Exploring the role of user empowerment in shaping behavioral intention and actual use of mHealth: An empirical study of an extended Technology Acceptance Model

doi:10.21203/rs.3.rs-3325534/v1

Many studies have highlighted mobile health (mHealth)’s positive impact on health promotion and disease management. However, technological features in mHealth alone to address challenges in retention may not ensure sustained utilization of mHealth if users lack interest or motivation for their own health management. Therefore, we aimed to explore the role of user empowerment, users' active involvement and ability to regulate health behaviors as influencing factors in mHealth adoption and usage. We extended the technology acceptance model (TAM) by incorporating the concept of user empowerment and tested the model by leveraging structural equation modeling with survey data collected from 612 respondents. Our findings revealed that positive and negative perceived health status (β = 0.24, t = 4.28, p < 0.001; β = 0.12, t = 2.50, p < 0.05) and health literacy (β = 0.64, t = 9.03, p < 0.001) resulted in a significant positive association with user empowerment in addition to users’ perception on ease of use of mHealth apps except for self-efficacy. User empowerment showed significant positive associations with all constructs related to TAM: perceived ease of use (β = 0.27, t = 7.11, p < 0.001), perceived usefulness (β = 0.15, t = 4.03, p < 0.001), behavioral intention (β = 0.48, t = 4.78, p < 0.001), and actual use (β = 0.31, t = 5.28, p < 0.001). Our findings shed light on the dynamic relationship among user empowerment, mHealth adoption, and usage by offering insights into enhancing user engagement and fostering healthier lifestyles through technology-driven interventions.

User Empowerment

mHealth

Technology Acceptance Model

Health Self-Management

mHealth, an integration of information and communication technologies (ICT) with healthcare services, has emerged as a promising avenue for delivering person-centered interventions, fostering user engagement, and enhancing self-management capabilities. By enabling continuous monitoring of vital signs and physical activity parameters for health and well-being, mHealth empowers users to maintain optimal health status and healthy behaviors [1]. Many studies have demonstrated the positive impact of mHealth on health promotion, disease prevention, and treatment activities by intervening users’ behaviors (e.g., physical activity and dietary habits) [2], [3] as well as by providing timely notifications for managing their health effectively [4].

Despite the benefits of mHealth and its growing popularity, mHealth faces challenges in its adoption and actual use. According to a report [5], the annual retention rate of mHealth apps in 2020 was as low as 18%, with an average 90-day retention rate of 31%. Furthermore, a systematic review of 17 studies revealed an average overall attrition rate of mHealth targeting for chronic disease management was 43% [6]. Similarly, a large-scale study conducted on 50,000 users of the MyHeart Counts app reported a substantial drop-off rate of 4.1 days as a measure of user engagement. These findings highlight that mHealth research needs to further investigate factors that can address challenges in the adoption and continued use of mHealth[7]. Studies in this line of mHealth research have explored the various factors affecting the adoption of mHealth technologies from multiple perspectives, including personal, social, technical, organizational, and healthcare-related angles [8].

However, the role of users’ proactive approach to their health self-management in the process of the adoption and use of mHealth technologies is relatively underexplored. Regardless of the motivational features incorporated into mHealth apps for user engagement, if individuals lack interest or motivation in managing their health and engaging in health-related activities, the effectiveness of mHealth diminishes for them. Hence, gaining an understanding of the degree to which users actively participate in their health self-management and their ability to regulate and sustain health-related behaviors could offer valuable insights into mHealth adoption and usage from this perspective.

To this end, we attempted to extend the technology acceptance model (TAM) by incorporating the concept of user empowerment, which encompasses self-awareness, active self-care, and the ability to make self-determined decisions while maintaining control over one's health and wellbeing. Thus, user empowerment can serve as an important motivational factor in health behavior change and self-management, contributing to the issue of low retention rates pertaining to users' interests and motivation. Within the framework of TAM, which primarily delves into users' perceptions of mHealth technologies on perceived usefulness and perceived ease of use, the study investigated the following research questions:

How can user empowerment be conceptualized and operationalized in both healthcare and the adoption and use of mHealth?
What are the factors that are positively associated with user empowerment in this context?
Is user empowerment positively associated with behavioral intention to use mHealth apps and subsequent actual use of mHealth apps?

2.1. mHealth as a tool of user empowerment

mHealth encompasses a diverse range of technologies, including mobile computing, medical sensors, and communication specifically for healthcare. With the increased availability of wearables and biomedical sensors, mHealth enables users to take charge of their healthcare needs through self-care and self-directed management. By offering personalized interventions and accessible healthcare services, mHealth enables users to set health-promoting goals, monitor their physiological data, and access necessary support, ultimately leading to informed decision-making and improved health outcomes. Previous studies have shown various impacts of mHealth in promoting healthy behaviors and lifestyle, encouraging users to manage symptoms and treatment [9]–[11]. For instance, individuals with diabetes can reduce blood glucose levels and hospitalization rates by controlling their physical activity and dietary habits. To maximize the benefits of mHealth and achieve better health outcomes, it is essential for users to be equipped with both the capability and commitment to actively engage and participate in their health management, which has resulted in growing attention to the concept of empowerment as a focal point in emerging patient or person-centered healthcare research and reform [12], [13]. Empowerment at an individual level is viewed as a dynamic process of individuals’ personal control over and proactive approach to life through self-management and shared decision making[14]–[16].

Recent mHealth studies have also attempted to explore the role of user empowerment in addressing the challenges of mHealth adoption and continued use [17]–[19]. Lee et al. [17] suggested that users’ commitment to self-monitoring keeps them continuing to use mHealth applications based on the usage patterns of mHealth app functions derived from the usage logs of 1,439 individuals over an 18-month period. König et al.[19] adapted the Precausion Adoption Process Model (PARM), a behavior change stage model for measuring patient empowerment, to investigate the mHealth adoption process in terms of behavioral characteristics and decision-making style. They demonstrated that the adoption process of the mHealht app varied according to different movitational stage. Nicola Brew-Sam[18] suggested a multidimensional empowerment approach as an antecedent of mHealth adoption and use for self-management to understand an overall underlying process.

However, previous empirical studies[17], [19] did not explicitly delve into the concept of empowerment, and another study [18] suggested methodologically that the concept of empowerment should be tailored specifically for the mHealth context to understand and investigate influencing antecedents of mHealth adoption and use. The various interpretations of empowerment and the intricacies involved in its operationalization in the line of mHealth research results in relatively few empirical studies examining the association between user empowerment and the adoption and sustained use of mHealth apps.

2.2. Technology Adoption Models (TAMs) in mHealth Adoption and Use

Technology adoption models such as the technology acceptance model (TAM) [20], the unified theory of acceptance and use of technology (UTAUT) model [21], and the extended UTAUT (UTAUT2) model [22] have been extensively utilized in the healthcare context to understand the influencing factors behind technology adoption. The TAM primarily delves into users’ perceptions of mHealth technologies, focusing on perceived usefulness and perceived ease of use. On the other hand, UTAUT and its variations consider broader constructs such as performance expectancy, effort expectancy, social influence, and facilitating conditions in the adoption of mHealth technologies[23]–[25]. Notably, these models were originally developed for information technology in general. Consequently, key constructs used in mHealth research studies have been defined or measured differently to suit various study contexts. Moreover, the “added variable approach” has been employed in testing additional constructs that fit a specific study context [26]. For instance, mHealth adoption among physicians or nurses utilized constructs such as personal innovativeness and perceived service availability as influencing factors [27], whereas patients’ use of mHealth for self-management utilized different constructs such as price value and perceived reliability [28]. Similarly, a systematic review categorized sociotechnical factors affecting patients’ mHealth adoption, including social and personal factors (e.g., social influence, motivation, locus of control, age), technical and material factors (e.g., usefulness, ease of use, privacy, cost), and health-related factors (e.g., health behaviors, risk perception, insurance status)[8]. Accordingly, it might not be adequate to include all factors to achieve successful mHealth adoption and use due to multiple stakeholders in multiple settings.

Numerous studies have demonstrated mHealth as a means of empowering users to manage their health and well-being. These studies also shed light on the importance of users’ personal and behavioral elements, such as intrinsic motivation, self-efficacy, health perception and locus of control, in successfully utilizing mHealth[6]. For instance, users who perceive that they are more in control of their behaviors and health condition are more intrinsically motivated to adopt and continuously use mHealth for their self-management [29]–[31]. These personal and behavioral factors are closely linked to user empowerment in the healthcare context, which encompasses self-awareness, active self-care, and the ability to make self-determined decisions regarding their health and well-being.

Despite the significant impact of user empowerment on mHealth adoption, only a few studies have incorporated the user empowerment construct or similar constructs into technology acceptance models (TAMs). Pai & Alathur [32] found a positive effect of individuals’ motivational factor – mobile service enabled empowerment – on the awareness and use of mHealth services. In their study, the construct empowerment is defined as the intrinsic motivation – competence, self-determination, and impact – that stimulates users’ cognitive assessments of mobile services. Similarly, Salgado et al.[33] added personal empowerment as a second-order construct into UTAUT2, using broader perspectives: 1) professional logic – expert knowledge acquisition to act, 2) consumer logic – personal affirmation to make decisions, and 3) community logic – a sense of community and participation. The study revealed that personal empowerment was positively associated with behavioral intention and use of mHealth for both chronic conditions and healthy groups. Jin et al. [34] examined how users’ perceptions on using mHealth apps were influenced by an event (i.e., the COVID-19 pandemic) and how the use of mHealth apps during pandemics affected psychological empowerment during pandemics. In this study, users’ psychological empowerment refers to their sense of self-efficacy and self-control pertaining to health management activities and outcomes.

Due to the broad and multifaceted nature of empowerment, adopting constructs from other studies may not be sufficient. While studies such as [33] and [34] have examined mHealth usage from the lens of empowerment, these constructs may not directly align with our research context that specifically focuses on health self-management. Given this gap in the literature, understanding how user empowerment influences their behavioral intention to adopt and utilize mHealth technologies for self-management could provide valuable insights into encouraging more active and engaged participation in utilizing mHealth technologies. Consequently, it becomes imperative to precisely conceptualize and operationalize user empowerment within the TAM to shed light on its role in driving mHealth adoption.

2.3. An Extension of TAM –User Empowerment as a Key Mediator

Despite a wide range of definitions[35]–[38], empowerment is referred to as a process that enables individuals to develop the knowledge, skills, and confidence to take control of their health as well as to actively participate in behavior change activities such as goal setting, self-monitoring, and decision-making. Pekonen et al.’s [39] systematic review of 31 studies in the healthcare context classified the elements of empowerment and their subscale items into four areas: 1) patients’ capacities, 2) patients’ knowledge, 3) patients’ behaviors and 4) support from others.

Empowerment can be attained through the enhancement of knowledge (e.g., health literacy, skills, competence) or the development of certain capacities (e.g., self-efficacy, autonomy) to make decisions related to one’s health. In addition, individuals can also attain empowerment through their own intrinsic traits and commitment[35], [37], [39]. Empowerment encompasses not only psychological constructs such as self-efficacy, perceived competences, or self-determination but also involves specific actions and behaviors that an individual undertakes while actively engaging in their health-promoting behaviors. Therefore, we argue that empowerment is not limited solely to users' ability and knowledge but is also extended to users' self-management behaviors. In this study, we aim to conceptualize and operationalize the multifaceted nature of user empowerment in the context of mHealth by incorporating relevant constructs from TAMs. By doing so, we can gain a deeper understanding of the factors that contribute to user empowerment and its impact on the acceptance and use of mHealth technologies.

3.1. Perceived Health Status

Perceived health status, as a motivating factor, plays a critical role in evoking individuals to activate or take actions for their health more attentively and objectively in healthcare contexts. According to the Patient Activation Measure (PAM), a patient empowerment measurement tool, patients begin to activate and take actions to maintain and improve their health by acknowledging their health status first [40]. The health belief model (HBM) has also identified perceived health status as perceived susceptibility and perceived severity that contribute to patients’ awareness of health issues and conditions [41], while the health action process approach (HAPA) has introduced risk perception or threat in the motivation process and the self-regulation process [42]. Furthermore, in mHealth adoption studies, few studies have investigated the impact of perceived health status as a threat or an awareness on user attitudes towards mHealth technologies[43].

However, evidence regarding the impact of perceived health status on health-promoting behaviors and mHealth adoption and use is mixed. Some empirical studies found the effectiveness of risk perception on promoting health-related behaviors[44], whereas some studies found its negative impact on health-promoting behaviors as a barrier to adopting a healthy lifestyle [45], [46]. Moreover, these studies have primarily emphasized the negative or pessimistic aspects of one’s health status, highlighting threats, risks, or disease, which leaves a gap in understanding the impact of positive or optimistic perceptions of health status on empowering behaviors and the adoption of mHealth technologies. It is noteworthy to the potential benefits and motivational impact of positive perceptions of health status. For example, individuals who maintain a positive outlook regarding their health are more likely to engage in proactive health behavior change activities. They may perceive their health status as an opportunity for growth, improvement, and overall well-being. This optimistic perspective can foster a sense of empowerment, encouraging individuals to take charge of their health and seek out resources, including mHealth technologies, to support their well-being. Therefore, we propose the following research question and hypothesis:

R1) How do different perceptions on health status impact user empowerment?

H1a) Perceived health status, when viewed optimistically, will be positively associated with user empowerment.

H1b) Perceived health status, when viewed pessimistically, will be positively associated with user empowerment.

3.2. Self-efficacy (SE)

Bandura’s social learning theory [47] defined self-efficacy as an individual’s assessment of one’s capability to perform a specific behavior successfully. In healthcare research, self-efficacy is one of the fundamental constructs that significantly impacts individuals’ motivations for behavioral change and their ability to control the environment and health behavior. Maibach & Murphy [48] asserted that self-efficacy influences individuals’ four main functions (i.e., choice behaviors, effort expenditure and persistence, thought patterns and emotional reactions). For instance, individuals with high efficacy tend to choose healthy behaviors and put more effort into mastering and maintaining those behaviors with high and positive expectations and emotions on their performance and affective states. In short, self-efficacy is a psychological factor that positively influences an individual’s level of effort and control over determinants of his or her health and well-being, eventually contributing to an enhancement in empowerment [35], [49]. Considering this, we argue that self-efficacy can be seen as a precursor to user empowerment since users require the ability to deliberate and to formulate and choose users’ health-promoting actions and behaviors (e.g., setting health goals, self-monitoring, etc.). Therefore, we formulated the following hypothesis:

H2a) Self-efficacy is positively related to user empowerment.

Similarly, in TAMs, self-efficacy has been identified as a significant influencing factor in the adoption and use of mHealth technologies, as it both directly and indirectly impacts perceived ease of use (PEU) and usefulness (PU) [23], [50]. Perceived usefulness and ease of use are important determinants in the adoption of information systems. In the context of mHealth adoption, perceived usefulness refers to the degree of users’ perception of how mHealth would enhance their task performance, while perceived ease of use refers to the degree of users’ perception of how using mHealth would be easy. However, there have been some mixed results regarding its effect on mHealth adoption intention. Interestingly, some studies found no or even negative effects of computer self-efficacy or general self-efficacy on mHealth adoption, which may be attributed to factors such as privacy concerns and UX motivational features[51], [52]. Despite these mixed empirical findings, self-efficacy remains a theoretically well-established predictor of mHealth adoption. Therefore, we formulated the following hypotheses:

H2b) Self-efficacy will be positively associated with users’ perceptions on ease of use.

3.3. eHealth Literacy

In addition to self-efficacy, eHealth literacy (eHL) is also a prominent factor in mHealth with behavior change interventions. As defined by the WHO Health Promotion Glossary[53], health literacy is “the cognitive and social skills that determine the motivation and ability of individuals to gain access to, understand and use information in ways of promoting and maintaining good health”. Health literacy skills are generally categorized into three types: basic/functional, communicative, and critical. Ishikawa & Yano [54] demonstrated that health literacy influences patients’ participation in information sharing, decision making, self-management, self-management behaviors and adherence and health care service use. For example, an individual with higher health literacy tends to exhibit greater confidence in their ability to accomplish health-promoting behavior and experience a higher success rate in adapting or adjusting their behaviors for health promotion. On the other hand, even with helpful resources, a user with a lower level of health literacy feels relatively overwhelmed and finds it difficult to adhere to treatment or interventions. Many studies emphasize that individuals need to acquire ability and knowledge and increase skills that are important for their health and well-being, which ultimately leads to the goal of empowerment by effectively controlling their health and quality of life [35], [37], [38], [55]. Furthermore, with the proliferation of digital health tools, online health communities, and information search capabilities, eHealth literacy skills related to computers and media have been recognized as an essential skill. eHealth literacy includes six core skills, namely, traditional literacy, health literacy, information literacy, media literacy, scientific literacy, and computer literacy, which refer to more comprehensive aspects[56]. Therefore, we formulated the following hypothesis:

H3a) eHealth literacy is positively related to user empowerment.

eHealth literacy is not only a critical factor in healthcare but also important for technology-based interventions, as health care services and features are increasingly delivered through information and communication technologies. Monkman et al. [57] highlighted the importance of eHealth literacy in usability for a mobile app (i.e., blood pressure tracker), suggesting that mHealth technologies are designed with eHealth literacy considerations to improve usability for users with limited health literacy. Vervier [58] showed a positive effect of eHealth literacy on users’ online privacy perception when using mHealth services or apps, consequently leading to a positive attitude towards mHealth apps. Similar to self-efficacy, eHL also plays an important role as a predictor of mHealth adoption and use. Thus, we proposed the following hypothesis:

H3b) eHealth literacy is positively related to perceived ease of use.

3.4. Mapping User Empowerment into Health-Promoting Behaviors

User empowerment within Information Systems(IS) contexts originates from the concept of psychological empowerment in workplace as a motivational construct that reflects a sense of control over one’s work and an active role in one’s work [16], [59]. Spreitzer [59] tested the following four cognitions defined by [60] as properties of empowerment: 1) Meaning : an individual’s judgment on the value of a work goal or purpose, 2) Competence: an individual’s belief in their capability in their performance with skill, 3) Self-determination: an individual’s sense of having choice in initiation and continuation of work behaviors, and 4) Impact : the extent to which an individual can influence outcomes of work.

Integrating with these four cognitions in the line of IS research, Kim & Gupta [61] defined user empowerment as an active motivational orientation towards using IT applications in the workplace and further established four corresponding cognitions for IS usage: meaning of system usage, competence of user, self-determination of user, and impact of system usage. This research demonstrated the significant role of user empowerment in influencing three different user behaviors in utilizing systems for task completion: extended use, integrative use, and emergent use.

In the healthcare context, empowerment is defined and used diversely. Initially, health promotion adopted empowerment to underscore patients’ autonomy and participation in making decisions about their health. Empowerment was later used for patients with chronic disease, serving as a strategy for motivation, enablement, and engagement in health management. However, the dimensions and measurement tools of empowerment vary across participants (e.g., patients, healthcare professionals) and healthcare settings (e.g., hospital care, primary care, generics)[37], [39]. While consensus on the definitions and psychometric properties of empowerment is lacking, three fundamental elements persist across various definitions and measurements: individual capacities, knowledge, and health-related behaviors.

In our study, we attempted to map psychological empowerment onto users’ personal and self-health-promoting behaviors, focusing on health goal setting and self-monitoring within the context of mHealth adoption since users need self-regulatory mechanisms to continuously control over their health and well-being, being aware of current behavior through mHealth interventions[62], [63]. While actively participating in their own health care and making informed health-related decisions with support from mHealth interventions [15], [18], [64], users perceive that mHealth is useful for their health management, which increases the possibility of behavioral intention to use mHealth for health care purposes. The behavioral intention (BI) in our study refers to the strength of one’s intention to use mHealth apps [20]. Consequently, the behavioral intention and actual utilization of mHealth are expected to be positively influenced by user empowerment as well as users’ perceived usefulness. Hence, we proposed the following hypotheses:

H4a) User empowerment will be positively associated with behavioral intention towards mHealth use.

H4b) User empowerment will be positively associated with the perceived usefulness of mHealth.

H4c) User empowerment will be positively associated with behavioral intention towards mHealth use.

H4d) User empowerment will be positively related to the actual use of mHealth apps.

Given that BI is determined by PU and PEU, we extended our hypotheses as follows:

H5a) Perceived ease of use will be positively associated with behavioral intention towards mHealth use.

H5b) Perceived ease of use will be positively related to perceived usefulness.

H5c) Perceived usefulness will be positively associated with behavioral intention towards mHealth use.

H5d) Behavioral intention will be positively related to the actual use of mHealth apps.

4.1. Data Collection

We used the Qualtrics panel service [65] to recruit participants who were 18 years or older and had used any mobile health technologies for their health and well-being. After agreeing on the informed consent, participants responded to a series of questions regarding self-efficacy, e-health literacy, perceived health status, and user empowerment, followed by TAM-related questions. We also collected demographic data (i.e., race/ethnicity, income, education, employment status, age). We retained 612 respondents for the main data analysis. Table 1 presents detailed demographic information of the participants.

Table 1

Participants' demographics
Characteristics		Frequency	Percentage
Age	18–25	169	27.6
	26–35	87	14.2
	36–45	79	12.9
	46–55	88	14.4
	56–65	97	15.8
	66+	92	15
Gender	Female	318	52
Gender	Male	291	47.5
Race	White	452	73.9
	Black or African American	81	13.2
	Other	45	7.4
	Asian	24	3.9
	American Indian or Alaska Native	5	0.8
	Native Hawaiian or Pacific Islander	5	0.8
Education	Less than high school	15	2.5
	High school graduate	138	22.5
	Some college	147	24
	2-year degree	63	10.3
	4 -year degree	164	26.8
	Doctorate	15	2.5
	Professional degree	70	11.4
mHealth apps	Apple Health	181	29.58
	Fitbit	138	22.55
	MyFitnessPal	80	12.8
	Samsung Health	23	3.76
	Others	190	31.05
Health devices	Scales	326	53
	Blood Pressure Monitor	239	39.1
	Wearable EKG	40	6.5
	Pulse Oximeter	114	18.6
	Smartwatch	231	37.7
	Blood Glucose Meter	82	13.4
	Others	30	4.9
	No health devices	81	13.2

4.2. Construct Measurements

All the scales and items were adopted from previous studies except for the user empowerment construct, which was newly added, as indicated in Table 2. The constructs for user empowerment were formulated based on self-regulatory behaviors to promote and maintain users’ health and well-being. Table 2 details the source of each scale and items utilized. All items were measured on 7-point Likert-type scales, requiring an answer ranging from 1 (strongly disagree) to 7 (strongly agree).

Table 2

Measurement Items and Reliability and Convergent Validity Test Results
Items	Measurement	Factor Loading	Cronbach's Alpha	CR	AVE
Self-Efficacy(SE) [66], [67]
SE_1	It is easy for me to stick to my aims and accomplish my goals.	.715	.862	.866	.563
SE_2	I am confident that I could deal efficiently with unexpected events.	.816
SE_3	Thanks to my resourcefulness, I know how to handle unforeseen situations.	.771
SE_4	I can remain calm when facing difficulties because I can rely on my coping abilities.	.779
SE_5	No matter what comes my ways, I am usually able to handle it.	.791
eHealth Literacy(eHL) [68]
HL_1	I know where to find helpful health resources on the mobile internet.	.788	.889	.809	.514
HL_2	I can complete a mobile-based medical consultation with a doctor using my mobile phone.	.755
HL_3	I usually check the evidence mentioned in mobile-based health information.	.707
HL_4	I can evaluate the reliability of the evidence mentioned in mobile-based health information.	.772
HL_5	I usually check the source of health information.	.705
HL_6	I can evaluate the reliability of a source of health information.	.730
HL_7	I can evaluate the quality of health information available on the mobile phone.	.723
Positive Perceived Health Status (PHS) [69]
PHS_1	According to the doctors I’ve seen‚ my health is now excellent.	.85	.87	.870	.573
PHS_2	I feel better now than I ever have before.	.777
PHS_3	My health is excellent.	.847
PHS_4	In the future‚ I expect to have better health than other people I know.	.761
PHS_5	I expect to have a very healthy life.	.821
Negative Perceived Health Status (NHS) [69]
NHS_1	I am somewhat ill.	.811	.812	.818	.530
NHS_2	I have been feeling bad lately.	.78
NHS_3	I will probably be sick a lot in the future.	.812
User Empowerment (UE) modified from [70]
SM_1	I attempted to collect results from health monitoring tools or apps.	.815	.888	.895	.551
SM_2	I have been collecting results from health monitoring tools or apps.	.828
SM_3	I have been analyzing results from health monitoring tools or apps.	.818
SM_4	I have been reviewing results from health monitoring tools or apps.	.795
GM_1	I attempted to set goals to promote my health.	.685
GM_2	I have been completing and adjusting goals and activities to manage side effects and symptoms.	.722
Perceived Usefulness (PU) [71]
PU_1	Using the mobile health apps helps me manage my health condition efficiently.	.851	.908	.907	.661
PU_2	Using the mobile health apps increases my productivity in managing my health condition.	.861
PU_3	Using the mobile health apps enhances my effectiveness in managing my health condition.	.862
PU_4	Using the mobile health apps improves my ability to manage my health.	.854
PU_5	Using the mobile health apps makes it easier to complete my health routines	.848
Perceived Ease of Use (PEU) modified from [71]
PEU_1	Operating the mobile health apps is easy for use.	.864	.806	.803	.579
PEU_2	I find the mobile health apps easy to use.	.881
PEU_3	Using the mobile health apps does not require a lot of my mental effort.	.803
Behavioral Intention (BI) [21]
BI_1	I plan to use the mobile health apps frequently.	.895	.856	.860	.670
BI_2	I will continue using the mobile health apps in the future.	.879
BI_3	I always try to use the mobile health apps in my daily life.	.871
Actual Use (AU) [72]
AU_1	To improve my physical activities	.792	.855	.859	.607
AU_2	To keep track of my physical activities	.821
AU_3	To monitor calories I burn	.756
AU_4	To achieve my personal activity goals	.809

The data analysis was conducted in two stages. First, we performed confirmatory factor analysis (CFA) with all variables to ensure the reliability of the measurement items. We assessed the reliability and two types of validity: content validity and construct validity. Composite reliability (CR) and average variance extraction (AVE) indicate good construct reliability when the cutoff values of CR and AVE are greater than.07 and.05, respectively. The convergent validity checks item loadings with the contracts. All item loadings must exceed 0.70 to support good convergent validity [73]. The discriminant validity can test intercorrelations between constructs based on the square roots of the AVE of constructs. The square roots of AVE must be higher than the correlation between the construct and the other constructs[74].

As demonstrated in Table 3, the CR for all other constructs varied from 0.81 to 0.90, and the AVEs ranged from 0.053 to 0.062, indicating construct reliability. After excluding unreliable items for the final CFA, the item loadings of constructs ranged from 0.72 to 0.895, which was higher than 0.7, in support of the convergent validity of constructs. As shown in Table 3, we find that all correlations of these groups were below the square roots of AVE, indicating good discriminant validity. However, the square root of the AVE of BI, PU, and PEU are slightly less than the absolute values of the correlations with other constructs. The items or constructs in our study were adopted from the items from a model that has been theoretically and empirically validated multiple times using established methods. The low discriminant validity is not due to problematic constructs[75] but possibly due to our study sample or context. According to the final CFA, the measurement model fit well to the data (χ²/df = 1498.474/724 = 2.07, p < .000; Root Means Square Error of Approximation (RMSEA) = .042 [LO: .039, HI: .045, 90% CI]; comparative fit index (CFI) = .945; Tucker-Lewis index (TLI) = .941).

Table 3. Discriminant Validity Test Results. Note: PHS, Positive Perceived Health Status; NHS, Negative Perceived Health Status; SE, Self-efficacy; eHL, eHealth Literacy; UE, User Empowerment; PEU, Perceived Ease of Use; Perceived Usefulness; BI, Behavioral Intention; AU, Actual Use.

	CR	AVE	MSV	MaxR(H)	SE	HL	PHS	NHS	EU	AU	BI	PEU	PU
SE	0.866	0.563	0.358	0.867	0.750
HL	0.809	0.514	0.457	0.812	0.598	0.717
PHS	0.870	0.573	0.280	0.874	0.404	0.277	0.757
NHS	0.818	0.530	0.280	0.823	-0.219	-0.129	-0.529	0.728
EU	0.895	0.551	0.521	0.905	0.408	0.569	0.310	-0.075	0.742
AU	0.860	0.607	0.365	0.870	0.340	0.449	0.370	-0.191	0.595	0.779
BI	0.859	0.670	0.897	0.861	0.430	0.608	0.277	-0.131	0.722	0.604	0.819
PEU	0.803	0.579	0.769	0.823	0.469	0.676	0.285	-0.174	0.631	0.502	0.877	0.761
PU	0.907	0.661	0.897	0.907	0.421	0.601	0.272	-0.180	0.658	0.588	0.947	0.870	0.813

After completing the measurement model test, the hypotheses were validated by structural model tests. The model fit well to the data (χ2/df = 1511.188/732 = 2.064 < 3.00; RMSEA = 0.042 < .05[LO: .039, HI: .045, 90% CI ]; CFI = 0.945 > 0.90; TLI = 0.941 > 0.90). All the hypotheses were supported in the model except self-efficacy-related hypotheses (H2a ~ b). Both positive and negative perceived health status (β = 0.24, t = 4.28, p < 0.001; β = 0.12, t = 2.50, p < 0.05) and health literacy (β = 0.64, t = 9.03, p < 0.001) had significant positive impacts on user empowerment, supporting hypotheses H1 and H3. However, self-efficacy had no effect on user empowerment and perceived ease of use towards behavioral intentions to use mHealth apps. Thus, the hypotheses in H2 were rejected. On the other hand, health literacy (β = 0.41, t = 6.95, p < 0.001) had significant positive effects on perceived ease of use. User empowerment had significant positive effects on all constructs related to TAMs: perceived ease of use (β = 0.27, t = 7.11, p < 0.001), perceived usefulness (β = 0.15, t = 4.03, p < 0.001), behavioral intention (β = 0.48, t = 4.78, p < 0.001), and actual use (β = 0.31, t = 5.28, p < 0.001), explaining 35% of the variances in user empowerment, thus supporting H4a ~ d. Consistent with findings from previous studies, perceived ease of use had significant positive effects on perceived usefulness (β = 0.85, t = 14.75, p < 0.001) and behavioral intention (β = 0.178, t = 2.55, p < 0.05), while perceived usefulness had a significant positive effect on behavioral intention (β = 0.76, t = 10.09, p < 0.001) towards the actual use of mHealth (β = 0.43, t = 6.85, p < 0.001), thus supporting H5a ~ d. The hypothesis test results are shown in Fig. 2 and Table 4.

Table 4

Hypotheses testing results. Note: ***: p < 0.001, *: p < .05, ns: not significant. PHS, Positive Perceived Health Status; NHS, Negative Perceived Health Status; SE, Self-efficacy; eHL, eHealth Literacy; UE, User Empowerment; PEU, Perceived Ease of Use; Perceived Usefulness; BI, Behavioral Intention; AU, Actual Use.
Hypothesis	Path Description and Direction	Standardized Path Coefficient	T value	Supported?
H1a	PHS to UE	β = .239***	4.28	Yes
H1b	NHS to UE	β = .115*	2.50	Yes
H2a	SE to UE	β = .055^ns	.787	No
H2b	SE to PEU	β = .073 ^ns	1.45	No
H3a	eHL to UE	β = .635***	9.03	Yes
H3b	eHL to PEU	β = .412***	6.95	Yes
H4a	UE to PEU	β = .274***	7.11	Yes
H4b	UE to PU	β = .146***	4.03	Yes
H4c	UE to BI	β = .147***	4.78	Yes
H4d	UE to AU	β = .309***	5.28	Yes
H5a	PEU to PU	β = .849***	14.75	Yes
H5b	PEU to BI	β = .177*	2.55	Yes
H5c	PU to BI	β = .761***	10.09	Yes
H5d	BI to AU	β = .430***	6.85	Yes

The purpose of our study was to investigate mHealth adoption and use through the lens of user empowerment, particularly focusing on health-promoting behaviors that represent users’ control over their health and wellbeing, by adding a new construct to TAM.

First, to conceptualize and operationalize empowerment within the study scope, we utilized four constructs – negative perceived health status, positive perceived health status, self-efficacy, and health literacy. Consistent with prior findings, perceived health status and health literacy were positively associated with user empowerment except for self-efficacy. In our study, both positive and negative perceived health status yielded a substantial influence on user empowerment. While many studies have focused on observing the effects of negative perception on health status or consciousness, such as risk reception and health severity, on mHealth adoption[45], [46], [76], our study introduced an insight regarding positive perception on health status, demonstrating that the impact of positive perceived health status was considerably stronger effect, compared to that of negative perceived health status.

As expected, our study showed that health literacy plays a critical role in user empowerment. In general, health literacy refers to an individual’s knowledge and skill to obtain, understand, use, and communicate health-related information[55]. Consequently, users with a higher level of health literacy are likely to take a more active role in regulating and maintaining their health-promoting behaviors than those with a lower level of health literacy. Due to the context of our study, eHealth literacy was utilized to cover users’ abilities and skills that are associated with health as well as technologies, contributing to users’ perception on ease of use of mHealth technologies.

Surprisingly, self-efficacy did not appear to have an effect on either user empowerment or perceived ease of use, which is not consistent with previous research findings. This lack of significant impact may be due to several underlying factors. One possibility is that an individual's confidence and belief in their abilities might not always translate into the actual performance of health-promoting behaviors. Despite the implications of self-efficacy in health behavior research as a highly predictive motivational construct, some authors have argued that self-efficacy scales[67] indicate a reflection of motivation rather than a determinant of motivation to perform health-related behaviors [77], [78] because the scales assess one’s confidence in a certain target behavior. Accordingly, self-efficacy for health-related behaviors may not always be a motivational argument for individuals to perform health-related behaviors. For example, individuals with higher self-efficacy may not consistently engage in health-promoting activities, such as physical exercise or maintaining a healthy diet, unless they perceive a specific need to do so. Similarly, if they do not prioritize their health and well-being, they might not perceive a need to use mHealth apps at all. Another factor might be that measures of self-efficacy did not fully capture its relationship with user empowerment in the context of mHealth adoption since we utilized general self-efficacy as a construct.

As hypothesized, we observed that empowerment was positively associated with users’ perceptions on ease of use and usefulness of mHealth apps. Additionally, both behavioral intention and actual use were also significantly influenced by user empowerment. These findings suggest that user empowerment is a pivotal factor that influences users’ perceptions of mHealth technologies and their subsequent utilization of mHealth apps. Serving as a mediator, the process of activities in empowerment may lead to enhanced perception of usability of mHealth apps - ease of use and usefulness as well as behavioral intention to use and actual use of mHealth technologies.

Finally, our study findings regarding the TAM construct confirmed prior TAM research findings, demonstrating that perceived ease of use had a greater impact on the perceived usefulness of mHealth apps, while these constructs also impact behavioral intention to ultimately engage in the actual use of mHealth apps.

Our study findings will make several contributions to the current line of research on mHealth adoption and design, highlighting underlying factors that influence user empowerment in healthcare as well. First, to the best of our knowledge, this is the first empirical study that goes beyond mHealth technology acceptance and use models not only by adding user empowerment as a key determinant of mHealth use and adoption but also by proposing new paths between the main antecedents and user empowerment. Second, the study’s findings revealed new insight pertaining to the effects of four antecedent factors on user empowerment in health self-management. Perceived health status and eHealth literacy lead users to take a more active role in their health self-management by setting a goal and monitoring their health status through mHealth apps. In particular, our study emphasized the importance of optimistic perception on one’s health status in user empowerment. Additionally, self-efficacy might not be as important as other constructs for users to be activated and take actions for their health and well-being. Third, the findings will help healthcare providers and mHealth developers deliver evidence-based, user-centered approaches, focusing on users’ different levels of user empowerment while promoting self-efficacy and educating users to improve their health.

However, our study also has several limitations. Given that gender and age could potentially moderate the effects of the antecedents of user empowerment, an additional analysis of data is imperative to establish the validity of these factors. Regarding the concept of empowerment, our study utilized only goal setting and self-monitoring behaviors as measures of the construct. However, to achieve a more comprehensive understanding of empowerment, we need to apply second-order variables, which will be a future direction of our study. Finally, to further investigate the association between self-efficacy and user empowerment, the study needs to look at self-efficacy from a different angle, such as task-specific efficacy or outcome expectations.

Ethical Approval

Not applicable.

Competing interests

There is no conflict of interest.

Authors' contributions

Jinie Pak contributed to the study's concept, design, and manuscript writing. Both Jinie Pak and Hyang-Sook Kim were responsible for material preparation, data collection, and analysis. The original draft of the manuscript was authored by Jinie Pak and Both Jinie Pak and Hyan-Sook Kim reviewed and edited the manuscript.

Funding

This study was supported by the Faculty Development & Research Committee (FDRC) grant from Towson University.

Availability of data and materials

No applicable

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No competing interests reported.

Exploring the role of user empowerment in shaping behavioral intention and actual use of mHealth: An empirical study of an extended Technology Acceptance Model

Status:

Version 1

Abstract

Figures

1. Introduction

2. Literature Review

2.1. mHealth as a tool of user empowerment

2.2. Technology Adoption Models (TAMs) in mHealth Adoption and Use

2.3. An Extension of TAM –User Empowerment as a Key Mediator

3. Hypotheses Development

3.1. Perceived Health Status

3.2. Self-efficacy (SE)

3.3. eHealth Literacy

3.4. Mapping User Empowerment into Health-Promoting Behaviors

4. Research Methodology

4.1. Data Collection

4.2. Construct Measurements

5. Data analysis and Results

6. Discussion

7. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1