The digital divide (DD) of older people is widening due to the rapid adoption of information and communication technologies (ICT) and the effects of COVID-19. This study aims to explore the impact of urban-rural, gender, and health inequalities on the DD of older people. First, the survey collected data on the use of ICT and the DD of older people (N = 624) in urban-rural communities. Then, an evaluation index system for the DD of older people was constructed. The DD indices for older people and the weights of indicators can be calculated via the entropy weight method (EWM). Next, linear and hierarchical regressions were applied to analyze the disparities in the DD of older people in urban-rural areas, gender, and health. Finally, the heterogeneity of the access divide (AD), use divide (UD), and knowledge divide (KD) were analyzed. The results indicated that the DD and KD of older people in rural communities are significantly higher than those in urban communities, mainly because of the poor infrastructure conditions in rural areas. Moreover, the migration of youth to work also reduced opportunities for intergenerational support. DD, AD, and UD were higher for older females than for older males because older females are more restricted by access to and use of digital technology. In addition, the poorer the health of older people is, the higher the AD and UD. Focusing on urban-rural, gender, and health disparities helps bridge the DD among older people and is critical to the sustainable development of digitally inclusive aging societies.

1. Introduction

The digital divide (DD) in the context of aging populations has gained considerable attention, particularly as intelligent services are rapidly integrated into daily life [1, 2]. The Chinese Government’s Work Report (2021) identifies the potential obstacles that information and communication technologies (ICT) pose to the daily lives of older people and highlights the importance of intelligent services adapted to the demands of older people. By the end of 2024, the population of individuals aged 60 and above in China had surpassed 310.3 million, constituting 22.0% of the nation’s total population. Moreover, the number of internet users over 60 has reached 156.2 million, accounting for only 14.1% of the total users. However, 49.7% of the older people had not yet joined the internet. To bridge DD, China has promoted smart home care services. However, previous studies have found that there are some problems such as a lack of age-appropriate design, and poor implementation effect [3, 4]. Despite these efforts, there are still clear gaps in China’s digital inclusion policies compared to international frameworks. For example, Project Wire Up in Singapore ensures that older people can effectively participate in the digital economy by providing personalized digital skills training and access to digital tools [5]. Similarly, the Society 5.0 strategy integrates artificial intelligence and Internet of Things to support an aging population through smart healthcare and digital public services in Japan [6]. In contrast, China’s policies mainly focus on infrastructure and service delivery but lack comprehensive measures to improve digital literacy and ensure sustainable, long-term participation of older people. Given rapid digitalization and the aging population in China, this gap poses a critical challenge to social equity and public service delivery. Therefore, there is an urgent need to address DD in older people.

The development of ICT is intended to be convenient and beneficial for people [7, 8]. However, DD poses a significant barrier to the use of ICT, involving technical, physical, psychological, and cognitive aspects [9–11]. Older people are a more vulnerable social group than younger individuals and adults regarding the use of ICT, particularly in media usage, web-based patient portals, and telemedicine [12–17]. Furthermore, COVID-19 has further exacerbated the DD of older people, with most forms of activity shifting to distance learning, telemedicine, and virtual activities [18, 19]. Therefore, the importance of mastering digital skills has been highlighted. However, the inability to use the internet to obtain information makes the healthcare of older people very inconvenient. The use of telemedicine can also widen the gap between technological haves and have-nots [20]. A lack of online social activities has a very serious impact on the mental health of older people, which may lead to autism, anxiety, and depression [21, 22]. In addition, mental health problems can affect the attitudes and motivation of older people for digital engagement, thus further widening disparities in health outcomes and social inequalities. Hence, the issue of DD in older people deserves special attention.

Academic exploration of DD traces back to the concept of internet access in the early 1990s, and the access gap is known as the “first-level DD” or access divide (AD) [1, 23]. With the deepening of the research, factors such as internet use skills and content accessibility are added, and more attention is given to the gaps in technical means, use power, and use skills. These usage gaps are called “second-level DD” or use divide (UD) [1, 9, 24]. Some studies have focused on the impact of psychological factors such as psychological barriers and network anxiety on UD [9, 25–27]. To construct a more comprehensive concept of the DD, the consequences and outcomes of ICT use were considered [28]. Differences in skills and knowledge about internet use affect outcomes and benefits for individuals, and the knowledge gap can lead to a “third-level DD” or knowledge divide (KD) when benefits are not available to all individuals [26, 29]. Dividing the DD according to the three dimensions of AD, UD, and KD has been supported by many studies [30]. Nonetheless, the definition of DD remains ambiguous, and its empirical measurement in China lacks a clear framework, especially for the older population [31]. Therefore, the relative importance of each dimension of the DD in this context needs to be further explored.

The growing DD has given rise to digital exclusion, a social phenomenon closely related to unequal access to and utilization of information [32]. Digital exclusion can be manifested as sequential or compounded, and its intensity is closely linked to the depth of the DD. Existing studies point to demographic, economic, and social factors (e.g., age, health, education, income, digital literacy, institution, and infrastructure) as the main drivers of DD [33–36]. In addition, some studies have indicated that the attitude of older people toward digital technology has also become an important cause of DD [37]. AD and UD are highly correlated with demographic characteristics, such as age and income [38], but the causes of KD are unclear [39, 40]. Some scholars have suggested focusing on cultural and political influences [28, 41]. Studies on older people have emphasized that government policies, digital education, age-appropriate design, and emotional support are critical to bridging the divide [42–48]. However, a comprehensive assessment of DD in older people and its influencing factors is still lacking.

Recent studies have further revealed the problem and impact of DD on older people in China. However, there are significant differences in perspectives and conclusions. Cultural capital has been shown to indirectly enhance the health of older people by narrowing the DD [49, 50], and its effect is more pronounced in urban men [49] and in the rural group of men aged 60–69 years [50]. Academic views on the relationship between digital technology use and health are still divided. Some studies have argued that the positive impact of digital technology use on health relies on access rather than skill level [51], whereas other studies have emphasized the determinative role of digital skills and technology anxiety in DD [52, 53]. In terms of policy response, digital inclusion policies are of high quality but suffer from a lack of implementation details [54], and older people, for example, face technical barriers to specific operational steps in the case of online car rental, highlighting the need for policies to be improved in conjunction with scenario-based improvements. In addition, social factors play an important role in the DD of older people. Intergenerational support types [53] and social exclusion [54] have been confirmed to have significant effects on the digital media use and psychological DD of older people. More importantly, in the selection of evaluation indicators of DD, existing studies have an obvious “dimensional imbalance” problem, and most of them focus on the investigation of AD [49, 51] and UD [51, 52, 55] and pay insufficient attention to KD. A review of 50 studies on the DD further corroborates the issue [30]. Therefore, the evaluation of DD is one-sided and limited.

Based on the above these gaps, this study aims to construct an evaluation index system for the DD of older people and explore the disparities and inequalities of DD between urban-rural, gender, and health. The core questions are as follows: (a) How can the strength of the DD of older people be evaluated? (b) Is there a disparity in the DD concerning urban-rural, gender, and health inequities? (c) Are there significant disparities in the second dimension concerning the DD (AD, UD, and KD) of older people? To solve these problems, an evaluation index system for the DD of older people is constructed based on J. Van Dijk’s digital exclusion theory [32]. The entropy weight method (EWM) is subsequently used to calculate the contribution of each index and the value of the DD of older people. In addition, the impacts of urban-rural, gender, and health inequalities on the DD of older people were obtained via linear regression analysis. Finally, the influence mechanism in the second dimension of the DD of older people is explored through hierarchical regression analysis.

The main contributions of this study are summarized as follows. First, a systematic method for assessing the DD of older people is proposed. Second, the disparities and inequalities in the second dimension of the DD of older people are explored. Third, the DD of older females and rural older people is highlighted, and providing technical and social support to promote digital inclusion is recommended. In conclusion, this study has implications for identifying the disparities in the DD and solving the problems of the DD.

The rest of this paper is structured as follows: Section 2 describes the participants, relevant variables and indicators, data analysis methods, and reliability and validity tests. Section 3 constructs the evaluation index system for the DD of older people and presents descriptive statistics for each variable. Moreover, linear and hierarchical regression analyses and robustness tests are performed for essential variables. A comprehensive discussion and analysis of the findings are presented in Section 4. Additionally, the research limitations and future directions are given. Finally, Section 5 summarizes some conclusions.

2. Methods

2.1. Participants

Field investigations were used to select the sample to get information about the subjects. To ensure the sample’s representativeness and relevance to the objectives, participants were required to be at least 60 years old, and gender balance was maintained as far as possible in sample sizes. The reasons for choosing the age limit of older people as over 60 years old are as follows: (1) the official age limit for older people by the World Health Organization (WHO) is 60 years old; (2) China’s Law on the Protection of the Rights and Interests of Older People stipulates that the starting age standard for older people is 60 years old; (3) at the turning point of China’s retirement age of 60, this group is more representative in terms of demand for digital services. The respondents are mainly concentrated in the southwest region of China, including Sichuan, Chongqing, Guizhou, and Kunming. The sampling was from September 2022 to January 2023, and the strategy involved two phases to ensure the representation and validity of the collected data. During the presurvey phase, 25 valid questionnaires were distributed and collected by using convenience sampling. The questionnaire’s content was adjusted based on the subjects’ completion and feedback. In the formal investigation stage, volunteers were recruited to conduct field investigations and complete questionnaires, and a total of 687 questionnaires were collected by stratified sampling. After data cleaning and outlier elimination, 63 invalid questionnaires were excluded, and 624 valid questionnaires were recovered, with a recovery rate of 90.83%.

2.2. Variables and Indicators

This study constructs a DD assessment indicator system based on Van Dijk’s theoretical framework of DD and digital exclusion [30, 32, 56]. The theory states that digital exclusion is not a dichotomy limited to access to digital technologies but rather a multidimensional, dynamic, and evolutionary process that encompasses access opportunities, skills of use, and the application of knowledge. As one of the highly influential theoretical paradigms in the field of DD research, the framework provides important theoretical support and analytical perspectives for an in-depth understanding of the complex composition and multidimensional characteristics of the DD. Therefore, this study categorizes DD among older people into three core dimensions: AD, UD, and KD. This dimensional division aims to provide a more comprehensive and in-depth analysis of DD among older Chinese people. Figure 1 shows the framework of related indicators.

Details are in the caption following the image — **Figure 1**
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Evaluation index system for the digital divide of older people.

AD reflects the digital exclusion experienced by older people at the physical or material level due to a lack of access to digital technologies. As J. Van Dijk’s theory emphasizes, digital access is a prerequisite for subsequent digital use. Therefore, based on this theoretical foundation, ‘network access’ (AD₁) and ‘digital device access’ (AD₂) were selected as key indicators to quantify the extent of the AD among older people in this study. Specifically, AD₁ is measured by “the main way your smart devices access the internet.” The ways include wired and wireless access, and if no access is counted as 0. AD₂ is measured by “the type of smart devices you use,” and smart devices include smartphones, computers, smart bands, smart televisions, and smart homes (e.g., robot vacuum cleaners), with one point for each choice. If no smart devices are used, the value is 0.

UD refers to the digital exclusion of older people who lack the necessary skills and autonomy to use digital technologies. Even if individuals have access to digital technologies, if they do not acquire effective use skills, it is still difficult for them to fully utilize the functions and services provided by digital technologies, thus creating a UD. To assess the extent of the usage divide effectively among older people, this study quantified the two dimensions of ‘digital skills’ (UD₁) and ‘internet use’ (UD₂). UD₁ is measured by “some of the types of actions you have access to.” Types include 10 daily applications such as public transportation, mobile payment, telemedicine, online shopping, daily payment, social entertainment, and information retrieval, with one point for each choice. UD₂ is measured by “the assessment about the speed of access to information via the internet.” The selections include slow (one point), general (two points), and fast (three points).

KD exemplifies the gaps and imbalances that exist in older people’s access to information, knowledge, and benefits from the use of digital technologies. According to the theory, if individuals only stay at the level of access and use of digital technology but fail to use it effectively to improve their knowledge level and quality of life, there will be a KD. Based on the digital life of older people, this study constructs quantitative indicators of the KD in two dimensions: ‘digital security’ (KD₁) and ‘digital payment’ (KD₂). KD₁ is measured by “I can identify the authenticity of information on the internet.” The selections include strongly disagree (one point), general (two points), and strongly agree (three points). KD₂ is measured by “I check my digital payment account information,” and choices include never (one point), sometimes (two points), and often (three points).

The dependent variable is the DD of older people. The independent variables include urban-rural status, gender, and health. Among them, urban-rural and gender are dichotomous variables. Health is measured by participants self-reporting their health on a five-point scale, from particularly unhealthy (one point), relatively unhealthy (two points), general (three points), relatively healthy (four points), and particularly healthy (five points). The control variables include age, living, and education. Education is divided into five stages: primary school (one point), secondary school (two points), universities, and colleges (three points), bachelor (four points), and graduate (five points). Linear and hierarchical regressions are used to explain the significance of the effect of independent variables on the DD of older people. Table 1 presents the results of the descriptive statistical analysis.

Table 1. Descriptive statistics (N = 624).

Variables and indicators	Mean	Std. dev.	Min	Max
Urban-rural (1 = urban)	0.697	0.460	0	1
Gender (1 = male)	0.486	0.500	0	1
Health	3.010	1.545	1	5
Age	72.950	9.373	60	94
Living (1 = living with others)	0.659	0.475	0	1
Education	2.115	1.139	1	5
AD₁	0.880	0.747	0	2
AD₂	1.548	1.263	0	5
UD₁	2.274	1.538	1	10
UD₂	1.370	1.187	0	3
KD₁	1.515	0.501	1	3
KD₂	1.971	0.844	1	3

2.3. Determining the Weights of Indicators

The EWM is used to analyze and determine the weights of indicators. This is an objective and scientific weight determination method based on information entropy theory. In this method, the importance of the indicators can be accurately obtained by the information content of the indicators to avoid bias caused by subjective evaluation. In the calculation process, assume that there are m objects and n indicators. Then, the evaluation matrix

can be defined as

(1)

The calculation steps are as follows:

•
Step 1: Obtain the standardized evaluation matrix .
(2)

(3)
where dd_{_j} = {dd_1j, dd_2j, …, dd_mj}. All indicators show positive results, and there is no need to address negative indicators in this study.
•
Step 2: Calculate the weight of the i − th object under the j − th indicator and get the proportion p_ij by
(4)
•
Step 3: Calculate the information entropy e_j.
(5)
where 1/(ln n) is a non-negative constant, and 0 ≤ e_j ≤ 1.
•
Step 4: Calculate the information divergence d_j.
(6)
•
Step 5: Determine the weight w_j of the j − th indicator.
(7)

2.4. Data Analysis

All the data were analyzed via SPSS 21.0 and R 4.3.3 software. First, the characteristics of the participants are shown via descriptive analysis. Reliability and factor analysis are used to test the reliability and structural validity of indicators. Next, the EWM is employed to determine the weights of each indicator and evaluate the DD indices for older people. Finally, the degree of correlation between variables is measured via correlation analysis. Linear regression is used to analyze the relationships between the DD of older people and independent variables. To verify the reliability of the results and identify the disparities in the primary indicators, the robustness test and heterogeneity analysis are carried out via hierarchical regression. Linear regression modeling is rapid and can effectively analyze the relationships among variables in small data problems, and the regression results have good interpretability. Hierarchical regression divides the sample into different groups, which are then subjected to independent regression analysis to better analyze the disparities between groups.

3. Results

3.1. Reliability and Validity Test

The internal consistency coefficient alpha, exploratory factor analysis, and confirmatory factor analysis are used to test reliability and validity. The results are shown in Table 2. The Cronbach’s α coefficient is 0.919 (> 0.9), and the values of AD, UD, and KD are 0.875, 0.924, and 0.893, respectively, indicating that the internal consistency reliability is good. Exploratory factor analysis was used to evaluate the scale. The Kaiser–Meyer–Olkin value is 0.931 (> 0.9), and the Bartlett sphericity test value is less than 0.001, indicating that the scale is very effective. Confirmatory factor analysis results show that the standardized load coefficient of each indicator is greater than 0.774. The average variance extraction (AVE) of each variable is greater than 0.572 (> 0.5). Moreover, the composite reliability (CR) of each indicator is greater than 0.801 (> 0.7), indicating that the extraction degree of the measurement index within the factor is excellent [57, 58]. Therefore, the reliability and validity tests have passed.

Table 2. Results of the reliability and validity tests.

Primary indicators	Secondary indicators	Loading	Cronbach’s α	AVE	CR
AD	AD₁	0.865	0.875	0.735	0.838
AD	AD₂	0.835	0.875	0.735	0.838

UD	UD₁	0.796	0.924	0.711	0.834
UD	UD₂	0.893	0.924	0.711	0.834

KD	KD₁	0.774	0.893	0.572	0.801
KD	KD₂	0.807	0.893	0.572	0.801

3.2. Weights of Indicators

This study includes 624 objects and six indicators, which cover three aspects of the DD of older people. The EWM is used to calculate the weights of indicators in each aspect. The weights, entropy, and divergence of all the indicators are shown in Table 3.

Table 3. Results of the weights, entropy, and divergence.

First indicators	Second indicators	Weights	Entropy	Divergence
AD	AD₁	0.141	0.913	0.087
AD	AD₂	0.146	0.909	0.091

UD	UD₁	0.197	0.878	0.122
UD	UD₂	0.154	0.904	0.096

KD	KD₁	0.206	0.872	0.128
KD	KD₂	0.156	0.903	0.097

3.3. Measurement and Correlation Analysis

According to the evaluation index system and weight assignment, the DD index for the i − th older people is expressed as

(8)

The larger the value of DD_i is, the higher the DD for the i − th older people. Table 4 shows the distributions of the DD indices from different aspects. Among them, age, living, and education are treated as control variables for downward categorization. This study reveals that the mean DD index in rural areas is 0.140 higher than that in urban areas. Moreover, the mean DD index for older females is 0.096 higher than that for older males. In addition, advanced older people tend to have higher DD, and older people living with others exhibit weaker DD.

Table 4. Classification summary of the digital divide evaluation.

Variables		N	%	Mean of DD	Std. dev.
Urban-rural	Rural	189	30.288	0.728	0.189
Urban-rural	Urban	435	69.712	0.588	0.196

Gender	Female	321	51.442	0.677	0.200
Gender	Male	303	48.558	0.581	0.197

Health	Particularly unhealthy	168	26.923	0.626	0.217
	Relatively unhealthy	84	13.462	0.688	0.184
	Commonly	99	15.865	0.627	0.179
	Relatively healthy	120	19.231	0.666	0.198
	Particularly healthy	153	24.519	0.577	0.213

Age	1 (60 to 69)	261	41.827	0.596	0.191
	2 (70 to 79)	201	32.212	0.617	0.221
	3 (80 to 89)	132	21.154	0.692	0.201
	4 (90 to 94)	30	4.807	0.749	0.114

Living	Alone	204	39.692	0.695	0.217
Living	Living with others	420	67.308	0.597	0.190

Education	Primary school	234	37.500	0.661	0.210
	Secondary school	201	32.212	0.607	0.215
	Universities and colleges	93	14.904	0.690	0.172
	Bachelor	75	12.019	0.545	0.176
	Graduate	21	3.365	0.557	0.141

Table 5 lists the correlation coefficients of the key measured variables. The Pearson correlation coefficient matrix shows that the DD is significantly correlated with urban-rural, gender, age, living, and education. However, DD is not correlated with health status, so further analysis is needed.

Table 5. Results of the correlation analysis.

	DD	Urban-rural	Gender	Health	Age	Living	Education
DD	1.000
Urban-rural	−0.316^∗∗∗	1.000
Gender	−0.236^∗∗∗	0.138^∗∗	1.000
Health	−0.079	−0.016	0.012	1.000
Age	0.212^∗∗∗	−0.093	−0.126^∗	−0.131^∗	1.000
Living	−0.227^∗∗∗	0.187^∗∗∗	0.131^∗	−0.015	0.002	1.000
Education	−0.132^∗	0.122^∗	0.079	−0.012	−0.152^∗∗	0.037	1.000

Note:^∗∗∗, ^∗∗, and ^∗ represent significance levels of 0.1%, 1%, and 5%, respectively.

Table 6 presents the test results for urban-rural, gender, and health disparities in the DD of older people. Stepwise regression is used to ensure the stability of the estimation results. Model 1 examines the influence of urban-rural areas on the DD without adding control variables. The results show that the DD indices of older people in urban areas are significantly lower than those in rural areas (β = −0.140, p < 0.001). Model 2 shows a gender difference in the DD of older people, and the DD indices for older females are significantly greater than those for older males (β = −0.096, p < 0.001). Model 3 examines the effect of health status on DD without adding control variables, and the results reveal no significant effect (β = −0.010, p > 0.05). Model 4 tested the effects of urban-rural, gender, and health status on DD in older people when control variables were added, and the effect of health status was still insignificant. A noticeable increase in R² indicates that the model fits well. The DD of older people in rural areas was significantly higher than that in urban areas was (β = −0.108, p < 0.001), and the DD of older females was significantly greater than that of older males (β = −0.064, p < 0.01). These results are consistent with those of previous studies, which revealed that urban-rural and gender are important factors influencing DD, especially for older people [59]. Among the control variables, age had a significant positive effect on the DD of older people, and the DD of advanced older people was significantly greater than that of younger people. The DD of older people living with others was significantly lower than that of older people living alone. The results further suggested that advanced older people and those living alone should be the focus of attention.

Table 6. Results of regression analysis.

Variable	Model 1	Model 2	Model 3	Model 4
Urban-rural	−0.140^∗∗∗ (0.029)			−0.108^∗∗∗ (0.029)
Gender		−0.096^∗∗∗ (0.028)		−0.064^∗∗ (0.024)
Health			−0.010 (0.009)	−0.009 (0.008)
Age				0.034^∗∗ (0.013)
Living				−0.068^∗∗ (0.026)
Education				−0.011 (0.012)
Constant	0.728^∗∗∗ (0.024)	0.677^∗∗∗ (0.019)	0.651^∗∗∗ (0.023)	0.758^∗∗∗ (0.053)
N	624	624	624	624
Control	No	No	No	Yes
R²	0.100	0.056	0.006	0.196
F	22.856^∗∗∗	12.153^∗∗∗	1.293	8.154^∗∗∗

Note:^∗∗∗, ^∗∗, and ^∗ represent significance levels of 0.1%, 1%, and 5%, respectively.

3.4. Robustness Test and Heterogeneity Analysis

To distinguish the disparities in the effects of gender and urban-rural on the DD of older people, linear regression is performed with urban-rural, and older males-older females as the control group. The results of the heterogeneity analysis are shown in Table 7. Model 5 shows that DD in females is significantly higher than that in males among the urban older people (β = −0.070, p < 0.01). Age and living are also significant influencing factors. While Model 6 is not significant (p > 0.05). Moreover, there is no significant difference in the DD between older males and older females in rural. Model 7 shows that the DD of older males in urban is significantly lower than that in rural (β = −0.122, p < 0.001). Age, living, and education are also essential factors. Model 8 shows the significant disparities between urban and rural in the DD of older females. The DD of older females in rural is significantly higher than that in urban (β = −0.103, p < 0.01).

Table 7. Results of the heterogeneity analysis.

Variable	Urban-rural		Gender
Variable	Model 5 Urban	Model 6 Rural	Model 7 Male	Model 8 Female
Urban-rural			−0.122^∗∗∗ (0.036)	−0.103^∗∗ (0.039)
Gender	−0.070^∗∗ (0.027)	−0.055 (0.053)
Health	−0.008 (0.010)	−0.013 (0.018)	−0.012 (0.012)	−0.008 (0.012)
Age	0.049^∗∗∗ (0.013)	0.005 (0.027)	0.054^∗∗ (0.019)	0.017 (0.021)
Living	−0.076^∗∗ (0.029)	−0.057 (0.049)	−0.077^∗ (0.039)	−0.074^∗ (0.036)
Education	−0.012 (0.014)	−0.008 (0.022)	−0.026^∗ (0.012)	0.005 (0.017)
Constant	0.633^∗∗∗ (0.063)	0.810^∗∗∗ (0.093)	0.715^∗∗∗ (0.076)	0.759^∗∗∗ (0.075)
N	435	189	303	321
Control	Yes	Yes	Yes	Yes
R²	0.135	0.073	0.197	0.139
F	4.323	0.904	4.659	3.262
P	0.001	0.485	0.001	0.009

Note:^∗∗∗, ^∗∗, and ^∗ represent significance levels of 0.1%, 1%, and 5%, respectively.

A heterogeneity analysis is conducted to observe the influences of urban-rural, gender, and health factors on the three types of DD. The analysis results are listed in Table 8. Model 9 shows that gender (β = −0.016, p < 0.01) and health (β = −0.008, p < 0.01) significantly influence the AD of older people. There is no difference in the effects of urban and rural areas on the AD (p > 0.05). However, it should be noted that age (β = 0.022, p < 0.01), living (β = −0.021, p < 0.05), and education (β = −0.015, p < 0.001) significantly affect AD. Older people living with others or with high levels of education have lower DD. Moreover, there are significant inequalities in access to technology between older males and older females, and AD is higher in the latter [60]. Model 10 shows that gender (β = −0.024, p < 0.01) and health (β = −0.011, p < 0.001) significantly influence UD. The UD of older females is greater than that of older males, and the empirical results are consistent with those of Model 2, Model 4, and Model 5. Living (β = −0.020, p < 0.05) and education (β = −0.013, p < 0.001) are significantly negatively correlated with UD, whereas age (β = 0.021, p < 0.001) is significantly positively correlated with UD. The results are consistent with those of Model 9. Older females living alone or with lower levels of health or education are likely to have higher UD. Model 11 shows that urban-rural (β = −0.026, p < 0.01) and education (β = 0.015, p < 0.05) significantly affect the KD. The KD of older people in rural areas is higher than that in urban areas, and the higher the education level is, the higher the KD. This phenomenon may be due to disparities in barriers to independent learning. Urban areas provide better digital education for older people; for example, universities for the aged offer courses about smartphone use.

Table 8. Digital divide hierarchical regression.

Variable	Model 9 AD	Model 10 UD	Model 11 KD
Urban-rural	−0.012 (0.012)	−0.014 (0.011)	−0.026^∗∗ (0.010)
Gender	−0.016^∗∗ (0.006)	−0.024^∗∗ (0.008)	−0.030 (0.018)
Health	−0.008^∗∗ (0.003)	−0.011^∗∗∗ (0.003)	0.007 (0.006)
Age	0.022^∗∗∗ (0.006)	0.021^∗∗∗ (0.006)	−0.001 (0.010)
Living	−0.021^∗ (0.010)	−0.020^∗ (0.010)	−0.028 (0.019)
Education	−0.015^∗∗∗ (0.004)	−0.013^∗∗∗ (0.005)	0.015^∗ (0.007)
Constant	0.214^∗∗∗ (0.022)	0.296^∗∗∗ (0.021)	0.187^∗∗∗ (0.036)
N	624	624	624
Control	Yes	Yes	Yes
R²	0.186	0.223	0.064
F	7.680^∗∗∗	9.603^∗∗∗	2.290^∗

Note:^∗∗∗, ^∗∗, and ^∗ represent significance levels of 0.1%, 1%, and 5%, respectively.

4. Discussion

This study examines the impact of urban-rural, gender, and health on the DD of older people. The EWM is employed to determine the weights of indicators about the DD, and correlation analysis is used to analyze the influence of independent variables on the DD. Moreover, linear regression and hierarchical regression are used to analyze heterogeneity. The analysis results provide a possible explanation for the disparities in the DD. This study is conducive to quickly distinguishing the vulnerable groups in the DD and providing targeted support. Therefore, the results are an essential contribution to the knowledge base in this field.

First, there are significant disparities in the DD of older people. Among them, the DD of older people in rural is higher than that in urban, and the DD of older females is higher than that of older males. These results are consistent with previous analyses in Western countries [61–64]. Internet penetration in urban is significantly higher than in rural, and ICT infrastructure is responsible for the difference in the DD of older people [19, 49, 65, 66]. Rural youth migrating for work also reduces older people’s access to digital intergenerational support. Meanwhile, in urban the DD of older females is higher than that of older males [67]. Gender significantly impacts older people’s access to smart devices, with older males using them more frequently than older females. In general, older females have lower incomes and education than older males, which means higher barriers to internet access and fewer opportunities to acquire digital skills. The unequal status of females in the internet world was a projection and amplification of the gender order of inequality in the real world, and structural sexism may be the main reason [68–70]. Although disparities in the effect of health on the DD are not significantly reflected, it is believed that health is a key factor in affecting the DD. The decline in visual and joint movement, as well as cognitive and other abilities, profoundly impacts the use and learning of digital devices. A deepening DD could lead to inequalities in health outcomes for older people.

Second, the effects of urban-rural, gender, and health on the AD, UD, and KD are different. Urban-rural has no significant influence on AD, while gender and health significantly affect AD. Digital access is an essential prerequisite for digital use, and it is a necessary step for individuals to develop digital skills [27]. Compared with males, females are deprived of more access rights and must take on more family responsibilities [71, 72]. The degree of health dramatically limits the opportunity for older people to use smart devices, especially for those with health problems and disabilities. The UD of older females is higher than that of older males, and the relationship between health and the UD is also significantly negative. These findings remained consistent with those for the AD. Additionally, older people with poorer health conditions may face more significant physical and cognitive challenges, making it more difficult to learn and apply new digital technologies. The KD of older people in rural is higher than that in urban. At the same time, the higher the education level, the higher the KD of older people. The reason may be that older people in rural have fewer opportunities and resources for digital education. Older people with less education may have lower digital awareness and be more vulnerable to online fraud and security risks. The above results demonstrate the inequality and disparities of different types of DD in urban-rural, gender, and health. This study is of great significance for identifying the disparities in the DD.

In addition, the deep application of artificial intelligence technology provides the possibility of facilitation and precision services for older people. It should be noted that this may also exacerbate the complexity of the DD for older people, forming the “algorithm divide.” On the one hand, the urban-rural difference may be further widened by the uneven distribution of intelligent infrastructure. For example, the lagging coverage of artificial intelligence assistants and smart care devices in rural areas may make older people more vulnerable in health management. On the other hand, the impact of health status on DD may be amplified. For older people with visual or motor impairments, the lack of interaction design due to technology will make DD more prominent. Only through the collaborative innovation of the humanization of technology, the precision of policy, and the normalization of education can the development of an age-friendly digital society be promoted.

4.1. Implications

Based on the findings, three recommendations are made to strengthen digital inclusion for older people. First, governments and relevant institutions should develop more comprehensive and targeted policies for digital inclusion. For example, infrastructure improvements and technical training can be provided to help older people better use digital technologies. Set up digital tech help stations in community centers where volunteers or professionals provide one-on-one tech support to older people. Second, the training and educational resources of rural areas and females should be increased to improve their digital skills. Regular training activities are carried out in rural areas to ensure that older people in remote areas have access to learning opportunities. Finally, there is a difference in the DD experience between baby boomers and the silent generation. Develop individualized support programs for older people according to the specific needs of different generations. Therefore, governments should pay attention to this phenomenon and provide targeted support to older people based on the technological adaptation process.

4.2. Limitations and Future Directions

There are some limitations to this study. First, the findings may be less applicable to countries with low internet penetration and only apply to digitally developed regions like China. Second, the analysis data come from the self-report of older people, which may have potential sampling bias, so it is necessary to add objective information to explore a more scientific evaluation method of the DD. Third, this study is exploratory and may lack a dynamic observational process to identify important factors influencing changes in DD. Finally, the linear relationship between DD and health is mainly analyzed, but the relationship may be nonlinear.

Future studies may expand the coverage of research samples, include more indicators, and adopt expert evaluation and self-report to build a more scientific evaluation index system. It is possible to explore in depth the impact of health conditions on older people’s exposure to and adoption of digital technologies, as well as the bridging effect of digital health services on DD. In addition, future studies may use panel data and nonlinear models to validate and explore other inequalities in the DD of older people.

5. Conclusion

This study revealed the pervasive inequality of DD and disparities in urban-rural areas, gender, and health. Rural older people and older females are vulnerable groups in terms of digital access and use. Health inequalities compound the challenges faced by DD. These findings indicate that the challenges faced by older people include declining physical health and lower educational opportunities. Moreover, older people face various technological barriers that exacerbate feelings of isolation and reduce happiness. The decline in physical function, coupled with the prevalence of diseases, often leads to a trend of living alone. This style of living often exacerbates loneliness and social exclusion in older people. Therefore, digital literacy and skills training should encompass the entire life cycle of an individual. Additionally, it can foster a digitally inclusive environment to mitigate the adverse impact of DD on older people. In summary, bridging the DD of older people requires the joint efforts of all stakeholders, including governments, social organizations, and communities.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This work was supported by the National Social Science Fund of China (23XSH008), the Sichuan University Fundamental Research Funds for the Central Universities of China (2023SRPA07), the Project of Sichuan Province Social Science Key Research Base “Research center of undertakings for the aged” (XJLL2024002), and the Project of Gerontological Society of Sichuan Province (24SCLN009).

Open Research

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

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The Digital Divide of Older People in Communities: Urban-Rural, Gender, and Health Disparities and Inequities

Abstract

1. Introduction