Enhancing Digital Health Engagement Among Asian Seniors: Investigating the Acceptance and Use of Fitness Apps in Promoting Healthy Aging

Abstract

This study investigated how older Asian adults (aged 50+) accept fitness apps, focusing on experiential factors derived from the technology acceptance model (TAM) and the Diffusion of Innovations (DOI). Between December 2023 and March 2024, a convenience sampling method was used to recruit older adults with three months of experience using fitness apps from social media platforms and community groups in multiple Asian countries. Of the 700 initial respondents, 587 valid questionnaires were retained (an 83.8% validity rate). Structural equation modeling (SEM) assessed relationships among relative advantages, compatibility, trialability, gamification, observability, perceived ease of use (PEU), perceived usefulness (PU), behavioral intention, and actual usage. Trialability, relative advantages, and compatibility significantly enhanced PEU (p < 0.01), while gamification and observability did not. By contrast, gamification and observability positively influenced PU (p < 0.05). Both PEU and PU in turn predicted behavioral intention, which explained actual app usage (p < 0.01). These findings underscore the importance of designing fitness apps that accommodate older adults’ skills and preferences, while also incorporating engaging features that reinforce perceived usefulness. Healthcare professionals and developers may leverage these insights to tailor digital health interventions, potentially improving exercise habits and well-being among aging populations in Asia.

1. Introduction

Apps related to digital health and physical activity are very common in app stores, including fitness apps. These applications are third-party software for smartphones or wearable devices that assist users in recording fitness data, guiding motor learning, and navigating a healthy lifestyle [1]. They offer integrated services combining educational, tracking, social, gamification, and motivational features that support healthier lifestyles and exercise habits [2]. Fitness apps have emerged to facilitate the more efficient integration of user needs and fitness resources, enhancing user engagement. Moreover, the end users of these applications are not only the customers of fitness app services but also a source of valuable data. Hence, the industry must identify strategies to attract more seniors to engage with fitness apps and actively participate in digital healthcare.

The growing popularity of fitness apps and the rapid growth of the internet economy have attracted the attention of scholars in the fields of sociology, design, and psychology. Despite a significant amount of research in this area, there is little research on older Asian users [2]. Technology supporting personal health management may benefit seniors [3], as 80% suffer from at least one chronic condition [4]. It is postulated that Asian seniors may benefit from exercise in terms of both physical conditioning and quality of life [5]. The available evidence suggests that digital health technology can significantly enhance quality of life and promote physical and mental well-being [6].

Fitness apps provide seniors with a valuable service combining education, tracking, social interaction, gamification, and motivation, significantly enhancing physical and mental well-being. However, the intention to utilize and adopt such apps among Asian seniors remains relatively low [7]. As individuals age, they may experience cognitive decline, including impairments in memory and learning, making the acceptance of new technologies challenging [8]. Kim et al. [9] specifically argue that there are differences in motivation, planning, and habits of using new information technology between seniors above 50 years of age and those below the age of 50. This indicates that the age factor can be used for market segmentation. It is necessary to conduct further research to determine the specific needs of this group of users. Therefore, the population targeted is classified as Asian seniors above 50 years of age.

Furthermore, most current studies on user motivation regarding fitness apps have taken expectation confirmation theory (ECT), the technology acceptance model (TAM), and the unified theory of acceptance and use of technology (UTAUT) as the theoretical basis to inspect the issue of general users’ continuous use of fitness apps, focusing on the problem of decision intention to engage more users in fitness apps [10,11,12]. Nevertheless, only a limited number of studies have been conducted on the factors that influence user experience, with insights drawn from the diffusion of innovation theory. There remains a need for further research on Asian seniors, since their technology acceptance may differ from that of younger users. It is also possible that a gap exists in understanding how fitness apps influence exercise adherence in this demographic. Most existing research focuses on technological characteristics rather than experiential factors shaping older Asian users’ engagement [13].

This study integrated the technology acceptance model and diffusion of innovations to propose a theoretical model aimed at addressing seniors’ acceptance behavior of fitness apps based on user experience factors. The principal advantage of integrating these theories was the use of the structure of the diffusion of innovations as a key input for forming the technology acceptance model. Combining these theories offers a novel approach to understanding how older Asian users adopt fitness apps, contributing to both theoretical integration and practical insights for digital health design.

2. Theoretical Model and Hypotheses

The continued acceptance and use of fitness apps among older adults is a complex phenomenon influenced by user perceptions—such as perceived ease of use and perceived benefits—as well as technology characteristics, including compatibility and relative advantage. In the broader context of information systems research, two influential theoretical frameworks have been frequently applied to explore these dynamics: the technology acceptance model (TAM) and the diffusion of innovations (DOI). This study aims to critically examine and integrate these two theories to identify the key factors influencing seniors’ acceptance of fitness apps, while also clarifying the relationships among these factors. Subsequently, a synthesis of the two frameworks will be presented, followed by the development of corresponding hypotheses.

2.1. Technology Acceptance Model and Diffusion of Innovations

The technology acceptance model (TAM) was constructed to explore the factors determining the acceptance of computer technology by users. The two key variables Davis et al. [14] put forward included perceived ease of use (PEU) and perceived usefulness (PU). According to Davis et al. [14], PU is the degree to which an individual believes that using an innovation improves performance, while PEU is “the perception of the extent to which a person believes that using an innovation is easy to do” [15]. This paper thus proposes that the technology acceptance model (TAM) can be applied to new technology adoptions, with a particular interest in seniors’ adoption of fitness apps. However, TAM-based research tends to concentrate more on the subjective views of users and their usage behavior towards new technology products and less on the characteristics of the product and the social factors involved [16]. This limitation can be overcome by including it in the diffusion of innovations.

The diffusion of innovation (DOI) is a theory that explains why people adopt or reject innovations based on their beliefs about them [17]. Rogers [18] argued that users typically progress through a series of natural steps, from first learning about an innovation to ultimately accepting it into their lives. Additionally, research has demonstrated a direct relationship between innovation acceptance and decision-making, with innovation attributes being measurable across five key dimensions: relative advantage, compatibility, complexity, trialability, and observability [19]. By combining TAM’s focus on user perceptions with DOI’s emphasis on the characteristics of the innovation, we can gain a more comprehensive understanding of the factors influencing older adults’ acceptance of fitness apps.

2.2. A Research Model for Evaluating User Experience Factors

Based on the theoretical intersection of TAM and DOI, this section outlines how these frameworks are integrated into a research model designed to assess the user experience factors influencing older adults’ adoption of fitness apps. A review of the IT adoption literature reveals that many of the characteristics attributed to an innovation are inherent in the process of adopting new technologies [20]. Both TAM and DOI share a common premise: adopters evaluate innovations based on their perceptions of the embedded characteristics. Consequently, this study is analyzed from the perspectives of both DOI and TAM, which are often regarded as highly similar due to their complementary constructs [21]. Furthermore, it has been demonstrated that the constructs in TAM form integral components of the perceptual innovation profile [22], highlighting how the integration of these two theories can lead to the development of more robust models [23].

The innovation attributes outlined in the DOI theory help explain the continued use of fitness applications. Considering that seniors vary in their ease of adoption and learning of new technologies, this study considered as exploratory factors the DOI’s relative advantages (RA), compatibility (CO), trialability (TR), and observability (OB), and the TAM’s perceived ease of use (PEU) and perceived usefulness (PU) of TAM. Also, the study identified behavioral intention of use (BIU) and behavior of use (BU) as objects to be validated. Furthermore, considering the distinctive design characteristics of fitness apps, this study adapted the DOI by incorporating gamification (GA) as a perceptual assessment variable.

Gamification has received considerable attention [24] because it is a means to support user engagement and enhance positive service use patterns. This can be achieved by enhancing the quality and productivity of user activities, social interactions, and behaviors [25]. The desired usage patterns are thought to emerge from the positive, intrinsically motivating experiences [26] that gamification fosters through elements of play and motivation within services [27]. While gamification has been shown to yield positive outcomes, these effects are highly context-dependent, varying with the environment in which it is applied and the characteristics of the users engaging with it [28]. Therefore, the gamification experiences provided by the specific population and fitness apps in this study offer a unique opportunity for a targeted evaluation.

Building on the theoretical foundations discussed earlier, the adapted technology acceptance model (TAM) and Decomposed Innovation (DOI) theory serve as the theoretical frameworks for this study. Based on the findings, a model has been developed to explain how older adults accept and use fitness apps. For a visual representation of this model, please refer to Figure 1.

Having established a combined theoretical model, the next step is to formulate specific hypotheses that emerge from these integrated constructs, thereby guiding the empirical investigation into seniors’ acceptance behaviors.

2.3. Based on the Hypotheses of the Research Framework Model

In this subsection, this study presents the hypotheses derived from the integrated TAM-DOI framework, linking each conceptual element—relative advantages, compatibility, trialability, gamification, observability, perceived ease of use, and perceived usefulness—to the proposed outcomes of behavioral intention and actual usage.

Relative advantages (RA) are defined as the extent to which seniors utilize fitness apps superior to traditional forms of exercise. It reflects the degree to which an innovation is seen as better than its alternatives [29]. A higher perceived relative advantage suggests that the innovation is viewed as a superior choice. Fitness apps offer distinct advantages over traditional exercise models for older adults, such as the ability to work out anytime and anywhere, regardless of weather, time, or equipment availability. Additionally, fitness apps allow users to track their health status and receive professional analyses through automatic data uploads to a cloud database. Seniors also gain access to more expert fitness guidance, all of which only require a smartphone and internet access, thereby reducing costs and expenses [30]. Van Slyke et al. [31] suggested that perceived usefulness (PU) and RA are related but distinct constructs. Furthermore, other studies indicate that RA serves as a prerequisite for PU [32]. Based on this reasoning, the following hypotheses are proposed:

H1a: 

The relative advantages of fitness apps positively influence the perceived ease of use of Asian seniors.

H1b: 

The relative advantages of fitness apps positively influence the perceived usefulness of Asian seniors.

Compatibility (CO) refers to “the degree to which fitness apps can support older adults across the full spectrum of exercise”. It measures the alignment between the innovation and the existing technological and social environments of an individual [29]. The multi-platform development of fitness apps enables Asian seniors to engage in exercise and learning through any electronic device from which they can access their fitness app accounts. For seniors, the primary goal of exercise is to improve strength and health. Furthermore, wearable smart devices can provide personalized recommendations based on the user’s physical data and exercise habits. An important aspect of compatibility is its support for socialization, as Asian seniors tend to fear loneliness more than younger populations [33]. Fitness apps facilitate the creation and maintenance of relationships by allowing seniors to share experiences and knowledge with like-minded individuals [34]. These apps help expand social networks while maintaining connections with family and friends in an online fitness-oriented environment. Research has demonstrated significant positive effects of compatibility on perceived ease of use (PEU) and perceived usefulness (PU) [35]. Given this evidence, the following hypotheses are proposed:

H2a: 

The compatibility of fitness apps positively influences the perceived ease of use of Asian seniors.

H2b: 

The compatibility of fitness apps positively influences the perceived usefulness of Asian seniors.

Trialability (TR) refers to the extent to which Asian senior users can experiment with fitness apps on a small scale to test their functionality. For individuals who are less familiar with technology, the opportunity to trial products provides a valuable chance to explore innovations and become more acquainted with their features. Moreover, the initial experience with a product plays a crucial role in shaping an individual’s “first impression” [36], which subsequently influences continued use. Expectation confirmation occurs when users perceive that their first experience aligns with their expectations [37]. Specifically, users confirm their expectations based on their initial interaction with the product, which in turn affects their decision to continue using it [38]. Studies have shown that individuals with higher levels of trialability tend to view the system as both more practical and easier to use [39]. Based on these considerations, the following hypotheses are proposed:

H3a: 

The trialability of fitness apps positively influences the perceived ease of use of Asian seniors.

H3b: 

The trialability of fitness apps positively influences the perceived usefulness of Asian seniors.

Gamification (GA) is a method of assessing Asian seniors’ evaluation of the settings and processes of gamification in fitness apps. Although gamification in fitness apps can potentially modify the user’s state, supporting engagement and learning, the application of feedback and point modification is likely to enable the feeling of purpose [24]. Additionally, the social gamification components in these apps foster social comparisons and create connections among users, enabling them to support one another while working toward shared goals [40]. Krath et al. [41] outlined key principles of gamification that can be applied to explain goal setting and its significance, provide guidance for older users through a predetermined journey, offer immediate feedback, and amplify users’ achievements. Moreover, gamification can provide real-time feedback, enhance positive performance, and break down complex tasks into simpler, manageable steps. Fitness apps incorporating gamification allow users to set personal goals, choose various progression paths, and adjust difficulty levels based on individual capabilities. Based on these considerations, the following hypotheses are proposed:

H4a: 

The gamification of fitness apps positively influences the perceived ease of use of Asian seniors.

H4b: 

The gamification of fitness apps positively influences the perceived usefulness of Asian seniors.

Observability (OB) is the extent to which others can observe the results brought by using a fitness app by Asian senior users. This concept deals with the observability of the innovation itself in a social context. Most fitness apps not only provide sports rankings, but also provide users with display platforms such as electronic medal walls as honorary awards to enable participants to visualize their results [42]. These results are not only displayed on social software but also linked to other social media. In this context, the higher the level of older users on the platform, the more chances they have to gain attention and appreciation from other users. Furthermore, these high levels of older users also report a stronger sense of personal fulfilment [43]. The greater the exposure an innovation receives, the greater the likelihood that potential users will adopt it [44]. The ease with which the benefits of an innovation can be demonstrated to others is a significant factor in its adoption. In this context, OB, which refers to the ability of individuals to learn the use of innovation with less effort and to clearly explain its use and benefits to others [45], plays an important role. Based on this, it is hypothesized that outcome demonstrability will positively influence perceived usefulness (PU) and perceived ease of use (PEU).

H5a: 

The observability of fitness apps positively influences the perceived ease of use of Asian seniors.

H5b: 

The observability of fitness apps positively influences the perceived usefulness of Asian seniors.

Perceived ease of use (PEU) refers to the degree to which older adults find fitness apps easy to navigate and understand. A reduction in the complexity of the innovation leads to a decrease in the cognitive and effort resources required to learn and use it, thereby lowering the overall cost of adoption. Perceived usefulness (PU), on the other hand, is the extent to which older adults believe that using fitness apps enhances their physical and mental well-being. PU increases an individual’s sense of utility and performance through innovation, providing additional motivation to continue using the app due to the perceived benefits. Given that the study focuses on seniors, it is important to recognize that their acceptance of new technology may be limited, with PEU playing a crucial role in this process. Davis [46] proposed that PEU is a precursor to PU, rather than being merely parallel to it. Therefore, the proposed relationship between PU and PEU is reasonable.

TAM is able to reasonably describe the intent of Asian seniors to use fitness apps. A substantial body of research has been conducted to examine the effects of PEU and PU on behavioral intentions across a range of domains [47]. The existing literature suggests that PEU and PU positively and directly impact behavioral intentions [48]. Researchers like Wu and Wang [49] have found significant links between behavioral intentions and perceived usefulness. However, some studies have shown that while PU is a crucial predictor of attitudes and intentions, PEU may not be [50]. This discrepancy highlights the necessity for further investigation in the current study. Accordingly, the objective of this study is to examine the mediating influence of PEU and PU on behavioral intention, with the formulation of specific hypotheses.

H6: 

The perceived ease of use of fitness apps by Asian seniors positively influences their perceived usefulness.

H7: 

The perceived ease of use of fitness apps by Asian seniors positively influences their behavioral intention to use.

H8: 

The perceived usefulness of fitness apps by Asian seniors positively influences their behavioral intention to use.

The behavioral intention of use (BIU) refers to the intention of older adults to regularly use fitness apps. Behavior of use (BU) indicates the actual behavior of older adults who consistently engage with fitness apps. Behavioral intentions may be defined as self-instructions to perform specific actions in order to achieve a desired outcome [51]. Intention reflects both the level of the stated goal or behavior. A number of social psychological models, including the theory of reasoned action [52], the theory of planned behavior [53], attitude–behavior theory [54], and the protection motivation theory [55], posit that the most direct and significant predictor of an individual’s behavior is their behavioral intention. In accordance with the technology acceptance model (TAM), both PEU and PU exert a direct influence on behavioral intention, which in turn affects actual use behavior. Behavioral intention is regarded as the most crucial determinant of actual use behavior [56]. In light of the above, the following hypothesis is proposed.

H9: 

The behavioral intention of Asian seniors who use fitness apps positively influences their behavior of use.

3. Methods

3.1. Measurement Item

The present study resulted in the development of nine constructs. In accordance with the modified DOI, the innovation attributes were evaluated based on the following five criteria: relative advantages, compatibility, trialability, gamification, and observability. In contrast, the TAM was used to assess the user experience, and its two key variables were perceived ease of use and perceived usefulness. Additionally, the constructs of behavioral intention and behavioral use were assessed. All items in the constructs were adapted from the existing literature and modified to align with the context of the target user group in this study.

In terms of demographics and sampling, the questionnaire was administered to a cohort of seniors in Asia, defined as individuals aged 50 and above. To ensure that we could obtain a diverse group of respondents and achieve accurate translation, the questionnaire was first drafted in English and then went through a comprehensive translation process. Firstly, a researcher who was not a native English speaker translated all the entries of the original text into other languages. Subsequently, another researcher undertook an independent translation of the entries back into English. Moreover, both researchers confirmed the semantic accuracy of the translation by comparing the two English versions.

Prior to administering the formal questionnaire, the readability of the questionnaire was verified by five individuals with over three years of experience using fitness apps. The questionnaire was subsequently revised in accordance with the feedback provided. Three academic experts in the fields of information technology and service design were invited to assess the suitability of the items for measuring the usage behavior of fitness apps by seniors, with a view to validating the content. Three fitness app practitioners were asked to review the survey items to determine if any items needed to be removed, added, or reworded. Additionally, the questionnaire was pre-tested with 30 Asian seniors aged 50 and over who had used fitness apps within the previous six months. The Amos software was employed to validate the questionnaire items, the adjusted scale, and the sources, as detailed in

Table 1. Definition of variables, specific measured variables, and sources of reference scales.

Measure	Reference		Measurement Term	Reference
Relative Advantages (RA)	The extent to which seniors’ use of fitness apps is superior to traditional exercise	RA1	Low investment and expenditure	Arning & Ziefle (2009) [57]
		RA2	Less interference from others
		RA3	Low site impact
		RA4	Less affected by weather
Compatibility (CO)	The extent to which fitness apps support exercise for seniors	CO1	Multi-project options	Robertson (1967) [17] Waheed et al. (2015) [58]
		CO2	Multi-platform support
		CO3	Multi-friend interaction
		CO4	Multi-functional experience
Trialability (TR)	The extent to which seniors can try to experience fitness apps on a limited basis	TR1	Easy handling	Agarwal & Prasad (1997) [59]
		TR2	With senior citizen mode
		TR3	Smooth system
		TR4	Guided step by step
Gamification (GA)	Acceptance of gamified fitness apps programs by seniors	GA1	Positioning	Eppmann et al. (2018) [60] Hamari & Koivisto (2014) [61]
		GA2	Challenges
		GA3	Reward and feedback model
		GA4	Bring entertainment
Observability (OB)	The extent to which the results of seniors’ use of fitness apps are visible to others	OB1	Physical monitoring data	Aizstrauta et al. (2015) [62]
		OB2	Fitness Achievement Showcase
		OB3	Social media linkage
		OB4	Concerns about use
Perceived Ease of Use (PEU)	Ease of learning and proficiency when older adults use fitness apps	FEU1	Easy to learn to use	Venkatesh & Davis (1996) [63] Smith (2008) [64]
		FEU2	Proficiency is easy
		FEU3	Does not take long to learn
		FEU4	Easy to teach others to use
		FEU5	Easy to use supporting electronics
Perceived Usefulness (PU)	The extent to which seniors subjectively perceive physical and mental health gains when using fitness apps	PU1	Harvesting health	Hendrickson et al. (1993) [65] Smith (2008) [64]
		PU2	Improve fitness efficiency
		PU3	Improvement of exercise regimen
		PU4	Expansion of exercise categories
		PU5	Reduce loneliness and boredom
Behavior Intention of Use (BIU)	Intention of orders to continue using fitness apps	BIU1	Intend to invest more time	Kim et al. (2016) [66] Hess et al. (2014) [67]
		BIU2	I intend to use it all the time
		BIU3	Want to invite friends to use together
		BIU4	Willingness to explore new features
		BIU5	Maintain a positive attitude towards use
Behavior of Use (BU)	Behavior of seniors who are continuing to use fitness apps	BU1	Longer usage time	Davis (1989) [46] Sheeran (2002) [68]
		BU2	Frequency of use becomes higher
		BU3	Already recommended to family and friends
		BU4	Have mastered the use of
		BU5	Proactive promotion on social media platforms

.

All items pertaining to the nine constructs were measured on a five-point Likert scale, with responses ranging from ‘strongly disagree’ (value 1) to ’strongly agree’ (value 5) [69]. Furthermore, general questions were formulated to address the overall user experience of fitness apps, including average time spent, average frequency of use, primary device utilized, consumption patterns, and top-ups. Additionally, the socio-demographic characteristics of the survey were measured using three items: gender, age, and educational attainment. A preliminary briefing preceded the questionnaire, during which participants were informed of the nature of the research and asked to confirm their consent to take part. They were also asked to indicate whether they belonged to the target readership of the survey. Only those who responded in the affirmative were requested to answer the questionnaire presented in Appendix A.

3.2. Research Methodology

This study applied structural equation modeling (SEM), a technique for the sophisticated analysis of statistics, as postulated by Barrett [70], in order to investigate relationships that tend to be causal between variables. The flexibility and adaptability of SEM to different research designs and types of data can be applied to provide a robust analytic framework for addressing interdisciplinary research questions [71].

The current study analyzed the questionnaire using the SPSS statistical software package (https://www.ibm.com/spss, 24 October 2024). Moreover, the main data analysis was carried out with the help of Amos. Amos is a widely used software in the performance of structural equation modeling analysis; it not only assists in model specification but also analyzes models within a graphical interface. Amos is mainly used in social science research for theory testing through developing and evaluating theoretical models, most commonly with observed and latent variables [72]. The objective of the current research was to explore how and why older Asians adopt and use fitness applications for healthy aging. Amos was very helpful in this analysis. Its path, CFA, and full SEM functions allowed the research to empirically verify the relationships among motivational cognitive factors, user experience variables, and overall outcomes. This deeper level of statistical rigor supports the implications for app developers and healthcare professionals that can be taken from this study.

The structural equation modeling framework allows two main forms of analysis: a path analysis of observed variables and a factor analysis of latent variables. These analyses are integrated within Amos to enable the assessment of model fit, which refers to the extent to which the observed data align with the theoretical model predictions. It is particularly suited to the exploration of complex data structures and the identification of potential relationships between variables [73].

The process of using the SEM test in this study was divided into two main stages: the assessment of the structural, convergent, and discriminant validity of the test and an analysis of the structural model [74]. This study employed path modeling analysis to examine the influence of seniors’ engagement with fitness apps on their continued use of such apps, with a particular focus on the role of user experience factors in the technology acceptance model (TAM) and the Decomposed Innovation Model (DOI). The results are presented and compared in the discussion of hypothesis testing based on the electric user experience factors.

3.3. Data Collection

Online surveys have become an increasingly valuable tool for straightforwardly and cost-effectively gathering information from respondents around the globe [75]. In this study, the survey was distributed through several popular social media platforms commonly used to discuss fitness in various Asian countries, including but not limited to WeChat (China), LINE (Thailand, Japan, Taiwan), KakaoTalk (South Korea), and Facebook groups (Singapore, Malaysia, Hong Kong). Consequently, these platforms are selected based on the wider variety of users who have also supported active communities focusing on health and fitness. Thus, this survey was conducted over a three-month period beginning on 20 December 2023 and ending on 20 March 2024, and the older adult population was invited to discuss and become survey respondents in randomly selected, publicly available older adult fitness discussion groups targeting fitness apps. In the end, a total of 700 completed questionnaires were received, which is enough for a large and varied sample in subsequent analyses.

The online survey was designed and administered using the Qualtrics platform, chosen for its ease of use and state-of-the-art data security features, with advanced options to customize the design of instruments. This allowed for intuitive development and real-time response-rate monitoring of the survey. Considering the higher penetration of digital health solutions and increased smartphone-based internet literacy in Asian seniors, this approach has given convenient access to participants already acquainted with health-related technologies.

The collected questionnaire data needed to be analyzed after removing outliers, eliminating inappropriate responses, and filling in missing data. For instance, if a respondent provided responses to a survey question over an insufficiently lengthy period of time, or if their answers exhibited a ’response set’ pattern (e.g., all 1s or all 5s), the respondent’s data were excluded on the grounds that such a pattern would indicate unreliability or untrustworthiness in the answers provided. In particular, the data were subjected to a process of screening to ensure their relevance, to identify any outliers, and to ascertain the time spent answering the survey. In instances where these criteria are not met, the responses are excluded from the final dataset.

4. Result

The final analysis of this study was finalized by collecting valid questionnaire data from 587 respondents. The validity rate of questionnaire collection was 83.8%.

4.1. Descriptive Statistical Analysis

The demographic information pertaining to the 587 respondents is presented in Appendix B. The gender distribution was equitable, comprising 48% males and 52% females. Discussions among users on various platforms indicate that female senior users are more prevalent due to their inclination to cluster fitness apps of this nature, such as square dancing. The majority of respondents (67.1%) were between the ages of 51 and 59, which may be indicative of a general decline in technological engagement among older adults. Almost half of the sample (46.8%) had obtained a college degree. A notable 46.2% of the senior cohort indicated a preference for utilizing fitness apps on a mobile phone or tablet. Furthermore, 70.9% of the same cohort have spent money on social media apps. It is evident that 92% of seniors utilize fitness apps at least twice a week, with 15.8% doing so almost every day. Furthermore, 73.9% of seniors use fitness apps for four hours or more per week, which suggests that those with some experience using fitness apps have a certain degree of dependence on them.

4.2. Measurement Model

The first one is related to reliability. Cronbach’s alpha coefficient can vary from 0 to 1, with the higher values meaning better internal consistency of the questionnaire or test used. As revealed in

Table 2. Reliability analysis.

Variable	Cronbach’s Alpha	N of Items
Relative Advantages	0.899	4
Compatibility	0.827	4
Trialability	0.897	4
Gamification	0.895	4
Observability	0.858	4
Perceived Ease of Use	0.899	5
Perceived Usefulness	0.927	5
Behavioral Intention of Use	0.884	5
Behavior of Use	0.915	5

below, Cronbach’s alpha lies between 0.827 and 0.927, which reflects good internal consistency among the constructed items.

Secondly, as shown in

Table 3. Common method bias analysis.

Extraction Sums of Squared Loadings
Total	% of Variance	Cumulative %
12.951	32.378	32.378
3.452	8.631	41.008
2.486	6.216	47.224
2.411	6.029	53.252
1.883	4.707	57.959
1.790	4.475	62.434
1.634	4.084	66.518
1.452	3.631	70.149
1.438	3.594	73.743

, the results of Harman’s single-factor test performed on the data showed that the unrotated first factor accounted for 32.378% of the variance, which is less than 50%. The total variance explained was 73.743%, which can be explained by the fact that there is no severe issue of heteroscedasticity in this study.

Thirdly, the data presented in

Table 4. Confirmatory factor analysis.

Measurement Indicator	CMIN	DF	CMIN/DF	SRMR	GFI	IFI	TLI	CFI	RMSEA
Measured value	821.272	704	1.167	0.026	0.935	0.992	0.992	0.992	0.017
Reference standard	-	-	<3	<0.08	>0.9	>0.9	>0.9	>0.9	<0.08

Description of abbreviations: CMIN—Chi-square Statistic; DF—Degrees of Freedom; CMIN/DF—Chi-square Divided by Degrees of Freedom; SRMR—Standardized Root Mean Square Residual; GFI—Goodness-of-Fit Index; IFI—Incremental Fit Index; TLI—Tucker–Lewis Index; CFI—Comparative Fit Index; RMSEA—Root Mean Square Error of Approximation.

represent a series of model fit indices from a validated factor analysis (CFA). The data show that the CMIN/DF ratio is 1.167, which is lower than the standard 2, indicating that the model is well fitted. The SRMR value of 0.026 represents the average size of the model residuals, which is lower than 0.08, indicating a good model fit. The GFI, which measures how well the data fit the model in relation to a perfect fit, is 0.935, close to 1, indicating a good fit. The IFI, TLI, and CFI values, all incremental fit indicators, are each 0.992, very close to 1, signifying an excellent model fit. The RMSEA value, which assesses the magnitude of the model error, is 0.017. A value under 0.05 is considered a good fit. These results indicate that the model fits the data excellently.

Fourthly, in the validity test, the composite reliability (CR) values exceed the established cutoff of 0.7 for the constructs, reaching 0.933 for IN and 0.828 for SP. This indicates that the construct is reliably measured and exhibits excellent internal consistency. A higher average variance extracted (AVE) value indicates that the construct’s indicator explains the construct better, thus reflecting higher convergent validity.

Table 5. Convergent validity test results.

Variable			Estimate	CR	AVE
RA1	←	RA	0.895	0.900	0.693
RA2	←	RA	0.797
RA3	←	RA	0.828
RA4	←	RA	0.805
CO1	←	CO	0.722	0.828	0.547
CO2	←	CO	0.777
CO3	←	CO	0.768
CO4	←	CO	0.689
TR1	←	TR	0.868	0.900	0.694
TR2	←	TR	0.707
TR3	←	TR	0.867
TR4	←	TR	0.878
GA1	←	GA	0.736	0.896	0.685
GA2	←	GA	0.846
GA3	←	GA	0.801
GA4	←	GA	0.916
OB1	←	OB	0.760	0.859	0.604
OB2	←	OB	0.761
OB3	←	OB	0.731
OB4	←	OB	0.852
PEU1	←	PEU	0.836	0.901	0.645
PEU2	←	PEU	0.771
PEU3	←	PEU	0.835
PEU4	←	PEU	0.745
PEU5	←	PEU	0.825
PU1	←	PU	0.843	0.933	0.738
PU2	←	PU	0.791
PU3	←	PU	0.895
PU4	←	PU	0.904
PU5	←	PU	0.856
BIU1	←	BIU	0.701	0.886	0.610
IU2	←	BIU	0.812
BIU3	←	BIU	0.865
BIU4	←	BIU	0.722
BIU5	←	BIU	0.793
BU1	←	BU	0.874	0.916	0.687
BU2	←	BU	0.777
BU3	←	BU	0.894
BU4	←	BU	0.825
BU5	←	BU	0.767

Description of abbreviations: CR—composite reliability; AVE—average variance extracted.

displays the AVE values (ranging from 0.547 to 0.738), and a value above 0.5 was considered acceptable, as it means that the latent factor explains a large part of the variance of its indicator.

Finally, as illustrated in

Table 6. Correlation and differential validity analysis.

Correlations and Discrimination
	RA	CO	TR	GA	OB	PEU	PU	BIU	BU
RA	0.832
CO	0.462 **	0.740
TR	0.465 **	0.354 **	0.833
GA	0.518 **	0.467 **	0.403 **	0.827
OB	0.186 **	0.208 **	0.160 **	0.180 **	0.777
PEU	0.409 **	0.348 **	0.476 **	0.328 **	0.132 **	0.803
PU	0.435 **	0.408 **	0.414 **	0.427 **	0.243 **	0.457 **	0.859
BIU	0.425 **	0.401 **	0.334 **	0.414 **	0.339 **	0.361 **	0.379 **	0.781
BU	0.348 **	0.359 **	0.340 **	0.314 **	0.329 **	0.325 **	0.295 **	0.496 **	0.829

** Correlation is significant at the 0.01 level (2-tailed).

, the bold numbers in the diagonal indicate the square root of each construct’s AVE. As all the numbers in the diagonal are greater than those in the non-diagonal, it can be concluded that the discriminant validity is satisfactory for all constructs.

The reliability and validity analysis of the aforementioned questionnaire demonstrated that the reliability, validity, and correlation of the questionnaire were sufficient to permit the application of structural equation modeling.

4.3. Structural Model

Prior to empirical testing, a structural equation model was constructed and then AMOS 24.0 was used to perform calculations using the maximum likelihood method to obtain Figure 2 and

Table 7. Measurement model fit indices.

Measurement Indicator	CMIN	DF	CMIN/DF	SRMR	GFI	IFI	TLI	CFI	RMSEA
Measured value	984.75	716	1.375	0.078	0.925	0.983	0.981	0.983	0.025
Reference standard	-	-	<3	<0.08	>0.9	>0.9	>0.9	>0.9	<0.08

. Table 7 shows that all data results meet the criteria, indicating that the model satisfies both the recommended level of independence of fit and the rule of combination, so it has a good fit and a good degree of fit.

4.4. Model Evaluation

Table 8. Path coefficients.

				STD Estimate	Estimate	S.E.	C.R.	p	Result
H1a	PEU	←	RA	0.192	0.145	0.042	3.458	***	Valid
H2a	PEU	←	CO	0.139	0.133	0.053	2.524	0.012	Valid
H3a	PEU	←	TR	0.356	0.348	0.047	7.322	***	Valid
H4a	PEU	←	GA	0.029	0.027	0.051	0.537	0.591	Invalid
H5a	PEU	←	OB	0.014	0.012	0.037	0.33	0.741	Invalid
H1b	PU	←	RA	0.106	0.097	0.048	2.027	0.043	Valid
H2b	PU	←	CO	0.143	0.165	0.06	2.753	0.006	Valid
H3b	PU	←	TR	0.118	0.14	0.056	2.488	0.013	Valid
H4b	PU	←	GA	0.161	0.187	0.058	3.201	0.001	Valid
H5b	PU	←	OB	0.128	0.139	0.042	3.318	***	Valid
H6	PU	←	PEU	0.245	0.296	0.057	5.233	***	Valid
H7	BIU	←	PEU	0.285	0.317	0.055	5.724	***	Valid
H8	BIU	←	PU	0.296	0.273	0.045	6.023	***	Valid
H9	BU	←	BIU	0.57	0.57	0.047	12.026	***	Valid

*** p < 0.001.

below demonstrates the efficacy of the model and the outcomes of the hypothesis testing, showcasing the values of the unstandardized coefficients and standard errors of the structural model. Table 8 indicates that the GA for H4a is not statistically significant for PEU (CR = 0.537, p = 0.591 > 0.05), and similarly, the OB for H5a is not significant for PEU (CR = 0.33, p = 0.741 > 0.05). Therefore, H4a and H5a were found to be ineffective. In comparison, the standardized path coefficients were positive for each of the other paths (p < 0.05), indicating that all other hypotheses had a significant positive effect and were therefore valid.

The results of the data analysis indicated that RA, CO, and TR had a notable positive impact on PEU, with TR exhibiting the most pronounced effect (STD Estimate = 0.356). This suggests that TR played a pivotal role in explaining PEU. Conversely, GA and OB demonstrated a non-significant effect on PEU (p > 0.05), indicating that these variables possess limited explanatory power with regard to perceived ease of use. All DOI variables (RA, CO, TR, GA, OB) had a significant positive effect on PU, with GA having the strongest effect (STD Estimate = 0.161). Additionally, PEU had a significant positive effect on PU (STD Estimate = 0.245), indicating that an increase in perceived ease of use is associated with an increase in perceived usefulness. Both PEU and PU had a significant positive effect on BIU, with PU having a slightly stronger effect (STD Estimate = 0). The results indicated that perceived usefulness exerted a slightly stronger influence on the intention to use BIU (296 vs. 0.285), suggesting that it is a slightly more powerful predictor of actual use behavior. Moreover, the effect of behavioral intention on actual use behavior was found to be the strongest single effect in the model (STD Estimate = 0.57), which is consistent with the notion that behavioral intention is a crucial predictor of actual use behavior.

5. Discussion

This study aims to assess the acceptance behavior of Asian seniors regarding fitness apps, focusing on the user experience factors involved. A detailed analysis of the data led to the following conclusions: Firstly, relative advantages, compatibility, and trialability significantly impacted Asian seniors’ perceived ease of use of fitness apps, while the effects of gamification and observability on this perception were not evident. Secondly, relative advantages, compatibility, trialability, gamification, observability, and perceived ease of use are all key user experience factors influencing Asian seniors’ perception of the usefulness of fitness apps. Lastly, the perceived usefulness and ease of use of fitness apps among Asian seniors positively relate to their intention to use, influencing their actual use behavior. These findings enhance understanding of behavioral or technology acceptance, emphasizing the key factors affecting seniors’ acceptance and use of fitness apps.

Prior research [76] has shown that the relative advantages of a product or service can positively affect its perceived ease of use and usefulness. Asian seniors can identify these relative advantages and apply them to real-life scenarios, which significantly improves the software’s perceived ease of use and usefulness. Additionally, with technological advancements, fitness app algorithms increasingly incorporate adaptive learning technologies that adjust exercise programs based on user feedback and progress [77]. This approach offers older users a more tailored and adaptable exercise experience, further boosting the software’s perceived usefulness and ease of use.

The significant influence of compatibility on perceived ease of use and usefulness is supported by previous research [64]. Asian seniors are more likely to utilize technologies that they perceive as compatible with their existing knowledge base and for which they have access to the requisite support and resources. It is of paramount importance to extend existing behaviors and familiar technologies in order to enhance the perception of ease of use and usefulness among Asian solder users. The capacity to utilize fitness applications on devices with which they are already conversant can markedly diminish the obstacles to learning, thereby enhancing their perceived ease of use and the value they ascribe to the software.

Trialability significantly positively impacts the perceived ease of use and usefulness of fitness apps among Asian seniors, aligning with findings from previous research [78]. This effect is related to self-efficacy, and the ease of trialing fitness apps has been shown to increase the self-efficacy of older Asian users, enhancing their belief that the fitness apps will help them achieve their fitness goals. Additionally, the trialability of fitness apps can provide a positive user experience by reducing the learning curve and offering immediate positive feedback [79], which enhances older users’ overall impression of the software. This positive experience further reinforces the perception of the fitness apps’ ease of use and usefulness.

Gamification plays a driving role in Asian seniors that fitness apps are useful, which is consistent with a study from Lister et al. [80] study that gamification possesses usability and is effective in encouraging behavior change. The gamification of fitness apps can have a positive impact on the health behaviors of older Asian users. These results highlight the importance of an iterative, user-centered design approach in developing gamified experiences.

However, the effect of gamification on the perceived ease of use of fitness apps was found to be relatively minor among the senior group. This contrasts with some studies that suggest gamification enhances the appeal and usability of technology and information software by guiding users [81]. This difference may be due to the complexity of the gamification-related initial settings of fitness apps, which contain more detailed and cumbersome information, making it more difficult for Asian seniors to learn. Despite this, once Asian seniors became familiar with the fitness app, they were easily attracted to the interactive experience provided by the gamification settings.

The present study lends support to the existing research findings [82] that observability has a direct and significant effect on the perceived usefulness of Asian seniors. Furthermore, it was found that observability enhances the confidence of Asian seniors to consistently use fitness apps. Positive expectations regarding interactions with innovative technologies have been demonstrated to result in the sustained adoption of such innovations when they are observed [83]. Concurrently, the social-related features of fitness apps can serve to bolster self-confidence in older adults. When older adults receive positive reinforcement regarding their fitness results from other users, they are more likely to perceive fitness apps as useful.

Nevertheless, the data indicate that there is no positive correlation between OB and perceived ease of use. These findings are at odds with those presented by Nezamdoust et al. [84], who posited that OB signifies that Asian seniors can learn to utilize innovation with diminished effort and elucidate its usage and advantages to others with greater ease. This may be attributed to the fact that the utilization of fitness applications necessitates a certain degree of persistence and time accumulation in order to facilitate a substantial alteration in visualized body data, which is contingent upon the Asian seniors’ effort and desired outcomes, irrespective of the perceived ease of use. Furthermore, the capacity to integrate social media functionality represents a significant additional feature, particularly appealing to Asian older users. However, this inevitably introduces additional complexity and reduces the perceived ease of use.

The perceived ease of use of a product or service has a significant effect on the perceived usefulness of that product or service. Furthermore, it plays a positive role in the behavioral intention to use that product or service, due to its influence on the perceived usefulness of the product or service in question. This finding aligns with the results of Li et al. [85], who demonstrated that perceived usefulness mediated the relationship between perceived ease of use and social influence on sustained intention. Accordingly, the relationship between PEU and PU was incorporated into a generic model of health-related software acceptance [86]. For Asian seniors, the ease of learning and mastery of fitness apps is a significant factor influencing their subsequent use. Fitness apps that are straightforward to learn and master are beneficial, which is crucial in developing age-appropriate and user-friendly fitness apps for Asian seniors.

The perceived usefulness of fitness apps significantly influences the intention to use them. Among Asian seniors, perceived usefulness is a primary motivator for adopting fitness apps. As a result, those with positive perceptions and attitudes toward the novelty and uniqueness of new technologies are more likely to engage in sustainable behaviors [87]. Generally, perceived usefulness refers to the extent to which users believe using fitness apps will improve their health and well-being. For older Asian users, fitness apps have been shown to benefit not only physical health but also mental well-being, as they provide a way for family members and friends to connect and spend time together despite being physically apart.

There is a positive correlation between the behavioral intention to use fitness apps and the actual use of fitness apps. These findings are consistent with those of previous research, which indicates that among Asian seniors, a stronger intention to download and use fitness apps is associated with more predictable usage behavior [88]. The intention to engage with a given behavior is a significant predictor of older Asian users’ acceptance of fitness apps. For Asian seniors, the perception of technology as valuable and easy to use is associated with an enhanced intention to use it, which is more likely to result in actual usage behavior.

This study provides important insights into how older adults in Asia are accepting fitness apps, but there are several limitations to consider. First, the sample was mainly drawn from active online communities in countries like China, Thailand, South Korea, and Japan. This could introduce selection bias, as the participants are likely to be more tech-savvy than the broader population. Second, the reliance on self-reported data might lead to response biases, where participants could either exaggerate or downplay their actual behaviors. Finally, although this study used structural equation modeling, it focused mainly on user experience factors and did not take into account other contextual influences, like cultural, social, or healthcare-related factors, that might also affect app acceptance.

Moreover, although the findings provide valuable insights into fitness app adoption in several Asian countries, we should be cautious about generalizing the results. The sample was primarily made up of people already engaged in online fitness discussions, meaning those who are less involved in digital health may not be adequately represented. Additionally, the factors influencing app acceptance might differ across cultures, and older adults in rural or less digitally connected areas could face different challenges not explored in this study. Further research is needed to determine whether these findings apply to a wider range of groups.

Furthermore, these results are particularly relevant for developers of fitness apps targeting older adults in Asia. This study shows that seniors tend to prioritize ease of use, compatibility with existing technology, and the ability to try apps before making a long-term commitment. While gamification and visibility did affect perceived usefulness, they were less influential on ease of use, underscoring the importance of simplicity in app design. Ultimately, this research highlights the need for a user-centered, adaptive design that can increase adoption rates and engagement, leading to better health outcomes for aging populations.

6. Conclusions

Fitness apps are a simple, cost-effective, and affordable digital healthcare solution that improves access to fitness services, especially for Asian seniors. This study confirms the relevance of the TAM and the DOI theory in assessing the acceptance behavior of fitness apps among seniors, focusing on user experience factors. Moreover, the generalized methodology allows for the straightforward adaptation of the results of this study to inform other population-specific investigations of online fitness services.

The market for fitness apps represents a promising landscape for the dissemination of health behavior change interventions. The findings of this study indicate that further modifications are required to design fitness apps that are efficient, reliable, meaningful, and usable for Asian seniors. Data derived from such studies help identify key barriers and facilitators affecting technology adoption, guiding developers to address older adults’ needs and preferences.

Recent software innovations have prioritized user-centered design, including simplified interfaces, compatibility with familiar devices, and trialability features that help seniors build confidence before fully committing. In addition, adaptive learning algorithms and social engagement tools encourage sustained use, improve satisfaction, and support better overall health management. By integrating health-monitoring capabilities, telehealth portals, and real-time tracking of vital signs, seniors can make informed decisions about their fitness routines, enhancing their overall health management.

New technologies provide a strong opportunity to boost exercise engagement among Asian seniors, potentially driving social change and enhancing the quality of life, health, self-efficacy, and self-directed behaviors in this group. Setting clear intentions for integrating emerging technologies with behavior change strategies supports the practical implementation of robust information systems. This will have a significant impact on the silver aging industry by offering designers strategies and best practices to develop more inclusive, effective solutions.