Advances in Web 2.0 technology have resulted in the application of mobile health (mHealth) care to the field of health management with increasing popularity. According to the Mobile Medical Survey Report of 2016, there were about 90,000 mHealth apps on the iOS platform in the United States in 2015. Compared with that of 2013, the growth rate was 106%. In October 2015, the China State Council implemented the Internet Plus policy, which enhanced the status of internet technology in health care. In October 2016, the outline of the Healthy China 2020 plan was proposed to establish a healthy concept to realize the health care needs of the entire nation. In January 2017, the Chinese government proposed the 13th Five-Year Plan for Health Promotion and Work and established the activities and plans for health promotion. In February 2017, the Chinese government proposed a long-term plan for the prevention and treatment of chronic diseases in China, which fully expressed the concept of the management of chronic diseases in daily lives. Studies have shown that health management apps have a positive impact on the improvement of personal health [1,2]. However, realistically, in all health management apps, the actual usage rate is rather low and the effects of health management are not obvious [3]. In our research, we ask the following questions: What are the reasons for the low utilization of health management apps? What factors affect users’ low compliance with mHealth apps and health behavior changes?

Prior literature suggests that the availability and ease of the use of health management apps play a crucial role in a patient’s choice of whether or not to use an app [4]. In 2009, Sykes et al [5] indicated that the professional support provided by a health management app to patients exerts a positive impact on the use of the app. Another study found that the patient’s personal intrinsic motivation has a positive effect on his or her health behavior changes [6]. Chen also explored the attitude toward organic food among the Taiwanese, which is related to health consciousness, environmental attitudes, and the mediating effects of a healthy lifestyle [7]. In fact, he found that health consciousness and the environment are the two most important motives for purchasing organic food. In addition, healthy lifestyle acts as a positive mediating effect between health consciousness and environmental attitudes, as well as the consumer’s attitudes toward organic foods [7]. Most of the previous studies have shown that system and information service quality, one’s motivation and ability, as well as social influence could affect the usage of apps. However, there is no clear evidence on the types of information that are most effective in influencing a patient’s continuous usage intention and whether these influences are temporally persistent.

Bhattacherjee and Sanford found that the influence processes for information technology (IT) acceptance are based on an elaboration-likelihood model (ELM) [8]. This theory enhanced our understanding of influence processes in IT acceptance based on the ELM. Indeed, the ELM includes two types of routes: central and peripheral routes are based on the type of information processed by a given user. The ELM also explains how one route may cause more influence than the other and how each route can have a long-term impact on users’ behavior changes [8,9]. Therefore, in this study, we conducted in-depth research on the central route (ie, intrinsic motivation) and peripheral route (ie, external motivation) of the ELM as well as on health awareness through multifactor perspectives at the individual, organizational, and social levels. We also determined which route—the central or the peripheral route—has a stronger impact on a patient’s use of health management apps. In particular, we planned to answer to the following research questions:

Seeking the answers to these three questions can enable us to better understand the factors that affect patients’ continuous usage of mHealth.

To meet the aforementioned objectives, a survey was conducted to test the main and mediated effects of mHealth services on a patient’s continuous usage intention. Our participants were users of mHealth apps in China. We tested the proposed model using structural equation modeling (SEM) with a partial least squares (PLS) estimation.

This study provides several theoretical contributions. First, we expanded the ELM and applied it to the health sector. Second, we explored the central and peripheral routes of the ELM, each with a different motivation and ability regarding the effects of patients’ continuous usage intention. Third, we redefined the cognitive-attitude-change process based on the original ELM.

Moreover, this study also contributes to the practical understanding of mHealth services in several important ways. First, as a result of the verification of our model, hospital administrators are provided with a theoretical basis, which enables them to better understand a patient’s psychological characteristics and accordingly construct a more comprehensive informatization process. Second, our model can provide support for health management entrepreneurs in terms of the design of health management apps so that they can clearly identify the problems and directions facing them. Third, the patients themselves can also gain a clear understanding of their inner tendencies, so that they can identify health management systems that best suit their personal characteristics through many health management apps and, thus, better manage their own health problems.

The rest of this paper proceeds as follows. In the next section (Theoretical Background), we present a brief overview of prior research and identify gaps in health behavior through mHealth service management. In the following section (Research Model and Hypotheses), we describe the key constructs and relationships in the ELM and present five hypotheses. The proposed hypotheses are tested using a survey based on a dataset of the app users of the mHealth service. This is followed by a discussion of our findings and their implications for information service research and practice. We end with some conclusions.

To test our model, we conducted a survey. This method was chosen because it is a way to access perceived information quality, perceived system quality, social media influence, attitude, and continuous usage intention; as well, it can enhance the broader applications of the research findings [23].

Bentler and Chou stressed that data can be structured for analysis on the premise that the sample dataset satisfies the normal distribution [24]. Although the results of our study indicate that the software program SmartPLS (SmartPLS GmbH) is suitable for nonnormal distribution, the results are better under conditions of a normal distribution. The condition by which the sample data satisfies a normal distribution occurs after the descriptive test of each constructed measurement item index, when the absolute value of the skewness is less than 2, and while the absolute value of the kurtosis is less than 5.0. We used SPSS Statistics for Windows, version 23.0 (IBM Corp), for statistical analysis of our sample data. The main output indicators were measured (ie, mean, SD, minimum, maximum, skewness, and kurtosis). The specific results are shown in Table 2.

We can perceive from this table that the largest value of skewness of 1.433 is less than 2, while the largest value of kurtosis of 2.269 is less than 5. Therefore, our survey data basically conforms to the above normal distribution and can be used for further structural equation analysis.

The survey questionnaire was distributed to the participants via WJX [25], a questionnaire website that is the oldest and most-used online survey software and serves as an examination and voting platform in China. Since its launch in 2006, users have posted more than 28.22 million questionnaires and have collected more than 1.88 billion responses while maintaining a growth rate of more than 100% yearly. The platform’s users have covered more than 90% of universities and research institutions in China, and the platform is a well-known portal for questionnaires, examinations, and voting systems that are trusted by leading companies in various industries.

The survey was posted on the questionnaire website. The objectives of the survey and the requirements for the recruitment of our participants were introduced at the beginning of the survey, and each participant would receive an incentive of RMB 15 (approximately US $2.19). The participants were from all over China and were users of mHealth management apps, such as Haodf, XYWY, or Guahao, which are large-scale mHealth apps in China. The data collection process was divided into two phases. In the first phase, a pilot analysis was conducted and the measurement model (eg, the reliability, validity, common method bias, and multicollinearity of the constructs) was assessed to ensure its appropriateness. In the second phase, we collected the data. A total of 262 questionnaires were distributed. Of these, 255 contained acceptable responses, yielding a response rate of 97.3%. Of these respondents, 55.3% (141/255) were male and 79.6% (203/255) were under the age of 40 years. Most of the participants were employed (241/255, 94.5%). Among the participants, 67.1% (171/255) had graduated with a bachelor’s degree.

The demographics of the respondents are shown in Table 3. A 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree), was used in our research. The scores indicate different levels of health consciousness for the two groups in our sample and we believe this approach was appropriate for our research.

The measurement of the majority of constructs was adopted from prior relevant studies (see Multimedia Appendix 1). Slight modifications were necessary to make the text suitable for the research context, and all measures used a 7-point Likert scale. Sustained participation was measured using the instrument of continuous usage intention that was adopted from Bhattacherjee and Sanford [8] and from Petty and Cacioppo [9]. Perceived mHealth information quality was measured using methods by Park et al [26] and by Bhattacherjee and Sanford [8]. Perceived mHealth system quality was measured using methods by Cheong and Park [27] and by Brady and Cronin [28]. Social media influence was measured using methods by Keery et al [29]. Attitude was measured using methods by Kim [30] and by Yang and Yoo [31]. The six items used to measure health consciousness were adopted from Mai and Eisenberg [32] and from Chen [7].

To enhance the validity of the measures of the constructs, we followed Moore and Benbasat [33] when adapting the measurements for our study. All the measures were adapted from prior research along with their sources (see Multimedia Appendix 1). Other control variables, such as age, gender, education, and work experience, were measured using a single-item measure. To verify the adapted survey items, individual meetings were held with university colleagues and postgraduate students to discuss the following: (1) the appropriateness of the questionnaire items, (2) the possibility of ambiguity in the questionnaire items, and (3) the appearance and layout of the survey instrument. Based on the feedback received, a revised questionnaire was developed. This was then sent to the same individuals for a second review. A minor revision was made based on their further suggestions.

This study examined the factors that affect the continuous usage intention of mHealth services based on the ELM and resulted in several findings. First, patients perceived mHealth services to have positive information quality; we also observed that the system quality of mHealth services had a positive effect on patients’ personal attitudes, indicating that when they perceived the provided mHealth apps to have a higher quality, they would change their attitudes. Our results are consistent with findings in prior studies [46]. Second, the results reveal that social media influence does have a positive effect on a patient’s attitude toward mHealth services, which is consistent with findings in existing research [47]. It indicates that effective social media influence can help patients improve their attitudes toward mHealth services.

Third, our study purports that patients’ health consciousness does moderate the influence of perceived information quality, perceived system quality, and social media on their attitudes. In particular, our findings suggest that a patient’s health consciousness has a positive effect on the relationship between social media influence and attitude, in contrast with Hypothesis 4c. This may be because patients with high health consciousness are more likely to pay attention to the management of personal health; this could lead them to discover new and effective health management modes and to try and continue to find more useful functions for health management, in order to manage their own health. However, patients with low health consciousness may not have a strong awareness of personal health management, and information about health promotion may be directly ignored. Boontarig also illustrated that the participant with a lower level of health consciousness is more concerned about value and convenience in supporting the use of the system. They also elaborated that the participant’s personality as a factor affecting a user’s perception is important in technology adoption and must be considered when developing and designing these services [48]. Thus, we infer that the effect of social media influence on patients’ attitudes toward mHealth services may be mediated through the patients’ health consciousness. Moreover, the moderation effects of health consciousness on the relationship between the perceived information quality of the content of mHealth services and the perceived system quality of mHealth services, as well as attitudes toward them, were found to be insignificant. This means that the patient’s health consciousness has no moderating effect on the central route (ie, perceived mHealth information quality and perceived mHealth system quality) and the patient’s attitude toward mHealth services. This may be because patients pay more attention to the quality of information and the system when using mHealth services and this has nothing to do with personal health consciousness. As long as the quality of mHealth services is verified, patients will choose to use mHealth services. Biduski at el illustrated that the determining aspect in user experience is whether the app features meet users’ health needs [49]. As long as the quality of mHealth services can meet the expectations of patients, patients will continue to use mHealth services.

To the best of our knowledge, this is the first study to use the ELM to explore the factors that affect a patient’s continuous usage intention on mHealth services based on multidimensional perspectives. Specifically, this study contributes to the mHealth service literature in three aspects.

First, in our research, we expanded the ELM and applied it to the mHealth care domain. Previous researchers have applied the ELM theory to many fields, including e-commerce, consumer behavior advertising, health care, and so on. Rollins and Bhutada illustrated that more highly involved consumers had more positive attitudes, behavioral intentions, and greater information-searching behavior [50]. Sher and Lee studied the effects of consumer skepticism on online shopping based on the ELM [51]. Angst and Agarwal also found that privacy concerns plays a significant role in the adoption of electronic health records [52]. Based on the ELM, this study explained the mechanism of influencing patients’ continuous usage intention toward mHealth services.

Second, we explored the central and peripheral routes, with their different motivations and abilities and their effects on user behavior. The central route (ie, perceived mHealth information quality and perceived mHealth system quality) is the factor that patients are most concerned with, which directly affect whether patients accept the mHealth services mode. As well, the peripheral route (ie, social media influence) can affect patients’ attitudes toward mHealth services through effective communication channels, such as TV shows, magazines, or advertisements, then change their continuous usage intention of mHealth services. However, due to individual differences (ie, patients’ health consciousness), the process and response of these things are not the same, so patients’ health consciousness plays a significant role between the central route, the peripheral route, and patients’ attitudes toward mHealth services.

Third, we redefined the cognitive-attitude-change process based on the original ELM. The continuous usage behavior of mHealth services needs to go through two stages: the first stage is a process from cognitive-to-attitude change and the second stage is a process from the attitude change to continuous usage, which can be well explained by the ELM theory.

Our research provides relevant practical implications. First, through the verification of this model, hospital administrators are provided with theoretical support, which enables them to better grasp the patient’s psychological characteristics and make their information construction more comprehensive in the process of informatization. Second, for health management entrepreneurs, this model can provide support for the design of health management apps; it can help entrepreneurs clearly identify the problems they encounter and help them decide which directions to take. Third, the patients themselves can gain a clear understanding of their inner tendencies so they can identify health management systems that suit their characteristics within many health management apps, in order to better manage their own health care.

Some limitations of this study need to be considered. First, our data was collected through online questionnaires. Although the participants were from all over the country, and though they were representatives of each province, there were also certain limitations. In a future field study, we will need to use a health management software app designed by our laboratory so that we can measure the users’ actual usage. Second, we measured continuous usage intention rather than actual usage. Although many theories, such as the theory of reasoned action [53] and the theory of planned behavior [54], and empirical studies have shown that behavioral intention is a reliable proxy for actual human behavior, future studies need to explore how and which factors influence actual usage behavior. Finally, we tried to conduct group experiments for health consciousness—high and low groups—to test whether there were different effects on continuous usage intention when patients experienced different degrees of health consciousness, but because of data constraints, the results did not differ significantly. Hence, we will measure and analyze the effects of this variable in the future.

This study explored the impacts of mHealth services on patient’s continuous usage intention from three dimensions, including personal intrinsic factors, organizational factors, and social factors. We found the following: (1) the influence of the individual’s internal motivation on his or her health behavior is positively correlated, (2) the influence of external factors, such as social media, on a patient’s health behavior is positive, and (3) a patient’s health consciousness plays a positive role in changing a person’s health continuous usage behavior. The findings of this article are important contributions to the field for both scholars and practitioners.