Content area
Perceived Ease of Use (PEU), Health Literacy (HL), and Usage Behavior (UAOA) are three important categories that are the focus of this study, which examines the factors that influence older individuals in Pune, India, when using mobile health (mHealth) applications. The research, which was informed by Health Literacy theory and the Technology Acceptance Model (TAM), aimed to investigate how these characteristics affect an elderly metropolitan population's initial uptake and ongoing engagement with mHealth applications. An online survey link was sent to roughly 3479 prospective participants via a variety of outreach channels, such as social media sites constructs. Structural Equation Modeling (SEM) was used to analyze the data and test the proposed connections between PEU, HL, and UAOA. This methodological approach made it possible to evaluate several connected variables at once, providing a thorough picture of the factors impacting older individuals' use of mHealth in Pune. The results show that mHealth usage is considerably and favorably influenced by both PEU and HL, with HL being the more reliable predictor. Higher PEU scores demonstrated the significance of intuitive design, straightforward navigation, and low cognitive load, whereas higher health literacy was linked to increased confidence in utilizing app features and comprehending health information. The report also highlights the need for focused solutions by identifying enduring obstacles such generational digital divides, technology phobia, privacy concerns and personal referral networks, to start the data gathering process. The goal of this strategy was to reach as many older persons as possible and promote their involvement. 953 people out of the entire outreach answered the survey. After that, a thorough data purification procedure was started, which included checking for statistical outliers, duplicate entries, inconsistent responses, and incomplete submissions. 700 valid responses were kept for analysis following the implementation of these quality control procedures, offering a strong and representative sample for the goals of the study. 30 items on a five-point Likert scale made up the final survey instrument, which was intended to gauge participants' opinions on the three. These observations have the following two implications: In theory, they broaden TAM by incorporating HL into the context of senior citizens in poor nations. To encourage uptake, they advise politicians, healthcare providers, and app developers to give priority to user-friendly interface design, digital health literacy training, and localized content. Promotion in the community that uses family members and medical professionals as reliable influencers could increase consumption even further. In addition to providing a basis for future research examining longterm health outcomes, the effects of certain app features, and the role of social influence in maintaining engagement, this study increases knowledge of mHealth uptake among aging urban populations.
Introduction
The application of mHealth has grown exponentially worldwide because of the increasing smartphone penetration and a highly felt need for easily accessible healthcare solutions. In 2021, the global mHealth market was valued at about USD 50 billion and is expected to surge to USD 310 billion by 2027 [11]. This surge is driven by increased demand for remote patient monitoring and chronic disease management. Over 5 million mHealth apps were available in app stores alone by 2020, thus catering to a myriad of user needs across the globe [33]. mHealth applications in India surged to be driven by increasing smartphone penetration and the government’s Digital India initiative. In 2020, India has recorded a 45% increase in health app downloads, reaching over 300 million downloads of health-related apps only [21]. The Indian mHealth market was valued at USD 1.2 billion in 2021 and is likely to grow at an approximate CAGR of 31 by 2026, said a source. This will be driven in large part by demand from telemedicine, chronic disease management, and fitness apps [16]. The COVID-19 pandemic accelerated this trend and underlined the need for digital health solutions.
Perceived Ease of Use (PEOU) in interaction with health literacy, turns out to be a significant determinant for the use of mHealth apps among older adults. In this regard, as in other regions, it is common to find the older adults' ability to use the mHealth technologies with ease acting as the main determinant for their adoption in Pune City of India. It has been shown that perceived ease of use significantly influences the acceptance of technology, particularly in older individuals, in whom usability problems may well occur due to age-related declines in processing resources [10]. Furthermore, health literacy, which is considered as the ability to access, process, understand, and interpret information in health, has relevance to the effective use of mHealth apps.
The level of health literacy among older adults is as high as 58% in India alone, which serves as an additional barrier to the correct use of mHealth apps [11, 29]. Specifically it applies to Pune also being, the geographic region of India has a rising senior citizen population for whom such accessible and user-friendly mHealth solutions will be trapping for improved health result. The aim of this study is to analyse the effect of perceived ease of use and health literacy on mHealth apps usage amongst older adults from Pune City, India using structural equation modelling (SEM) approach. The conceptual model produced in the research shows practices as predicted by the perceived ease of use and health literacy.
The model is used to test the following hypothesis:
Hypothesis 1 (H1)
The null hypothesis posits that there is no significant relationship between Perceived Ease of Use (PEU) and Health Literacy (HL) among older adults in Pune city. Conversely, the alternate hypothesis suggests a significant relationship between PEU and HL in this demographic.
Hypothesis 2 (H2)
The null hypothesis asserts that Perceived Ease of Use does not significantly influence the usage of mHealth apps among older adults in Pune city. In contrast, the alternate hypothesis posits that Perceived Ease of Use significantly impacts mHealth app usage in this population.
Hypothesis 3 (H3)
The null hypothesis states that Health Literacy does not significantly affect mHealth app usage among older adults in Pune city. The alternate hypothesis states that Health Literacy has a significant influence on the use of mHealth apps among this group.
Literature review
Many factors influencing the acceptance and use of mobile health applications among older adults have been evaluated in recent studies. The evidence presented by several studies targeting elderly people indicates that perceived usefulness and perceived ease of use are key factors for the intention to adopt mHealth [9, 24, 28, 40]. Other factors, such as mHealth literacy, the size of the screen on the smartphone, and the general influence exerted by healthcare providers, positively affect usage intention, while storage consumption does the exact opposite [40]. Thus, technology readiness components would be the grounds for perceived usefulness and perceived ease of use, including optimism, innovativeness, and insecurity [9]. The Unified Theory of Acceptance and Use of Technology (UTAUT) model shows that performance expectancy, effort expectancy, social influence, and perceived credibility have a significant construct effect on the elderly people's intention to use the mHealth services [26]. In these dynamics, there are factors involved that are important to be understood by governments, policymakers, and healthcare providers for the successful adoption of mHealth among older adults, not only in developed but also in developing countries [1].
The adoption of mHealth by older adults has been the focus of a growing body of research that has identified several key factors influencing intentions to use and continued use of these technologies. Perceived usefulness, perceived ease of use, and social influence are shown to have significant impact on the adoption intention in this study [6, 25]. Additionally, privacy issues, lack of reliability, and intergenerational differences are some of the essential hindrances [25]. Gamification, usability and empathic collaborative design are some of the enablers while perceived risk, sunk cost and technological anxiety are the inhibitors of continued use [36]. These include recommendations by the researchers [25] regarding technical, free trial, information clarification, and participatory design approaches to improve the adoption rate. Moreover, engagement is recognized to be one of the strongest predictors of continued intention to use mHealth applications [36]. Taking these factors into consideration in co-developing mHealth interventions could enable older adults to engage in what would be meaningful self-management which, ultimately, may enhance quality of life in chronic conditions like heart failure [6].
Despite some potential benefits for older adults, there are challenges to the adoption of Digital health technologies DHTs. Factors influencing the use of DHTs by seniors were studied, especially in fall risk assessment and in heart failure management. Technology readiness, eHealth literacy, and perceived usefulness and ease of use were determined as key determinants for the adoption of DHTs [7, 17]. Facilitators are prior experience with technology, willingness to learn, and a physician's recommendation though barriers include lack of knowledge, sensory limitations, and cost concerns [7]. A fall risk mobile app developed for older adults demonstrated high usability. The clear instructions and prevention strategies further improved this [15]. Researchers suggest that person-related, technology-related, and contextual barriers be addressed while facilitators are capitalized upon to increase the adoption of DHT among seniors [7]. Age-related sensory changes can be factored into the design of apps to make a smartphone as potent as a tablet for older people [15].
Mobile health (mHealth) applications have received significant attention as tools that can act in support of public health in contemporary times, such as during COVID-19 [5]. Health consciousness directly affects the use of health apps, mediated by health information orientation and eHealth literacy to use health apps effectively [8]. Social influence, trust, and behavioral intention play crucial roles in mHealth adoption [27]. Usefulness, perceived ease of use, subjective norm, trust [38], perceived risk, and attitude factors all significantly influence the behavioral intention to use mHealth services [45]. The adoption process will thus be moderated by age, where perceived ease of use, perceived vulnerability, and perceived severity are of greater concern to middle-aged and older users [45]. These determinants can be understood to help in improving the design of mHealth services and their implementation for better health outcomes, increasing user satisfaction [5, 27].
mHealth applications potentially benefit health care delivery, more so for older adults. Nevertheless, their acceptance is scarce in this population. Outside of the aging process, other factors affecting mHealth adoption include perceived usefulness, perceived ease of use, social influence, and facilitating conditions of the mHealth app [2, 14]. Certain variables, such as age and gender, may modify the influence of both factors on adoption intentions [2]. Despite concerns of cost, privacy, and usability, many older adults express interest in mHealth usage for health management and medication review, and health provider contacts [32]. In conclusion, mHealth initiatives need to consider the apprehensions of the users and require an approval from the healthcare operators to improve the uptake rates. Through a meta-analysis, it provides a comprehensive model of mHealth acceptance [1]. It has mostly centred upon the relationship of perceived usefulness, perceived ease of use, attitude, subjective norms and facilitating conditions on behavioral intention to use mHealth [3].
Based on existing preliminary evidence, mHealth applications enhance health literacy and outcomes, especially among high-risk populations. In India, an mHealth app was successful in increasing hypertension knowledge for individuals with low and high literacy levels [12]. Still, the limited 2016 mHealth technology diffusion rates for the older population is worrisome. With regard to acceptance, a study founds some of the most important constructs in acceptance are: perceived ease of use, social influence, and trust [18, 42]. Third, the intention of the older adults to use mHealth apps may also be influenced by perceived risks (privacy and performance risks etc.) [18]. In addition, as Fox and Connolly [23] stated, perceived risks including privacy and performance risks might negatively influence older adults intention of using mHealth apps. These findings imply that mHealth has the promise to enhance health care access and health outcomes, but that targeted strategies may be necessary to promote adoption among older populations.
Not all patients and caregivers are immediately ready to adopt digital health solutions, this readiness or motivation is influenced by varying degrees of trust and the contextual factor of each individual. The research results on the disclosure behaviors support recent studies showing that deviants also rationalize their misrepresentation of personal information use through techniques neutralization is a double play, online engagement has dark factors [46]. In times of crisis such as COVID-19, this study may assist in discrediting mandates to fight against misinformation by identifying rumor belief and sharing patterns on social media, highlighting perceived credibility and collective responsibility [13]. Knowledge-sharing online about health by doctors results in differing outcomes, from patient satisfaction to gratitude—which is dependent on the type of the information and how it is presented [35],motivations are not just financial, but include altruism and professional reputation too [41].
Policy environments being of utmost importance regarding the adoption of mHealth are assuredly shown in China, where focused policy measures address user hesitance and gaps in the market [44]. Further, acceptance is influenced by aspects of the service such as relevance, matching, and competence, which influence users' cognitive and affective attitudes toward mHealth services [39]. The personalization-privacy paradox in mHealth states that perceived usefulness and user attitude mediate the balancing act between tailored services and privacy concerns as a factor in adoption decisions [43]. Together, these studies provide a rough perspective on the way digital trust, motivation, and systemic factors play a role in technology use in health and beyond.
Several recent studies have emphasized the possibility of mobile health (mHealth) applications to reinforce older adults' positive behaviors. Two important factors indirectly related to mHealth adoption among older adults, digital health literacy and perceived ease of use, played a major influence on these conditions [4, 30]. Participation in digital health platforms is linked with elevated levels of digital health literacy [37]. Rather than criteria based on age alone, there might be better predictors for mHealth app usability in cognitive function [20]. The digital health literacy of older adults could be influenced by sociodemographic factors, device-related factors and social support [31]. The mediating role of technological interactivity and anxiety between perceived usefulness and intent to use smart healthcare devices was established. (Sheng [34]). Increased eHealth literacy has also been significantly associated with self-perceived health status along with increasing use of digital health services [19]. The evidence suggests favorable outcomes in older adults after using mHealth interventions [22], with one preliminary study showing an increase in frailty scores and step counts.
This study draws its conceptual base from the Technology Acceptance Model (TAM), introduced in 1989 by Davis: According to TAM, two factors Perceived Ease of Use (PEU) and Perceived Usefulness (PU) are what accept and use technology by an individual: In the case of mHealth applications, PEU stands very critical for elderly users because of their cognitive, physical, and technological barriers. Further, this research incorporates ideas from the HL area, admitting how crucial it is to obtain, process, and comprehend health information to interact effectively with digital health tools.
There are very fewer studies that have focused on how PEU interacts with HL with elderly populations, especially when it comes to developing countries such as India. The literature on TAM with regard to younger groups, clinicians, or general users and ignoring the specific barriers and facilitators for older adults residing in urban Indian settings, is vast.
On the contrary, some researchers have worked to extend TAM into broader frameworks like UTAUT or PMT. In contrast, we purposefully limit ourselves to the fundamental construct of Perceived Ease of Use, paired with Health Literacy, to form a somewhat parsimonious yet highly focused model. This in turn makes room for the thorough examination of these two important aspects that are adaptable and therefore bear on user interface design, educational interventions, and policy formulation toward inclusive mHealth adoption.
The study builds upon TAM and proposes integrating Health Literacy theory so that the existing knowledge about digital health adoption can grow and a significant research gap on elderly technology users in urban India can be addressed.
Research gap
Numerous factors, such as perceived utility, perceived simplicity of use, social impact, trust, and conducive conditions, are identified in the body of existing literature on older persons' adoption of mobile health (mHealth) [9, 26, 27, 40]. Although research has also looked at barriers like technological anxiety, privacy concerns, and usability issues [25, 36], the majority of the evidence comes from developed nations or general populations, with little attention paid to the particular sociocultural and technological contexts of older adults in urban India. Adoption is influenced by a number of factors, including policy environment, service characteristics, and digital health literacy [37, 39, 44]. However, little is known about how Perceived Ease of Use (PEU) and Health Literacy (HL) interact for this population.
Research on mHealth in India has mostly focused on younger users, medical professionals, or rural health initiatives, which has left a knowledge gap about the complex factors that influence senior adoption in cities like Pune where generational digital disparities and urban infrastructure coexist. According to earlier research, HL has a major influence on digital engagement and self-perceived health status [19, 31], and PEU is a crucial factor in light of age-related cognitive and physical obstacles [20]. Nevertheless, in the Pune context, no empirical study has carefully investigated these constructs collectively.
In order to fill this knowledge gap, the current study uses the Technology Acceptance Model (TAM) in conjunction with Health Literacy theory to examine the ways in which PEU and HL affect older adults' use of mHealth apps in Pune. This provides specific insights for inclusive design, policy development, and digital health literacy initiatives.
This research focuses on addressing gaps in the existing literature by specifically examining the roles of Perceived Ease of Use and Health Literacy as determinants of mHealth app usage among older adults in Pune.
Research methods
Survey design
The survey design targets the older adult population of Pune, focusing on three key constructs: Perceived Ease of Use (PEU), Health Literacy (HL), and Usage of mHealth Apps Among Older Adults (UAOA). Each construct is operationalized by 10 questions, which measure participants' experiences and attitudes in relation to using mHealth apps. This will be followed by the administration of the survey to a sample of 700 to ensure a good representation of the old age population. Such questions will be framed so as to capture quantitative data and will further allow an in-depth analysis of factors influencing the adoption of mHealth among elderly citizens in Pune.
Participants and data collection
The target demographic for this study comprised older adult residents of Pune, India. Data collection was undertaken via an online questionnaire produced using Google Forms, administered over a period of 1 month in the month of September 2024. This period was purposefully selected to coincide with a time when the majority of Pune's senior citizens were at home because of less travel during the monsoon. This allowed for higher response rates while maintaining a short enough recall period to guarantee accurate and consistent responses. The link to the questionnaire was disseminated extensively via community networks, messaging groups, and social media platforms, utilizing both personal connections and nearby community organizations to optimize reach.
The survey link was distributed to about 3479 possible respondents in total. Of these, 953 people answered. 700 valid replies were kept for study after a thorough data purification procedure that eliminated outliers, inconsistent responses, duplicate entries, and incomplete submissions. In order to facilitate statistical reporting and computation, the final sample size was rounded. Both automated validation rules and manual screening for missing data were part of the quality checks.
It is admitted that sampling bias may have been introduced by the online-only data collection strategy, which favored participants who were accustomed to using digital platforms and already had internet access. This restriction is especially pertinent to research with senior citizens, some of whom can experience difficulties with computer literacy. Nonetheless, this approach was selected because to its effectiveness, affordability, and capacity to quickly reach individuals who are spread out geographically within Pune.
Results
The demographic characteristics of 700 older adults (Table 1) reveal a balanced distribution across various age groups, with the largest group being those aged 66–70 (22%), followed closely by those aged 81+ and 71–75 (21.5% each). Gender distribution shows a significant proportion identifying as Other/Don’t wish to mention (37%), followed by males (32%) and females (31%). Education levels are diverse, with 22% having a primary education and 21.5% a secondary education. Marital status is varied, with 28% divorced and 27.5% married. Health condition data indicate that 35% are healthy, while the rest manage chronic conditions (33.25%) or minor issues (31.75%).
Table 1. Demographic characteristics of older adults (n = 700)
Item | Frequency | Percentage | |
|---|---|---|---|
Age group | 66–70 | 154 | 22.0 |
81+ | 150 | 21.5 | |
71–75 | 150 | 21.5 | |
60–65 | 128 | 18.25 | |
76–80 | 118 | 16.75 | |
Gender | Other/Don’t wish to mention | 259 | 37.0 |
Male | 224 | 32.0 | |
Female | 217 | 31.0 | |
Education level | Primary | 154 | 22.0 |
Secondary | 150 | 21.5 | |
No formal education | 145 | 20.75 | |
Postgraduate | 128 | 18.25 | |
Graduate | 123 | 17.5 | |
Marital status | Divorced | 196 | 28.0 |
Married | 193 | 27.5 | |
Widowed | 161 | 23.0 | |
Single | 150 | 21.5 | |
Health condition | Healthy | 245 | 35.0 |
Chronic Condition | 233 | 33.25 | |
Minor Issues | 222 | 31.75 |
Descriptive statistics (Table 2) of Perceived Ease of Use indicate varying degrees of agreement on all the ten items measured. Most of the responses to items PEU 7, PEU 8, and PEU 9 were rated positively; 36%, 33%, and 40% of those responded with a “3” on the scale, which means they agreed moderately. By contrast, items such as PEU 4 and PEU 10 received the lowest ratings, with 31% and 15%, respectively, choosing “1,” which means they disagreed. The scale indicates a tendency toward moderate to high perceived ease of use, but there is quite conspicuous variation—the more significant disagreement on some of the items, which reflects aspects less favorable to user experience.
Table 2. Descriptive statistics of the measurement of Perceived Ease of Use
Frequency (%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
5 | 4 | 3 | 2 | 1 | ||||||
N | % | N | % | N | % | N | % | N | % | |
PEU 1 | 207 | 30 | 111 | 16 | 199 | 28 | 61 | 9 | 122 | 17 |
PEU 2 | 91 | 13 | 202 | 29 | 181 | 26 | 88 | 13 | 138 | 20 |
PEU 3 | 88 | 13 | 168 | 24 | 176 | 25 | 95 | 14 | 173 | 25 |
PEU 4 | 59 | 8 | 105 | 15 | 170 | 24 | 147 | 21 | 219 | 31 |
PEU 5 | 191 | 27 | 159 | 23 | 144 | 21 | 67 | 10 | 139 | 20 |
PEU 6 | 157 | 22 | 153 | 22 | 145 | 21 | 78 | 11 | 167 | 24 |
PEU 7 | 58 | 8 | 215 | 31 | 249 | 36 | 103 | 15 | 75 | 11 |
PEU 8 | 164 | 23 | 215 | 31 | 232 | 33 | 48 | 7 | 41 | 6 |
PEU 9 | 76 | 11 | 242 | 35 | 280 | 40 | 52 | 7 | 50 | 7 |
PEU 10 | 55 | 8 | 222 | 32 | 269 | 38 | 103 | 15 | 51 | 7 |
PEU 1 The interface of the mHealth app is easy to navigate, PEU 2 I find it easy to learn how the mHealth app works, PEU 3 The mHealth app responds quickly to what I want to do, PEU 4 It is easy to get access to the features I want to use in the mHealth app, PEU 5 The mHealth app is easy to use for someone of my age, PEU 6 I can accomplish all of my health-related tasks effectively using the application, PEU 7 I find the instructions available in the app clear and easy, PEU 8 I can use the mHealth application without any help from others, PEU 9 The design of the mHealth app makes its use easy, PEU 10 I can easily rectify my mistakes while using the mHealth app
When asked about their gender, an exceptionally high percentage of respondents (37%) chose “Other/Don’t wish to mention.” This might be an indication of an increasing inclination for anonymity in online questionnaires, particularly among senior citizens who might be hesitant to divulge specific demographic information because of privacy concerns. It’s also likely that some participants chose not to disclose because they felt uneasy or unsure about how the information would be utilized. This pattern may also have been influenced by cultural factors, such as a reluctance to disclose personal identification qualities in questionnaires connected to the community.
There may be an overrepresentation of particular social groups in this sample, since the percentage of divorced respondents (28%) is significantly greater than the national average for older adults in India. The online data gathering strategy may have reached seniors who are more socially independent or live in urban areas, which are known to have higher divorce rates. In cities like Pune, where shifting social norms and a higher acceptance of marital breakup are more common, it can also be a reflection of demographic changes.
Descriptive statistics (Table 3) of HL show that, overall, the levels of health literacy among the respondents are at a medium to high level across all the measured items. Items like HL6 and HL7 have very high proportions of responses rating them high: 38 percent chose “3” and 37 percent chose “4,” indicating strong health literacy. Although most items, like HL1 and HL2, had 16% and 15%, respectively, choosing “2,” which represents lower literacy levels. This gives the information that even though most respondents were good at understanding health-related information, there is still room for improvement in health literacy in some areas.
Table 3. Descriptive statistics of the measurement of health literacy
Frequency (%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
5 | 4 | 3 | 2 | 1 | ||||||
N | % | N | % | N | % | N | % | N | % | |
HL1 | 84 | 12 | 242 | 35 | 202 | 29 | 115 | 16 | 57 | 8 |
HL2 | 62 | 9 | 216 | 31 | 270 | 39 | 105 | 15 | 47 | 7 |
HL3 | 156 | 22 | 198 | 28 | 237 | 34 | 64 | 9 | 45 | 6 |
HL4 | 99 | 14 | 251 | 36 | 231 | 33 | 69 | 10 | 50 | 7 |
HL5 | 113 | 16 | 243 | 35 | 214 | 31 | 84 | 12 | 46 | 7 |
HL6 | 98 | 14 | 258 | 37 | 268 | 38 | 40 | 6 | 36 | 5 |
HL7 | 71 | 10 | 258 | 37 | 265 | 38 | 63 | 9 | 43 | 6 |
HL8 | 73 | 10 | 238 | 34 | 271 | 39 | 65 | 9 | 53 | 8 |
HL9 | 99 | 14 | 206 | 29 | 276 | 39 | 80 | 11 | 39 | 6 |
HL10 | 117 | 17 | 249 | 36 | 227 | 32 | 69 | 10 | 38 | 5 |
HL1 I understand the health information given by the mHealth app. HL2 The app has clear explanations about the medical terms used. HL3 I can easily find information related to my health conditions within the app. HL4 I feel confident to use the app to manage my health., HL5 The mHealth app helps me to understand my treatment options, HL6 I can keep the health recommendations given in the application, HL7 The app helps me keep track of my health progress., HL8 I am able to make informed decisions about my health with the help of the app, HL9 I can understand what is written in the app, HL10 The mHealth app makes resources and support easily accessible to me
The descriptive statistics (Table 4) for the usage of mHealth apps among older adults reveal varied levels of engagement. Items like UAOA7 and UAOA8 show high usage, with 38% and 40% of respondents selecting “3,” indicating frequent app usage. On the other hand, items such as UAOA4 and UAOA3 have higher proportions of lower ratings, with 31% and 25% selecting “1,” reflecting less frequent usage. The overall trend suggests that while a significant portion of older adults are actively using mHealth apps, there is a substantial group that engages less frequently, indicating a need for strategies to improve usage among this demographic.
Table 4. Descriptive statistics of the measurement variables of usage mHealth app among older adults
Frequency (%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
5 | 4 | 3 | 2 | 1 | ||||||
N | % | N | % | N | % | N | % | N | % | |
UAOA1 | 206 | 29 | 111 | 16 | 199 | 28 | 61 | 9 | 123 | 18 |
UAOA2 | 90 | 13 | 202 | 29 | 181 | 26 | 89 | 13 | 138 | 20 |
UAOA3 | 88 | 13 | 167 | 24 | 177 | 25 | 95 | 14 | 173 | 25 |
UAOA4 | 58 | 8 | 106 | 15 | 170 | 24 | 147 | 21 | 219 | 31 |
UAOA5 | 98 | 14 | 251 | 36 | 232 | 33 | 69 | 10 | 50 | 7 |
UAOA6 | 113 | 16 | 242 | 35 | 214 | 31 | 85 | 12 | 46 | 7 |
UAOA7 | 98 | 14 | 256 | 37 | 268 | 38 | 42 | 6 | 36 | 5 |
UAOA8 | 76 | 11 | 240 | 34 | 280 | 40 | 53 | 8 | 51 | 7 |
UAOA9 | 54 | 8 | 222 | 32 | 269 | 38 | 104 | 15 | 51 | 7 |
UAOA10 | 84 | 12 | 239 | 34 | 204 | 29 | 115 | 16 | 58 | 8 |
UAOA1 It helps me monitor my health regularly, UAOA2 I depend on the mHealth app in taking my medication., UAOA3 The mHealth app aids in keeping me connected to my health caregiver. UAOA4 I set reminders for medical appointments using the mHealth app. UAOA5 I find it very useful to track my physical activity with the help of the mHealth app. UAOA6 I prefer using the mHealth app rather than visiting a healthcare facility over minor issues. UAOA7 The mHealth app has increased my self-management in terms of my chronic conditions. UAOA8 I regularly access health information through the mHealth app. UAOA9 I do share data from the mHealth app with healthcare providers. UAOA10 Since using the mHealth app, I have a better feeling of control over my health
Table 5 shows the metrics that can be used to evaluate the measurement model’s reliability and validity. All constructs have Cronbach’s alpha above 0.9, thus demonstrating good internal consistency. Composite reliability as measured with rho_a and rho_c is also very high, way above 0.9, thus establishing excellent reliability. Convergent validity, as represented through AVE, ranged from 0.585 to 0.642, thus meeting the threshold of being above 0.5. These results imply that the constructs are both reliable and valid for further analysis.
Table 5. Construct validity and reliability
Cronbach's alpha | Composite reliability (rho_a) | Composite reliability (rho_c) | Average variance extracted (AVE) | |
|---|---|---|---|---|
HL | 0.937 | 0.940 | 0.947 | 0.642 |
PEU | 0.913 | 0.916 | 0.929 | 0.621 |
UAOA | 0.911 | 0.912 | 0.927 | 0.585 |
Table 6 shows the descriptive Statistics and Factor Loadings of Measurement Variables: Perceived Ease of Use (PEU), Health Literacy (HL), and User Attitude and Outcome Acceptance (UAOA).
Table 6. Descriptive statistics of the measurement variables
Latent constructs | Descriptive statistics | Factor loading | |||
|---|---|---|---|---|---|
Mean | Median | Standard deviation | Initial | Final | |
PEU 1 | 3.314 | 3.000 | 1.424 | 0.847 | 0.864 |
PEU 2 | 3.029 | 3.000 | 1.312 | 0.819 | 0.828 |
PEU 3 | 2.861 | 3.000 | 1.359 | 0.802 | 0.826 |
PEU 4 | 2.483 | 2.000 | 1.297 | 0.732 | 0.746 |
PEU 5 | 3.280 | 4.000 | 1.460 | 0.776 | 0.794 |
PEU 6 | 3.079 | 3.000 | 1.475 | 0.776 | 0.793 |
PEU 7 | 3.023 | 3.000 | 1.346 | 0.670 | Dropped |
PEU 8 | 3.213 | 3.000 | 1.532 | 0.606 | Dropped |
PEU 9 | 3.346 | 3.000 | 1.010 | 0.746 | 0.735 |
PEU 10 | 3.181 | 3.000 | 1.018 | 0.715 | 0.706 |
HL1 | 3.259 | 3.000 | 1.118 | 0.754 | 0.755 |
HL2 | 3.201 | 3.000 | 1.020 | 0.756 | 0.756 |
HL3 | 3.509 | 4.000 | 1.124 | 0.709 | 0.709 |
HL4 | 3.400 | 4.000 | 1.072 | 0.777 | 0.776 |
HL5 | 3.419 | 4.000 | 1.096 | 0.791 | 0.791 |
HL6 | 3.489 | 4.000 | 0.976 | 0.858 | 0.858 |
HL7 | 3.359 | 3.000 | 0.991 | 0.852 | 0.852 |
HL8 | 3.304 | 3.000 | 1.030 | 0.812 | 0.812 |
HL9 | 3.351 | 3.000 | 1.036 | 0.859 | 0.859 |
HL10 | 3.483 | 4.000 | 1.052 | 0.828 | 0.828 |
UAOA1 | 3.309 | 3.000 | 1.425 | 0.790 | 0.777 |
UAOA2 | 3.024 | 3.000 | 1.311 | 0.766 | 0.755 |
UAOA3 | 2.860 | 3.000 | 1.359 | 0.739 | 0.709 |
UAOA4 | 2.731 | 3.000 | 1.423 | 0.680 | Dropped |
UAOA5 | 3.397 | 3.000 | 1.070 | 0.710 | 0.722 |
UAOA6 | 3.416 | 4.000 | 1.097 | 0.715 | 0.728 |
UAOA7 | 3.483 | 4.000 | 0.979 | 0.780 | 0.797 |
UAOA8 | 3.339 | 3.000 | 1.015 | 0.789 | 0.808 |
UAOA9 | 3.177 | 3.000 | 1.017 | 0.774 | 0.788 |
UAOA10 | 3.251 | 3.000 | 1.120 | 0.782 | 0.795 |
The range for the PEU from the initial factor loadings is from 0.606 to 0.847, where PEU 1 has the highest initial loading of 0.847. Looking at the final loadings, the changes have improved for most variables, except for the case where PEU 7 and PEU 8 were dropped, and this brings out low factor loadings. Generally, this would mean that loadings for the PEU variables onto the construct are strong, reflecting good measurement for perceived ease of use.
Factor loadings for the Health Literacy construct are all high, ranging from 0.709 to 0.859. HL6 and HL9 have the highest loadings and thus contribute the most to the health literacy construct.
For UAOA, the factor loadings are moderate, ranging from 0.680 to 0.808. Variables UAOA 4 and UAOA 5 were dropped due to low loadings, while others show a reasonable improvement in final loadings, demonstrating a moderate-to-good fit for the UAOA construct.
Correlation Matrix (Table 7) Between Health Literacy, Perceived Ease of Use, and mHealth App Usage Among Older Adults—A high correlation across all constructs indicates excellent relationships. HL highly correlates with UAOA (0.908) and PEU (0.737). PEU7 and PEU8 were dropped from the SEM model due to low factor loadings, likely because many older participants either did not use in-app instructions or depended on others for navigation. This resulted in limited response variability, reducing their statistical contribution despite their theoretical relevance. It is also strongly correlated with UAOA (0.919), which implies that perceived ease of use has a strong influence on the use of mHealth applications. Taken together, these results suggest that constructs are heavily interrelated and thus relevant for understanding how mHealth is adopted.
Table 7. Correlation matrix
HL | PEU | UAOA | |
|---|---|---|---|
HL | 1.000 | 0.737 | 0.908 |
PEU | 0.737 | 1.000 | 0.919 |
UAOA | 0.908 | 0.919 | 1.000 |
Figure 1 is SEM model for this paper. It shows the path coefficients of the relationship Health Literacy (HL), Perceived Ease of Use (PEU), and mHealth App Usage Among Older Adults (UAOA). The path coefficient of PEU → HL is 0.737. The relationship is, therefore, highly positively influenced by Perceived Ease of Use in relation to Health Literacy. On the other hand, the path coefficient of PEU → UAOA is 0.546, which signifies a moderate positive impact due to Perceived Ease of Use with respect to mHealth App Usage.
[See PDF for image]
Fig. 1
Measurement Model
The presence of a 0.506 coefficient value for HL- > UAOA in Table 8 suggests that health literacy influences mHealth App Usage positively and significantly. Justification for such coefficients would mean that ease of use and health literacy are important determinants of mHealth app usage.
Table 8. Post-hoc minimum sample size
Path coefficients | P-value | Results | |
|---|---|---|---|
HL—> UAOA | 0.506 | 0.000 | Supported |
PEU—> HL | 0.737 | 0.000 | Supported |
PEU—> UAOA | 0.546 | 0.000 | Supported |
Discussion and implications
The current study provides a detailed knowledge of the factors impacting older persons in Pune's use of mHealth applications, as interpreted by the Health Literacy theory and the Technology Acceptance Model (TAM). These results throw quite some light on various factors that affect the usage of mHealth applications by the elderly from across the city of Pune. Demographically, most of the participants were between the ages of 66–70. There was also a fair representation of male and female users, and of those identifying as 'Other' or not reported. A varied sample ensures a wide understanding of different or all factors likely to influence mHealth utilization among older adults. This urban sample from Pune represents a mix of older users who are tech-savvy and tech-averse, placing findings in the context of a metropolitan Indian environment where infrastructure is advanced but levels of digital literacy are still low.
Based on these, the analysis of some PEU showed that, while on the one hand, a large part of the respondents perceived mHealth applications as easy to use (high rates on PEU1, PEU5, and PEU7), on the other hand, quite many respondents faced problems (low scores on PEU4 and PEU10). TAM's focus on effort expectation is consistent with this, indicating that usability enhancements may directly improve behavioral intention and actual usage. This goes in line with results from Xie and Or [40] and Dash and Mohanty [9], who have recently pointed out that though the elderly value mHealth application benefits, their usability stands out conspicuously among the technologically naive and the sensorily impaired.
Other key descriptors used in the study are the perceived health literacy of the population investigated. Each of these indicators—HL6, HL7, and HL8—the fact that each is rated high means that the majority of all older adults would exhibit the requisite skills needed to successfully utilize mHealth applications. However, the lower ratings for HL2 and HL3 might be indicative of issues that some users will face when trying to understand and navigate health information as presented by these applications. This reflects the external factors of TAM that affect perceived usefulness and ease of use within the HL construct. It also confirms the findings of Cajita et al. [7] that a higher level of eHealth literacy is associated with a higher likelihood of adoption.
This study also evaluated how older individuals were using the mHealth apps and the findings were quite mixed. On the off chance of a good number of respondents indicating regular use of these apps, considering high scores on UAOA1, UAOA5, and UAOA7, a good proportion of those responding raised concerns relating to the usability, privacy, and reliability of the apps reflected in lower scores on UAOA4 and UAOA9. The privacy–personalization paradox [43], in which users balance perceived risks and advantages, is reflected in these worries and influences long-term use.
These findings are supported by research from Rajak and Shaw [27], which identifies trust, perceived risk, and behavioral intention as the most core factors in mHealth adoption. The concerns expressed by the elderly with regard to these technologies mirror a more profound uneasiness that is further documented in the literature, whereby older users show particular concern for data security and the accuracy of health information provided by apps [18].
Furthermore, current study findings demonstrate the role played by social influencers and recommendations by healthcare providers in supporting mHealth app utilization for older adults. Guidance and influence by healthcare professionals or family members were frequently reported to increase rates of utilization, as also previously shown by Saare et al. [2], and Hsieh et al. [15]. This supports UTAUT's social impact framework and emphasizes how crucial reliable middlemen are to elder populations' uptake of technology.
It has also pinpointed some difficulties in the adoption of mHealth apps, including technology anxiety, generational gaps, and sensory limitations. Indeed, these are challenges that have been documented in earlier studies by Pan et al. [25], and Tandon et al. [36], who indicate that older adults usually experience problems adapting themselves to new technologies in the face of such challenges. These results suggest that, in the Pune setting, localized interventions—like step-by-step tutorials, community training, and content in regional languages—could lessen these obstacles.
Hence, the findings of this study are in keeping with the literature on mHealth adoption by older adults, in that perceived ease of use, health literacy, social influence, and privacy and reliability concerns are leading reasons for use. The study also pointed out that interventions would have to be tailored to the challenges of older adults in Pune city, including localization of content, personalized support, and improvement in e-health literacy. The study expands TAM by incorporating HL as an enabling concept and placing these interventions inside Pune's socioeconomic and technological reality, thereby advancing theoretical knowledge and the development of useful strategies. This provides healthcare providers and policymakers with an opportunity to address these lacunae and promote effective adoption and sustained use of mHealth apps among this demographic group, eventually contributing to improved health outcomes and quality of life for older adults.
Perceived ease of use (PEU)
Results indicate that the acceptance and sustained use of mHealth apps by older adults are strongly influenced by perceived ease of use. Items about how simple navigation and how intuitive the app interface is were observed to have been strongly loaded. The hypothesis that PEU exerts a positive influence on mHealth app use is supported, which was in line with prior literature arguing for the case of ease of use among elderly people [9, 40]. Improving usability might thus be one of the most important strategies to foster adoption of mHealth apps among this particular group of people.
Health literacy (HL)
Another major predictor was health literacy. The likelihood of the effective use of mHealth apps among older adults shows a positive relationship to higher levels in health literacy. The health literacy constructs were represented by understanding health information and navigational ability of health-related content, which were both represented with high reliability according to the robust factor loadings in the SEM model. The finding is in line with previous literature, which identifies health literacy as a major enabler of technology adoption among seniors [7, 12]. Therefore, increasing health literacy would improve participation among older adults in using mHealth technologies.
Usage behavior (UAOA)
SEM analysis also confirmed that perceived ease of use and health literacy affect usage behaviour. Seniors with higher perceived ease of use and better health literacy are expected to use the mHealth apps more frequently. Since all factor loadings for the variables related to usage behaviour were significant, the constructs helped in validating the aspect of capturing usage for older adults. Thus, this result validates an approximation of the Unified Theory of Acceptance and Use of Technology (UTAUT) that defines performance expectancy and effort expectancy as the principal predictors of technology use [26].
Implications for mhealth app design
Study results highlighted the need for mHealth app developers to provide support in usability (ease of use, ease of access to results, and accessibility for the older population) and health literacy support features in mHealth apps. We may close this gap with simpler interfaces and built-in clear step-by-step instructions and educational resources within the app. Finally, results from this study additionally highlight the importance of providers actively encouraging mHealth use, and orienting older adults to these tools within the use contexts and technologies.
Implications of the study
Theoretical implications
This study shows that Health Literacy (HL) has a major impact on older persons’ acceptance of mHealth in an urban Indian setting by incorporating it as a complementary construct into the Technology Acceptance Model (TAM). When applied to people with different educational and digital histories, the results point to the necessity of extending TAM to incorporate literacy-related characteristics by demonstrating that HL directly improves perceived ease of use and usage behavior. The findings support the importance of trusted intermediaries in older adults’ adoption of technology and correlate with UTAUT's social impact dimension. They also validate the relevance of the two main TAM constructs, effort expectancy and performance expectancy. Furthermore, by placing these concepts within Pune's socioeconomic and cultural context, the study adds to the body of research on mHealth adoption. It demonstrates that, despite favorable opinions about usability, privacy concerns, technology anxiety, and generational digital divides continue to have an impact.
Practical implications
The findings highlight the necessity of localized mHealth interventions that integrate targeted literacy support with user-friendly design from the perspective of policy and practice. To accommodate a range of literacy levels and sensory skills, developers should give priority to bilingual content, clear in-app instructions, and streamlined navigation. In order to establish familiarity and trust, community health workers and healthcare professionals can act as important influencers by actively advocating and showcasing mHealth tools during consultations. To encourage adoption, public health initiatives might make use of social support systems, such as peers, family, and local authorities. Furthermore, the security issues raised by respondents can be resolved by implementing privacy assurance features, open data use guidelines, and user-controlled information-sharing choices. In the end, these combined actions can improve self-management of health issues and the quality of life for older individuals in Pune by encouraging both initial adoption and ongoing participation.
Conclusion
The objective of this study was to identify and confirm the constructs of Perceived Ease of Use (PEU), Health Literacy (HL), and Usage Behaviour (UAOA) in the adoption and use of mHealth applications among the geriatric population of Pune city. The results of the SEM study validate the Technology Acceptance Model (TAM) in conjunction with Health Literacy theory in this urban Indian context by confirming that both PEU and HL significantly and favorably influence usage behavior. While privacy concerns, technology anxiety, and navigation challenges appeared as persistent hurdles, seniors with higher levels of digital competence and health literacy reported using mHealth more frequently and confidently.
By incorporating HL into the TAM framework for older individuals in a developing country setting and providing empirical data from a sizable, heterogeneous urban sample, this study adds to the body of knowledge in academia. From a practical standpoint, it emphasizes the necessity of localized app design, literacy improvement programs, and trust-building strategies involving social support networks and healthcare providers.
Policymakers should find ways to improve digital competence in the elderly, particularly in terms of health-related technology use. Campaigns for public health, organized training courses, and in-app learning materials can all improve preparedness and long-term participation.
Limitations include the online-only sampling method, which may bias results toward more digitally literate seniors. To evaluate generalizability, future studies should test this model in a variety of geographical and cultural contexts. The effect of particular mHealth features—like personalized suggestions, interactive training, and privacy protections—on engagement might be investigated in more detail. While qualitative methods could offer deeper insights into user perceptions, cultural hurdles, and design preferences for mHealth solutions targeted at the elderly, longitudinal research could evaluate the impact of mHealth usage on long-term health outcomes.
Author contributions
YM: Paper Writing, Editing PA: Concept, paper writing AC: Data analysis RP: Data curation and analysis HB: Research methodology VS: Discussion, Funding.
Funding
Open access funding provided by Symbiosis International (Deemed University). No funding received for this project.
Data availability
We agree to make the data and materials supporting the results or analyses presented in your paper available upon request.
Declarations
Ethics approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The survey instrument was approved by the Academic Integrity Committee of Symbiosis Centre for Management and Human Resource Development, Symbiosis International (Deemed University), India, wide letter reference number: SCMHRD/2024/58 dated 22nd July 2024 in writing.
Consent to participate
All participants provided informed written consent before completing the survey. The questionnaire contained an online consent form that participants must read and acknowledge before proceeding. The questionnaire did not ask for personal information such as their name, email address, or contact number. The participants were encouraged to ask any question before agreeing to participate and they could revoke their consent at any point during the research period. Additionally, they were informed that the data would be utilized exclusively for research and that there would be no commercial use.
Consent for publication
Not applicable.
Clinical trial number
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher’s note
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