Design features of interfaces and acceptance of ePHR among elderly individuals in China: Insights from an expanded UTAUT framework

Abstract

Objective

This study aims to investigate the factors influencing the adoption of electronic Personal Health Records (ePHR) among China’s aging “new silver” generation, with a focus on understanding how user interface (UI) design affects older users’ adoption intentions.

Methods

An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, incorporating elements from the Technology Acceptance Model (TAM), was used as the analytical framework. The model also includes seven key UI design characteristics. A total of 420 valid responses were collected through online questionnaires and snowball sampling. Structural equation modeling (SEM) was applied to analyze the data.

Results

The analysis revealed that behavioral intention (BI) to adopt ePHR was most strongly influenced by perceived ease of use (PEOU; β = 0.218, p = 0.002). Performance expectancy, effort expectancy, perceived usefulness (PU), and self-efficacy were also found to be significant predictors. Among the UI design features, platform features (E1) significantly impacted PEOU, PU, and BI. Additionally, font clarity (E2), color and icon features (E3), operational cognition (E5), and feedback perception (E4) all had significant positive effects on PU, PEOU, and BI. Information retrieval features (E6) influenced PU and PEOU, but had a minor impact on BI.

Conclusion

The findings highlight the relevance of the UTAUT model in the context of digital health adoption among older users, emphasizing the importance of user interface design in shaping adoption intentions. The study underscores the need for tailoring ePHR systems to meet the specific needs of older individuals and provides practical recommendations for improving inclusive digital health solutions.

Keywords

electronic personal health records (ePHR)elderly population in China unified theory of acceptance and use of technology (UTAUT)user interface design attributes

Introduction

Due to global population growth and the increasing trend of aging, it is anticipated that by 2050, those aged 65 and over will represent 16% of the world’s population.¹ This demographic transition places escalating pressures on global healthcare systems, necessitating more accessible, high-quality, and sustainable medical services. A comprehensive comprehension of the particular requirements of older adults—who are significantly reliant on medical services—is essential for constructing effective and equitable healthcare systems,² particularly considering the increased need for telemedicine during the COVID-19 pandemic.³

Against this backdrop, the rapid advancement of digital and mobile health technologies has introduced new possibilities for delivering cost-effective, safe, and personalized services to older populations.⁴ Electronic Personal Health Records (ePHR) have emerged as a fundamental element of digital health systems. ePHR are online services enabling patients to continually access, review, modify, download, and share their health information, surpassing traditional, static paper records by providing interactive, secure, and interoperable platforms.⁵ By supporting data integration across healthcare settings, ePHR facilitate a shift toward more active patient participation in health management.

Despite over 90% of hospitals in China adopting electronic medical records, older adults rarely engage with ePHR systems to schedule appointments, send messages, request prescriptions, or review clinical notes. Although prior research has examined the usability and Effectiveness of digital health systems,^6–8 limited attention has been given to older adults’ perspectives and adoption barriers. Age-related cognitive, perceptual, and physical declines⁹ often hinder the elderly from effectively engaging with digital technologies, contributing to negative emotions, resistance, and dependency on external assistance.¹⁰ Usability challenges—particularly in interface layout—remain a critical barrier, as complex designs may overwhelm older users and discourage continued use.¹¹In China, the adoption rate of ePHR among older adults remains low due to unfamiliarity with digital health tools,^12,13 unintuitive interface design, and unclear operational processes.^14,15

The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely applied to explain technology adoption across diverse contexts, including healthcare. Its core constructs—such as performance expectancy and effort expectancy—have been shown to influence older adults’ technology use in areas ranging from e-commerce to e-health services. Nevertheless, UTAUT was originally developed for organizational settings and emphasizes cognitive evaluations of usefulness and effort. As a result, it offers limited explanatory power when applied to elderly users interacting with complex, consumer-facing systems such as ePHR, where usability, interface clarity, and interaction design play a decisive role. This limitation becomes particularly salient for older adults, whose adoption decisions are often shaped by perceptual load, interface accessibility, and confidence in interacting with digital systems. Recent research increasingly suggests that user interface characteristics influence technology acceptance indirectly by shaping users’ perceptions of usefulness and ease of use. Yet, these insights remain weakly integrated into mainstream acceptance models, especially in the context of ePHR and aging populations. Consequently, there is a theoretical gap in understanding how and through which mechanisms user interface design affects older adults’ behavioral intention to adopt ePHR systems. Addressing this gap requires extending existing acceptance frameworks to explicitly incorporate interface-related factors that are central to older users’ experiences.

Addressing these issues requires identifying which user interface and experience features influence older users’ behavioral intentions and satisfaction. This study aims to fill that gap by examining the adoption determinants of ePHR among China’s “silver” generation, offering insights for improving the user experience design of digital health systems to better serve the aging population. “Silver” refers to individuals aged 55 years and above who aspire to a high-quality life; they are sometimes also referred to as “digital immigrants.^16,17 This study, grounded on the UTAUT theoretical framework, examines the behavioral adoption mechanisms of ePHR systems among elderly individuals in China. This research highlights the mediating role of behavioral intention (BI) in influencing user engagement, in contrast to previous studies that largely concentrated on system performance or technical aspects. Additionally, it integrates user experience design components tailored for aged populations into the research, seeking to offer theoretical insights and practical recommendations for the design and dissemination of digital health systems aimed at aging people. Accordingly, this study is guided by the following research question:

RQ1:

What are the key determinants influencing the adoption of ePHR systems among older adults in China,

RQ2:

How do user interface design characteristics impact their adoption behavior?

Materials and methods

Research design

This study aims to systematically examine the factors influencing older adults’ behavioral intention to adopt electronic Personal Health Records (ePHR). A quantitative research approach was employed, utilizing a structured questionnaire for data collection. Structural Equation Modeling (SEM) was selected as the primary statistical analysis method due to its ability to simultaneously examine the complex relationships between multiple independent and dependent variables, while also accounting for the influence of measurement errors. The analysis was conducted using IBM AMOS 24 for model testing, and SPSS 26 for data processing, ensuring high statistical power and reliability during the hypothesis testing phase.

Relevant studies and hypothesis framework

EPHR have shown significant potential in transforming healthcare delivery.¹⁸ Studies suggest that ePHR enhance patients’ medication adherence and improve communication between patients and clinicians, contributing to better care quality and safety. Interest in electronic health records patient portals, and role-based PHRs has steadily increased in recent years.^19,20 By 2020, the adoption rate of ePHR exceeded 75% and has continued to grow.²¹ Unlike traditional paper records, ePHR enable patients to access their health information anytime and anywhere, empowering them to take a more active role in their healthcare management. Patients can view, edit, comment on, download, and integrate their health data—embodying a patient-centered model. While older adults have shown interest and motivation in using digital health technologies, their adoption rate of ePHR remains significantly lower than that of younger populations. This gap is largely attributed to age-related barriers, including limited digital literacy, reduced cognitive functioning, and lower technology self-efficacy.²² Consequently, ePHR usage is not solely dependent on the system’s usability but also on whether it aligns with the specific preferences and capabilities of older users.²³ Notably, existing ePHR studies focus on user satisfaction or experiences of current users. At the same time, limited attention has been given to non-users—particularly the behavioral intention stage that precedes actual use.²⁴

While ePHR systems have been widely implemented in some developed countries, their adoption remains early across many Asian and developing regions. Common barriers include fragmented healthcare infrastructures, low public health awareness, and underdeveloped digital ecosystems.²⁵In China, for example, although over 90% of hospitals have adopted electronic medical records, many ePHR functions have yet to reach end-users effectively. Patients still face difficulties in accessing appointment information, sending secure messages, obtaining prescription refills, or viewing doctors’ notes via digital platforms.^26,27A growing body of research underscores the importance of designing ePHR systems prioritizing individual needs and user-centered experiences.²⁸ Given the widespread use of mobile devices in China and the increasing public awareness of personal health management, there is significant potential for large-scale ePHR implementation. However, realizing this potential requires a deeper understanding of the core factors influencing older adults’ acceptance and behavioral intention. Addressing these challenges is essential to advancing the accessibility, inclusivity, and impact of digital health technologies in aging societies.

Unified theory of acceptance and use of technology

The acceptance and dissemination of technology mostly rely on consumers’ readiness to accept and interact with it. Researchers have established many theoretical models to systematically forecast and elucidate user adoption of information technologies, notably the Technology Adoption Model (TAM) and the Innovation Diffusion Theory (IDT), which have achieved considerable prominence. Davis (1989) proposed the Technology Acceptance Model (TAM), which identifies two primary determinants—Perceived Usefulness (PU) and Perceived Ease of Use (PEOU)—as crucial in shaping users’ intentions and actual behaviors. These notions have been experimentally substantiated across several technology environments.

Venkatesh et al. (2003) developed the Unified Theory of Acceptance and Use of Technology (UTAUT) by expanding on the Technology Acceptance paradigm (TAM) and other previous frameworks, establishing it as a significant paradigm for elucidating user behavior about technology adoption. The UTAUT model synthesizes essential elements from eight prior models and comprises four fundamental constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC). Furthermore, it includes gender, age, experience, and voluntariness as moderating variables. The model demonstrates substantial explanatory capability and has been extensively utilized in corporate environments, offering significant insights for technology implementation and policy assessment.

However, despite its strengths, UTAUT has limitations when applied in consumer-oriented or healthcare contexts, particularly those involving older adult populations [28]. The model was initially developed for organizational settings, and its constructs may not fully capture the cognitive, emotional, and usability concerns of elderly users. Notably, there has been limited research on integrating UTAUT with interface design attributes in ePHR. In the aging population segment, where usability and interface accessibility play crucial roles, further theoretical refinement is necessary to better align UTAUT with the specific needs of elderly ePHR users.

The UTAUT model has been widely employed to explore digital technology adoption among older adults across various contexts. Prior research indicates that Performance Expectancy (PE) and Facilitating Conditions (FC) are critical factors influencing older adults’ technology usage, with an overall usage rate of around 55%.²⁹ A study of elderly users in Malaysia identified PE, Social Influence (SI), and FC as major predictors of online shopping behaviors.³⁰Meta-UTAUT analyses of smartphone use further confirmed that PE was the strongest determinant of Behavioral Intention (BI).³¹In the context of e-health services among users aged 60–69, PE, Effort Expectancy (EE), and SI were found to significantly influence usage behavior in descending order of effect.³² Additionally, it was revealed that when supported by strong social networks, technological self-efficacy among older adults moderated the relationship between perceived usefulness and adoption intention—highlighting the interplay between personal confidence and social support.³³

The relationship between user interface design elements and elderly users’ acceptance behavior

User interface (UI) usability is a crucial intermediary between users and technical systems, particularly for older adults with limited cognitive and perceptual capacities. The quality of UI design has been shown to significantly influence elderly users’ intention to adopt new technologies. In mobile health applications, heuristic evaluation methods have been widely used to assess UI effectiveness and identify design preferences specific to older users. These studies emphasize the importance of human-centered design in enhancing user experience and satisfaction.³⁴ For example, Tsai et al. integrated Nielsen’s heuristics with ISO 9241-11 standards and reported a strong positive correlation between interface design and overall system usability.³⁵ Kim et al. highlighted that “simplicity” and “intuitiveness” were the most preferred UI features among older adults.³⁶ Similarly, Saeidnia et al., in the context of COVID-19, found that personalized design elements significantly improved user satisfaction in customized smartphone applications.³⁷ Zhou et al. further demonstrated that elderly users favored systems with clear navigation paths and minimalist visual layouts, such as login pages with white backgrounds, which enhanced task completion in daily use scenarios.³⁸ Although these findings confirm the importance of UI design in improving product usability for aging populations, most existing research has focused primarily on functional experience and usability assessment, with limited theoretical integration into technology acceptance frameworks. Specifically, there is a lack of systematic analysis regarding how UI characteristics influence behavioral intention (BI) in the context of electronic Personal Health Records (ePHRs). Given the complex interaction requirements and self-directed nature of ePHRs, it is essential to explore the mechanistic pathways through which interface design affects acceptance behavior in elderly users.

Integration of UI design and technology acceptance models

Recent research has increasingly emphasized the interplay between user interface (UI) usability and technology acceptance. Sumak et al. demonstrated that UI design quality significantly influences users’ evaluations of Performance Expectancy (PE) and Effort Expectancy (EE), thereby shaping their eventual adoption behavior.³⁹ Turken et al. further noted that users’ familiarity with system navigation modulates their perceptions of PE and EE, which is constrained by individual cognitive abilities and task complexity.⁴⁰ Despite these developments, limited attention has been paid to how distinct UI attributes influence the behavioral intentions of older adults. In ePHR and other digital health contexts, elderly users often interact with complex interfaces, where excessive functionality and unclear navigation can directly suppress usage intentions. Exploring the effects of UI properties—such as information organization, visual hierarchy, and operational simplicity—on BI can refine the UTAUT framework and guide the development of age-friendly health systems. To bridge the theoretical disconnect between technology acceptance models and UI design research, some scholars have proposed integrating Shneiderman’s heuristic principles into the UTAUT model. These hybrid models suggest treating UI usability attributes as independent predictors of BI, or as mediators/moderators in the relationships between PE/EE and BI.⁴¹ Such integrative approaches provide a more comprehensive framework for interpreting the acceptance behavior of older adults in systems like ePHR, accounting for both subjective motivations and objective usability factors.

This study follows the theoretical principles outlined by Venkatesh et al.⁴² in the original formulation of the Unified Theory of Acceptance and Use of Technology (UTAUT). In alignment with their extensive validation work, all core constructs in the model are expected to influence Behavioral Intention (BI). Drawing on these foundational insights, we developed hypotheses by linking each construct to using ePHR systems. The theoretical framework illustrating these relationships is presented in Figure 1.

Figure 1.

Extended model of the theoretical model of this study.

To examine how user interface (UI) design features affect older adults’ intention to use ePHR, this study incorporates Nielsen–Shneiderman heuristics⁴³ as key usability indicators. We conceptualize Design Elements (DEs) as seven interface-specific usability factors:

• E1: System platform characteristics

• E2: Font readability

• E3: Visual clarity (colors and icons)

• E4: Feedback transparency

• E5: Operational guidance

• E6: Information retrieval efficiency

• E7: Interaction consistency

These elements are hypothesized to influence users’ PU and PEOU, key mediators within the extended UTAUT framework. In addition, Performance Expectancy (PE), Effort Expectancy (EE), Self-Efficacy (SE), and Facilitating Conditions (FC) are considered as direct predictors of BI, particularly relevant in the context of older Chinese users. The integration of these constructs enables a more comprehensive understanding of the cognitive, perceptual, and usability-related factors that shape ePHR adoption behavior in aging populations.

Theoretical constructs and hypotheses development

Performance expectancy (PE)

PE denotes the extent to which consumers believe that utilizing a technology would facilitate the attainment of their intended objectives. During the initial phases of technology adoption, older persons may encounter apprehension or ambiguity over usability, mostly attributable to age-related deterioration in physical, visual, or cognitive faculties, which diminishes their adoption rates.⁴⁴ Nevertheless, technologies can also enhance communication and support health-related tasks, increasing the likelihood of adoption. Prior studies have shown that expectations of tangible benefits—such as improved self-management, easier access to healthcare services, and enhanced quality of life—positively shape adoption behaviors.⁴⁵

Performance Expectancy (PE) positively influences older people’s Behavioral Intention (BI) to utilize ePHR.

Effort expectancy (EE)

EE reflects the degree of ease associated with using a system. For older adults, perceived availability of support (e.g., from family or institutions) influences their willingness to adopt new technologies.⁴⁶ External support increases trust and perceived usefulness, thereby strengthening adoption intention. Empirical studies in healthcare contexts confirm that EE is a critical determinant of actual use, as it reflects the perceived assistance of systems in completing tasks.

Effort Expectancy (EE) positively influences older people’s Behavioral Intention (BI) to utilize ePHR.

Self-efficacy (SE)

Originates from Social Cognitive Theory and refers to an individual’s belief in their ability to complete a specific task or activity. It influences activity preferences, perceptions of workload, and the effort devoted to overcoming obstacles. When integrated into technology acceptance research, SE is defined as the individual’s self-assessment of their capability to use information systems. Dogrueld described the “new silver generation” as users whose confidence in adopting mobile applications depends largely on prior experience, emphasizing that effective interaction and transaction require adequate knowledge, skills, and experience. Prior research has identified self-efficacy (SE) as a pivotal factor influencing the use of interactive technologies, including the internet, wherein greater levels of SE facilitate individuals’ ability to navigate and adapt to new technological environments.⁴⁷In the context of this study, incorporating SE into the extended UTAUT framework enables a deeper understanding of older adults’ self-perceptions when using ePHR. SE is expected to significantly enhance perceived ease of use, fostering adoption of new digital health technologies.

Self-Efficacy (SE) positively affects older adults’ Behavioral Intention (BI) to use ePHR

Facilitating conditions (FC)

FC refer to the degree to which organizational and technical infrastructures are in place to support system use. These conditions may vary across contexts and technologies, ranging from technical assistance and IT support to reliable network accessibility.Prior studies have highlighted that older adults place greater importance on FC than younger users, as age-related limitations in vision, memory, and physical ability make them more dependent on supportive infrastructures.⁴⁸ Variations in FC may partially explain why adoption rates are lower among elderly users with physical or cognitive impairments. Research on user acceptance of medical and health-related software technologies in the healthcare domain has shown that FC is often determined by the availability of management support, technical resources, and computing infrastructure.⁴⁹

Facilitating Conditions (FC) positively affect older adults’ Behavioral Intention (BI) to use ePHR.

Perceived usefulness (PU)

PU denotes the extent to which individuals feel that employing a system would enhance their task performance. This study adapts PU, given that most participants are retired older persons, to encompass ePHRs’ capacity to meet everyday requirements, increase self-management, and boost overall well-being beyond work-related performance. Perceived Usefulness (PU) is a significant predictor of Behavioral Intention BI.⁵⁰

Perceived Usefulness (PU) positively affects older adults’ Behavioral Intention (BI) to use ePHR.

Perceived ease of use (PEOU)

PEOU denotes the degree to which people consider a technology to be effortless and uncomplicated to utilize. It is a fundamental concept in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). For the “new silver generation,” perceiving ePHR as simple and intuitive is fundamental to adoption. Early empirical evidence by Venkatesh⁵¹ confirmed that PEOU significantly influences attitudes toward technology adoption. Similarly, Nysveen and Pedersen (2016) highlighted the role of effort expectancy in shaping personal attitudes, while Dunnebeil et al. found that PEOU directly influenced user attitudes toward eHealth systems in outpatient contexts.⁵² These findings consistently underscore the central role of effort perception in digital service adoption.

Perceived Ease of Use (PEOU) positively affects older adults’ Behavioral Intention (BI) to use ePHR.

Design elements (DE)

DE pertains to the features of system-specific interface and interaction design that influence user acceptability and behavior. They include many elements of system design, such as visual aesthetics (e.g., layout, colors, fonts, icons).⁵³ Interaction design (e.g., navigation, buttons, swiping, and other touch operations), usability (ease of learning and efficiency in task completion).⁵⁴ Functionality (the extent to which system features meet user needs and expectations), and compatibility (the degree to which the system integrates with existing hardware, software, and standards). DEs are regarded as critical antecedents of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU), which subsequently influence Behavioral Intention (BI). As the first layer of user interaction, DEs shape older adults’ first impressions of ePHR and their subsequent acceptance decisions. Based on prior studies,^41,55 this study categorizes DEs into seven factors, as shown in Table 1.

H7a–H7g: Design Elements (E1–E7) positively influence Perceived Usefulness (PU).

H8a–H8g: Design Elements (E1–E7) positively influence Perceived Ease of Use (PEOU).

H9a–H9g: Design Elements (E1–E7) positively influence Behavioral Intention (BI).

Table 1.

Design elements (E1–E7) definitions and dimensions.

Design element	Description	Code
System Platform Features	Overall interface stability, layout logic, and operational fluency	E1
Font Readability	Font size, spacing, and typographic readability	E2
Visual Clarity: Colors & Icons	Color contrast and intuitive iconography	E3
Feedback Transparency	Instant feedback and prompts after system operation	E4
Operational Guidance	Clarity of task flow and consistency of operational logic	E5
Information Retrieval Efficiency	Ease of information search and retrieval	E6
Interaction Consistency	Consistency of interaction rules and design between different functional modules.	E7

Questionnaire

All measurement items were adapted from prior studies, primarily Venkatesh,⁵¹ Wilson and Lankton,⁵⁶ with modifications to reflect the Chinese context. The questionnaire was based on widely used English versions in previous research, translated, and expanded to ensure cultural and contextual appropriateness for Chinese respondents. To assess the clarity and reliability of the questionnaire, prior to the formal survey, three experts in the field of digital health were invited to evaluate the content validity of the translation and contextual adaptation to ensure the accuracy of the item wording. According to existing research, a sample size of 10-30 participants is sufficient for a pilot study.⁵⁷ Van Belle suggests that at least 12 participants should be considered.⁵⁸Therefore, a sample size of 20 participants (not included in the final sample) was used in the pilot study to effectively assess the reliability of the questionnaire.The formal data analysis revealed that the Cronbach’s alpha coefficients for all dimensions were greater than 0.70, and the structural validity indices were satisfactory, demonstrating the applicability of the scale within the sample of this study.

The survey consisted of two sections: (1) demographic information, (2) UTAUT-related constructs, a total 50 measurement items were included, each rated on a five-point Likert scale anchored at 1 (strongly disagree) and 5 (strongly agree). Before responding, participants were given a brief introduction to the concept of ePHR to ensure a common understanding and alignment with the study’s objectives. The questionnaire in this study is primarily based on the UTAUT model and user interface design elements. The variables involved referencethe works of Venkatesh et al. and Davis et al.,^50–52 with adjustments and extensions made to these theoretical models, particularly in the context of electronic personal health record system (ePHR) adoption among older adults. The measurement items for user interface design elements are mainly derived from the research of Lidwell, Nielsen, and Shneiderman, focusing on aspects such as visual clarity, icon design, font readability, and feedback transparency.^43,59These design principles contribute to improving the system’s usability and user engagement. Additionally, studies on information retrieval efficiency,⁵⁴ and the discussions on task flow and operation guidance by Carroll and Norman.⁶⁰ Were also incorporated. The measurement of dimensions such as system platform functionality, font readability, visual clarity, and feedback transparency was conducted to ensure an accurate assessment of the subjective experience of older adults using the ePHR system.^41,53,55The integration of these theoretical foundations and supporting literature provides a solid theoretical framework for further analyzing the usage behavior of older adults in ePHR systems. Questionnaire items are included in Additional File 1.

Data collection and sample

Data was collected through an online platform ‘Wenjuanxing’ and the survey was conducted from April to June 2024.The target respondents were older adults with the cognitive and behavioral abilities required to use mobile digital health services. Through various channels, this study ultimately identified participants from the regions of Beijing, Shanghai, Guangzhou, and Shenzhen in China. The survey link was distributed via Wenjuanxing (a Chinese online survey tool) and WeChat using a snowball sampling method. The target respondents were Chinese adults aged 60 and above who were familiar with basic digital devices and could provide informed consent. Participants with severe cognitive impairments or those unable to voluntarily participate were excluded. These respondents were recruited from elderly communities in China, with the age threshold aligning with the digital divide concept, which is typically considered to begin at age 50.^61,62 Throughout the data collection phase, this research adhered to ethical academic standards. Prior to beginning the questionnaire, all participants were provided with clear information regarding the study’s objectives to ensure that their participation was both informed and voluntary.

A total of 486 questionnaires were collected. Only fully completed responses were retained for analysis, such as those completed in less than one minute or with a high rate of repetitive answers, resulting in a final sample of 420 valid responses. This sample size meets the recommendations of Hair et al., which suggest that the number of observations should be at least ten times the number of predictors. Furthermore, Creswell recommends a sample size between 350 and 500 for robust quantitative research.⁶³ Accordingly, the final sample was deemed sufficient to test the study model.

Statistical analysis

Data analysis was conducted in two stages. First, Confirmatory Factor Analysis (CFA) was performed using SPSS Statistics 26 to validate the measurement model. This process assessed the reliability and validity of the measurement items, ensuring that each construct was adequately measured. Specifically, we evaluated content validity, construct reliability, and discriminant validity. These measures were crucial for confirming that the scale was both reliable and valid in the context of older Chinese adults’ adoption of ePHR systems. Second, the structural relationships among constructs were tested using Structural Equation Modeling (SEM) with AMOS 24.⁶⁴ Model fit was assessed using several goodness-of-fit indices, including Chi-square (χ²), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). These indices provided a comprehensive understanding of how well the hypothesized model fit the data, allowing for an evaluation of the overall model fit and the strength of relationships between variables. In terms of model reliability and construct validity, we reported internal consistency (Cronbach’s alpha) and composite reliability for all latent constructs. Convergent validity was assessed through average variance extracted (AVE), ensuring that the constructs were well represented by their respective items.

Ethical statement

Ethical review and approval were exempted for this study due to the fact that the respondents were only required to answer questions without any control over humans. There is no relevant experimental research on the human body itself. (For this study due to the fact that the respondents were only required to answer questions without any control over humans). There is no relevant experimental research on the human body itself. It should also be emphasized that Participants were adults and voluntarily participated in the questionnaire consent was informed. Because all participants were of Chinese nationality, therefore, according to article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings, No. 4, “Ethical Review Measures for Life Science and Medical Research Involving Human Beings” of the People’s Republic of China, February 18, 2023, article 32: Ethical review may be exempted if it does not cause harm to human beings, or if it does not involve sensitive personal information or commercial interests. This statement is intended to safeguard the ethical compliance of the research project and to protect the rights and privacy of the participants.

Results

As shown in Table 2, the detailed demographic characteristics of the respondents are presented. Each participant was informed of the study’s purpose before participation, and written consent was obtained before completing the survey. A total of 486 questionnaires were collected, of which 420 were valid, yielding a response rate of 87.86%. Among the valid respondents were 226 males and 194 females, indicating a slightly higher proportion of male participants. 39%were aged between 65 and 69 years, and more than 90% had attained at least a high school level of education. Regarding smartphone experience, the majority (58.6%) reported having 3–4 years of usage experience, highlighting a moderate level of digital familiarity within the sample.

Table 2.

Detailed demographic characteristics of participants.

Profile	Items	Number	Percentage(%)
Gender	Male	226	53
Gender	Female	194	47
Age	60∼64	258	61
Age	65∼69	162	39
Education	Graduate and above	17	4
	High school	44	10.5
	Junior high school	359	85.5
Smart phone usage experience	3-4 years	246	58.6
	1-2 years	73	17.4
	> 5	101	24
Monthly Income	Over 10,000 yuan	18	4.3
	5,000 to 10,000 yuan	278	66.2
	Below 5,000 yuan	124	29.5
Daily Usage Time of Smart Devices	3-4 hours	127	30.2
	Within 2 hours	243	57.9
	Over 5 hours	50	11.9
Familiarity with Electronic Personal Health Records	Have a little understanding	227	54
	Basic understanding	57	13.6
	Well-informed	34	8.1
	Know but do not understand	102	24.3
Have you ever used a paper-based Personal Health Record (PHR)?	Yes	378	90
	No	42	10
Have you ever used an electronic Personal Health Record (ePHR)?	Yes	278	66.2
	No	142	33.8

Model measurement evaluation

As shown in Table 3, the values of composite reliability (CR) ranged from 0.656 to 0.880, and the average variance extracted (AVE) for the constructs ranged from 0.505 to 0.604, all exceeding the recommended threshold of 0.50.⁶⁵ Cronbach’s alpha coefficients were further calculated to assess the internal consistency of the scales. Cronbach’s alpha estimates the degree of inter-item consistency within each construct.⁶⁶ A coefficient above 0.70 is generally acceptable, indicating that the scale explains at least half of the construct’s variance.⁶⁷ In this study, both Cronbach’s alpha and CR values for all constructs surpassed 0.70, thus affirming the reliability of the measuring scales. Furthermore, the content validity of the questionnaire was guaranteed as all items were derived from reputable previous studies. Moreover, all factor loadings are above 0.70, affirming the robust convergent validity of the scales.⁶⁸ Therefore, all items were retained for subsequent analysis.

Table 3.

Reliability and validity test results.

Construct	Item	Factor loading	CR	Cronbach’s α	AVE
BI	BI1	0.642	0.749	0.807	0.583
	BI2	0.679	0.712
	BI3	0.644	0.748
E1	E11	0.577	0.676	0.753	0.505
	E12	0.595	0.656
	E13	0.574	0.680
E2	E21	0.673	0.754	0.820	0.604
	E22	0.678	0.748
	E23	0.671	0.755
E3	E31	0.660	0.808	0.843	0.519
	E32	0.619	0.819
	E33	0.679	0.803
	E34	0.665	0.807
	E35	0.623	0.819
E4	E41	0.587	0.743	0.783	0.551
	E42	0.650	0.674
	E43	0.629	0.699
E5	E51	0.695	0.881	0.895	0.553
	E52	0.709	0.879
	E53	0.662	0.885
	E54	0.741	0.875
	E55	0.693	0.880
	E56	0.705	0.879
	E57	0.678	0.882
E6	E61	0.627	0.741	0.798	0.571
	E62	0.636	0.733
	E63	0.666	0.700
E7	E71	0.652	0.839	0.861	0.554
	E72	0.679	0.832
	E73	0.684	0.831
	E74	0.661	0.836
	E75	0.719	0.822
EE	EE1	0.598	0.693	0.768	0.531
	EE2	0.610	0.688
	EE3	0.607	0.684
FC	FC1	0.647	0.769	0.817	0.600
	FC2	0.691	0.724
	FC3	0.668	0.749
PE	PE1	0.635	0.709	0.790	0.555
	PE2	0.624	0.721
	PE3	0.632	0.713
PEOU	PEOU1	0.652	0.738	0.802	0.577
	PEOU2	0.655	0.730
	PEOU3	0.655	0.724
PU	PU1	0.634	0.716	0.792	0.559
	PU2	0.636	0.715
	PU3	0.631	0.721
SE	SE1	0.642	0.732	0.800	0.578
	SE2	0.661	0.718
	SE3	0.646	0.735

To evaluate the convergent and discriminant validity of the measurement model, confirmatory factor analysis (CFA) was performed in accordance with the two-step modeling approach recommended by Anderson and Gerbing.⁶⁹ The convergent validity of the constructs was evaluated by examining standardized factor loadings, composite reliability (CR), and average variance extracted (AVE). In assessing internal consistency, CR was utilized as it incorporates standardized path coefficients and is widely recognized as a statistically superior metric compared to Cronbach’s alpha.⁷⁰ AVE indicates the degree to which a latent construct captures the variance of its observed measures, thereby serving as an essential indicator of construct convergence (Viswanathan, 2005). Following Hair et al., satisfactory convergent validity is demonstrated when CR values are greater than 0.70 and AVE values exceed 0.50.

As shown in Table 3, all standardized loadings, CR values, and AVE values met these recommended thresholds, confirming adequate convergent validity. Furthermore, discriminant validity was assessed using Fornell and Larcker’s criterion.⁷¹Specifically, the square root of each construct’s AVE was compared with its correlations with other constructs. As presented in Table 3, the square root of AVE for each latent variable exceeded the highest correlation with any other construct, thus supporting satisfactory discriminant validity for all constructs.⁷²

The measurement model’s goodness-of-fit was evaluated using absolute and incremental fit indices.(Table 4) Absolute fit indices assess the degree to which the proposed model fits the observed data directly, serving as a fundamental measure of model adequacy.⁷⁰,⁷³ As shown in Table 5, the results indicated χ²/df = 1.091 and RMSEA = 0.015, both far superior to the conventional cut-off criteria (χ²/df < 3; RMSEA < 0.08), suggesting an excellent model fit.⁷⁴ Incremental fit indices were further examined to evaluate the improvement of the estimated model over a null model, which assumes no correlations among the observed variables.⁷⁵ To evaluate model fit, the Tucker–Lewis Index (TLI) and Comparative Fit Index (CFI) were adopted, as both indices are widely recommended for assessing the adequacy of complex structural equation models.⁷⁶ The findings showed TLI = 0.986 and CFI = 0.988, both exceeding the recommended threshold of 0.90, thereby confirming the superior fit of the model. In addition, other indices demonstrated satisfactory results: SRMR = 0.034, GFI = 0.904, IFI = 0.988, and PGFI = 0.768, all meeting conventional standards. These results indicate that the model exhibits excellent fit in terms of residuals, overall adequacy, and parsimony.⁷⁰

Table 4.

Discriminant validity testing.

Construct	Mean	S.D.	PU	PEOU	BI	E3	E1	E5	E6	E7	E4	E2	SE	FC	PE	EE
PU	3.706	0.823	0.748
PEOU	3.956	0.814	0.413	0.759
BI	3.862	0.881	0.493	0.511	0.764
E3	3.937	0.772	0.062	0.035	0.073	0.721
E1	3.785	0.829	0.489	0.556	0.521	-0.140	0.711
E5	3.728	0.768	0.130	0.123	0.097	0.020	-0.067	0.744
E6	3.673	0.878	0.106	0.104	-0.009	-0.011	-0.041	0.012	0.756
E7	2.340	0.845	0.066	-0.052	-0.056	0.044	-0.072	0.058	-0.081	0.744
E4	3.625	0.834	0.194	0.101	0.111	-0.047	-0.012	0.009	0.105	-0.063	0.742
E2	2.497	0.958	-0.029	0.053	0.008	-0.033	-0.068	-0.022	-0.018	-0.024	-0.021	0.777
SE	3.683	0.850	0.512	0.473	0.543	-0.150	0.531	0.039	0.100	-0.041	0.070	0.048	0.760
FC	3.833	0.863	0.489	0.455	0.518	-0.045	0.543	0.011	0.004	-0.038	0.031	-0.044	0.531	0.774
PE	3.971	0.840	0.377	0.414	0.522	-0.046	0.461	-0.003	0.042	0.001	-0.007	0.048	0.509	0.452	0.745
EE	2.074	0.750	0.414	0.340	0.493	-0.041	0.487	-0.027	-0.033	-0.033	0.061	-0.089	0.439	0.445	0.498	0.729

Table 5.

Goodness-of-fit test.

	χ²/df	RMSEA	SRMR	TLI	CFI	GFI	IFI	PGFI
Recommended Standard Value	3	0.08	0.08	0.9	0.9	0.9	0.9	0.5
Predicted Value	1.091	0.015	0.034	0.986	0.988	0.904	0.988	0.768

After confirming the model’s goodness-of-fit, this study further tested the hypothesized structural paths. As illustrated in Figure 2, the path analysis results highlight that Design Elements (DE) are critical factors influencing older Chinese adults’ acceptance of ePHR systems.

Figure 2.

Path analysis model for the hypotheses. *** p < 0.001.

As presented in Table 6, Perceived Ease of Use (PEOU) had the strongest effect on Behavioral Intention (β = 0.218, p = 0.002). Performance Expectancy (PE) also exerted a significant positive impact (β = 0.183, p = 0.012), followed by Effort Expectancy (EE) (β = 0.174, p = 0.017). Perceived Usefulness (PU) was likewise significant (β = 0.145, p = 0.037), and Self-Efficacy (SE) showed a positive influence as well (β = 0.135, p = 0.042). These findings suggest that “new silver” users with stronger digital competence are more confident in their technical capabilities, reinforcing their intention to adopt ePHR.

Table 6.

Results of the hypothesis tests.

Code	Path	β	SE.	CR.	P	Outcome
H1	BI←PE	0.183	0.073	2.521	.012*	Sig.
H2	BI←EE	0.174	0.073	2.384	.017*	Sig.
H3	BI←SE	0.135	0.067	2.034	.042*	Sig.
H4	BI←FC	0.129	0.067	1.922	.055	N.S.
H5	BI←PU	0.145	0.07	2.084	.037*	Sig.
H6	BI←PEOU	0.218	0.07	3.134	.002**	Sig.
H7a	PU←E1	0.604	0.072	8.379	.000***	Sig.
H7b	PU←E2	0.029	0.046	0.614	.539	N.S.
H7c	PU←E3	0.155	0.055	2.837	.005**	Sig.
H7d	PU←E4	0.198	0.054	3.674	.000***	Sig.
H7e	PU←E5	0.161	0.053	3.048	.002**	Sig.
H7f	PU←E6	0.106	0.05	2.143	.032*	Sig.
H7g	PU←E7	0.105	0.048	2.167	.030*	N.S.
H8a	PEOU←E1	0.731	0.081	9.013	.000***	Sig.
H8b	PEOU←E2	0.106	0.05	2.103	.035*	Sig.
H8c	PEOU←E3	0.15	0.059	2.561	.010*	Sig.
H8d	PEOU←E4	0.115	0.057	2.021	.043*	Sig.
H8e	PEOU←E5	0.185	0.057	3.241	.001*	Sig.
H8f	PEOU←E6	0.118	0.053	2.207	.027*	Sig.
H8g	PEOU←E7	0.001	0.052	0.024	.981	N.S.
H9a	BI←E1	0.687	0.078	8.804	.000***	Sig.
H9b	BI←E2	0.062	0.049	1.268	.025*	N.S.
H9c	BI←E3	0.188	0.057	3.267	.001**	Sig.
H9d	BI←E4	0.138	0.055	2.498	.013*	Sig.
H9e	BI←E5	0.151	0.055	2.737	.006**	Sig.
H9f	BI←E6	0.011	0.051	0.221	.825	N.S.
H9g	BI←E7	-0.011	0.05	-0.219	.826	N.S.

***p < 0.001, **p < 0.01, *p < 0.05.

Sig: supported; N.S: not supported.

By contrast, Facilitating Conditions (FC) demonstrated only a marginal effect (β = 0.129, p = 0.055). This indicates that although external support—such as family assistance, institutional training, or policy measures—plays a role, intrinsic system usability and individual competence remain the more decisive factors shaping adoption intention among older adults.

Focusing on Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Behavioral Intention (BI) as key variables, this study systematically examined the effects of seven user interface design elements (E1–E7) on older adults’ acceptance of ePHR systems. The results demonstrate that most design features significantly affected the perception pathways.

First, System Platform Features (E1) showed strong and significant positive effects across all three paths—PEOU (β = 0.731, p < 0.001), PU (β = 0.604, p < 0.001), and BI (β = 0.687, p < 0.001). This suggests that overall interface integrity, layout clarity, and system stability are central determinants of older adults’ perceptions and intentions, reflecting their heightened sensitivity to structural consistency and operational coherence.

Second, Font Readability (E2) exerted a modest but significant effect on PEOU (β = 0.106, p = 0.035), yet showed no significant impact on PU or BI (p > 0.05). This implies that while appropriate font size and spacing help reduce visual burden and enhance readability, such improvements cannot directly alter perceptions of system utility or adoption motivation.

Third, Visual Clarity: Colors & Icons (E3) had significant positive effects on all three outcomes (PU: β = 0.155, p = 0.005; PEOU: β = 0.150, p = 0.010; BI: β = 0.188, p = 0.001), with the strongest effect observed on BI. This highlights that visual recognizability and aesthetic appeal are pivotal in driving older adults’ behavioral intentions during system interaction.

Fourth, Feedback Transparency (E4) significantly influenced PU (β = 0.198, p < 0.001) and BI (β = 0.138, p = 0.013), with a marginal effect on PEOU (β = 0.115, p = 0.043). These results underscore the importance of real-time system feedback and response transparency in enhancing users’ sense of control and security, strengthening cognitive and behavioral expectations.

Fifth, Operational Guidance (E5) demonstrated significant effects on PU (β = 0.161, p = 0.002), PEOU (β = 0.185, p = 0.001), and BI (β = 0.151, p = 0.006). This indicates that logical task flows and comprehensible instructions are critical for enabling older users to complete data entry and retrieval tasks.

Sixth, Information Retrieval Efficiency (E6) significantly affected PU (β = 0.106, p = 0.032) and PEOU (β = 0.118, p = 0.027), but its effect on BI was not significant (β = 0.011, p = 0.825). This finding suggests that while retrieval efficiency enhances perceived usability and usefulness, it does not directly translate into adoption intentions unless reinforced by other system factors.

Finally, Interaction Consistency (E7) had a marginally significant effect on PU (β = 0.105, p = 0.030), yet showed no significant effects on PEOU or BI (p > 0.05). This may be attributable to older adults’ preference for single-device use (e.g., smartphones) when accessing ePHR, implying that cross-device compatibility and multi-platform experience are of relatively limited concern for this user group.

Discussion

This study makes three main theoretical contributions. First, the validation of the UTAUT model in the context of ePHR adoption among older Chinese adults is broadly consistent with prior digital health research, such as the study by Hoque and Sorwar,⁷⁷ which confirmed the explanatory value of UTAUT-related constructs in health technology adoption. The present findings extend this line of evidence to an aging-society context and provide additional empirical support for the applicability of UTAUT in digital health research involving older populations. Second, this study extends the model by incorporating seven user interface design aspects (E1–E7) as exogenous variables, thereby highlighting the critical role of interface design in shaping perceived ease of use (PEOU) and perceived usefulness (PU). Consistent with Yang et al., who emphasized the importance of interface-related perceived value in digital interactions.⁷⁸ At the same time, the present study advances prior work by operationalizing interface usability into seven specific design dimensions and empirically testing their effects within the ePHR context. In this way, the study links UTAUT with user experience (UX) theory and provides a more comprehensive framework for explaining technology adoption behavior among older adults. Third, the findings identify self-efficacy (SE) as a key psychological driver of behavioral intention (BI), suggesting that older adults’ sense of confidence and control plays an important role in digital health adoption. Taken together, these findings broaden the explanatory scope of technology adoption models and deepen current understanding of the mechanisms underlying ePHR uptake among older adults.

The findings also underscore the importance of age-friendly interface design in promoting ePHR adoption. Features such as clear platform structure, readable fonts, intuitive icons, transparent feedback, and efficient information retrieval significantly enhance usability perceptions and, in turn, encourage adoption. This is in line with prior research emphasizing the importance of interface simplicity and clarity for older users.⁷⁹ In practical terms, simplified registration procedures and time-efficient functions may further reduce barriers to use. In contrast, the relatively limited effect of facilitating conditions (FC) suggests that external support, such as family assistance, institutional training, or governmental guidance, may be less influential than expected in shaping behavioral intention. This finding is consistent with Shah et al., who also reported that FC was not positively associated with behavioral intention.⁸⁰ However, it differs from some conventional UTAUT-based studies conducted in organizational settings, where facilitating conditions often play a stronger role. One possible explanation is that, in the context of ePHR adoption among older adults, intrinsic usability and personal confidence may exert a more direct influence on intention than external support alone. Accordingly, system developers should place greater emphasis on improving usability and strengthening users’ confidence.

The significant role of self-efficacy further suggests that ePHR developers and policymakers should provide onboarding guidance, interactive tutorials, and supportive training programs to gradually strengthen older adults’ confidence in using such systems.^81,82 More broadly, improving PU and PEOU requires continuous optimization of system architecture and operational simplicity. Practical strategies include simplifying interaction procedures, reducing unnecessary steps, enhancing visual clarity, and incorporating supportive features such as voice guidance and real-time feedback to improve comprehension and trust. Standardizing and emphasizing frequently used navigation buttons may also reduce cognitive burden.^37,83Designers should align system functions with the cognitive characteristics and usage patterns of the “new silver” generation by simplifying functionality, using clear language, and adopting more human-centered interaction approaches. Consistent with Yang et al., enhancing the perceived value of technological interaction is crucial, as it shapes user satisfaction and ultimately affects the success of digital services.⁷⁸

Conclusions

Theoretical contributions

This study expands the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating key user interface design elements, providing a more comprehensive framework for understanding the adoption of digital health technologies among China’s “new silver” demographic. By integrating seven design factors—such as platform attributes, feedback systems, and visual clarity—the research enhances UTAUT’s explanatory power in the context of ePHR adoption.

Empirical findings demonstrate that these design elements significantly influence older adults’ views of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU), which indirectly enhance their Behavioral Intention (BI). Notably, Facilitating Conditions (FC) exhibited a negative correlation with BI, aligning with specific prior studies. This suggests that older users may rely more on their internal cognitive processing and emotional states than on external technical support when forming usage intentions. Moreover, the study underscores the critical role of Self-Efficacy (SE); the sense of control and confidence generated during interaction markedly improves older persons’ willingness to adopt ePHR devices.

Practical implications

From a practical standpoint, this research provides actionable guidance for both interface design and healthcare management. First, for developers, the incorporation of interface elements tailored to older adults—such as clear navigation, intuitive layouts, and efficient feedback systems—can significantly reduce cognitive load. Second, for healthcare managers and policymakers, the results suggest that implementation strategies should go beyond technical installation. Promotion efforts must focus on psychological empowerment (Self-Efficacy), helping older users build confidence through supportive training rather than just providing passive technical tools. By aligning ePHR systems with the cognitive and emotional capabilities of older users, stakeholders can foster greater participation in healthcare management and improve the quality of care.

Limitations and future research directions

While this study offers important insights, it is not without limitations. First, the use of a snowball sampling method represents a significant limitation. This approach may introduce bias by over-representing individuals who are more technologically proficient or have stronger social networks, while under-representing those who are isolated or less digitally literate. Consequently, this restricts the external validity of the results, as the findings may reflect the behaviors of a more tech-savvy subgroup rather than the general older population. Future studies should employ stratified random sampling to ensure a more representative sample. Second, the sample was restricted to mainland China. Future research should extend to other cultural and geographic settings to facilitate cross-cultural comparisons. Finally, while design aspects were treated as exogenous variables, potential interrelationships among these constructs persist. Future research should explore the causal pathways connecting design cognition, emotional reactions, and behavioral expectations. Incorporating broader psychological, social, and technological dimensions will help create inclusive frameworks that support the sustainable development of smart health technology for aging populations. Ultimately, this study lays the foundation for further research into how digital health tools, particularly ePHRs, can be optimized for older users, ensuring that these systems meet the evolving needs of an aging global population.

Footnotes

Acknowledgement

The authors would like to thank all the participants who generously gave their time to complete the survey. We also acknowledge the valuable support provided by our colleagues during the data collection and analysis process.

ORCID iD

Sainan Zhang

Ethical consideration

Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings, No. 4, “Ethical Review Measures for Life Science and Medical Research Involving Human Beings” of the People’s Republic of China, February 18, 2023, Article 32: Ethical review may be exempted if it does not cause harm to human beings, or if it does not involve sensitive personal information or commercial interests. Therefore, review and approval by the Zhejiang A&F University Institutional Review Board is not required.

Consent to participate

Each participant has been made aware of the study and has only agreed to use the data in academic research.

Author contributions

Conceptualization, S,Z; Methodology, S,Z; Data curation, S,Z; Formal analysis, S, Z; Writing – original draft S, Z; Statistical analysis, S,Z. Data collection, X,J; Validation, X, J; Writing – review & editing. X, J; Technical support X, J. Investigation, Z, Z; Resources, Z, Z. Literature review, C, Z; Supervision, C,Z; Project administration, C,Z review & editing, C,Z. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research Start-up Project of Zhejiang A&F University [Grant No. 2024FR043]. Supported by the Rural Culture and Common Prosperity Research Institute of Zhejiang A&F University (a newly established key think tank in Hangzhou).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The raw data used during the current study are available from the corresponding author upon reasonable request.*

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