Health Literacy Suppresses the Negative Association Between ICTs Usage and Mental Health Among the Aged: A Cross-Sectional Survey

Abstract

Although the association between Health Literacy and Mental Health has been explored in previous research, their interplay within digital technology contexts—especially among the aged—remains insufficiently studied. This study aims to explore the association between ICTs usage and the Mental Health of older adults in China and examine the moderating role of Health Literacy in this relationship. This study is a secondary data analysis based on a cross-sectional survey: 2021 Psychological and Behavioral Investigation of Chinese Residents. Descriptive statistical analysis and hierarchical multiple regression analysis were applied. In zero-order correlation analyses, the relationship between ICTs usage and Mental Health was not significant. After controlling for Health Literacy, ICTs usage had a significant negative association with Mental Health (B = −0.129, β = −.16, P < .001). Health Literacy had a positive relationship with Mental Health (B = 0.027, β = .243, P < .001). Further moderation effect analysis showed that introducing the interaction term did not increase the model’s explanatory power (ΔR² = .000). Although no moderating effect was found between ICTs usage and Mental Health, Health Literacy may act as a suppressor variable to offset the negative impact of digital technology. Future Mental Health management for the aged should focus on reducing harmful ICTs usage and enhancing Health Literacy levels.

Keywords

health literacy ICTs usage mental health aged cross-sectional study

Introduction

The “silver wave” is accelerating globally, with the current global population over 60 years old exceeding 1 billion. It is estimated that by 2030, this number will rise to 1.4 billion.¹ Elderly individuals face numerous challenges such as aging, disease, discrimination, and loneliness, which may trigger mental health issues among the elderly population.^2,3 According to WHO data,⁴ more than 20% of adults aged 60 and over have mental or neurological disorders. In China, the elderly face significant challenges related to mental health. Epidemiological studies indicate that middle-aged and older adults in China have a noticeable incidence of depression, ranging from approximately 17.4% to 46.15% .^5,6 Mental health issues are critical causes of diseases and behaviors such as peptic ulcers, primary hypertension, and suicide in the elderly.⁷ Poor mental health can deteriorate the physical health of the elderly, leading to increased morbidity and mortality rates,⁸ while physiological decline can exacerbate mental issues,^9,10 ultimately imposing a dual burden of caregiving and economic pressure on individuals, families, and society.^11,12

Information and communication technologies (ICTs), including mobile smart terminals and portable computing devices, have rapidly embedded in contemporary life, impacting personal, and collective social well-being. Some studies have found that ICTs usage promotes the mental well-being of the elderly.^13-15 Shapira et al¹⁶ found through controlled experiments that elderly groups who socialize using digital technology have higher well-being and empowerment. Peng et al,¹⁷ based on CGSS data from 2012, 2013, and 2015, found that elderly groups maintain social interaction and engage in entertainment activities through information technology, effectively enhancing their subjective well-being. Digital technology products (such as apps and online platforms) not only address mental health challenges with their innovation and effectiveness,¹⁸ but also hold significant potential for delivering timely, personalized, and accessible mental health interventions.¹⁹ Nonetheless, concerns about the potential adverse effects of ICTs have emerged as a major area of scholarly inquiry.²⁰ Some researchers suggest that the use of smartphones and social media may lead to increased suicidal behavior, worsening depressive symptoms, and rising feelings of loneliness.²¹ Digital technology can trigger mental health crises through convenient practical effects²² and may indirectly affect mental health through the influence of online misinformation on the elderly due to usage barriers.^23,24

Health Literacy, reflected in an individual’s ability to acquire health information, understand its implications, and apply it effectively, is crucial for the elderly to discern the quality of information and make informed health decisions in a digital environment.²⁵ Existing research has found that Health Literacy positively impacts the maintenance of Mental Health, with higher levels of Health Literacy associated with lower prevalence of mental disorders.^26,27 In current academic research and practical contexts, the terms “Health Literacy ” and “Digital Health Literacy” are largely synonymous, both denoting an individual’s capacity to manage health information.²⁸ Health Literacy serves as the cornerstone of Digital Health Literacy,²⁹ it represents a refinement and expansion of the overarching notion of “Health Literacy” in the “digital era,”³⁰ with both adhering to the same operational definition.³¹ The fundamental competencies of Health Literacy are globally relevant in a digital context. Research indicates a substantial positive association between Health Literacy and Digital Health Literacy: as Health Literacy increases, so does an individual’s self-evaluated digital Health Literacy.³² Fast et al³³ noted that rural communities with insufficient Health Literacy frequently struggle to evaluate the reliability of digital health information. Moreover, those with diminished Health Literacy are less inclined to utilize e-health services.³⁴

It is worth noting that although the intrinsic link between Health Literacy and Mental Health has been validated by a substantial amount of academic research, its role in specific contexts—namely, the digital technology usage environment—especially for elderly groups facing challenges in cognitive ability and technological adaptability, remains underexplored. The relationships among variables in statistical modeling may demonstrate various mechanisms due to the influence of a third variable, including moderation, mediation, or suppression effects.^35,36 According to Sansakorn et al³⁷ Health Literacy has a significant moderating effect, significantly negatively moderating the relationship between Cyberchondria and Health Anxiety. However, there is currently an absence of systematic investigation into these relational mechanisms between Health Literacy and variables such as ICTs and Mental Health. In view of this, the present study aims to examine the moderating effect of Health Literacy on the relationship between ICTs usage and Mental Health in the elderly, exploring whether Health Literacy can enhance the positive impact of digital technology while mitigating its potential negative effects. By revealing this moderating mechanism, this study will provide empirical evidence for optimizing digital health intervention strategies for the elderly and offer new ideas for bridging health inequalities in the digital age.

Research Design

Data Source

This study is a secondary data analysis based on the 2021 Psychological and Behavioral Investigation of Chinese Residents (PBICR),³⁸ which is a nationally representative cross-sectional survey conducted from July 10, 2021 to September 15, 2021.This survey utilized a multi-stage stratified quota sampling method and covered the China’s capital cities of 23 provinces, 5 autonomous regions, as well as 4 municipalities directly under the central government, ensuring that the obtained sample is generally consistent with the population distribution characteristics of China. According to the original survey protocol,³⁹ eligible participants should be aged ≥ 12 years, possessing Chinese nationality or being permanent residents of China (with ≤1 month per year living outside), with voluntary participation by signing informed consent, capable of completing the online questionnaire independently or with supervision, and able to comprehend of all questionnaire items; individuals with severe cognitive impairment or psychiatric disorders, participating in other similar studies, or unwilling to cooperate, were excluded.

Based on the study’s objectives, for this secondary analysis, we further restricted our sample to individuals aged between 60 and 85. Although the PBICR original data included samples aged 60 to 101 years, this study referenced the selection range of some mainstream literature⁴⁰ and considered that the extremely elderly population (such as those aged 85 and above) might have heterogeneity in psychological characteristics compared to those aged 60 to 85 years.⁴¹ Therefore, only the population aged 60 to 85 years was included, excluding respondents aged 85 and above. Since the data obtained in this study is already cleaned data, there are no individuals with missing “key variables” (such as age/gender/mental health-related scales), or obviously abnormal records, etc. Therefore, our inclusion and exclusion criteria are only limited to age, and we finally selected 1112 elderly individuals.

The dataset used in this study is entirely sourced from public channels and contains no personally identifiable information, meaning formal approval from an ethics committee was not required for this secondary data analysis according to research ethics guidelines. This study strictly adheres to relevant data usage regulations to ensure that the data analysis process complies with academic ethical standards. Moreover, this study adhered to the STROBE guidelines for reporting observational studies.⁴²

Variable Selection

Dependent Variable

Mental Health Level

Presently, Mental Health surveys for the general populace often encompass assessment instruments pertaining to life satisfaction, well-being, positive emotions, negative emotions, psychosomatic symptoms, anxiety, and depression.⁴³ Anxiety disorders and depressive disorders are 2 categories of mental illnesses characterized by significant prevalence rates,⁴⁴ particularly among individuals aged 60 and older in the global population.⁴⁵ This study employs the Depression Screening Scale (PHQ-9)⁴⁶ and the Generalized Anxiety Disorder Scale (GAD-7)⁴⁷ from the PBICR to evaluate Mental Health levels, as these instruments are widely recognized assessment tools^48,49 and serve as fundamental indicators for assessing the psychological health status of populations in prominent longitudinal studies in the UK.⁵⁰

The PHQ-9 is a self-report depression symptom screening tool developed by Kroenke et al,⁴⁶ containing 9 items and utilizing a four-point scoring system (0 = “not at all” to 3 = “nearly every day”), with a total score range of 0 to 27. Higher scores indicate more severe depressive symptoms. In this study, the Cronbach’s alpha coefficient⁵¹ for this scale is .92. The GAD-7 is a brief screening tool developed by Spitzer et al⁴⁷ for assessing symptoms of generalized anxiety disorder. This scale contains 7 items, also using a four-point scoring system (0 = “not at all” to 3 = “nearly every day”), with a total score range of 0 to 21. Higher scores indicate more severe anxiety symptoms. In this study, the Cronbach’s alpha coefficient for this scale is .94. Due to the different score ranges of the PHQ-9 and GAD-7, and to facilitate direct comparison and comprehensive analysis on a unified scale, as well as to reduce the risk of multicollinearity,⁵² this study first standardizes the total scores of PHQ-9 and GAD-7 into Z-scores, then calculates the average, and multiplies the result by −1 to obtain a composite score representing Mental Health level, where higher scores indicate better Mental Health status.

Independent Variable

ICTs Usage

ICTs usage is measured by the frequency of “personal computer (including tablets)” and “smartphone” usage from a 7-item self-developed scale in the PBICR questionnaire. Older adults utilize ICTs across several contexts, including information retrieval, social engagement, and enjoyment.⁵³ This study, constrained by data restrictions, could not distinguish between specific forms of ICTs usage and instead concentrated on “general ICT usage,” defined as the utilization of PCs, cellphones, and tablets with internet access capabilities.⁵⁴ Moreover, alterations in the awareness and conduct of older individuals are contingent upon the “continuous use” of ICTs.⁵⁵ Numerous research conducted by Cotten et al^56,57 indicate that regular use of ICTs correlates with beneficial results for older persons. This study employs the frequency of “mobile phone” and “computer” usage as indicators, grounded in validation from prior research rather than subjective selection. A four-point scoring system is used for this scale (0 = “rarely used” to 4 = “nearly every day”), and the average of the 2 item scores represents the composite score for ICTs usage, where higher scores indicate higher levels of ICTs usage. The correlation coefficient between the 2 items is r = .39, P < .001, indicating acceptable internal consistency for this composite indicator.

Moderating Variable

Health Literacy Level

Health Literacy level is assessed using the Health Literacy Scale (HLS-SF12; Duong et al 2019) from the PBICR questionnaire. This scale contains 12 items, adopting a four-point scoring system (1 = “very difficult” to 4 = “very easy”), evaluating respondents’ perceived difficulty in handling health information, with a total score range of 12 to 48. This study follows the standard scoring procedure of the scale, first calculating the average score of all items, then using the formula ((average score − 1)/3) × 50 to convert the scores to a standardized score range of 0 to 50, where higher scores indicate higher levels of Health Literacy. This conversion aligns with the European Health Literacy Survey framework,⁵⁸ facilitating clinical interpretation. In this study, the Cronbach’s alpha coefficient for this scale is .92.

Control Variables

Gender: Collected as a nominal variable, participants self-report as male (coded as 0) or female (coded as 1). Age: Collected in an ordinal classification, divided into 5 age groups: 60 to 65 years (1), 66 to 70 years (2), 71 to 75 years (3), 76 to 80 years (4), 81 to 85 years (5). Education Level: Measured as an ordinal variable, categorized into no formal education (1), primary school (2), middle school (3), vocational school (4), high school (5), associate degree (6), bachelor’s degree (7), master’s degree (8), doctoral degree (9). Monthly Income: Collected in an ordinal classification, divided into less than 1500 yuan (1), 1501 to 3000 yuan (2), 3001 to 4500 yuan (3), 4501 to 6000 yuan (4), 6001 to 7500 yuan (5), 7501 to 9000 yuan (6), 9001 to 10 500 yuan (7), 10 501 to 12 000 yuan (8), 12 001 to 13 500 yuan (9), 13 501 to 15 000 yuan (10), and above 15 001 yuan (11). All control variables are presented as single items.

Statistical Analysis

This study verifies the hypotheses by using SPSS29.0.1.0⁵⁹ to conduct descriptive statistical analysis and hierarchical multiple regression analysis. Since the main variables in this study are continuous variables rather than ordinal categorical variables, they satisfy the basic assumptions of hierarchical multiple regression analysis.

Before conducting multiple regression analysis, multicollinearity among variables was assessed by examining the variance inflation factor (VIF). According to the recommendations of Hair et al,⁶⁰ a VIF value of less than 10 is considered an acceptable level, indicating that multicollinearity does not have a significant impact on the regression results. To further reduce the risk of multicollinearity, especially when creating interaction terms, the variables of ICTs usage and Health Literacy were centralized. All statistical tests used a significance level of P < .05.

Results

Descriptive Statistics Results

Based on the descriptive statistics (see Table 1), among the 1112 valid samples collected in this study, the gender ratio of participants was equal (M = 0.50, SD = 0.50); the age was mainly distributed between 66 and 75 years old (M = 2.57, SD = 1.19); the level of education was below average (M = 3.35, SD = 2.01), with the average level being between junior high school and vocational school; the overall Monthly Income was at a low to middle level (M = 3.40, SD = 2.19).

Table 1.

Descriptive Statistics of Key Research Variables (N = 1112).

Variable	Mean	Standard deviation	Minimum value	Maximum value	Skewness	Kurtosis
ICTs usage	1.48	1.18	0.00	4.00	0.26	−0.85
Health literacy level	30.05	8.43	0.00	50.00	0.02	0.89
Mental health level	0.00	0.95	−4.25	0.97	−1.02	0.72
Monthly income	3.40	2.19	1.00	11.00	1.39	2.18
Gender	0.50	0.50	0.00	1.00	0.02	−2.00
Age	2.57	1.19	1.00	5.00	0.24	−0.81
Education level	3.35	2.01	1.00	9.00	0.73	−0.49

The data shows that the frequency of ICTs usage among the study subjects is generally at a low level (M = 1.48, SD = 1.18, theoretical range 0-4), with a slightly positive skew (skewness = 0.26), indicating that most respondents have a low frequency of ICTs usage and that there are significant differences in the respondents’ digital technology application capabilities. This may reflect the imbalance in digital adaptation among the elderly. Although the Health Literacy score (M = 30.05, SD = 8.43, theoretical range 0-50) is close to a normal distribution (skewness = 0.02), reflecting relatively balanced individual differences in Health Literacy among the study subjects, the coefficient of variation based on the standard deviation is 28%. This suggests a substantial but controllable variation within the sample, which aligns with the current situation of considerable educational level differences among the elderly population of different age groups.⁶¹

Correlation Analysis

The results indicate that at a 99% confidence level, there is no significant correlation between the use of ICTs and Mental Health (r = −.035, P = .248). However, Health Literacy shows a significant positive correlation with Mental Health (r = .185, P < .001), suggesting that higher levels of Health Literacy may be associated with better Mental Health. Additionally, there is a strong positive correlation between the use of digital technology and Health Literacy (r = .437, P < .001). Regarding control variables, Monthly Income shows a significant positive correlation with both the use of digital technology (r = .343, P < .001) and Health Literacy (r = .238, P < .001), but no significant correlation with Mental Health (r = .034, P = .258). Educational level displays the strongest correlation with the use of digital technology (r = .520, P < .001), and also shows a significant positive correlation with Health Literacy (r = .360, P < .001). Age exhibits a significant negative correlation with the use of ICTs (r = −.252, P < .001; See Table 2).

Table 2.

Correlation Matrix of Key Research Variables (N = 1112).

Variable	Monthly income	Gender	Age	Education level	ICTs usage	Health literacy level	Mental health level
Monthly income	1
Gender	−.039	1
Age	−.097**	−.006	1
Education level	.488**	−.165**	−.181**	1
ICTs usage	.343**	−.090**	−.252**	.520**	1
Health literacy level	.238**	−.091**	−.120**	.360**	.437**	1
Mental health level	.034	−.04	−.029	.024	−.035	.185**	1

Note: ** P < .01.

Hierarchical Multiple Regression Analysis

Although there is no significant direct relationship between the use of ICTS and Mental Health, there is a significant relationship between Health Literacy and Mental Health, as well as between the use of digital technology and Health Literacy. According to Hayes,⁶² even if there is no significant direct relationship between the independent variable and the dependent variable, a moderating effect may still exist and be theoretically meaningful. Therefore, this study uses a hierarchical regression analysis strategy to explore the moderating effect of Health Literacy to verify the hypothesis.

Before conducting multiple regression analysis, multicollinearity among variables was assessed by examining the variance inflation factor (VIF). Collinearity statistical analysis showed (Table 3) that all variables had VIF values far below the critical value of 5, and tolerance values greater than 0.5, indicating that there was no significant multicollinearity issue among the variables,⁶⁰ allowing for the safe conduct of multiple regression analysis.

Table 3.

Collinearity Statistical Analysis Results.

Variable	Tolerance	VIF
Monthly income	0.748	1.337
Gender	0.967	1.034
Age	0.931	1.074
Education level	0.594	1.684
Centralized ICTs usage	0.63	1.587
Centralized health literacy level	0.782	1.278

In hierarchical multiple regression analysis, control variables (gender, age, income, and education level) were entered in the first layer; main effect variables (ICTs usage and Health Literacy) were added in the second layer; and the interaction term of the 2 was added in the third layer. The results showed that the control variables only explained 0.3% of the variance in Mental Health and were not significant (R² = .003, F-change = 0.923, P = .450), indicating that demographic variables may have little impact on Mental Health. The second layer significantly increased the explanatory power of the model compared to the first layer (ΔR² = .050, F-change = 29.344, P < .001). Notably, after including the main effect variables in the second layer, there was a significant negative relationship between ICTs usage and Mental Health level (B = −0.129, β = −.16, P < .001), suggesting that higher digital use may be associated with lower Mental Health level. Health Literacy level had a significant positive relationship with Mental Health level (B = 0.027, β = .243, P < .001), indicating that in this research the higher the level of Health Literacy may be associated with the higher Mental Health level. However, further analysis of the moderation effect showed that the interaction term of ICTs usage and Mental Health level was not significant (P = .923), and the introduction of the interaction term did not increase the explanatory power of the model (ΔR² = 0.000), indicating that Health Literacy level may not moderate the relationship between ICTs usage and Mental Health (See Table 4).

Table 4.

Results of Hierarchical Multiple Regression Analysis of ICTs Usage and Health Literacy level on mental health level.

Predictor variables	Model 1		Model 2		Model 3
Predictor variables	B (SE)	β	B (SE)	β	B (SE)	β
Constants	0.050 (0.102)		0.079 (0.101)		0.079 (0.101)
Control variables
Monthly income	0.014 (0.015)	.031	0.013 (0.015)	.03	0.013 (0.015)	.03
Gender	−0.074 (0.058)	−.039	−0.061 (0.057)	−.032	−0.061 (0.057)	−.032
Age	−0.022 (0.024)	−.027	−0.031 (0.024)	−.039	−0.031 (0.024)	−.039
Education level	−0.001 (0.017)	−.003	−0.003 (0.018)	−.007	−0.003 (0.018)	−.007
Main effect variables
ICTs usage			−0.129 (0.030)***	−.16	−0.129 (0.030)***	−.16
Health literacy level			0.027 (0.004)***	.243	0.027 (0.004)***	.243
Moderating variable
Interaction term (X × M)					0 (0.003)	−.003
Model statistics
R²	.003		.054		.054
ΔR²	.003		.050***		0
F change	0.923		29.344***		0.009

Note. B = unstandardized regression coefficient; SE = standard error; β = standardized regression coefficient.

***

P < .001.

Discussion

This study explored the complex relationship between the use of ICTs, Health Literacy, and Mental Health, yielding 2 major findings. After controlling for demographic variables and Health Literacy, ICTs usage was significantly negatively related to Mental Health (β = −.129, P < .001); secondly, Health Literacy was significantly positively related to Mental Health (β = .027, P < .001). Notably, although the relationship between ICTs usage and Mental Health was not significant in the zero-order correlation analysis, its negative impact became significant after incorporating Health Literacy, suggesting that Health Literacy may play a suppressor role in this relationship. However, we did not find a moderating effect of Health Literacy on the relationship between ICTs usage and Mental Health (ΔR² = .000).

This study’s findings align with existing literature, while also presenting new discoveries. On one hand, the insignificant relationship between ICTs usage and Mental Health in zero-order correlation analysis is not an exception. The narrative review by Odgers and Jensen⁶³ indicates that the majority of studies on ICTs usage and Mental Health are correlational, showing small and inconsistent associations, with positive, negative, and non-significant results. On the other hand, the significant negative relationship between ICTs usage and Mental Health, after controlling for demographic variables and Health Literacy, supports Wolniewicz et al,⁶⁴ who found that the use of ICTs, such as mobile phones, leads to Mental Health problems or crises. The positive correlation between Health Literacy and Mental Health is consistent with existing literature. The study by Guo et al⁶⁵ suggests that sufficient Health Literacy could be an independent and critical factor in preventing individuals from experiencing adverse Mental Health conditions in the long term. Haeri-Mehrizi et al⁶⁶ found that individuals with lower Health Literacy had 2.56 times the likelihood of having Mental Health issues than those with higher Health Literacy. Improving public Health Literacy may be a crucial way to enhance the psychological status of the population. The study by Dadgarinejad et al⁶⁷ confirmed that raising Health Literacy levels can effectively reduce the incidence of anxiety caused by the fear of the coronavirus.

However, no moderating effect of Health Literacy was found between ICTs and Mental Health level. Instead, Health Literacy in this research may act as a suppressor variable, meaning that without considering Health Literacy, the relationship between ICTs usage and Mental Health may be not significant. Once Health Literacy is controlled for, the negative impact of ICTs usage is “revealed,” showing a significant negative relationship. The suppressor effect refers to a phenomenon where a variable enhances the relationship between 2 other variables by controlling or eliminating the variance influence of other variables.⁶⁸ The predictive validity is evaluated by the magnitude of the regression coefficient.³⁶ This study demonstrates a statistically significant suppression effect (B = −0.129, β = −.16), albeit with a rather small effect size. This may be due to the presence of range constraint among pertinent variables. MacKinnon et al³⁶ asserted that “mediation, confounding, and suppression are specific instances of third-variable effects in multiple regression models.” All adhere to the mathematical principles of parameters in multivariate statistical analysis; specifically, when the value range for either the independent or dependent variable is constrained, the correlation coefficient is likely to be underestimated, subsequently influencing effect sizes derived from correlation .⁶⁹ This study established a theoretical range of Health Literacy scores from 0 to 50; however, the actual sample yielded a mean of 30.05 and a standard deviation of 8.43, with the effective values predominantly clustered in the mid-range (22.66-36.74) and failing to encompass the extreme ranges. Consequently, the modest impact size in this study may be somewhat attributed to sample distribution. Ferguson⁷⁰ noted that small to medium impact sizes are prevalent and has practical significance in psychological and social science research.

Notwithstanding restricted statistical power, the impact of Health Literacy is still significant, indicating potential underlying mechanisms that merit further investigation. Fleary et al⁷¹ found that individuals with higher Health Literacy level might be better at critically analyzing negative attitudes toward Mental Health. In this study, Health Literacy may similarly “purify” the negative effects of ICTs usage. People with high Health Literacy may be better at critically evaluating and using digital technology, thereby avoiding its negative impacts. Research indicates that persons possessing high Health Literacy exhibit superior skills in processing and assessing digital information relative to those with low Health Literacy.^72,73 When obtaining digital health information, individuals typically choose reputable sources such as medical websites and exhibit increased skepticism regarding the trustworthiness, validity, and authenticity of online health information.⁷⁴ They may rectify their assumptions by seeking proper information pertaining to online rumors.⁷⁵ Chowdhury and Gibb’s research⁷⁶ suggests that various factors, including information overload and dispersion throughout the information retrieval process, can heighten anxiety levels. A survey of 380 adults in Korea and Israel indicates that information overload significantly enhances 4 dimensions: negative emotions linked to mental unhealthiness, depressive symptoms, trait anxiety, and trait rage.⁷⁷ Individuals possessing extensive knowledge reserves (Health Literacy) and familiarity with specific information types are less susceptible to feelings of overload when confronted with related information and their sense of information overload diminishes when engaging with familiar content.⁷⁸ Consequently, the anxiety and adverse feelings stemming from information overload may be alleviated. Individuals possessing elevated Health Literacy are less prone to uncritically adhere to trends and are less susceptible to the effects of online social comparison. A study on the particular expression of Health Literacy in social media contexts reveals that social media literacy contributes to mitigating the envy generated by social comparisons in online platforms.⁷⁹ Furthermore, a systematic evaluation of Health Literacy indicates that its meaning encompasses effective self-management and self-adjustment capabilities.⁸⁰ Effective self-regulation facilitates emotional management and the fulfillment of internal demands within the dynamic digital landscape of social media, hence preserving psychological equilibrium.⁸¹

The results of this study may be intricately linked to China’s own cultural values and practices. In Chinese older individuals, Health Literacy’s “information discernment ability” may be more evident in “identifying official and reliable sources” than merely in “information screening skills.” Older adults often perceive traditional ICTs (eg, CCTV) and official government and community channels (eg, community WeChat groups) as credible sources of information, demonstrating a greater propensity to focus on health information disseminated by professional medical institutions via WeChat.^82,83 Research indicates that older adults who regularly engage with traditional media exhibit more proficiency in discerning health information on WeChat, hence mitigating the detrimental effects of harmful online content.^84,85 Moreover, the health knowledge possessed by middle-aged and older individuals mostly comprises fundamental information that can be readily utilized and they tend to depend on their prior life experiences to evaluate the information they encounter and more inclined to dismiss information that contradicts their own beliefs, rather than seeking out and validating it.^86,87 This form of “pragmatic” Health Literacy may assist in mitigating anxiety induced by information overload when utilizing technological technologies. Furthermore, in light of the rising incidence of online fraud aimed at the elderly in recent years, certain provinces and localities have initiated “digital literacy” programs for seniors, and pertinent organizations have created digital learning resources, thereby enhancing the elderly’s knowledge of digital fraud.⁸⁸ Moreover, middle-aged and elderly individuals tend to be inherently more careful and watchful,⁸⁹ hence, older adults with excellent Health Literacy may be more adept at safeguarding themselves against detrimental online information.

In light of our findings, future Mental Health management for the elderly should adopt a dual intervention strategy, focusing both on reducing harmful ICTs usage and enhancing Health Literacy levels. This also supports the development of more scientific guidelines for ICTs usage, especially for groups vulnerable to Mental Health issues, by setting reasonable usage limits and recommending best practices. Finally, Health Literacy education should be integrated into community health programs for the elderly as an important protective factor for improving Mental Health.

Limitations

First, despite the considerable size of sample in this study, the comprehensive analytical method could not adequately investigate the correlations between Health Literacy, ICTs utilization, and Mental Health across various age, urban-rural, and gender demographics. A national survey in China conducted heterogeneity analyses across various age, urban-rural, and gender groups, revealing that the enhancement of risk identification through digital literacy is more pronounced among rural residents due to their limited digital technology foundation. The preventive effect is more pronounced among women and the elderly.⁸⁹ Future studies may further examine the mechanisms underlying disparities among distinct subpopulations, cultural, economic, and infrastructural factors to augment the universality of the findings and improve the relevance of policy suggestions.

Second, the utilization of ICTs was assessed only using 2 metrics: the frequency of computer usage and the frequency of smartphone usage. While these may illustrate prevalent daily ICTs behaviors, they may not sufficiently distinguish between different types of ICTs (such as social media, gaming, information retrieval, etc.) or the “purposes of use” and acceptance levels among older adults, which could have varying effects on Mental Health. Previous research indicates that the utilization of ICTs for leisure activities or social interactions positively influences the Mental Health of older adults.^55,90 Conversely, when older adults are compelled to use ICTs for “involuntary tasks,” such as managing government affairs or scheduling vaccine appointments during the pandemic, these technologies may adversely affect their Mental Health.⁹¹ Future studies should develop more comprehensive metrics of ICTs usage to elucidate the correlation between ICTs utilization and Mental Health more accurately.

Third, the scales employed in this study primarily concentrated on the 2 aspects of depression and anxiety in Mental Health, excluding other pertinent dimensions of Mental Health. In the future, more comprehensive Mental Health metrics, such as subjective well-being, may be integrated to enhance the explanatory breadth of the research findings.

Forth, Although the inhibition effect was identified, the study did not explore the specific mechanisms by which ICTs usage affects Mental Health. Additionally, this study employed conventional Health Literacy scale data and did not thorough Health Literacy assess participants’ digital Health Literacy levels. Future research might focus on digital Health Literacy, a variable more closely related to the study topic, to further explore its role in the relationship between ICTs usage and Mental Health. Moreover, further validation of this inhibition mechanism is necessary, along with the investigation of other potential moderating or mediating variables or adopting longitudinal or mixed-methods approaches to gain a deeper understanding of the underlying mechanisms.

Conclusion

Based on the results of this study, no moderating effect of Health Literacy was found between digital technology and Mental Health; instead, Health Literacy may act as a suppressor variable. When Health Literacy was not controlled, the relationship between ICTs usage and Mental Health was not significant. However, after controlling for Health Literacy, a significant negative impact of ICTs usage on Mental Health emerged. This indicates that high Health Literacy may offset the negative impact of digital technology. Therefore, future Mental Health management for the elderly should adopt a dual intervention strategy, focusing on reducing harmful ICTs usage and enhancing Health Literacy levels.

Footnotes

Acknowledgements

Sincere gratitude to the research team of the Psychological and Behavioral Investigation of Chinese Residents (PBICR) for their diligent efforts in conducting the survey and meticulously cleaning the data for public use. Their commitment to making this valuable dataset accessible to the research community has made this study possible.

Authors Note

Ailifeila Akepaer (1994—), female, master’s degree in social work, graduated from Xinjiang University in 2020; From November 2020 to November 2023, worked as a college counselor at Xinjiang Medical University, specializing in the mental health of college students; Currently studying as a vocational graduate student majoring in English Language and Literature at Renmin University of China. She is currently an independent researcher.

ORCID iD

Ailifeila Akepaer

Ethical Considerations

This study utilized data from the 2021 Psychological and Behavioral Investigation of Chinese Residents (PBICR), which was accessed entirely through public channels. The research did not involve any direct interaction with human subjects, and the dataset contained no personally identifiable information. Therefore, in accordance with institutional guidelines for secondary data analysis of publicly available, anonymized datasets, this study was exempt from formal ethical review. The research was conducted in compliance with relevant ethical standards for data management and reporting of public health information

Consent for Publication

The author consents to the publication of this manuscript in this journal.

Author Contributions

As the sole author, I was responsible for all aspects of this study including conception, design, data collection, analysis, and manuscript preparation.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

The data used in this study are from the 2021 Psychological and Behavioral Investigation of Chinese Residents (PBICR), which is accessible to researchers upon reasonable request. The PBICR questionnaire and related information are publicly available on the PBICR website (). The de-identified datasets analyzed during this study are not publicly available but can be obtained from the PBICR research team upon reasonable request (pbicr_zgjmxlyxwdc@163.com).

References

World Health Organization. Ageing. 2022. Accessed June 1, 2025. https://www.who.int/health-topics/ageing#tab=tab_1

Pandey

Tripathi

Kar

Vidya

Singh

Mental Health Promotion for elderly populations in World Health Organization South-East Asia region: needs and resource gaps. World J Psychiatry. 2022;12(1):117-127. doi:10.5498/wjp.v12.i1.117

Ribeiro-Gonçalves

Costa

Leal

Loneliness, ageism, and mental health: the buffering role of resilience in seniors. Int J Clin Health Psychol. 2023;23(1):100339. doi:10.1016/j.ijchp.2022.100339

World Health Organization. Mental health of older adults. 2017. Accessed June 1, 2025. https://www.who.int/news-room/fact-sheets/detail/mental-health-of-older-adults

Liu

Xia

, et al The mediation role of sleep quality in the relationship between cognitive decline and depression. BMC Geriatr. 2022;22(1):178. doi:10.1186/s12877-022-02855-5

Zhou

Wang

Yan

, et al Association between chronic diseases and depression in the middle-aged and older adult Chinese population-a seven-year follow-up study based on charls.. Front Public Health. 2023;11:1176669. doi:10.3389/fpubh.2023.1176669

Jiang

GR.

Suicidal behavior: influencing factors, theoretical models and research prospects. Adv Psychol Sci. 2015;23:1437-1452.

Guo

Cui

Xia

, et al Association between health literacy, depressive symptoms, and suicide-related outcomes in adolescents: a longitudinal study. J Affect Disord. 2023;327:15-22. doi:10.1016/j.jad.2023.01.054

Cunningham

Sullivan

Caserotti

Tully

P, MA

, Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports. 2020;30(5):816-827. doi:10.1111/sms.13616

10.

Sun

Tang

Social frailty is associated with physical functioning, cognition, and depression, and predicts mortality. J Nutr Health Aging. 2018;22(8):989-995. doi:10.1007/s12603-018-1054-0

11.

Zhang

Chen

Depression and cardiovascular disease in elderly: current understanding. J Clin Neurosci. 2018;47:1-5. doi:10.1016/j.jocn.2017.09.022

12.

Meng

Liu

, et al Association of depression with all-cause and cardiovascular disease mortality among adults in China. JAMA Netw Open. 2020;3(2):e1921043. doi:10.1001/jamanetworkopen.2019.21043

13.

Fang

Chau

AKC

Wong

Fung

Woo

Information and communicative technology use enhances psychological well-being of older adults: the roles of age, social connectedness, and frailty status. Aging Ment Health. 2018;22(11):1516-1524. doi:10.1080/13607863.2017.1358354

14.

Lifshitz

Nimrod

Bachner

YG.

Internet use and well-being in later life: a functional approach. Aging Ment Health. 2018;22(1):85-91. doi:10.1080/13607863.2016.1232370

15.

Neves

Franz

Judges

Beermann

Baecker

Can digital technology enhance social connectedness among older adults? A feasibility study. J Appl Gerontol. 2019;38(1):49-72. doi:10.1177/0733464817741369

16.

Shapira

Barak

Gal

Promoting older adults’ well-being through Internet training and use. Aging Ment Health. 2007;11(5):477-484. doi:10.1080/13607860601086546

17.

Peng

Lyu

MH.

Will using the internet make older adults feel happier? Empirical research from CGSS data. Nanjing Soc Sci. 2019;10(384):57-68.

18.

Naslund

Aschbrenner

Marsch

Bartels

SJ.

The future of mental health care: peer-to-peer support and social media. Epidemiol Psychiatr Sci. 2016;25(2):113-122. doi:10.1017/S2045796015001067

19.

Torous

Roberts

LW.

Needed innovation in digital health and smartphone applications for mental health: transparency and trust. JAMA Psychiatr. 2017;74(5):437-438. doi:10.1001/jamapsychiatry.2017.0262

20.

Kardefelt-Winther

Rees

Livingstone

Contextualising the link between adolescents’ use of digital technology and their mental health: a multi-country study of time spent online and life satisfaction. J Child Psychol Psychiatry. 2020;61(8):875-889. doi:10.1111/jcpp.13280

21.

Twenge

Joiner

Rogers

Martin

GN.

Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci. 2018;6(1):3-17. doi:10.1177/2167702617723376

22.

Elhai

Dvorak

Levine

Hall

BJ.

Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disord. 2017;207:251-259. doi:10.1016/j.jad.2016.08.030

23.

Xie

Yang

Lin

, et al

Does the Internet use improve the mental health of Chinese older adults?

Front Public Health. 2021;9:673368. doi:10.3389/fpubh.2021.673368

24.

Yuan

Internet use and mental health problems among older people in Shanghai, China: the moderating roles of chronic diseases and household income. Aging Ment Health. 2021;25(4):657-663. doi:10.1080/13607863.2020.1711858

25.

Baker

DW.

The meaning and the measure of health literacy. J Gen Intern Med. 2006;21(8):878-883. doi:10.1111/j.1525-1497.2006.00540.x

26.

Fiedler

Pfaff

Soellner

Pförtner

TK.

Exploring the association between health literacy and psychological well-being among industry managers in Germany. J Occup Environ Med. 2018;60(8):743-753. doi:10.1097/jom.0000000000001324

27.

Zhang

PPL

Chung

JWY

. How different health literacy dimensions influences health and well-being among men and women: the mediating role of health behaviours. Health Expect. 2021;24(2):617-627. doi:10.1111/hex.13208

28.

Ghorbanian Zolbin

Huvila

Nikou

. Health literacy, health literacy interventions and decision-making: a systematic literature review. J Doc. 2022;78(7):405-428. doi:10.1108/JD-01-2022-0004

29.

Azzopardi-Muscat

Sørensen

Towards an equitable digital public health era: promoting equity through a health literacy perspective. Eur J Public Health. 2019;29(Supplement_3):13-17. doi:10.1093/eurpub/ckz166

30.

Bodie

Dutta

MJ.

Understanding health literacy for strategic health marketing: eHealth literacy, health disparities, and the digital divide. Health Mark Q. 2008;25(1-2):175-203. doi:10.1080/07359680802126301

31.

Dunn

Hazzard

Technology approaches to digital health literacy. Int J Cardiol. 2019;293:294-296. doi:10.1016/j.ijcard.2019.06.039

32.

Del Giudice

Bravo

Poletto

, et al Correlation between eHealth Literacy and health literacy using the eHealth literacy scale and real-life experiences in the health sector as a proxy measure of functional health literacy: cross-sectional web-based survey. J Med Internet Res. 2018;20(10):e281. doi:10.2196/jmir.9401

33.

Fast

Deibert

Hruby

Glassberg

KI.

Evaluating the quality of Internet health resources in pediatric urology. J Pediatr Urol. 2013;9(2):151-156. doi:10.1016/j.jpurol.2012.01.004

34.

Meskó

Drobni

Bényei Gergely

Győrffy

Digital health is a cultural transformation of traditional healthcare. mHealth. 2017;3:38. doi:10.21037/mhealth.2017.08.07

35.

Baron

Kenny

DA.

The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173-1182. doi:10.1037/0022-3514.51.6.1173

36.

MacKinnon

Krull

Lockwood

CM.

Equivalence of the mediation, confounding and suppression effect. Prev Sci. 2000;1(4):173-181. doi:10.1023/a:1026595011371

37.

Sansakorn

Mushtaque

Awais-E-Yazdan

Dost

MKB

. The relationship between cyberchondria and health anxiety and the moderating role of health literacy among the Pakistani public. Int J Environ Res Public Health. 2024;21(9):1168. doi:10.3390/ijerph21091168

38.

PBICR Database. X-MOL website. Accessed June 11, 2025. https://www.x-mol.com/groups/pbicr/research

39.

Fan

Liu

Sun

Psychological and behavior investigation of Chinese residents: concepts, practices, and prospects. Chin Gen Pract J. 2024;1(3):149-156. doi:10.1016/j.cgpj.2024.07.006

40.

Chen

Wang

, et al Relationship between sleep duration and sociodemographic characteristics, mental health and chronic diseases in individuals aged from 18 to 85 years old in Guangdong province in China: a population-based cross-sectional study. BMC Psychiatry. 2020;20(1):455. doi:10.1186/s12888-020-02866-9

41.

Bowling

Farquhar

Browne

Use of services in old age: data from three surveys of elderly people. Soc Sci Med. 1991;33(6):689-700. doi:10.1016/0277-9536(91)90023-6

42.

von Elm

Altman

Egger

Pocock

Gøtzsche

Vandenbroucke

JP.

Strengthening the reporting of observational studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335(7624):806-808.

43.

Headey

Kelley

Wearing

Dimensions of mental health: life satisfaction, positive affect, anxiety and depression. Soc Indic Res. 1993;29(1):63-82. doi:10.1007/bf01136197

44.

Kalin

NH.

The critical relationship between anxiety and depression. Am J Psychiatr. 2020;177(5):365-367. doi:10.1176/appi.ajp.2020.20030305

45.

World Health Organization. Mental health of older adults. 2017. Accessed September 1, 2025. https://www.who.int/en/news-room/fact-sheets/detail/mental-health-of-older-adults

46.

Kroenke

Spitzer

Williams

JB.

The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613. doi:10.1046/j.1525-1497.2001.016009606.x

47.

Spitzer

Kroenke

Williams

Löwe

A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-1097. doi:10.1001/archinte.166.10.1092

48.

Mukuria

Franklin

Hinde

Mapping functions for the PHQ-9 and GAD-7 to generate EQ-5D-3L for economic evaluation. Eur J Health Econ. 2025;26(1):63-70. doi:10.1007/s10198-024-01692-0

49.

Robeson

Brasil

Adams

Zlomke

KR.

Measuring depression and anxiety in autistic college students: a psychometric evaluation of the PHQ-9 and GAD-7. Autism. 2024;28(11):2793-2805. doi:10.1177/13623613241240183

50.

Shevlin

Butter

McBride

, et al Measurement invariance of the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) across four European countries during the COVID-19 pandemic. BMC Psychiatry. 2022;22(1):154. doi:10.1186/s12888-022-03787-5

51.

Cronbach

LJ.

Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297-334. doi:10.1007/BF02310555

52.

Hill

Ziemer

RF.

The risk of general stein-like estimators in the presence of multicollinearity. J Econometrics. 1984;25(1-2):205-216. doi:10.1016/0304-4076(84)90047-2

53.

Wagner

Hassanein

Head

Computer use by older adults: a multi-disciplinary review. Comput Human Behav. 2010;26(5):870-882. doi:10.1016/j.chb.2010.03.029

54.

Khosravi

Rezvani

Wiewiora

The impact of technology on older adults’ social isolation. Comput Human Behav. 2016;63:594-603. doi:10.1016/j.chb.2016.05.092

55.

Kokubun

Ogawa

Browne

, et al Social capital mediates the association between the ICT usage and well-being of older people in Japan: implication for a new design paradigm. Sustainability. 2022;14(7):4148. doi:10.3390/su14074148

56.

Cotten

Anderson

McCullough

BM.

Impact of internet use on loneliness and contact with others among older adults: cross-sectional analysis. J Med Internet Res. 2013;15(2):e39. doi:10.2196/jmir.2306

57.

Cotten

Ford

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. doi:10.1093/geronb/gbu018

58.

Sørensen

Van den Broucke

Pelikan

, et al Measuring Health Literacy in populations: illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q). BMC Public Health. 2013;13(1):948. doi:10.1186/1471-2458-13-948

59.

IBM Corp. IBM SPSS Statistics. Version 29.0.1.0. IBM Corp; 2023. https://www.ibm.com/products/spss-statistics

60.

Hair

Black

Babin

Anderson

Tatham

, eds. Multivariate Data Analysis. 6th ed. Pearson Prentice Hall; 2006.

61.

Chinese Social Science Network. Constructing a Favorable Environment for Human Resource Development of Young-Old Population. Chinese Social Science Network. Published February 22, 2023. Accessed July 18, 2025. https://www.cssn.cn/skgz/skwyc/202302/t20230222_5590153.shtml [in Chinese].

62.

Hayes

AF.

Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford Publications; 2017.

63.

Odgers

Jensen

MR.

Annual research review: adolescent mental health in the digital age: facts, fears, and future directions. J Child Psychol Psychiatry. 2020;61(3):336-348. doi:10.1111/jcpp.13190

64.

Wolniewicz

Tiamiyu

Weeks

Elhai

JD.

Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. Psychiatry Res. 2018;262:618-623. doi:10.1016/j.psychres.2017.09.058

65.

Guo

Zhang

Huang

Zheng

Pan

Zheng

JZ.

Mental Health and related influencing factors among the empty-nest elderly and the non-empty-nest elderly in Taiyuan, China: a cross-sectional study. Public Health. 2016;141:210-217. doi:10.1016/j.puhe.2016.09.005

66.

Haeri-Mehrizi

Mohammadi

Rafifar

, et al Health literacy and mental health: a national cross-sectional inquiry. Sci Rep. 2024;14(1):13639. doi:10.1038/s41598-024-64656-7

67.

Dadgarinejad

Nazarihermoshi

Hematichegeni

, et al Relationship between health literacy and generalized anxiety disorder during the COVID-19 pandemic in Khuzestan province, Iran. Front Psychol. 2023;14:1294562. doi:10.3389/fpsyg.2023.1294562

68.

Preacher

Hayes

AF.

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-891. doi:10.3758/brm.40.3.879

69.

Cohen

West

Aiken

LS.

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge; 2013.

70.

Ferguson

CJ.

An effect size primer: a guide for clinicians and researchers. Prof Psychol Res Pr. 2009;40(5):532-538. doi:10.1037/a0015808

71.

Fleary

Joseph

Gonçalves

Somogie

Angeles

The relationship between health literacy and mental health attitudes and beliefs. Heal Lit Res Pr. 2022;6(4):e270-e279. doi:10.3928/24748307-20221018-01

72.

Chen

Huy

Lin

, et al Association of digital Health Literacy with future anxiety as mediated by information satisfaction and fear of COVID-19: a pathway analysis among Taiwanese students. Int J Environ Res Public Health. 2022;19:15617. doi:10.3390/ijerph192315617

73.

Diviani

van den Putte

Giani

van Weert

JC.

Low health literacy and evaluation of online health information: a systematic review of the literature. J Med Internet Res. 2015;17(5):e112. doi:10.2196/jmir.4018

74.

Chen

Hay

Waters

, et al Health literacy and use and trust in health information. J Health Commun. 2018;23:724-734. doi:10.1080/10810730.2018.1511658

75.

Lee

When do people verify and share health rumors on social media? The effects of message importance, health anxiety, and health literacy. J Health Commun. 2019;24:837-847. doi:10.1080/10810730.2019.1677824

76.

Chowdhury

Gibb

Landoni

. A model of uncertainty and its relation to information seeking and retrieval (IS&R). J Doc. 2014;70(4):575-604. doi:10.1108/JD-05-2013-0060

77.

Swar

Hameed

Reychav

Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search. Comput Human Behav. 2017;70:416-425. doi:10.1016/j.chb.2016.12.068

78.

Crook

Stephens

Pastorek

Mackert

Donovan

EE.

Sharing health information and influencing behavioral intentions: the role of Health Literacy, information overload, and the Internet in the diffusion of healthy heart information. Health Commun. 2016;31:60-71. doi:10.1080/10410236.2014.936336

79.

Schreurs

Meier

Vandenbosch

Exposure to the positivity bias and adolescents’ differential longitudinal links with social comparison, inspiration and envy depending on social media literacy. Curr Psychol. 2023;42(32):28221-28241. doi:10.1007/s12144-022-03893-3

80.

Liu

Wang

Liu

, et al What is the meaning of health literacy? A systematic review and qualitative synthesis. Fam Med Community Health. 2020;8(2):e000351. doi:10.1136/fmch-2020-000351

81.

Reinecke

Gilbert

Eden

Self-regulation as a key boundary condition in the relationship between social media use and well-being. Curr Opin Psychol. 2022;45:101296. doi:10.1016/j.copsyc.2021.12.008

82.

Chai

How has the nationwide public health emergency of the COVID-19 pandemic affected older Chinese adults’ health literacy, health behaviors and practices, and social connectedness? Qualitative evidence from urban China. Front Public Health. 2022;9:774675. doi:10.3389/fpubh.2021.774675

83.

Wang

Zhuang

Shao

Exploring health information sharing behavior of Chinese elderly adults on WeChat. Healthcare. 2020;8(3):207. doi:10.3390/healthcare8030207

84.

Wei

The impact of internet participation on social trust among the elderly: evidence from CGSS2018 data. Renkou Xuekan. 2023;45(01):54-67 [in Chinese]. doi:10.16405/j.cnki.1004-129X.2023.01.007

85.

Wang

Zhou

Elderly people’s ability to identify health information on social media and the factors influencing its improvement. Tushu Qingbao Gongzuo. 2022;66(24):14-29 [in Chinese]. doi:10.13266/j.issn.0252-3116.2022.24.002

86.

Meng

Yin

Research on elderly-friendly media adaptation in new media communication: Taking smartphones and apps such as WeChat and TikTok as examples. Chuan Mei Guan Cha. 2021;(08):74-79 [in Chinese]. doi:10.19480/j.cnki.cmgc.2021.08.006

87.

Wang

Shen

Zhou

, et al Analysis of the status quo and influencing factors of health information avoidance behavior in AIDS prevention among middle-aged and elderly people in rural areas. Xiandai Yufang Yixue. 2025;52(11):2015-2020 [in Chinese]. doi:10.20043/j.cnki.MPM.202502037

88.

Sun

Zhang

Zhong

Digital Inclusion among the elderly: status analysis, attribution reasoning and relief Strategies—Based on 2021 CGSS analysis. Adult Educ. 2025;45(02):44-52 [in Chinese].

89.

Does digital literacy help residents avoid becoming victims of frauds? Empirical evidence based on a survey of residents in six provinces of east China. Int Rev Econ Finance. 2024;91:364-377. doi:10.1016/j.iref.2024.01.056

90.

Chi

Internet use and loneliness of older adults over time: the mediating effect of social contact. J Gerontol B Psychol Sci Soc Sci. 2021;76(3):541-550. doi:10.1093/geronb/gbaa004

91.

Park

Zeng

Zhao

Tong

Information communication technology accessibility and mental health for older adults during the coronavirus disease in South Korea. Front Public Health. 2023;11:1126900. doi:10.3389/fpubh.2023.1126900