun, Y.; Chen, H.; She, J. The role of perceived e-health literacy in users’ continuance intention to use mobile healthcare applications: An exploratory empirical study in China. Inf. Technol. Dev. 2018, 24, 198–223.
Pyrogen Testing Market. Zion Market Research, New York, NY, USA. Available online: https://www.zionmarketresearch.com/report/mhealth-apps-market (accessed on 23 January 2019).
Nisha, N.; Iqbal, M.; Rifat, A. The changing paradigm of health and mobile phones: An innovation in the health care system. J. Glob. Inf. Manag. 2019, 27, 19–46.
Cho, J. The impact of post-adoption beliefs on the continued use of health apps. Inter. J. Med. Inform. 2015, 87, 75–83.
Kim, E.; Han, S. Determinants of continuance intention to use health apps among users over 60: A test of social cognitive model. Int. J. Environ. Res. Public Health 2021, 18, 10367.
Lee, E.; Han, S.; Jo, S.H. Consumer choice of on-demand mHealth app services: Context and contents values using structural equation modeling. Inter. J. Med. Inform. 2017, 97, 229–238.
Chiu, W.; Cho, H.; Chi, C. Consumers’ continuance intention to use fitness and health apps: An integration of the expectation-confirmation model and investment model. Inf. Technol. People 2021, 34, 978–998.
Filieri, R. What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. J. Bus. Res. 2015, 68, 1261–1270.
Ghose, A.; Guo, X.; Li, B.; Dang, Y. Empowering patients using smart mobile health platforms: A randomized field experiment. MIS Q. 2022. preprints.

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Pei Wu

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Update Date: 10 Feb 2022

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