Research on the Impact of Digital Inclusion Among Older Adults on Their Proactive Health Behaviors

Abstract

Against the backdrop of actively addressing population aging and digitalization, this study employs hierarchical regression estimation to explore the impact of digital inclusion among older adults on their proactive health behaviors and tests the direct effect of self-efficacy on proactive health behaviors. Furthermore, using the Bootstrap mediation effect test, this study further examines the mediation effect of self-efficacy in the process by which digital inclusion among older adults influences their proactive health behaviors. The results indicate: (1) Intermediate and advanced digital inclusion among older adults have significant positive impacts on their proactive health behaviors; (2) self-efficacy exhibits a significant positive influence on proactive health behaviors; (3) self-efficacy serves as a mediator in the positive impact of digital inclusion on proactive health behaviors among older adults. Based on these findings, the following practical suggestions are proposed: (1) Conduct education and training on digital health tools tailored for older adults; (2) establish a social support network for digital health management among older adults; (3) optimize digital health tools and update them to be more elderly-friendly.

Keywords

older adults digital inclusion self-efficacy proactive health behaviors

Introduction

The report of the 20th National Congress of the Communist Party of China outlined the significant policy of “implementing a national strategy to actively address population aging.” With the accelerated evolution of an aging society (W. Gao et al., 2023), the health issues of older adult population have increasingly become a topic of close attention for governments and all sectors of society. Proactive health, as a key measure to actively address population aging, can effectively enhance the health literacy and self-management abilities of older adults, reduce the burden on the healthcare system, and improve the quality of life for older adults (J. Liu et al., 2022; Sun et al., 2023). However, currently, most elderly individuals have relatively weak health literacy (Shah et al., 2021), insufficient social participation, and a lack of health awareness, resulting in many elderly people being in a state of passive health throughout the year (He & Wang, 2023). Numerous studies have shown that digital inclusion can effectively promote the physical and mental health of older adults, enhance social interactions, and improve self-management of health (Dan et al., 2023; Feng et al., 2024), thereby motivating proactive health behaviors among older adults. Therefore, how does digital inclusion among older adults influence their proactive health behaviors? This paper will investigate this issue.

In 2015, China first proposed the concept of “proactive health” (Li & Yu, 2020). Since then, academics have engaged in a series of discussions on the concept and connotations of proactive health. Although there is no unified consensus in the academic community on the definition and interpretation of proactive health, there is general agreement that it encompasses the following four core characteristics: Firstly, proactivity emphasizes individuals’ active role in maintaining their health status and engaging in health management processes; secondly, preventiveness requires the effective identification and avoidance of potential health threats through precise health risk assessment mechanisms before health issues arise; thirdly, intervention focuses on health intervention strategies for early-stage and chronic diseases, particularly advocating the use of non-pharmacological interventions to promote health recovery or maintenance; finally, participation involves mobilizing forces from all sectors of society to form a concerted effort to promote the sustainable development of the national health cause (Wolf et al., 2017; Xu & Zhou, 2023). Therefore, this paper defines proactive health behaviors as a multidimensional conceptual framework based on proactivity, with comprehensive consideration of these characteristics. Specifically, proactive physical exercise, as a typical manifestation of preventiveness, covers the frequency and duration of exercise activities in daily life; active participation in social activities serves as a marker of participation, encompassing individual involvement in daily social interactions and team or organizational activities; and proactive improvement of the living environment is considered a specific practice of intervention, referring to beneficial health-related adjustments and optimizations made by individuals to their physical environment (Wu & Yi, 2024). This classification standard profoundly highlights the core position of the “behavior” element within the scope of proactive health, which is highly consistent with the theme of this study and provides a perspective and path for comprehensively analyzing the specific manifestations of older adult population in proactive health practices and the underlying influencing factors.

The concept of digital inclusion represents a deepening and revision of traditional digital divide theory, emphasizing the deep integration of digital technology into and service to people’s daily lives, thereby achieving seamless and deep integration between technology and life (Fisk et al., 2023). It can promote individual participation and decision-making in health management (French & Richardson, 2017; Yongai et al., 2024). As the degree of digital inclusion among older adults continues to deepen, influenced by factors such as geography, family economic status, and social class (Pangbourne et al., 2010), the digital inclusion of older adults is increasingly differentiated, with varying degrees of mastery of digital skills and understanding of the digital society among different elderly individuals. Most scholars categorize this digital inclusion difference into three stages based on different levels of demand: basic digital inclusion, intermediate digital inclusion, and advanced digital inclusion. Elderly individuals at each stage are affected differently by digital inclusion (C. Liu, 2021). The digitization of elderly life has made their cognitive world more diverse and complex (F. Wang & Guo, 2022), and proactive health is also related to the cognitive status of older adults (S. Wang et al., 2023). Therefore, whether the various stages of digital inclusion among older adults will affect their behaviors, especially their proactive health behaviors, and how the mechanisms work remain to be explored and studied.

Furthermore, self-efficacy, as an individual’s confidence in their abilities in specific situations, undoubtedly plays an important role in the health behaviors of older adults (Lu et al., 2024). When elderly individuals obtain more health-related information through digital technology, their self-efficacy may increase, thereby motivating them to adopt more proactive health behaviors. Studies have shown that self-efficacy not only influences individuals’ health decisions and behavior choices but may also enhance their coping abilities when facing health challenges (Hur, 2018). Therefore, exploring the role of self-efficacy between digital inclusion and proactive health behaviors among older adults can provide deeper insights into this complex relationship and provide a solid theoretical foundation for practical operations and policy design, thereby more effectively promoting elderly health management and improving their quality of life.

Currently, some scholars have begun to pay attention to the impact of internet use on the health status of older adults. However, digital inclusion is not entirely equivalent to internet use; it also includes the use of other digital devices and technologies beyond internet usage. And there has been little research on how the self-efficacy of older adult changes after digital inclusion and how their proactive health behaviors change. Based on this, this study aims to introduce the concept of digital inclusion, combine existing research, sort out the changes in self-efficacy that may occur during the process of digital inclusion among older adults, and its impact on their proactive health behaviors, and construct a theoretical model among the three. Thereby, this study explores the influence mechanism of digital inclusion among older adults on their proactive health behaviors, with the goal of finding ways for older adults to better use digital technology to actively promote their health status.

Literature Review and Research Hypotheses

The Relationship Between Digital Inclusion and Proactive Health Behavior

The concept of digital inclusion originates from an American Internet development report titled “Net Laggards: Toward Digital Inclusion,” where digital inclusion is defined by the prevalence of personal and household computers and Internet access, as well as the usage of the Internet and computers among vulnerable groups (Jiang & Zong, 2024a). Based on previous research, current digital usage patterns, Maslow’s (1943) hierarchy of needs, and Katz’s (2017) uses and gratifications theory, digital inclusion can be categorized into three levels according to different needs: basic digital inclusion to fulfill the primary needs of love and belonging, intermediate digital inclusion to meet the secondary need for respect, and advanced digital inclusion to satisfy the higher-order need for self-actualization (C. Liu, 2021). This paper will deconstruct digital inclusion from the aforementioned three dimensions and construct hypothetical models by integrating digital divide theory with established research findings from mature theoretical frameworks (Park & Chun, 2024; Ueno et al., 2023). The following sections provide a detailed elaboration of these three stages of digital inclusion.

While academic circles have yet to reach a consensus on the definition and con-notations of proactive health, comparative analyses of research conducted in various countries reveal that there is general recognition of four core attributes embedded within this concept (MacEachern et al., 2024; Rasmussen et al., 2024). These attributes have been consistently identified across national studies as fundamental components of proactive health, despite the ongoing theoretical debates regarding its precise boundaries and operational definitions. This cross-national consensus suggests a nascent framework for understanding proactive health that transcends cultural and contextual differences, providing a foundational structure for future research and practice in this emerging field. This paper will also follow the four attributes of proactive health mentioned above to propose relevant hypotheses and select dimensions.

The Relationship Between Basic Digital Inclusion and Proactive Health Behavior

Older adults at the stage of basic digital inclusion are often constrained by their economic status or the degree of digitalization in their region, resulting in limited Internet exposure. They primarily use the Internet to facilitate communication with others and access information relevant to their surroundings within the digital society. At this stage, elderly individuals typically possess the ability to operate a computer for basic tasks, use the Internet for information searching and browsing, and engage in simple communication with family and friends through social media platforms like QQ and WeChat. These behaviors help fulfill their needs for love and belonging. Older adults temporarily at this stage are defined as belonging to the basic digital inclusion group. Some scholars argue that proactive health under the concept of “Healthy China” should be a networked system architecture based on the Internet (L. Gao & Gu, 2023). Additionally, research has shown that digital inclusion can promote social participation among older adults by expanding their social networks, thereby providing opportunities for proactive health behavior (Quan Haase et al., 2017). The aforementioned research provides preliminary evidence that the daily activities of older adults are being progressively influenced by the increasingly sophisticated internet technology. However, whether and to what extent such technological advancements exert specific effects on their health-related behaviors, particularly proactive health behaviors, remains an area that warrants further investigation and clarification. Based on this, the research hypothesis proposed in this paper is:

H1a: Basic digital inclusion has a significant positive impact on the proactive health behavior of older adults.

The Relationship Between Intermediate Digital Inclusion and Proactive Health Behavior

Older adults at the intermediate digital inclusion stage have already undergone a period of adjustment with the digital society compared to those at the basic stage. They are no longer content with simple communication and information acquisition through the Internet and start to gain confidence and validate their worth via online activities. Building on the digital skills acquired during basic digital inclusion, elderly individuals at this stage can use smartphones to download various apps for entertainment and leisure activities, present themselves online, express their thoughts, and forward, comment on, or post information they approve of. These behaviors satisfy their need for respect and recognition. Older adults at this stage are defined as belonging to the intermediate digital inclusion group. Based on data from the China Longitudinal Aging Social Survey (CLASS) 2020, further research has shown that Internet use can influence proactive health management among older adults through chat interactions, online entertainment, and information acquisition (Wu & Yi, 2024). Previous research has demonstrated that enhanced engagement of older adults in digital society may exert certain influences on their health management. However, existing studies have yet to achieve sufficient depth in examining the extent of their digital inclusion and, more specifically, its impact on proactive health behaviors. Based on this, the research hypothesis proposed in this paper is:

H1b: Intermediate digital inclusion has a significant positive impact on the proactive health behavior of older adults.

The Relationship Between Advanced Digital Inclusion and Proactive Health Behavior

Older adults at the advanced digital inclusion stage have a thorough understanding of digital media such as the Internet. They generally possess higher digital literacy among their peers and have mastered most of the digital skills required for daily life. Therefore, they pursue higher-level goals of self-actualization. After fulfilling the first two needs, they try to explore their potential by obtaining higher-level satisfaction through the Internet. At this stage, elderly individuals can engage in online transactions, participate in most advanced Internet activities, and even initiate Internet-related events or create group chats to drive further digital inclusion among other elderly populations. These behaviors fulfill their need for self-actualization. Older adults who have reached this stage are defined as belonging to the advanced digital inclusion group. As the proportion of the Internet and digital technology in the lives of older adults continues to increase, some scholars believe that digital inclusion cannot be simply summarized by the use of physical network equipment. The willingness of older adults to change their living conditions and meet their needs through digital technology should also be considered as manifestations of digital inclusion (H. Wang, 2021). For older adults themselves, good health is undoubtedly a prerequisite for both, as it provides the capital to improve their quality of life. Passive medical care alone can only maintain the current quality of life, so proactive health behavior is required to enhance it. The difference between proactive health behavior and traditional passive medical care lies in its holistic view and preventive concept of treating diseases before they occur. It emphasizes leveraging technology to continuously mobilize residents’ subjective initiative, thereby influencing their health awareness, habits, and even the environment, ultimately achieving the goal of universal health (He & Wang, 2023). The majority of existing studies continue to concentrate on analyzing the impacts of initial digital engagement on health behaviors among older adults, inadvertently overlooking the subpopulation that has attained relatively high levels of digital inclusion. The interrelationship between their advanced digital proficiency and conscious proactive health behaviors also warrants systematic scholarly examination. Based on this, the research hypothesis proposed in this paper is:

H1c: Advanced digital inclusion has a significant positive impact on the proactive health behavior of older adults.

The Relationship Between Self-Efficacy and Proactive Health Behavior

Self-efficacy influences numerous aspects of an individual’s behavior, such as the acquisition of new capabilities and behaviors, as well as the breaking of existing behavioral constraints. It also impacts individuals’ psychological and emotional activities, reflecting two pathways through which self-efficacy influences health outcomes: through behavioral practices and physiological stress responses. Thus, self-efficacy is closely related to the health behaviors of older adults. As an essential component of individual subjective initiative, self-efficacy explains and guides people’s health actions, and it is often used as a predictor of health behavior change and maintenance in policies aimed at altering people’s health behavior plans (Johnson et al., 2021; Lu et al., 2024). This aligns with the proactive health concept.

Some scholars have found that self-efficacy is the variable most closely related to healthy exercise behavior, and related research has confirmed the positive correlation between them (C. Liu, 2021). Subsequent studies by scholars have also shown that an increase in self-efficacy can prompt older adults to engage in physical exercise proactively, working in tandem with social support to produce a chained effect (Xie et al., 2023). Most scholars have reached consistent conclusions in this regard. Furthermore, studies have indicated that subjective support such as self-efficacy is a dynamic factor that has the most lasting impact on the health behaviors of older adults (Warner et al., 2011). While a small number of studies present contradictory findings, this paper aims to empirically examine and validate the relationship between these two constructs through rigorous methodological approaches. Based on this, this paper proposes the research hypothesis:

H2: Self-efficacy has a significant positive impact on the proactive health behavior of older adults.

The Relationship Among Digital Inclusion, Self-Efficacy, and Proactive Health Behavior

Some psychological studies have found that the Internet can influence people’s consciousness, attitudes, and motivations, further affecting their behaviors (Naveenan et al., 2024). Existing research presents dual perspectives on the societal implications of internet technology for older adults. On one hand, it is argued that excessive reliance on digital platforms may engender adverse psychosocial outcomes, including digital dependence and heightened vulnerability to social isolation, particularly among seniors with limited technological literacy. Such phenomena, as documented in studies examining the digital divide, often exacerbate existing disparities in social connectedness (Diana et al., 2025; Gutiérrez et al., 2024). Conversely, a more expansive body of literature emphasizes that the Internet provides a vast amount of health information, offering older adults more information channels and influencing their perceptions and attitudes toward their own health to a certain extent, thereby significantly enhancing their self-efficacy (Luo, 2024). This viewpoint aligns with the “Internet Gain Effect Theory” proposed by foreign scholars. Further research by scholars has shown that digital inclusion has a significant positive effect on enhancing self-efficacy by influencing the psychological perceptions and self-evaluations of older adults. The higher the degree of digital skill mastery among older adults, the stronger their health confidence and sense of self-accomplishment (Xie et al., 2023), making them more willing to engage in proactive health behaviors. However, existing research has only demonstrated the mediating role of factors such as social support in the relationship between digital inclusion and proactive health behaviors among older adults. There remains a significant knowledge gap regarding whether and how self-efficacy explicitly exerts mediating effects in this context. Based on this, this paper proposes the research hypothesis:

H3: Self-efficacy plays a mediating role in the positive impact of digital inclusion on the proactive health behavior of older adults.

Research Design

Data Sources and Screening

The data used in this paper are sourced from the Chinese General Social Survey (CGSS). The 2017 survey data are currently among the rare datasets in China that can represent national internet usage-related situations, exhibiting strong authority and reliability. The survey employed a cross-sectional survey design featuring single-occasion data collection, where all measurements were obtained through a one-time administration of the questionnaire. Meanwhile, this dataset contains information pertinent to this study, including digital inclusion, proactive health behaviors, and self-efficacy. With a broad sample coverage, it systematically reflects the characteristics of internet use, health status, and self-evaluation among older adults, and is frequently utilized in research on the internet and health. Based on the research object and design, the present study employed a two-phase data curation process. Initially, we filtered the original dataset to retain responses from individuals aged 60 years and above who completed the internet-related modules. Subsequently, we applied systematic data cleaning procedures: Missing values in critical variables were imputed using multiple imputation techniques, while records containing excessive missing data (>30% incomplete) were excluded to maintain analytical rigor. This methodology yielded a final analytic sample of 1,778 valid cases, representing older adults who provided complete responses to both internet usage and health-related modules in the 2017 wave of the survey.

Variable Description

Dependent Variable

The dependent variable in this paper is the proactive health behavior of older adults. Based on previous research on proactive health and considering the availability of data in the 2017 CGSS, integrating the three widely recognized attributes of proactive health—preventive, interventional, and participatory—and their shared proactive premise, corresponding dimensions were matched and extracted from the database. The following three dimensions are selected to measure the proactive health behavior of older adults: engaging in physical exercise proactively, actively participating in social activities, and proactive improvement of living environments. Corresponding questions in the questionnaire include: “Do you often participate in physical exercise in your spare time?”; “How frequently do you participate in leisure groups or sports organization activities?”; “Do you often engage in social activities in your spare time?”; “How often do you clean your home?”; “How often do you repair and maintain your home inside and outside?”; Responses are scored from 1 (never) to 5 (very frequently) or from 1 (never done) to 7 (almost every day) based on frequency (Wu & Yi, 2024). Higher scores indicate a higher degree of proactive health behavior among older adults. The specific scale design is presented in Table 1.

Table 1.

Explanation of Core Variables.

Variable	Dimension	Item	Range of values
Dependent variable
Proactive health behavior	Physical exercise	In the past year, have you often engaged in the following activities during your free time (participating in physical exercise)?	[1, 5]
	Social activities	In the past 12 months, how frequently have you participated in the following group activities (leisure, sports groups)?	[1, 5]
	Social activities	In the past year, have you often done the following during your free time (socializing)?	[1, 5]
	Living environment	How often do you perform the following household chores (cleaning the house)?	[1, 7]
	Living environment	How often do you perform the following household chores (repairs and maintenance inside and outside the house)?	[1, 7]
Independent variable
Digital inclusion level	Basic inclusion	Do you know how to use a computer to open websites?	[5, 25]
		Frequency of internet use for social activities (communicating with others via QQ, WeChat, email, etc.)
		Frequency of internet use for leisure and entertainment (e.g., games, music, videos)
		Frequency of internet use for information acquisition (e.g., searching for information, browsing news, etc.)
		Frequency of completing the task of browsing existing information on the internet
	Intermediate inclusion	Do you know how to download and install apps on a smartphone?	[7, 35]
		Agreement with the statement: “It’s not difficult for me to find the information I want online”
		Agreement with the statement: “When I want to express my thoughts online, I know how to do it”
		Frequency of internet use for self-expression (using WeChat Moments, QQ Space, etc. as platforms for self-expression and sharing feelings)
		Frequency of completing the task of forwarding existing information on the internet
		Frequency of completing the task of commenting on existing information on the internet
		Frequency of completing the task of publishing information on the internet
	Advanced inclusion	In the past 12 months, have you used WeChat Pay?	[6, 30]
		In the past 12 months, have you used Alipay’s mobile payment function?
		Frequency of internet use for online action (defending one’s rights or advocating for others online)
		Frequency of internet use for commercial transactions (e.g., online transfers, payments, online shopping, etc.)
		Have you ever created a QQ group as the group admin?
		Have you ever created a WeChat group as the group admin?
Mediating variable
Self-efficacy	Psychological stress	In the past 4 weeks, how often have you felt that difficulties were piling up so much that you couldn’t overcome them?	[1, 5]
	Perceived goal achievement	To what extent does the following statement apply to you: “I can easily achieve my goals?”	[1, 7]
	Life satisfaction	Overall, how satisfied are you with your current life situation?	[1, 7]
Control variable
Basic factors	Gender	Gender	[0, 1]
	Age	Calculated from the item “Your birth year”	[0, 1]
	Household registration	Your current household registration status	[0, 1]
	Education level	Your highest level of education completed	[1, 3]
	Marital status	Your current marital status	[0, 1]
	Children	Number of children you have	[0, 2]
	Family economic status	Your family’s economic status in your location	[1, 3]

Independent Variable

The independent variable in this paper is the level of digital inclusion among older adults. Based on previous research on digital inclusion, building upon the assessment of digital technology usage in daily life—specifically considering metrics such as frequency of use, proficiency level, technological dependency, and the range and sophistication of acquired skills—and integrating theoretical frameworks including Maslow’s Hierarchy of Needs and Katz’s Uses and Gratifications Theory, this paper chooses to examine the level of digital inclusion among older adults from three dimensions: “basic inclusion,”“intermediate inclusion,” and “advanced inclusion.” There are a total of 18 related questions in the questionnaire, and all questions are reassigned a score of 1 to 5, with higher scores indicating a higher degree of usage for the related questions. Scores for the three dimensions are calculated separately, and a total score for the level of digital inclusion is derived. Higher total scores indicate a deeper level of digital inclusion among older adults (C. Liu, 2021). The specific scale design is presented in Table 1.

Mediating Variable

The mediating variable in this paper is the self-efficacy of older adults. Based on previous research and relevant findings from the 2017 CGSS, this paper chooses to study the self-efficacy of older adults from three dimensions: psychological stress, perceived goal achievement, and life satisfaction. Corresponding questions in the questionnaire include: “How often do you have the following feeling: feeling that difficulties are accumulating to the point that you cannot overcome them?”; “To what extent does the following statement apply to your actual situation: I can easily achieve my goals”; “Overall, how satisfied are you with your current overall living situation?”; Responses are scored from 1 (very frequently) to 5 (never) or from 1 (completely disagree) to 7 (completely agree) based on frequency. Higher scores indicate higher self-efficacy among older adults (Jiang & Zong, 2024b). The specific scale design is presented in Table 1.

This study conducted a reliability analysis using the internal consistency reliability method, specifically Cronbach’s α coefficient. Through statistical analysis of the selected data, the measurement instrument was calculated to have a Cronbach’s α of .788, demonstrating high internal consistency and satisfactory reliability. To ensure the structural validity of the employed questionnaire, a Kaiser-Meyer-Olkin (KMO) test was performed to evaluate the inter-item correlations and factor analysis appropriateness of the measurement tool. The resulting KMO value of 0.830 indicated adequate sampling adequacy for conducting factor analysis.

Model Construction

Existing studies indicate that digital inclusion among older adults influences their self-efficacy through psychological perceptions and self-evaluative mechanisms, while fluctuations in self-efficacy subsequently affect their engagement in proactive health behaviors such as physical exercise and health management. Drawing on the digital divide theory, disparities in technological access and utilization capabilities amplify social inequalities, particularly by impeding older adults’ acquisition of health information and utilization of healthcare services, thereby exacerbating health disparities. Based on this theoretical framework, the present study develops a structural model to investigate the direct effects of digital inclusion on proactive health behaviors in older adults, while simultaneously examining the mediating role of self-efficacy in this relational dynamic. Hierarchical regression estimation methods can be utilized to examine the direct and mediation effects among variables. Therefore, this study employs hierarchical regression estimation methods to test the direct effect of digital inclusion on proactive health behaviors among older adults, the direct effect of self-efficacy on proactive health behaviors, and the mediation effect of self-efficacy in the process of digital inclusion influencing proactive health behaviors among older adults. The specific models are as follows:

Y = c_{0} + c_{1} X + μ_{1}

(1)

M = a_{0} + a_{1} X + μ_{2}

(2)

Y = c_{0}^{'} + c_{1}^{'} X + b_{1} M + μ_{3}

(3)

In Equations 1, 2, and 3, Y represents proactive health behaviors among older adults; X represents the degree of digital inclusion among older adults; M represents self-efficacy among older adults; $μ_{1}$ , $μ_{2}$ and $μ_{3}$ are error terms.

The first step is to test the direct effect, which involves determining whether the coefficient $c_{1}$ in Equation 1 is significant. $c_{1}$ represents the direct effect (the direct influence of digital inclusion on proactive health behaviors among older adults) without considering mediation.

The second step is to test the mediation effect. The coefficient $a_{1}$ in Equation 2 represents the first half of the mediation effect (the influence of digital inclusion on self-efficacy among older adults), and the coefficient $b_{1}$ in Equation 3 represents the second half of the mediation effect (the influence of self-efficacy on proactive health behaviors). To establish mediation, it is necessary not only to assess whether the coefficients in both halves are individually significant but also to verify the significance of the product of coefficients $a_{1} b_{1}$ .

While hierarchical regression estimation methods can be used to test mediation effects, they may suffer from issues of low statistical power. Therefore, this study further examines the mediation role of self-efficacy in the process by which digital inclusion among older adults influences proactive health behaviors using the Bootstrap method.

Empirical Results and Analysis

Common Method Bias Test

Given that the data from the Chinese General Social Survey (CGSS) used in this study may be influenced by subjective factors of the respondents, in order to ensure the validity of the research conclusions, this study employs the Harman’s single-factor test to assess common method bias in the data (Aguirre-Urreta & Hu, 2019). The variance explanation rate of the first exploratory factor before rotation is 18.42%, which is below the threshold of 40%. This indicates that there is no serious common method bias in this study.

Descriptive Analysis and Correlation Analysis

Basic descriptive analyses of digital inclusion, proactive health behaviors, and self-efficacy among older adults yield the following results: The overall score for basic digital inclusion among older adults is 16.30 out of 25, the score for intermediate digital inclusion is 17.61 out of 35, and the score for advanced digital inclusion is 9.42 out of 30. The overall score for digital inclusion is 43.33 out of 90. It can be observed that the scores for basic and intermediate digital inclusion among older adults are significantly higher than those for advanced digital inclusion, suggesting that while older adults have had a certain level of engagement with the digital society, their digital inclusion primarily remains at the basic and intermediate stages, with overall digital inclusion being only at a moderate level. The overall score for proactive health behaviors among older adults is 16.15, slightly above the median, indicating a moderate level of proactive health. The overall score for self-efficacy among older adults is 14.13, slightly below the median, and when compared with previous research (Bian et al., 2020), it suggests that self-efficacy among older adults is relatively low. Specific details are visualized in Figure 1.

Figure 1.

Description analysis of digital inclusion, self-efficacy, and active health behavior.

After conducting deviation standardization processing on the dimensions of each variable, this study employed the equal weighting averaging method for calculations. Subsequent correlation analysis was performed on the results, with particular attention to examining the correlation coefficients among the three variables. The analysis revealed that the correlation coefficients between the three stages of digital inclusion and self-efficacy among older adults are .162 (p < .01), .129 (p < .01), and .115 (p < .01), respectively, indicating significant positive correlations. The correlation coefficients between the three stages of digital inclusion and proactive health behaviors are .123 (p < .01), .146 (p < .01), and .185 (p < .01), respectively, also indicating significant positive correlations. The correlation coefficient between self-efficacy and proactive health behaviors is .070 (p < .01), indicating a positive correlation as well. The specific details are shown in Table 2.

Table 2.

Description and Correlation Analysis of Digital Inclusion, Self-Efficacy, and Active Health Behavior.

Variable	Total score	Proactive health behavior	Self-efficacy	Basic digital inclusion	Intermediate digital inclusion	Advanced digital inclusion
Proactive health behavior	16.15	1
Self-efficacy	14.13	.070**	1
Basic digital inclusion	16.30	.123**	.162**	1
Intermediate digital inclusion	17.61	.146**	.129**	.664**	1
Advanced digital inclusion	9.42	.185**	.115**	.388**	.492**	1

indicate significance at the 1% statistical levels.

Hypothesis Testing

In this study, hierarchical regression estimation methods were employed, with heteroscedasticity-robust standard errors used for all regressions, and multicollinearity diagnostics conducted on all regression models. The results indicate that the variance inflation factors (VIFs) are all well below the critical threshold of 5, suggesting that the degree of correlation and collinearity among the variables is within a reasonable range, and there are no serious multicollinearity issues.

The specific results of the hierarchical regression tests are presented in Table 3. Specifically, Model 1 tests the relationship between the dependent variable and the control variables. Model 2 examines the impact of three stages of digital inclusion among older adults on proactive health behaviors. Model 3 assesses the influence of self-efficacy on proactive health behaviors. Models 1, 2, 3, and 4 collectively test the direct effects of digital inclusion and self-efficacy among older adults on proactive health behaviors. Model 5 examines the relationship between the mediator variables and the control variables. Model 6 tests the impact of the three stages of digital inclusion among older adults on self-efficacy. Model 8 investigates the combined effects of digital inclusion and self-efficacy among older adults on proactive health behaviors. Models 5, 7, and 8 collectively test the mediating role of self-efficacy in the process by which digital inclusion among older adults influences proactive health behaviors.

Table 3.

Hierarchical Regression Test Results.

Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
	Dependent				Mediator			Dependent
	Proactive health behavior				Self-efficacy			Proactive health behavior
Gender	−1.151**	−1.152**	−1.159**	−1.149**	0.018	0.019	0.019	−1.154**
Age	−0.929**	−0.876**	−0.947**	−0.916**	0.044	0.043	0.046	−0.928**
Education level	0.273	0.157	0.273	0.200	−0.000	−0.015	−0.013	0.203
Household registration	0.148	0.195	0.177	0.230	−0.070	−0.055	−0.056	0.245
Family economic status	0.273	0.220	0.241	0.237	0.078**	0.073**	0.072*	0.217
Marital status	−0.801	−0.886	−0.805	−0.912	0.009	−0.013	−0.010	−0.909
Children	−0.441**	−0.360*	−0.425**	−0.383*	−0.037	−0.022	−0.027	−0.375*
Basic digital inclusion		0.179				0.110**
Intermediate digital inclusion		0.374*				0.011
Advanced digital inclusion		0.851**				0.087*
Total digital inclusion				1.148**			0.193**	1.096**
Self-efficacy			0.415**					0.269*
Constant	17.250**	14.618**	15.332**	14.673**	4.627**	4.136**	4.192**	13.547**
R ²	.048	.077	.053	.074	.005	.034	.031	.076

and * indicate significance at the 1% and 5% statistical levels, respectively.

From model 1 in Table 3, it can be observed that the gender, age, and number of children of older adults have significant negative correlations with their proactive health behaviors, while education level, household registration, family economic status, and marital status have insignificant impacts on proactive health behaviors. According to the results of Model 2, the positive impact of basic digital inclusion among older adults on their proactive health behaviors is not significant, rejecting Hypothesis H1a. However, intermediate and advanced digital inclusion have significant positive effects on proactive health behaviors, validating Hypotheses H1b and H1c. This indicates that when older adults’ digital proficiency reaches the mid-level or high-level, it can promote their engagement in proactive health behaviors. The results of model 3 show that self-efficacy also has a significant positive impact on proactive health behaviors, confirming Hypothesis H2. This suggests that an increase in older adults’ sense of self-identity can facilitate their proactive health behaviors. Model 5 reveals that the family economic status of older adults has a significant positive impact on their self-efficacy. Analysis suggests that older adults from families with stronger economic power have more opportunities to access high-end devices and products. They are also more likely to achieve their needs and goals in life, facing relatively lower life pressures, which contributes to an increase in self-efficacy. In contrast, the gender, age, education level, household registration, marital status, and number of children of older adults have insignificant impacts on their self-efficacy. The results of model 6 indicate that both initial and high-level digital inclusion among older adults have significant positive effects on their self-efficacy. This suggests that during the initial stage of digital inclusion when older adults first encounter the digital society and the high-level stage when they fully master digital skills, they can clearly perceive that their goals in daily life are more easily achieved due to digitization, thereby enhancing their self-efficacy. However, older adults at the mid-level stage of digital inclusion, being in a relatively transitional state, can use digital devices well but mostly for entertainment, even leading to addictions such as playing video games and watching short videos. Therefore, the improvement in self-efficacy is not significant at this stage. Comparing the results of model 8 with model 4, it can be found that after incorporating self-efficacy, the regression coefficient β of the impact of digital inclusion among older adults on their proactive health behaviors decreases, preliminarily proving that self-efficacy plays a mediating role in the influence of digital inclusion on proactive health behaviors among older adults, thus validating Hypothesis H3.

Mediation Effect Testing

As an integrated construct, digital inclusion encompasses the three aforementioned sub-dimensions, which are not isolated entities but rather interrelated and progressively layered. To prevent methodological limitations arising from conducting mediation analyses on each sub-dimension separately—such as overlooking holistic effects, fragmented results, overlooked factor interactions, and insufficient sample sizes for sub-dimensional mediation testing—the study opts to employ the overarching concept of digital inclusion as the independent variable in the mediation model. Table 4 presents the results of further testing the mediation effect of self-efficacy in the process of digital inclusion among older adults influencing proactive health behaviors using the Bootstrap mediation effect testing method.

Table 4.

Bootstrap Test for Mediating Effects.

Path	Total effect	Direct effect	Indirect effect	z-value	Test conclusion	Mediated effect (%)
Digital inclusion → self-efficacy → proactive health behavior	1.148**	1.096**	0.052	13.063	Partial mediation	4.529

indicate significance at the 1% statistical levels.

The results indicate that the mediation effect of self-efficacy in the process of digital inclusion among older adults influencing proactive health behaviors is significant, further validating Hypothesis H3. Specifically, the total effect was 1.148, with a direct effect of digital inclusion on proactive health behaviors measuring 1.096. The effect of digital inclusion on self-efficacy registered at 0.193, while the effect of self-efficacy on proactive health behaviors stood at 0.269. The product of these two effects, representing the indirect effect of self-efficacy in the relationship between digital inclusion and proactive health behaviors among older adults, was 0.052. This indirect effect accounted for 4.529% of the total effect, with a 95% confidence interval that excluded zero, indicating that the mediation effect reached statistical significance.

Discussions

This paper systematically analyzes the impact of digital inclusion on proactive health behaviors among older adults using hierarchical regression estimation methods and data from the Chinese General Social Survey (CGSS) 2017 for individuals over 60 years old. Furthermore, it examines the mediating role of self-efficacy in the relationship between digital inclusion and proactive health behaviors among older adults through the Bootstrap mediation effect testing method. The relevant research results obtained in this paper are as follows:

The relationship between digital inclusion and proactive health behaviors

The research results indicate that initial digital inclusion among older adults has an insignificant impact on their proactive health behaviors, but mid-level and high-level digital inclusion have significant positive effects. This suggests that the higher the degree of digital inclusion among older adults, the more frequent their proactive health behaviors are. The reasons for this result can be explained from the three stages of digital inclusion among older adults.

Firstly, older adults at the basic stage of digital inclusion, although satisfying their needs for love and belonging by establishing certain connections with relatives, friends, and surrounding people through digital means, generally receive too simple or basic information. They tend to focus on trivial matters in their daily lives through digital means and have not yet been exposed to much information or promotion related to their own health management. Therefore, they have fewer opportunities to further attempt proactive health behaviors. Moreover, older adults who have been at this stage for a long time are mostly older and cared for by their children. Their use of digital devices such as mobile phones is mostly limited to contacting their children. At the same time, their children are more inclined to accompany them for passive medical treatment when health issues arise, rather than encouraging them to learn more digital skills or proactive health-related knowledge.

Secondly, older adults at the intermediate stage of digital inclusion, after satisfying their need for respect through digital platforms and receiving certain respect, recognition, and positive evaluations, gain confidence in their own health status. They can obtain health and other various information from more channels and share simple information through various chat software or social platforms. This allows them to access promotions related to proactive health and understand the potential benefits of proactive health behaviors on their living conditions, thereby attempting more proactive health behaviors and being more willing to share their health information with others. This is crucial information in the process of proactive health, such as monitoring their health status through wearable intelligent devices and sharing it with the community or doctors for health prediction and other proactive health behaviors.

Furthermore, older adults at the advanced stage of digital inclusion have satisfied their higher-level self-actualization needs, gaining a strong sense of achievement through digital technologies such as the Internet. They are proficient in using various digital means and have a high degree of trust in them. They can perform most proactive health behaviors through important interactive channels such as online health platforms. Even some of these older adults can spontaneously call on other older adults around them to participate in proactive health activities or organize related activities for exercise and other proactive health behaviors to maintain health and improve their lives.

2. The relationship between self-efficacy and proactive health behaviors

The research results show that the self-efficacy of older adults has a significant positive impact on their proactive health behaviors, indicating that the stronger the self-efficacy of older adults, the higher the frequency of their proactive health behaviors. Possible reasons include:

Higher self-efficacy represents higher recognition of one’s abilities, lower life stress, and a happier life. These favorable conditions enable older adults to have more ability and willingness to make changes and efforts to their health status, increasing the possibility of them persisting in proactive health behaviors. Older adults with higher self-efficacy are more likely to gain a sense of achievement and happiness from the health improvements brought by proactive health behaviors and convert them into motivation. Because of this, these older adults are not only satisfied with the temporary stability of health brought by passive medical treatment but also pursue the prediction of proactive health behaviors and the improvement of overall health status.

3. The mediating role of self-efficacy in the impact of digital inclusion on proactive health behaviors among older adults

According to the research results, self-efficacy plays a significant mediating role in the process of digital inclusion affecting proactive health behaviors among older adults. However, the test results show that it is a partial mediator with an overall low degree of influence. The reasons can be explained in two parts:

The reason why self-efficacy can play a mediating role in the process of digital inclusion affecting proactive health behaviors among older adults may be that the deeper the degree of digital inclusion among older adults, the more opportunities they have to obtain health-related information through the Internet and other channels. More information and more communication and information sharing with others will deepen their cognition of their own health status. Specifically, they will have a clearer understanding of their potential health problems and will not experience excessive pressure when their health fluctuates. They will have more confidence to solve existing problems, and their self-efficacy will be improved. Therefore, older adults will be more willing to explore and attempt proactive health behaviors such as active monitoring and prevention of foreseeable health problems.

Regarding the partial mediating role of self-efficacy in the impact of digital inclusion on proactive health behaviors among older adults, it may be because digital inclusion not only affects proactive health behaviors by influencing the self-efficacy of older adults. The degree of digital inclusion among older adults may also affect external factors such as their participation in society. External social support may also have a certain impact on the willingness and related behaviors of older adults toward proactive health. Additionally, it must be acknowledged that third-order digital inclusion may exert varying degrees of influence on older adults’ self-efficacy. When combined with the previously observed differential impacts of third-order digital inclusion on older adults’ proactive health behaviors, these variations could further affect the mediation outcomes.

Despite the generally low R-squared values across various models, most variables exhibited significant positive correlations (p < .01) in different models, and numerous studies indicated a positive influence of digital inclusion on proactive health behaviors among older adults. Therefore, it is recommended to implement observational studies on proactive health behaviors among elderly individuals at varying stages of digital inclusion, with a focus on examining the distinct impacts of the three digital inclusion phases on the frequency and other dimensions of their proactive health behaviors.

In summary, the aforementioned research findings have broadened the application of theories related to digital inclusion and the digital divide in fields such as gerontology and health, providing a pathway for better integrating digital and internet-related theories with sociology.

Conclusions and Recommendations

This study concludes the following: First, digital inclusion among older adults demonstrates a certain promotional effect on their proactive health behaviors, contingent upon their achieving an intermediate level of digital inclusion or higher. Therefore, to enhance older adults’ proactive health behaviors, it is particularly important to elevate their digital proficiency. Second, digital inclusion can promote proactive health behaviors by influencing older adults’ self-efficacy. This underscores the need to pay special attention to the concurrent changes in both mentality and capability during older adults’ acquisition of digital skills, with timely implementation of corresponding support measures. By ensuring simultaneous improvements in digital competency and self-efficacy, older adults can better develop proactive health awareness and engage in proactive health behaviors.

Based on this, the following recommendations are proposed to enhance the level of proactive health behaviors among older adults through digital inclusion:

Conduct education and training on digital health tools for older adults to improve their awareness and ability to use these tools. This will not only help older adults further accelerate their digital inclusion process but also enhance their self-efficacy, laying the foundation for proactive health behaviors such as wearing intelligent health devices to intervene in their health. The government can cooperate with grassroots organizations to organize relevant training courses, and communities can hold health lectures to assist older adults in familiarizing themselves with digital health tools.

Build a social support network for digital health management among older adults, including support from families, communities, and medical institutions. Encourage older adults to share health information and health experiences among themselves, provide mutual support, and establish incentive mechanisms to encourage their active participation in digital health management. This will enhance their self-efficacy and increase their identification with and enthusiasm for proactive health behaviors.

Optimize digital health tools with age-appropriate updates to make them easier for older adults to understand and operate, reducing the difficulty of use and increasing acceptance of digital health tools. At the same time, provide personalized customization services. Due to the varying health conditions of older adults, digital health tools can be customized according to their different needs and preferences, enhancing their potential for digital inclusion and willingness to use, further improving their self-efficacy, and better engaging in proactive health-related behaviors.

To address the differential impacts of digital inclusion stages on proactive health behaviors, interventions must be stratified according to seniors’ technological proficiency.

For older adults at the basic digital inclusion stage, community health centers should implement low-threshold training programs focusing on fundamental e-health literacy—such as navigating telehealth apps, accessing credible health information via simplified interfaces, and using emergency alert functions. Concurrently, local governments could subsidize “digital health starter kits” (pre-configured tablets with one-touch access to essential services) and establish intergenerational mentorship schemes pairing seniors with youth volunteers for sustained skill reinforcement.

For those achieving intermediate inclusion, primary care systems should integrate gamified health modules into popular platforms (e.g., WeChat mini-programs) that convert step-tracking into community challenges, while social workers facilitate peer-led “digital health circles” where members co-create content on chronic disease management. Municipal policies could incentivize such engagement through “digital health vouchers” redeemable for preventive screenings upon completing behavioral milestones.

At the advanced inclusion stage, policy should leverage their expertise through “silver tech ambassador” initiatives: Training digitally fluent elders to co-design age-friendly app interfaces with developers, moderate online health communities, and organize hybrid (online-offline) preventive health campaigns. Health authorities could further institutionalize this by certifying senior-led digital health cooperatives eligible for public funding, thereby scaling peer-to-peer modeling while addressing the self-efficacy mediation pathway identified in our findings.

Critically, all interventions require embedded feedback mechanisms—such as community health clinics mapping participants’ digital tiers via rapid assessment tools—to dynamically adjust support intensity and ensure equitable resource allocation across the inclusion continuum.

Limitations

Despite having achieved certain breakthroughs by employing the most up-to-date data available to investigate the relationship between digital inclusion of older adults and their proactive health behaviors, this study acknowledges inherent limitations stemming from data constraints and methodological boundaries. These restrictions have resulted in a relatively circumscribed research scope and an oversimplified empirical framework. Future research endeavors should incorporate multidimensional empirical analyses to facilitate more substantial theoretical and practical advancements in this domain.

The current study utilizes data from the 2017 wave of the Chinese General Social Survey (CGSS), which remains one of the most authoritative and reliable datasets containing measures related to digital inclusion and proactive health behaviors. However, a limitation arises from the temporal gap between 2017 and the present, during which digital technologies have undergone rapid evolution and societal penetration. This interval raises potential concerns regarding the dataset’s capacity to fully capture contemporary digital-health dynamics. In addition, the exclusive selection of samples from China implies that cultural and infrastructural differences may limit the applicability of the research findings to other regions. Future research would benefit from incorporating updated databases that reflect more recent technological advancements and behavioral patterns, thereby addressing this limitation and enhancing the generalizability of findings.

Footnotes

ORCID iDs

Zhe Lu

XingLong Xu

Ethical Considerations

The data utilized in this study were sourced from the Chinese General Social Survey (CGSS). The CGSS project adhered to the ethical guidelines stipulated in the Declaration of Helsinki and received formal approval from the institutional review board prior to data collection. As this study exclusively employs de-identified, publicly available datasets, no additional ethical review was required.

Consent to Participate

Informed consent procedures for the CGSS survey were rigorously implemented. All participants provided voluntary, written consent after being fully informed of the study’s objectives, data usage protocols, anonymity safeguards, and their right to withdraw at any stage. Given the secondary nature of this analysis and the public accessibility of the data, no further consent was solicited from individual participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Natural Science Foundation of China General Project “Research on the Driving Pathways and Long-Term Mechanisms of Urban and Rural Residents' Proactive Insurance Enrollment Behavior Based on Policy Experience Utility (72574086),” the Jiangsu Provincial Social Science Project “Research on the Mechanisms and Policies of Digital Intelligence Empowering the Integration of Medical and Preventive Care at the Grassroots Level in Jiangsu Under the Context of Multimorbidity (24GLB023),” the Jiangsu University Scientific Research Project “Research on the Impact of Digital Inclusion on Proactive Health Behaviors Among Older Adults (Y23C057),” and the National Natural Science Foundation of China General Project “Research on the Realization Mechanism and Enhancement Strategies of Grassroots Healthcare Service Value Based on Residents' Proactive Utilization (72274081).”

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 datasets employed by the researchers in the analysis of the study is available by the authors upon reasonable request. We confirmed that informed consent was obtained from all participants.

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