This paper is in the following e-collection/theme issue:

Demographics of Users, Social & Digital Divide (724) eHealth Literacy / Digital Literacy (379) Use and User Demographics of mHealth (330) Information Seeking, Information Needs (458) Internet Access and Digital Technology Use in an Elderly Population (198) Geriatrics (18) Social Media in Aging (21)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49688, first published .
Mediating Effects of Information Access on Internet Use and Multidimensional Health Among Middle-Aged and Older Adults: Nationwide Cross-Sectional Study

Mediating Effects of Information Access on Internet Use and Multidimensional Health Among Middle-Aged and Older Adults: Nationwide Cross-Sectional Study

Mediating Effects of Information Access on Internet Use and Multidimensional Health Among Middle-Aged and Older Adults: Nationwide Cross-Sectional Study

Authors of this article:

Liping Fu1, 2 Author Orcid Image ;   Caiping Liu1, 2 Author Orcid Image ;   Yongqing Dong1 Author Orcid Image ;   Xiaodong Ma2 Author Orcid Image ;   Quanling Cai1, 2 Author Orcid Image ;   Dongli Li1, 3 Author Orcid Image ;   Kaisheng Di1, 2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1College of Management and Economics, Tianjin University, Tianjin, China

    2College of Politics and Public Administration, Qinghai Minzu University, Xining, China

    3College of Chunming, Hainan University, Haikou, China

    *these authors contributed equally

    Corresponding Author:

    Caiping Liu, PhD

    College of Management and Economics

    Tianjin University

    Weijin Road Campus No. 92

    Xuefu Street, Nankai District

    Tianjin, 300072

    China

    Phone: 86 13997138522

    Email: liucaiping@tju.edu.cn


    Background: With the exacerbation of population aging, the health issues of middle-aged and older adults have increasingly become a focus of attention. The widespread use of the internet has created conditions for promoting the health of this demographic. However, little is known about the effects of information access in promoting the relationship between internet use and the health of middle-aged and older adults.

    Objective: This study aims to examine the relationship between internet use and multidimensional health in middle-aged and older adults, as well as the mediating effect of information access. Moreover, this study will explore the relationship between other dimensions of internet use (purposes and frequency) and health.

    Methods: Data were sourced from the China General Social Survey conducted in 2018. Health outcomes, including self-rated, physical, and mental health, were assessed using the 5-level self-rated health scale, the 5-level basic activities of daily living scale, and the 5-level depression scale, respectively. The ordinal logistic regression model was used to examine the relationship between internet use and health among middle-aged and older adults. Additionally, the Karlson-Holm-Breen decomposition method was used to examine the mediation effect of information access. To address endogeneity issues, the two-stage least squares approach was applied.

    Results: In our sample, nearly half (n=3036, 46.3%) of the respondents use the internet. Regression analyses revealed that internet use was positively associated with self-rated health (odds ratio [OR] 1.55, 95% CI 1.39-1.74; P<.001), physical health (OR 1.39, 95% CI 1.25-1.56; P<.001), and mental health (OR 1.33, 95% CI 1.19-1.49; P<.001) of middle-aged and older adults. Various dimensions of internet use positively contribute to health. In addition, information access significantly mediated the relationship between internet use and self-rated health (β=.28, 95% CI 0.23-0.32), physical health (β=.40, 95% CI 0.35-0.45), and mental health (β=.16, 95% CI 0.11-0.20). Furthermore, there were significant differences in the relationship between internet use and health among advantaged and disadvantaged groups.

    Conclusions: The study showed that different dimensions of internet use are associated with better self-rated health, better physical health, and better mental health in middle-aged and older adults. Information access mediates the relationship between internet use and health. This result emphasizes the significance of promoting internet access as a means to enhance the health of middle-aged and older adults in China.

    J Med Internet Res 2024;26:e49688

    doi:10.2196/49688

    Keywords

    internet use; health; middle-aged and older adults; information access; mediation analysis 


    Background

    With the extension of human life expectancy and the decline in birth rates, the global trend of population aging is becoming increasingly pronounced [1]. This phenomenon poses significant challenges to health care and social security systems, making the promotion of healthy aging crucial for socioeconomic development and realizing global health governance [2]. Health is the foundation for enhancing life expectancy, improving quality of life, and achieving healthy aging [3].

    In today’s world, with the advancement of information technology, the internet is integrating into various aspects of society, profoundly changing people’s ways of production and living. According to Digital 2022: Global Digital Reports, as of January 2022, global internet users have reached 4.95 billion, with an internet penetration rate of 62.5% [4]. Meanwhile, as of December 2022, China’s internet users have surpassed 1.067 billion, including 153 million older people internet users [5]. The rapid spread and development of the internet have had a significant influence on the field of health. For instance, it enables access to health information [6], intervenes in health behaviors [7], monitors public health [8], formulates health policies [9], and provides medical services [10].

    Evidence is mixed on the influence of internet use on health in middle-aged and older adults. One perspective suggests that the internet positively influences health and is considered an effective tool for improving health status [11-13]. Evidence indicates that middle-aged and older adults view the internet as a communication platform for expressing emotions and maintaining social relationships [14]. The internet has established novel social connections, expanding the external social networks of older individuals and helping them regain social opportunities [15]. Furthermore, the internet has strengthened intergenerational family bonds [16]. It has substituted face-to-face communication methods, reconstructing a web-based family space for older individuals. Adult children can provide telecare services for older adults, thereby partially fulfilling the expectations of intimacy that older individuals have toward their children [17]. In summary, the internet has created a new social space for older adults not limited by physical boundaries, significantly enhancing their overall physical and mental health and positively influencing intergenerational relationships [18,19]. In addition, the internet’s web-based consultation and telemedicine have enhanced the accessibility of health care services for middle-aged and older adults [20,21]. However, another perspective suggests that internet use may adversely affect health [22-24]. Research from the Stanford Institute for the Quantitative Study of Society and the Kennedy School of Government has revealed that internet use does not reduce social isolation [25,26]. It may even be associated with a higher risk of severe social isolation in certain situations [27]. Similarly, some studies indicate that excessive internet use can lead to internet addiction, causing distress to individuals’ psychological, social, and occupational well-being [28]. Other research also suggests that excessive internet use can lead to reduced rest time, which may harm the health of middle-aged and older adults [29].

    A possible mediator between internet use and health association is information access. The health and wellness model supports this viewpoint, indicating that individuals with health consciousness can better understand their health status, including disease risks, preventative measures, and treatment options, through the acquisition of accurate health information [30,31]. This contributes to heightened health awareness and encourages people to engage in proactive health behaviors such as regular checkups, healthy eating, and exercise [32]. Additionally, information retrieval can provide psychological support and coping strategies, aiding in managing stress and anxiety, and thereby improving mental health [33,34]. In summary, information access plays a key role in maintaining and enhancing health conditions, facilitating individuals in effectively managing and preserving their health [35,36]. However, Chinese scholars have not yet given sufficient consideration to interpreting the influence of internet use on the health of middle-aged and older adults from an information perspective. Research on this issue is not only important for understanding the factors influencing the health of middle-aged and older adults but also for gaining insights into the socioeconomic consequences of internet use.

    Similarly, the literature debates the relationship between internet use and social inequality. Some studies conclude that during the early and middle stages of internet adoption, advantaged groups with higher income, higher education, and more excellent technological proficiency are likelier to access the internet and enjoy its benefits [37-39]. Conversely, disadvantaged groups faced information disadvantages due to limitations in cognitive abilities and lower digital literacy, contributing to the digital divide [40,41]. However, other studies have concluded that the widespread availability of the internet has lowered the barriers to entry, increasing the possibility for all social groups, especially disadvantaged groups, to access information resources and technology, thus promoting upward mobility among society’s marginalized populations [42,43]. According to the law of diminishing marginal utility, the benefits of internet use are limited in populations with substantial existing information resources. Instead, the benefits of the internet may be more concentrated among disadvantaged groups, leading to digital dividends [44,45]. So, whether the internet’s effect on health brings about a digital divide or digital dividend and whether it narrows health inequalities between advantaged and disadvantaged groups or widens disparities among different populations needs to be empirically tested in this study.

    There are several significant research gaps in the existing literature. First, although there is increasing interest in the potential influence of the internet on health [46], there is still a relative shortage of in-depth research explicitly targeting middle-aged and older adults in China. More comprehensive studies are required to investigate the relationship between multidimensional internet use and multidimensional health. Furthermore, limited existing literature categorizes middle-aged and older adults into advantaged and disadvantaged groups based on multiple dimensions within the same sample, and investigates the relationship between internet use and their health. In the diverse social context of China, it is essential to conduct in-depth research to investigate whether there are differences in the influence of internet use on the health of advantaged and disadvantaged groups. Such research can help us better understand the potential of the internet to reduce or widen health inequalities, thus providing a scientific basis for formulating more equitable and effective public health policies. In summary, there is significant room for further exploration in studying internet use and its relationship with health among middle-aged and older adults in China. More in-depth and comprehensive exploration is needed to address the current research gaps in this field.

    Objectives

    Based on the analysis provided in the background section, this study aims to analyze the relationship between internet use and the multidimensional health of middle-aged and older adults using a nationally representative survey in China. Additionally, it investigates the relationship between the purposes and frequency of internet use and multidimensional health. Furthermore, this study extends the existing literature by examining the mediating effects of information access on the association between information use and multidimensional health. Moreover, the study will categorize middle-aged and older adults into advantaged and disadvantaged groups based on 4 criteria: income level, educational attainment, urban-rural classification, and region. Subsequently, this study will unveil potential heterogeneous influences for different subgroups within the advantaged and disadvantaged groups.


    Data and Sample

    The China General Social Survey (CGSS) is China’s earliest, nationally representative, and comprehensive ongoing academic survey initiative [47]. The project uses a stratified, multistage probability proportional to size method to collect data, comprehensively gathering data at multiple levels including society, communities, households, and individuals. It covers 28 provinces, autonomous regions, and municipalities across China [48]. This study used nationwide survey data from CGSS2018. Following the age classification standards of the World Health Organization and considering the situation in China, individuals aged 45 years and older were defined as middle-aged and older adults [49]. Among the participants in CGSS2018, a total of 12,787 individuals were surveyed. After excluding 4344 individuals younger than 45 years, and additionally removing cases with missing data, a final valid sample of 6562 individuals was obtained. Figure 1 presents the specific data processing procedures.

    ‎
    Figure 1. Participant screening flowchart. CGSS: China General Social Survey.

    To examine whether internet use widens the digital divide between the advantaged and disadvantaged groups or brings more information welfare to the disadvantaged group, this study classified middle-aged and older adults into advantaged and disadvantaged groups based on 4 criteria. The criteria include income level, educational attainment, urban-rural classification, and region. The disadvantaged group refers to middle-aged and older adults with income below the median, an education level at or below junior high school, residing in rural areas, or living in western regions of China. Conversely, those not meeting these criteria are categorized as the advantaged group. This differentiation between advantage and disadvantage is measured using objective criteria and has no discriminatory nature.

    Ethical Considerations

    CGSS was a survey approved by the Ethical Review Committee of Renmin University of China. The survey data was also anonymous, and the answers were protected by privacy law. Each participant provided signed informed consent at the time of participation. There was no requirement for additional ethics approval for approved data users.

    Measures

    Dependent Variable

    Similar to prior research [50], health was measured using 3 indicators: self-rated health, physical health, and mental health.

    Self-rated health, although simple, has been demonstrated to predict mortality and disability rates successfully [51]. Self-rated health (“How do you rate your current health?”) was assessed using a 5-point scale (1=very unhealthy, 2=relatively unhealthy, 3=average, 4=relatively healthy, and 5=very healthy). The higher the value, the healthier.

    Physical health (“In the past 4 weeks, how frequently has your work or other daily activities been affected by health problems?”) was assessed using a 5-point scale (1=always, 2=often, 3=sometimes, 4=rarely, and 5=never), with higher values indicating better physical health.

    Mental health (“How frequently have you felt symptoms of depression or sadness in the past 4 weeks?”) is assessed using a 5-point scale (1=always, 2=often, 3=sometimes, 4=rarely, and 5=never), with higher values indicating better mental health status.

    Independent Variables

    The core independent variable in this study is internet use. Respondents were asked whether they use the internet (yes=1 and no=0). Additionally, the CGSS questionnaire includes questions regarding internet use frequency (never=1, rarely=2, sometimes=3, often=4, and very frequently=5), with higher values indicating more frequent internet use. Moreover, the CGSS questionnaire includes questions about the purposes of internet use, such as web-based payment (yes=1 and no=0), web-based socializing (yes=1 and no=0), web-based learning (yes=1 and no=0), and web browsing (yes=1 and no=0).

    Mediating Variable

    The mediating variable is information access. Information access was measured using the question “Is the internet (including mobile internet) your main source of information?” with “yes” coded as 1 and “no” as 0.

    Control Variables

    According to the health capital demand theory and existing research literature [52,53], this study controlled for individual sociodemographic and economic variables. These variables include gender (male=1 and female=0), age (continuous variable), educational level (illiterate=1, primary school or below=2, junior high school=3, high school=4, and college or above=5), marital status (married=1 and unmarried=0), residence (rural=0 and urban=1), household annual income (continuous variable), household size (continuous variable), and whether to exercise (yes=1 and no=0).

    Instrumental Variables

    We used 2 instrumental variables to address endogeneity issues. The first instrumental variable is the mean of the frequency of internet use by other community residents (excluding the respondent’s internet use). The second instrumental variable is the broadband download rate of the respondents’ province. Table S1 in Multimedia Appendix 1 presents the definition and coding of variables.

    Statistical Analysis

    All statistical analyses were conducted using Stata software (version 17.0; StataCorp LLC), with P values <.05 considered statistically significant (2-tailed).

    We performed a descriptive analysis of the sample, summarizing continuous data using means and SDs, and presenting categorical data as frequencies and percentages. In the context of univariate analysis, we examined the relationship between internet use and health. The chi-square test was applied for categorical variables, while the Mann-Whitney U test was used for continuous variables.

    Additionally, we used an ordinal logistic regression model to examine the odds ratios (ORs) and 95% CI for the association between internet use and health. Three models were constructed: model 1 included internet use as the independent variable and self-rated health, physical health, and mental health as the dependent variables, without any additional control variables. Model 2 incorporated sociodemographic variables such as gender, age, education, residency, marital status, and exercise. Model 3 introduced family variables, including family size and family income, on the basis of model 2. Models 2 and 3 both included provincial fixed effects to adjust for unobserved provincial-level factors.

    To provide a comprehensive understanding of the research, we conducted the following analyses: first, we explored the relationship between internet use and health, the purposes of internet use and health, as well as the relationship between the frequency of internet use and health. All 3 sets of regressions used an ordinal logistic regression model. Second, we used the Karlson-Holm-Breen (KHB) decomposition method to examine whether information access mediated the relationship between internet use and health. Third, to address potential endogeneity problems, we introduced instrumental variable methods, specifically using a two-stage least squares regression. Fourth, middle-aged and older adults were categorized into advantaged and disadvantaged groups based on 4 criteria. Further analysis was conducted using grouped regression analysis for the advantaged and disadvantaged subgroups. Finally, it should be noted that all our regression analyses incorporated provincial fixed effects to control for provincial disparities.


    Descriptive Statistics

    Table 1 summarizes the sociodemographic characteristics of the study participants (n=6562). From a demographic perspective, the average age of the participants was 61.41 (SD 10.69) years. The gender ratio between males and females (n=3392, 51.69% vs n=3170, 48.31%) was approximately balanced. Most participants (n=5342, 81.41%) had partners, while a minority (n=1220, 18.59%) were single. The educational level of the participants was relatively low (n=4875, 74.29%), with approximately three-quarters having yet to receive education beyond high school. Regarding geographical distribution, there were more participants from rural areas than urban areas (n=4067, 61.98% vs n=2495, 38.02%). In terms of family characteristics, the average household size was 2.55 (SD 1.33), indicating that at least two people lived together in each household, with few individuals living alone.

    Table 1. Descriptive statistics of variables (n=6562).
    Characteristics and variablesTotal sample (n=6562)Internet use (n=3036)Internet nonuse (n=3526)P value
    Self-rated health, n (%)<.001
    Very unhealthy301 (4.59)59 (1.94)242 (6.86)
    Relatively unhealthy1372 (20.91)391 (12.88)981 (27.82)
    Average1719 (26.20)802 (26.42)917 (26.01)
    Relatively healthy2355 (35.89)1282 (42.23)1073 (30.43)
    Very healthy815 (12.41)502 (16.53)313 (8.88)
    Physical health, n (%)<.001
    Very unhealthy286 (4.36)50 (1.65)236 (6.69)
    Relatively unhealthy834 (12.71)200 (6.59)634 (17.98)
    Average1245 (18.97)436 (14.36)809 (22.94)
    Relatively healthy2060 (31.39)1094 (36.03)966 (27.40)
    Very healthy2137 (32.57)1256 (41.37)881 (24.99)
    Mental health, n (%)<.001
    Very unhealthy85 (1.30)23 (0.76)62 (1.76)
    Relatively unhealthy533 (8.12)161 (5.30)372 (10.55)
    Average1561 (23.79)594 (19.57)967 (27.43)
    Relatively healthy2495 (38.02)1192 (39.26)1303 (36.95)
    Very healthy1888 (28.77)1066 (35.11)822 (23.31)
    Sex, n (%).003
    Male3392 (51.69)1510 (49.74)1882 (53.37)
    Female3170 (48.31)1526 (50.26)1644 (46.63)
    Age (years), mean (SD)61.41 (10.69)56.92 (9.17)65.27 (10.39)<.001
    Marital status, n (%)<.001
    Partnered5342 (81.41)2662 (87.68)2680 (76.01)
    Single1220 (18.59)374 (12.32)846 (23.99)
    Education, n (%)<.001
    Illiterate1227 (18.70)186 (6.13)1.41 (29.52)
    ≤Primary school1813 (27.63)501 (16.50)1312 (37.21)
    Middle school1835 (27.96)1.38 (34.19)797 (22.6)
    High school1150 (17.53)834 (27.47)316 (8.96)
    ≥College537 (8.18)477 (15.71)60 (1.70)
    Residency, n (%)<.001
    Urban2495 (38.02)1657 (54.58)838 (23.77)
    Rural4067 (61.98)1379 (45.42)2688 (76.23)
    Exercise, n (%)<.001
    Yes3122 (47.58)1811 (59.65)1311 (37.18)
    No3440 (52.42)1225 (40.35)2215 (62.82)
    Family size, mean (SD)2.55 (1.33)2.58 (1.24)2.53 (1.39)<.001
    Family income, mean (SD)10.10 (2.19)10.80 (1.66)9.49 (2.40)<.001

    Regarding health status, among the participants, 35.89% (n=2355) considered their self-rated health relatively healthy, and 12.41% (n=815) considered it to be very healthy. In terms of physical health, 31.39% (n=2060) reported themselves as relatively healthy, while 32.57% (n=2137) reported themselves as very healthy. Concerning mental health, 38.02% (n=2495) rated their mental health as relatively healthy, while 28.77% (n=1888) rated it as very healthy. Overall, the health status of middle-aged and older adults was relatively good.

    Among participants who rated their self-rated health as unhealthy (including very unhealthy and relatively unhealthy), 450 (6.86%) individuals had used the internet, while 1223 (18.64%) individuals had not used the internet. In the case of participants who assessed their physical health as unhealthy (including very unhealthy and relatively unhealthy), 250 (3.80%) individuals had used the internet, while 870 (13.26%) individuals had not used the internet. Likewise, among participants who considered their mental health as unhealthy (including very unhealthy and relatively unhealthy), 184 (2.80%) individuals had used the internet, while 434 (6.61%) individuals had not used the internet. The descriptive statistics showed that middle-aged and older adults with poor self-rated, physical, and mental health are more likely to not use the internet (P<.001). Additionally, females (P<.001), older individuals (P<.001), those with less than a high school education (P<.001), and those without a partner (P<.001) are more inclined not to use the internet. Furthermore, individuals with lower household income (P<.001), smaller household size (P<.001), and those living in rural areas (P<.001) were also more likely to refrain from using the internet.

    Table 2 displays the frequency and purpose of internet use. In the middle-aged and older adult population, internet use frequency is relatively low. Over half of the individuals (n=3526, 53.73%) have never used the internet, while only 15.33% (n=1006) use it often, and 13.2% (n=866) use it very frequently. A total of 27.52% (n=1806) of middle-aged and older adults use the internet for information access, 41.31% (n=2711) for web-based learning, 44.93% (n=2948) for web-based socializing, 17.16% (n=1126) for web-based payment, and 44.38% (n=2912) for web browsing.

    Table 2. Internet use frequency and purpose by respondents in the sample (n=6562).
    VariableParticipants, n (%)
    Internet use frequency
    Never3526 (53.73)
    Rarely542 (8.26)
    Sometime622 (9.48)
    Often1006 (15.33)
    Very frequently866 (13.20)
    Internet use purpose (reference: yes)
    Information access1806 (27.52)
    Web-based learning2711 (41.31)
    Web-based socializing2948 (44.93)
    Web-based payment1126 (17.16)
    Web browsing2912 (44.38)

    Internet Use and Multidimensional Health

    Table 3 presents the results of the regression analysis, illustrating the relationship between internet use and multidimensional health. Internet use demonstrated a significant positive association with self-rated health, physical health, and mental health. The findings indicated that middle-aged and older adults who used the internet were more likely to report good self-rated health compared to nonusers (OR 1.55, 95% CI 1.39-1.74; P<.001). Additionally, individuals who used the internet had significantly higher odds of better physical health than nonusers (OR 1.39, 95% CI 1.25-1.56; P<.001). Moreover, individuals who used the internet were more likely to have positive mental health compared to nonusers (OR 1.33, 95% CI 1.19-1.49; P<.001). This section presents only the results of model 3, while the regression results of models 1 and 2 can be found in Table S2 in Multimedia Appendix 1.

    Table 3. Regression results for health among study participants (n=6562).
    VariableSelf-rated health, ORa (95% CI)P valuePhysical health, OR (95% CI)P valueMental health, OR (95% CI)P value
    Internet use
    Yes (reference: no)1.55 (1.39-1.74)<.0011.39 (1.25-1.56)<.0011.33 (1.19-1.49)<.001
    Sex
    Male (reference: female)1.28 (1.17-1.41)<.0011.26 (1.14-1.38)<.0011.29 (1.18-1.42)<.001
    Age0.98 (0.97-0.98)<.0010.98 (0.97-0.98)<.0011.01 (1.00-1.01).009
    Marital status
    Married (reference: unmarried)1.07 (0.95-1.21).271.16 (1.02-1.31).021.26 (1.11-1.43)<.001
    Education
    ≤Primary school (reference: illiterate)1.06 (0.93-1.22).381.05 (0.92-1.20)<.0011.08 (0.94-1.23).29
    Middle school (reference: illiterate)1.18 (1.01-1.37).041.35 (1.16-1.57).511.28 (1.11-1.49).001
    High school (reference: illiterate)1.37 (1.15-1.63)<.0011.56 (1.31-1.86)<.0011.34 (1.12-1.60).001
    ≥College (reference: illiterate)1.37 (1.10-1.71).0061.60 (1.28-2.01)<.0011.21 (0.97-1.52).10
    Exercise
    Yes (reference: no)1.57 (1.43-1.73)<.0011.52 (1.38-1.68)<.0011.36 (1.24-1.50)<.001
    Residency
    Urban (reference: rural)0.85 (0.75-0.96).0091.02 (0.90-1.15).801.11 (0.98-1.25).09
    Family size1.02 (0.99-1.06).241.03 (0.99-1.07).141.02 (0.98-1.06).28
    Family income1.09 (1.06-1.11)<.0011.06 (1.04-1.09)<.0011.05 (0.02-1.07)<.001

    aOR: odds ratio.

    Frequency and Purpose of Internet Use and Health

    Table 4 displays the associations between the frequency and purpose of internet use and health. From the analysis results, it becomes evident that the higher the frequency of internet use among middle-aged and older adults, the better their self-rated health (very frequently: OR 1.48, 95% CI 1.26-1.74; P<.001), physical health (very frequently: OR 1.76, 95% CI 1.48-2.08; P<.001), and mental health (very frequently: OR 1.52, 95% CI 1.28-1.79; P<.001) tend to be. Regarding the purposes of internet use, except for web-based payment (OR 1.08, 95% CI 0.94-1.24; P=.30), which had no relationship on the physical health of middle-aged and older adults, all other purposes of internet use showed significant associations with health. Compared to the control group, middle-aged and older adults who used the internet showed improvements in self-rated, physical, and mental health.

    Table 4. Associations between health and internet use frequency and purpose (n=6562).
    Independent variables: internet useSelf-rated health, ORa (95% CI)P valuePhysical health, OR (95% CI)P valueMental health, OR (95% CI)P value
    Frequency (reference: never)
    Rarely1.64 (1.38-1.94)<.0011.25 (1.05-1.47).011.27 (1.07-1.51).006
    Sometimes1.41 (1.19-1.66)<.0011.28 (1.08-1.51)<.0041.20 (1.02-1.42).03
    Often1.67 (1.44-1.94)<.0011.44 (1.23-1.67)<.0011.39 (1.19-1.62)<.001
    Very frequently1.48 (1.26-1.74)<.0011.76 (1.48-2.08)<.0011.52 (1.28-1.79)<.001
    Purpose (reference: no)
    Web-based payment1.24 (1.08-1.42).0021.08 (0.94-1.24).301.22 (1.06-1.41).005
    Web-based learning1.49 (1.33-1.67)<.0011.41 (1.25-1.58)<.0011.29 (1.15-1.45)<.001
    Web-based socializing1.51 (1.34-1.70)<.0011.58 (1.41-1.78)<.0011.48 (1.3-1.66)<.001
    Web browsing1.40 (1.25-1.57)<.0011.39 (1.24-1.55)<.0011.29 (1.15-1.45)<.001

    aOR: odds ratio.

    Mediation Effect of Information Access

    Table 5 reports the mediation effect of information access using the KHB decomposition method. The results revealed that information access had a significant mediating effect (β=.28, 95% CI 0.23-0.32) on the relationship between internet use and self-rated health. Additionally, information access also had a significant mediating effect (β=.40, 95% CI 0.35-0.45) on the relationship between internet use and physical health. Moreover, the KHB results showed that the indirect effect of information access on internet use and mental health was significant (β=.16, 95% CI 0.11-0.20). Therefore, the mediating effect of information access on internet use and each dimension of health among middle-aged and older adults was significant.

    Table 5. Mediation of the relationship between middle-aged and older adults’ health and internet use by information access (n=6562).
    Mediation by information accessSelf-rated health, β (95% CI)P valuePhysical health, β (95% CI)P valueMental health, β (95% CI)P value
    Total effect.52 (0.47-0.57)<.001.63 (0.58-0.68)<.001.33 (0.29-0.38)<.001
    Direct effect.24 (0.18-0.31)<.001.23 (0.16-0.30)<.001.18 (0.12-0.24)<.001
    Indirect effect.28 (0.23-0.32)<.001.40 (0.35-0.45)<.001.16 (0.11-0.20)<.001

    Treatment: Instrumental Variables Approach

    Internet use as a form of individual decision-making may lead to endogeneity issues due to reverse causality or omitted variables. While significant efforts have been made in this paper to carefully select control variables to mitigate omitted variable issues, endogeneity concerns may still exist. To address this issue, the study sought instrumental variables for the internet use of middle-aged and older adults to mitigate potential bias and nonconsistency resulting from endogeneity. Ultimately, 2 instrumental variables were chosen, and a two-stage estimation was performed using the product of these two instrumental variables as instruments. The first instrumental variable is the mean internet use frequency among residents in the respondent’s community (excluding the respondent’s internet use). The second instrumental variable is the broadband download rate of the province. Average bandwidth data for each province is sourced from the 19th edition of the “China Broadband Speed Report,” published by the Broadband Development Alliance.

    Table S3 in Multimedia Appendix 1 displays the two-stage least squares regression. In the first-stage regression of the endogenous variables, the instrumental variables were statistically significant at the 1% significance level. The results from the second stage of the regression still suggested that individuals who used the internet had a higher likelihood of better self-rated health (β=.31, 95% CI 0.03-0.59; P=.03, better physical health (β=.55, 95% CI 0.25-0.86; P<.001), and better mental health (β=.33, 95% CI 0.08-0.59; P=.01) compared to nonusers. This suggested that the conclusions drawn earlier remained robust.

    Heterogeneity Analysis

    In existing research [41,45], some scholars argue that the internet may create a digital divide between advantaged and disadvantaged groups, while others believe it may bring about digital dividends. To address this controversy, this study categorizes middle-aged and older adults into advantaged and disadvantaged groups with 4 criteria: income level, educational attainment, urban-rural classification, and region.

    Table S4 in Multimedia Appendix 1 demonstrates that internet use had a significant facilitative effect on self-rated health (urban: OR 1.37, 95% CI 1.34-1.65; P=.001 vs rural: OR 1.67, 95% CI 1.45-1.93; P<.001), mental health (urban: OR 1.20, 95% CI 0.99-1.45; P=.06 vs rural: OR 1.40, 95% CI 1.22-1.62; P<.001) for both rural and urban samples, as well as physical health of the rural group (OR 1.57, 95% CI 1.36-1.81; P<.001). However, it had no significant effect on the physical health of the urban group (OR 1.12, 95% CI 0.93-1.36; P=.24). Therefore, for those in rural areas, the health effects of internet use were much larger.

    Table S5 in Multimedia Appendix 1 reveals that the association between internet use and self-rated health (eastern: OR 1.45, 95% CI 1.24-1.69; P<.001 vs western: OR 2.03, 95% CI 1.60-2.57; P<.001), physical health (eastern: OR 1.21, 95% CI 1.03-1.41; P=.02 vs western: OR 2.14, 95% CI 1.69-2.71; P<.001), and mental health (eastern: OR 1.40, 95% CI 1.20-1.64; P<.001 vs western: OR 1.29, 95% CI 1.02-1.63; P=.04), for both western and eastern region samples were also significantly positive. However, for those in western areas, the effect was much larger.

    Table S6 in Multimedia Appendix 1 displays that the association between internet use and self-rated health (high-income: OR 1.37, 95% CI 1.17-1.61; P<.001 vs low-income: OR 1.74, 95% CI 1.47-2.05; P<.001), physical health (high-income: OR 1.17, 95% CI 1.00-1.38; P=.05 vs low-income: OR 1.61, 95% CI 1.37-1.90; P<.001), and mental health (high-income: OR 1.16, 95% CI 0.98-1.36; P=.08 vs low-income: OR 1.49, 95% CI 1.26-1.76; P<.001) for both high-income and low-income samples were also significantly positive. However, for those in the low-income group, the effect was much larger.

    Finally, as indicated in Table S7 in Multimedia Appendix 1, the association between internet use and self-rated health (high education: OR 1.56, 95% CI 1.34-1.80; P<.001 vs low education: OR 1.61, 95% CI 1.36-1.92; P<.001), physical health (high education: OR 1.49, 95% CI 1.28-1.73; P<.001 vs low-education: OR 1.29, 95% CI 1.09-1.53; P=.004), and mental health (high education: OR 1.35, 95% CI 1.17-1.57; P<.001 vs low education: OR 1.27, 95% CI 1.07-1.52; P=.007) for both high education and low-education samples were also significantly positive.


    Principal Findings

    This study used data from China’s first nationwide publicly available database CGSS to examine the relationship between internet use and multidimensional health in middle-aged and older adults. We initially examined the significant relationships between internet use and self-rated, physical, and mental health. Subsequently, we conducted further analyses to explore the links between the purposes and frequency of internet use and multidimensional health. Then, we investigated the partial mediating role of information access in the relationship between internet use and health. Finally, we explored whether there were significant differences in internet use and health among advantaged and disadvantaged groups.

    Our research confirmed that internet use was currently influencing the daily lives of middle-aged and older adults: 46.3% (n=3036) of middle-aged and older adults use the internet in our sample. This indicated that over half of the older people in our sample had not yet accessed the internet and were unable to benefit from the advancements in digital technology. This was still a significant gap compared to the internet penetration rates among older people in other countries, which ranged from 76% to 82% [54,55]. The reasons behind this phenomenon are multifaceted. First, the availability of smart devices and services for older adults is not yet comprehensive [56], and their digital skills are generally inadequate [57], resulting in fundamental operational obstacles during their use [58]. Second, the ownership of internet access devices among older adults is lower than younger generations [59]. Third, older adults have weaker information processing abilities and lower discernment skills, making them more susceptible to false information and telecommunications fraud [60]. These factors make some older adults unwilling to use the internet.

    In this study, it was found that internet use was positively associated with the health of middle-aged and older adults. This contribution addressed the ongoing debate in the literature [18,22], as other surveys presented contradictory results. This could be related to the results of other studies [61] that the internet enables people to access health-related knowledge, gradually develop healthy lifestyle habits, and improve overall health. The internet provides middle-aged and older adults access to more health resources and convenient methods for managing their health, which positively affects their overall health [62,63]. For instance, middle-aged and older adults can use health apps and digital tools for medication management, health data recording, and health trend tracking, facilitating better self-management of health-related issues [64]. Furthermore, the internet offers crucial support for mental health, allowing middle-aged and older adults to access emotional support and resources to address psychological concerns through digital therapy [65]. Additionally, individuals’ internet use is associated with better financial and health care decision-making [66,67]. People who use the internet may have more financial resources, which can contribute to their health [38].

    We found that information access significantly mediated the relationship between internet use and each dimension of health. The internet provides middle-aged and older adults access to a wealth of health-related information, from disease prevention to treatment recommendations, to understand better and manage their health [68,69]. They can enhance their health knowledge through web-based resources and adopt healthier lifestyles such as regular exercise and balanced diets [70]. Furthermore, information access reduces uncertainty in medical decision-making as individuals consult health care professionals through web-based platforms to seek treatment advice [71]. In summary, the information access feature of the internet enhances middle-aged and older adults’ understanding and management of their health, granting them greater autonomy and control over their well-being, ultimately resulting in a positive influence on overall health [72].

    This study also confirmed that there were significant differences in the relationship between internet use and the health of middle-aged and older adults among advantaged and disadvantaged groups. Specifically, for those in rural and western areas, the effect was much larger. As pointed out in previous research [73], China faces an imbalance in the allocation of health care resources among regions, with a concentration of high-quality medical resources in urban and eastern regions [74]. In contrast, rural and western regions have relatively limited health care resources, resulting in insufficient accessibility to health care services [75]. Therefore, middle-aged and older adults in rural and western regions are more likely to improve their health through internet use [76]. This study also indicated that the association between internet use and the health of middle-aged and older adults was significantly positive for both higher-income and lower-income samples. However, the effect was greater for those in the lower-income group. Low-income disadvantaged group often faces challenges in accessing health care services than advantaged groups [77]. However, the internet provides them with abundant health information resources, including web-based consultations and medical advice, which help alleviate health care access issues linked to economic constraints [78]. In summary, internet use brings more benefits to disadvantaged groups [79]. This conclusion provides essential insights into how to mitigate health inequalities among advantaged and disadvantaged groups.

    Contributions

    This study makes significant contributions to the existing literature from 4 perspectives. First, unlike previous research that often examined the relationship between internet use and health from a single perspective, we adopt an innovative research approach. We categorize internet use into 3 dimensions (whether to use the internet, internet use frequency, and internet use purpose) to provide a multidimensional consideration of internet use. Second, the statistical analysis in existing studies often overlooks endogeneity issues and sample selection bias. This paper addresses these concerns by selecting broadband download rates and internet use frequency among community residents as instrumental variables to ensure the reliability of the research results. Third, our findings provide empirical evidence for the first time, demonstrating that information access serves as a crucial mediating variable in the relationship between internet use and the health of middle-aged and older adults. This not only supports the theoretical proposition that internet use improves the health status of middle-aged and older adults but also advocates for the promotion of active aging policies. Fourth, to our knowledge, limited literature categorizes middle-aged and older adults into advantaged and disadvantaged groups based on multiple criteria and investigates the relationship between internet use and multidimensional health. Our results emphasize that internet use tends to benefit disadvantaged groups more. These insights are of significant importance and provide evidence for narrowing the health and welfare gap between different population groups.

    Limitations

    Certainly, our study acknowledges specific limitations. First, due to data constraints, we have to rely on the question “How do you rate your current physical health?” to measure physical health. This may not comprehensively measure their health status. In future research, we will select more comprehensive physical health indicators, such as activities of daily living and instrumental activities of daily living, to better reflect the health status of individuals. Second, given the complexity of the relationship between internet use and health, future research should consider longitudinal analysis to understand better the causal relationships and temporal dynamics. Nonetheless, our research offers valuable evidence for low-income countries seeking a deeper understanding of the relationship between internet use and health.

    Conclusions

    In this study, it was found that internet use was positively associated with better self-rated health, physical health, and mental health after controlling for key determinants of health. Additionally, information access mediated the relationship between internet use and the health of middle-aged and older adults in China. Furthermore, there were significant differences in the relationship between internet use and health among advantaged and disadvantaged groups. This study suggests collaborative efforts between the government and the market to further enhance internet accessibility for the older people population in China. Additionally, society should strive to lower the technological and income barriers for middle-aged and older adults in using the internet so that more seniors can enjoy the digital dividends brought by the internet.

    Acknowledgments

    This study was funded by the Provincial Philosophy and Social Science Project of Qinghai Province (23ZCQ089), and the Qinghai Minzu University Foundation (39D2023003). We thank all members in LF’s laboratory at Tianjin University for their assistance with the manuscript review.

    Data Availability

    The data sets generated during or analyzed during this study are available in the China General Social Survey repository [80].

    Authors' Contributions

    LF was responsible for the study concept, and supervision. CL was responsible for the study design, data analysis, interpretation of the data, and writing of the manuscript. YD and XM were responsible for supervision. QC, DL, and KD reviewed the manuscript. All authors have read and approved the published version of the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Additional tables for the study.

    DOCX File , 69 KB
    1. Vollset SE, Goren E, Yuan CW, Cao J, Smith AE, Hsiao T, et al. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet. 2020;396(10258):1285-1306. [FREE Full text] [CrossRef] [Medline]
    2. Cesari M, Sumi Y, Han ZA, Perracini M, Jang H, Briggs A, et al. Implementing care for healthy ageing. BMJ Glob Health. 2022;7(2):e007778. [FREE Full text] [CrossRef] [Medline]
    3. Abud T, Kounidas G, Martin KR, Werth M, Cooper K, Myint PK. Determinants of healthy ageing: a systematic review of contemporary literature. Aging Clin Exp Res. 2022;34(6):1215-1223. [FREE Full text] [CrossRef] [Medline]
    4. Digital 2022: Global Overview Report. We are Social & Hootsuite. 2022. URL: https://datareportal.com/reports/digital-2022-global-overview-report [accessed 2024-05-03]
    5. The 51st statistical report on internet development in China. China Internet Network Information Center. 2023. URL: https://www.cnnic.cn/NMediaFile/2023/0807/MAIN169137187130308PEDV637M.pdf [accessed 2024-05-03]
    6. Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30(3):464-471. [CrossRef] [Medline]
    7. McDool E, Powell P, Roberts J, Taylor K. The internet and children's psychological wellbeing. J Health Econ. 2020;69:102274. [FREE Full text] [CrossRef] [Medline]
    8. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. 2, 2013;15(4):e85. [FREE Full text] [CrossRef] [Medline]
    9. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4. [FREE Full text] [CrossRef] [Medline]
    10. Wald HS, Dube CE, Anthony DC. Untangling the web—the impact of internet use on health care and the physician-patient relationship. Patient Educ Couns. 2007;68(3):218-224. [CrossRef] [Medline]
    11. Allcott H, Braghieri L, Eichmeyer S, Gentzkow M. The welfare effects of social media. Am Econ Rev. 2020;110(3):629-676. [CrossRef]
    12. Duplaga M, Szulc K. The association between internet use and health outcomes in the population of older adults. Eur J Public Health. 2020:165. [CrossRef]
    13. Amini R, Chee KH, Mendieta M, Parker S. Online engagement and cognitive function among older adults. Geriatr Gerontol Int. 2019;19(9):918-923. [FREE Full text] [CrossRef] [Medline]
    14. Burke M, Kraut RE. The relationship between Facebook use and well-being depends on communication type and tie strength. J Comput-Mediat Comm. 2016;21(4):265-281. [CrossRef]
    15. Schrempft S, Jackowska M, Hamer M, Steptoe A. Associations between social isolation, loneliness, and objective physical activity in older men and women. BMC Public Health. 2019;19(1):74. [FREE Full text] [CrossRef] [Medline]
    16. Wu HY, Chiou AF. Social media usage, social support, intergenerational relationships, and depressive symptoms among older adults. Geriatr Nurs. 2020;41(5):615-621. [CrossRef] [Medline]
    17. Cruz J, Brooks D, Marques A. Home telemonitoring in COPD: a systematic review of methodologies and patients' adherence. Int J Med Inform. 2014;83(4):249-263. [CrossRef] [Medline]
    18. Duplaga M. The association between internet use and health-related outcomes in older adults and the elderly: a cross-sectional study. BMC Med Inform Decis Mak. 2021;21(1):150. [FREE Full text] [CrossRef] [Medline]
    19. Bekalu MA, McCloud RF, Viswanath K. Association of social media use with social well-being, positive mental health, and self-rated health: disentangling routine use from emotional connection to use. Health Educ Behav. 2019;46(2_suppl):69-80. [CrossRef] [Medline]
    20. Kamal SA, Shafiq M, Kakria P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol Soc. 2020;60:101212. [CrossRef]
    21. Smailhodzic E, Hooijsma W, Boonstra A, Langley DJ. Social media use in healthcare: a systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Serv Res. 2016;16(1):442. [FREE Full text] [CrossRef] [Medline]
    22. Braghieri L, Levy R, Makarin A. Social media and mental health. Am Econ Rev. 2022;112(11):3660-3693. [CrossRef]
    23. Huang C. Internet use and psychological well-being: a meta-analysis. Cyberpsychol Behav Soc Netw. 2010;13(3):241-249. [CrossRef] [Medline]
    24. Błachnio A, Przepiórka A, Pantic I. Internet use, Facebook intrusion, and depression: results of a cross-sectional study. Eur Psychiatry. 2015;30(6):681-684. [CrossRef] [Medline]
    25. Nie NH. Sociability, interpersonal relations, and the internet: reconciling conflicting findings. Am Behav Sci. 2016;45(3):420-435. [CrossRef]
    26. Stepanikova I, Nie NH, He X. Time on the internet at home, loneliness, and life satisfaction: evidence from panel time-diary data. Comput Hum Behav. 2010;26(3):329-338. [CrossRef]
    27. Meshi D, Cotten SR, Bender AR. Problematic social media use and perceived social isolation in older adults: a cross-sectional study. Gerontology. 2020;66(2):160-168. [CrossRef] [Medline]
    28. Tavares AI. Self-assessed health among older people in Europe and internet use. Int J Med Inform. Sep 2020;141:104240. [FREE Full text] [CrossRef] [Medline]
    29. Kokka I, Mourikis I, Nicolaides NC, Darviri C, Chrousos GP, Kanaka-Gantenbein C, et al. Exploring the effects of problematic internet use on adolescent sleep: a systematic review. Int J Environ Res Public Health. 2021;18(2):760. [FREE Full text] [CrossRef] [Medline]
    30. McCloud RF, Okechukwu CA, Sorensen G, Viswanath K. Beyond access: barriers to internet health information seeking among the urban poor. J Am Med Inform Assoc. 2016;23(6):1053-1059. [FREE Full text] [CrossRef] [Medline]
    31. Pandey SK, Hart JJ, Tiwary S. Women's health and the internet: understanding emerging trends and implications. Soc Sci Med. 2003;56(1):179-191. [CrossRef] [Medline]
    32. McCully SN, Don BP, Updegraff JA. Using the internet to help with diet, weight, and physical activity: results from the health information national trends survey (HINTS). J Med Internet Res. 2013;15(8):e148. [FREE Full text] [CrossRef] [Medline]
    33. Chopik WJ. The benefits of social technology use among older adults are mediated by reduced loneliness. Cyberpsychol Behav Soc Netw. 2016;19(9):551-556. [FREE Full text] [CrossRef] [Medline]
    34. Magsamen-Conrad K, Dillon JM, Billotte Verhoff C, Faulkner SL. Online health-information seeking among older populations: family influences and the role of the medical professional. Health Commun. 2019;34(8):859-871. [FREE Full text] [CrossRef] [Medline]
    35. Jiang S, Street RL. Pathway linking internet health information seeking to better health: a moderated mediation study. Health Commun. 2017;32(8):1024-1031. [CrossRef] [Medline]
    36. Rowlands IJ, Loxton D, Dobson A, Mishra GD. Seeking health information online: association with young Australian women's physical, mental, and reproductive health. J Med Internet Res. 2015;17(5):e120. [FREE Full text] [CrossRef] [Medline]
    37. Oyedemi TD. Digital inequalities and implications for social inequalities: a study of internet penetration amongst university students in South Africa. Telemat Inf. 2012;29(3):302-313. [CrossRef]
    38. Van DAJAM, Helsper E. The third-level digital divide: who benefits most from being online? Commun Inf Technol. 2015;10:29-52. [CrossRef]
    39. Vassilakopoulou P, Hustad E. Bridging digital divides: a literature review and research agenda for information systems research. Inf Syst Front. 2023;25(3):955-969. [FREE Full text] [CrossRef] [Medline]
    40. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752-762. [FREE Full text] [CrossRef] [Medline]
    41. Bauer JM. The internet and income inequality: socio-economic challenges in a hyperconnected society. Telecommun Policy. 2018;42(4):333-343. [CrossRef]
    42. Mesch G, Mano R, Tsamir J. Minority status and health information search: a test of the social diversification hypothesis. Soc Sci Med. 2012;75(5):854-858. [CrossRef] [Medline]
    43. van Deursen AJ, van Dijk JAGM, Peters O. Rethinking internet skills: the contribution of gender, age, education, internet experience, and hours online to medium-and content-related internet skills. Poetics. 2011;39(2):125-144. [CrossRef]
    44. Chen L, Liu W. The effect of internet access on body weight: evidence from China. J Health Econ. 2022;85:102670. [CrossRef] [Medline]
    45. Lustria MLA, Smith SA, Hinnant CC. Exploring digital divides: an examination of eHealth technology use in health information seeking, communication and personal health information management in the USA. Health Inf J. 2011;17(3):224-243. [FREE Full text] [CrossRef] [Medline]
    46. Hunsaker A, Hargittai E. A review of internet use among older adults. New Media Soc. 2018;20(10):3937-3954. [CrossRef]
    47. Chyi H, Mao S. The determinants of happiness of China‘s elderly population. J Happiness Stud. 2011;13(1):167-185. [CrossRef]
    48. Wang J, Wang C, Li S, Luo Z. Measurement of relative welfare poverty and its impact on happiness in China: evidence from CGSS. China Economic Review. 2021;69:101687. [CrossRef]
    49. World Report on Ageing and Health. Geneva, Switzerland. World Health Organization; 2015:246.
    50. Boardman JD. Self-rated health among U.S. adolescents. J Adolesc Health. 2006;38(4):401-408. [FREE Full text] [CrossRef] [Medline]
    51. Franks P, Gold MR, Fiscella K. Sociodemographics, self-rated health, and mortality in the US. Soc Sci Med. 2003;56(12):2505-2514. [CrossRef] [Medline]
    52. Cotten SR, Ford G, Ford S, Hale TM. Internet use and depression among retired older adults in the United States: a longitudinal analysis. J Gerontol B Psychol Sci Soc Sci. 2014;69(5):763-771. [CrossRef] [Medline]
    53. Leibowitz AA. The demand for health and health concerns after 30 years. J Health Econ. 2004;23(4):663-671. [CrossRef] [Medline]
    54. Crouch E, Gordon NP. Prevalence and factors influencing use of internet and electronic health resources by middle-aged and older adults in a US health plan population: cross-sectional survey study. JMIR Aging. 2019;2(1):e11451. [FREE Full text] [CrossRef] [Medline]
    55. Zajac IT, Flight IHK, Wilson C, Turnbull D, Cole S, Young G. Internet usage and openness to internet-delivered health information among Australian adults aged over 50 years. Australas Med J. 2012;5(5):262-267. [FREE Full text] [CrossRef] [Medline]
    56. Hong A, Nam C, Kim S. What will be the possible barriers to consumers’ adoption of smart home services? Telecommun Policy. 2020;44(2):101867. [CrossRef]
    57. Nimrod G. Changes in internet use when coping with stress: older adults during the COVID-19 pandemic. Am J Geriatr Psychiatry. 2020;28(10):1020-1024. [FREE Full text] [CrossRef] [Medline]
    58. Beaunoyer E, Dupéré S, Guitton MJ. COVID-19 and digital inequalities: reciprocal impacts and mitigation strategies. Comput Human Behav. Oct 2020;111:106424. [FREE Full text] [CrossRef] [Medline]
    59. Seifert A, Cotten SR, Xie B. A double burden of exclusion? Digital and social exclusion of older adults in times of COVID-19. J Gerontol B Psychol Sci Soc Sci. 2021;76(3):e99-e103. [FREE Full text] [CrossRef] [Medline]
    60. Seo H, Blomberg M, Altschwager D, Vu HT. Vulnerable populations and misinformation: a mixed-methods approach to underserved older adults’ online information assessment. New Media Soc. 2020;23(7):2012-2033. [FREE Full text] [CrossRef]
    61. Arcury TA, Sandberg JC, Melius KP, Quandt SA, Leng X, Latulipe C, et al. Older adult internet use and eHealth literacy. J Appl Gerontol. 2020;39(2):141-150. [FREE Full text] [CrossRef] [Medline]
    62. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res. 2015;17(2):e52. [FREE Full text] [CrossRef] [Medline]
    63. Al Bassam N, Hussain SA, Al Qaraghuli A, Khan J, Sumesh EP, Lavanya V. IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19. Inform Med Unlocked. 2021;24:100588. [FREE Full text] [CrossRef] [Medline]
    64. Giustini D, Ali SM, Fraser M, Boulos MNK. Effective uses of social media in public health and medicine: a systematic review of systematic reviews. Online J Public Health Inform. 2018;10(2):e215. [FREE Full text] [CrossRef] [Medline]
    65. Forsman AK, Nordmyr J. Psychosocial links between internet use and mental health in later life: a systematic review of quantitative and qualitative evidence. J Appl Gerontol. 2017;36(12):1471-1518. [CrossRef] [Medline]
    66. Schilbach F, Schofield H, Mullainathan S. The psychological lives of the poor. Am Econ Rev. 2016;106(5):435-440. [CrossRef] [Medline]
    67. James BD, Boyle PA, Yu L, Bennett DA. Internet use and decision making in community-based older adults. Front Psychol. 2013;4:605. [FREE Full text] [CrossRef] [Medline]
    68. Deng Z, Mo X, Liu S. Comparison of the middle-aged and older users' adoption of mobile health services in China. Int J Med Inform. 2014;83(3):210-224. [CrossRef] [Medline]
    69. Madathil KC, Rivera-Rodriguez AJ, Greenstein JS, Gramopadhye AK. Healthcare information on YouTube: a systematic review. Health Informatics J. 2015;21(3):173-194. [FREE Full text] [CrossRef] [Medline]
    70. Leung L, Chen C. E-health/m-health adoption and lifestyle improvements: exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommun Policy. 2019;43(6):563-575. [CrossRef]
    71. Zhao Y, Zhang J. Consumer health information seeking in social media: a literature review. Health Info Libr J. 2017;34(4):268-283. [FREE Full text] [CrossRef] [Medline]
    72. Fischer SH, David D, Crotty BH, Dierks M, Safran C. Acceptance and use of health information technology by community-dwelling elders. Int J Med Inform. 2014;83(9):624-635. [FREE Full text] [CrossRef] [Medline]
    73. Tang S, Meng Q, Chen L, Bekedam H, Evans T, Whitehead M. Tackling the challenges to health equity in China. Lancet. 2008;372(9648):1493-1501. [FREE Full text] [CrossRef] [Medline]
    74. Evandrou M, Falkingham J, Feng Z, Vlachantoni A. Individual and province inequalities in health among older people in China: evidence and policy implications. Health Place. 2014;30:134-144. [FREE Full text] [CrossRef] [Medline]
    75. Wang Y, Li Y, Qin S, Kong Y, Yu X, Guo K, et al. The disequilibrium in the distribution of the primary health workforce among eight economic regions and between rural and urban areas in China. Int J Equity Health. 2020;19(1):28. [FREE Full text] [CrossRef] [Medline]
    76. Berg T, Winterton R, Petersen M, Warburton J. 'Although we're isolated, we're not really isolated': the value of information and communication technology for older people in rural Australia. Australas J Ageing. 2017;36(4):313-317. [CrossRef] [Medline]
    77. Jacobs B, Ir P, Bigdeli M, Annear PL, Van Damme W. Addressing access barriers to health services: an analytical framework for selecting appropriate interventions in low-income Asian countries. Health Policy Plan. 2012;27(4):288-300. [CrossRef] [Medline]
    78. Naslund JA, Aschbrenner KA, Araya R, Marsch LA, Unützer J, Patel V, et al. Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature. Lancet Psychiatry. 2017;4(6):486-500. [FREE Full text] [CrossRef] [Medline]
    79. Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: internet use patterns, eHealth literacy, and attitudes toward computer/internet use. J Med Internet Res. 2013;15(5):e93. [FREE Full text] [CrossRef] [Medline]
    80. Chinese General Social Survey. URL: http://cgss.ruc.edu.cn/ [accessed 2024-08-26]

    ‎
    CGSS: China General Social Survey
    KHB: Karlson-Holm-Breen
    OR: odds ratio

    Edited by T de Azevedo Cardoso; submitted 06.06.23; peer-reviewed by E Brainin, W He; comments to author 26.07.23; revised version received 08.10.23; accepted 02.08.24; published 09.09.24.

    Copyright

    ©Liping Fu, Caiping Liu, Yongqing Dong, Xiaodong Ma, Quanling Cai, Dongli Li, Kaisheng Di. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.09.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.