Path analysis of the influence of digital health literacy on self-management behaviour among elderly patients with chronic diseases in rural China

Path analysis of the influence of digital health literacy on self-management behaviour among elderly patients with chronic diseases in rural China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Path analysis of the influence of digital health literacy on self-management behaviour among elderly patients with chronic diseases in rural China Xuefang Liu, Xiaomin Gan, Guangqin Ren, Zhongrui Mao, Jiuying Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5647182/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in BMC Geriatrics → Version 1 posted 8 You are reading this latest preprint version Abstract Background : Chronic disease self-management is very important for the progression and treatment of diseases worldwide. The management of chronic diseases among elderly individuals in rural areas is an urgent public health concern in China. The purpose of this study was to investigate the relationship between digital health literacy and chronic disease self-management behaviour in elderly Chinese patients with chronic diseases in rural areas, as well as the chain mediating effects of social support and depression. The objective was to provide a scientific basis for improving the active health behaviour of rural elderly patients with chronic diseases in China and worldwide. Methods : Using convenience sampling, the survey subjects were elderly patients with chronic diseases in rural areas of Anhui Province, China. A self-designed questionnaire was used to collect general survey data, digital health literacy scale scores, social support scale scores, depression scale scores, and chronic disease self-management behaviour scale scores. Common method bias tests, descriptive statistics and correlation analyses were performed via SPSS 29.0. The structural equation model was constructed and tested via AMOS 27.0. Differences for which P <0.05 were considered statistically significant. Results: In all, 202 elderly patients with chronic diseases who resided in rural areas were enrolled. The digital health literacy score was 39.25±9.00 points, and the chronic disease self-management behaviour score was 27.82±9.56 points. The self-management behaviours of rural elderly patients with chronic diseases were positively correlated with digital health literacy and social support and were negatively correlated with depression ( P < 0.01). After the mediating effect test, the total indirect effect value of social support and depression was 0.167, which accounted for 36.07% of the total effect. Among them, social support and depression were partial mediators of digital health literacy and chronic disease self-management behaviour, with effect values of 0.055 (95% CI: 0.012, 0.127) and 0.094 (95% CI: 0.024, 0.201), which accounted for 11.88% and 20.3% of the total effect, respectively. Social support and depression were chain mediators of digital health literacy and chronic disease self-management behaviour, with an effect value of 0.018 (95% CI: 0.004, 0.055) and an effect share of 3.89%. Conclusion: The self-management level of elderly patients with chronic diseases in rural China is low. Digital health literacy not only directly affects the chronic disease self-management behaviour of elderly individuals but also indirectly predicts chronic disease self-management behaviour through the mediating effects of social support and depression. Digital health literacy Self-management behaviour of chronic diseases Social support Depression Chain mediation analysis Rural elderly patients with chronic diseases Figures Figure 1 Figure 2 Background The global population is ageing rapidly, and thus the population of elderly patients with chronic diseases continues to increase. According to a 2018 report from the World Health Organization, approximately 41.1 million deaths worldwide were caused by chronic noncommunicable diseases, which accounted for 71% of all deaths, and it is estimated that this figure may reach 52 million by 2030[ 1 ]. More than 180 million elderly individuals in China have chronic noncommunicable diseases that account for 75% of all cases of chronic disease, 88.5% of the total deaths and 70% of the total disease burden[ 2 , 3 ]. Therefore, medical service systems face enormous challenges. Rural elderly individuals are most strongly impacted by chronic diseases, thereby constituting a public health issue in China that urgently requires resolution. Chronic disease self-management refers to the undertaking of preventive or therapeutic health care activities by individuals with the assistance of health care professionals[ 4 ]. For rural elderly individuals with low educational levels, low economic income, and low availability of medical and health resources, the self-health management model is an affordable and highly efficient model for improving quality of life and preventing disease. In recent years, ageing and digitalization have become mainstream trends worldwide. However, due to the limitations of physiological and psychological factors, elderly individuals have limited levels of acceptance and application of digital media[ 5 , 6 ], which hinders the digital-based, precise, and long-acting development of smart elder care systems. Digital health literacy is a skill that should be mastered by elderly patients with chronic diseases[ 7 ]. Many scholars have noted that the current self-management behaviour among individuals with chronic diseases in a digital environment is limited by low levels of digital health literacy[ 8 – 10 ]. However, few studies have explored the mechanism underlying the influence of digital health literacy on chronic disease self-management behaviour; furthermore, most of the relevant studies have focused on a single chronic disease, such as hypertension[ 11 ], diabetes[ 9 ], or chronic heart failure[ 8 , 12 ]. Few studies have examined chronic disease groups as a whole[ 13 ], and even fewer studies have focused on chronic disease groups among rural elderly individuals. Therefore, the current study examined two important antecedent variables of chronic disease self-management behaviour, i.e., social support and depression, on the basis ofaccording to the biopsychosocial medicine model, and constructed a chain mediation model. The aim of this study was to elucidate the mechanism underlying the effect of digital health literacy on chronic disease self-management behaviour among rural elderly patients with chronic diseases and to provide a scientific basis for improving active health behaviours among rural elderly patients with chronic diseases in China and worldwide. Digital health literacy refers to the ability to use digital technology to search, select, evaluate, and apply online health information and to interact with doctors or service organizations online[ 14 , 15 ]. In recent years, the rapid development of the internet and the extensive integration of intelligent digital technologies have yielded considerable changes in the management model of chronic diseases, which has led to the integration of chronic disease medicine and prevention characterized by digitization, intelligence, and informatization[ 16 ]. In this context, the role of digital health literacy in improving individuals’ self-health management ability and improving health outcomes has become increasingly important[ 17 – 19 ]. Patients can not only consult a doctor who can evaluate their condition online, but they can also search for a large amount of health information with the aim of improving their health status[ 20 ]. Especially during the COVID-19 pandemic, the attention to the importance of self-health care has been widely raised[ 21 ], and digital tools (e.g., online consultation, and health apps) became an effective way to manage the health of patients with chronic diseases.The self-management behaviour of patients with chronic diseases may be limited according to their level of e-health literacy. Patients with chronic diseases who have higher levels of e-health literacy are more aware of the utility of internet health resources for health management and are more willing to use health applications[ 8 , 22 ]. Additionally, many domestic studies of patients with a single chronic disease, such as diabetes, stroke, or chronic heart failure, have confirmed the positive correlation between digital health literacy and health management behaviour [ 12 , 23 , 24 ]. Therefore, Hypothesis H1 is proposed as follows: Digital health literacy is positively associated with the self-management behaviour of rural elderly patients with chronic diseases. The concept of social support comes from psychiatric research in the 1960s and refers to the material and spiritual support that an individual can obtain from family, friends, and colleagues when facing stress; this support includes the establishment of good intimate relationships with others, social participation, helping others, recognition of self-worth and receiving help from others[ 25 ]. Many previous studies have confirmed the close relationship between higher levels of e-health literacy and higher levels of social support[ 8 , 26 , 27 ]. It has been suggested that a high level of digital health literacy could lead to higher levels of social support. Additionally, as a social determinant of individual health, social support strongly affects individuals’ emotions, quality of life, and health outcomes. Many previous studies have confirmed the significant positive correlation between social support and self-management behaviour in elderly patients with chronic diseases[ 28 – 32 ]. In other words, higher levels of social support can better enable individuals to manage their own disease, thereby leading to the maintenance of good physical health. Therefore, Hypothesis H2 is proposed as follows: Social support mediates the relationship between digital health literacy and self-management behaviour among rural elderly patients with chronic diseases (H2a: Digital health literacy is positively correlated with social support among rural elderly patients with chronic diseases; H2b: Social support is positively associated with self-management behaviour among rural elderly patients with chronic diseases). Depression is one of the most common mental health problems among the elderly. Numerous studies have shown that mental health status is correlated with self-management ability among patients with chronic diseases. Chen[ 11 ] noted that mental health was significantly associated with self-management ability in patients with hypertensive kidney disease and emphasized that mental health is the most important explanatory variable in the self-management of these patients. Zhang[ 29 ] also confirmed that well-being has an important effect on the self-management behaviour of elderly hypertensive patients and that well-being is positively correlated with disease self-management and lifestyle management. Additionally, the mental health status of an individual is related to the individual’s level of digital health literacy. Studies have shown a significant negative correlation between e-health literacy and psychological problems such as insomnia, anxiety and depression, which indicates that improvements in e-health literacy can help mitigate these psychological problems[ 33 , 34 ]. Therefore, Hypothesis H3 is proposed as follows: Depression mediates the relationship between digital health literacy and self-management behaviour among rural elderly patients with chronic diseases (including H3a: Digital literacy is negatively associated with depression among rural elderly patients with chronic diseases; H3b: Depression is negatively associated with self-management behaviours among rural elderly patients with chronic diseases). In summary, individuals with higher levels of digital health literacy are prone to receiving more social support from different groups and are thus more likely to perceive the benefits of social support. According to the social support theory, good social support can promote physical and mental health, and strong evidence suggests that social support can effectively alleviate patients' psychological distress; i.e., a relatively high level of social support can effectively alleviate depression, anxiety, loneliness and other psychological problems among elderly patients with chronic diseases [ 17 , 35 – 37 ]. This alleviation of psychological problems in turn improves the overall health and quality of life of individuals. Therefore, Hypothesis H4 is proposed as follows: Social support and depression play a chain mediating role in the relationship between digital health literacy and self-management behaviour among rural elderly patients with chronic diseases. Methods Study design and participants Using a convenience sampling method, the group recruited 20 students from a medical university in Anhui Province, who were from the rural areas of six cities in Anhui Province: Hefei, Huangshan, Fuyang, Wuhu, Chuzhou, Bengbu, and Anqing. The researchers were trained to conduct questionnaire surveys from July to September 2024 to the elderly in their respective villages. Since rural elderly people generally have a low level of education and limited literacy, the survey process was based on collecting data by asking questions one by one by the researcher, responding by the respondents, and filling in the questions after the researcher confirmed the answers.The inclusion criteria were as follows: aged ≥ 60 years; diagnosed with one or more chronic diseases (based on definitions provided by the International Classification of Diseases (ICD-10)), including diabetes, hypertension, coronary heart disease, and chronic obstructive pulmonary disease, by a secondary or one of the above medical institutions; rural residents; access to the internet via smart devices; no severe cognitive impairment; good communication skills; and voluntarily participated in this study and signed an informed consent form. The exclusion criterion was diagnosis of a critical illness, such as hearing impairment, serious vital organ or somatic diseases, and advanced malignant tumours, which may have inhibited participants from cooperating with the investigation. A total of 237 rural elderly people were surveyed by 20 researchers, of whom 35 were unwilling to continue answering and terminated early, which led to 35 questionnaires with missing data, and finally 202 valid questionnaires were obtained in this study, with an effective recovery rate of 85.23%. This study was ethically approved by the Biomedical Ethics Committee of Anhui Medical University (No. 83243452). Measures General information system (GIS) questionnaire The following general demographic data were collected: sex, age, educational level, retirement income, marital status, preretirement occupation, and medical payment methods. The following disease-related data were collected: years of illness, number of illnesses, and disease burden. Digital Health Literacy Assessment Scale The Digital Health Literacy Assessment Scale was developed by Siqi Liu[ 38 ]. This scale includes 15 items across 3 dimensions: digital health information acquisition and assessment ability, digital health information interaction ability, and digital health information application ability. Each item was scored on a 5-point Likert scale, and the maximum score was 75 points. Higher scores indicate higher levels of digital health literacy. In this study, the Cronbach’s α coefficient for the Digital Health Literacy Assessment Scale was 0.880. Social Support Rating Scale (SSRS) The Social Support Rating Scale [ 25 ] was developed by Shuiyuan Xiao to assess the degree of perceived social support among individuals. The SSRS includes 10 items across three dimensions: subjective support, objective support, and the utilization of social support. Due to the basic situation of the population included in the current study, Question 4 was deleted; thus, 9 items appeared on the questionnaire. Each item was scored on a 4-point Likert scale, and the maximum score was 62 points. Higher scores indicate a higher level of perceived social support. A total score ≤ 18 indicates a low level of social support, a score between 18 and 40 indicates a moderate level of social support, and a score > 40 indicates a high level of social support. The Cronbach’s α coefficient of this scale in this study was 0.727. Self-Rating Depression Scale (SDS) The Self-Rating Depression Scale (SDS) was developed by WKZung[ 39 ] in 1965 and was derived from the Zung Depression Scale (1965), which was used to measure the severity of depression and its changes in response to treatment. The SDS includes 20 items, among which 10 items are positively scored and 10 items are negatively scored. Each item is scored on a 4-point Likert scale. The items assessed the respondent’s depressive symptoms within the past week. The maximum total score is 80 points and can be categorized as follows: no depression risk (0–41), mild depression risk (42–50), moderate depression risk (51–57), or severe depression risk (58–80). The Cronbach’s α coefficient for the SDS in this study was 0.803. Chronic Disease Self-Management Behaviour Scale (CDSMS) The Chronic Disease Self-management Behaviour Scale was developed by Dr. Lorig of the Chronic Disease Education Research Center of Stanford University[ 40 ]. This scale can be used by patients with various chronic diseases according to the framework of the self-efficacy theory. Fu Dongbo[ 41 ] developed a localized chronic disease self-management questionnaire based on Lorig’s scale. The CDSMS used herein includes three dimensions: physical exercise, the management of cognitive symptoms, and communication with doctors. Each item in the exercise dimension (6 items) was scored on a 5-point Likert scale. Each item in the management of cognitive symptoms (6 items) and communication with the doctor (3 items) dimensions was scored on a 6-point Likert scale. The total score of the scale ranged from 0–69. Higher scores indicate stronger self-management ability. The Cronbach’s α coefficient for the CDSMS in this study was 0.843. Statistical analysis SPSS 29.0 and AMOS 27.0 were used for the statistical analysis. First, the common method bias test, descriptive statistical analysis, and correlation analysis were performed via SPSS 29.0. Next, AMOS 27.0 was used to perform structural equation modelling to analyse the chain mediating role of social support and depression in the relationship between digital health literacy and chronic disease self-management behaviour among rural elderly patients with chronic diseases. Statistical significance was indicated by P < 0.05. Results Participant characteristics The study included 202 rural elderly patients with chronic diseases, including 113 (55.9%) males and 89 (44.1%) females. Additional data are shown in Table 1 . According to the independent samples t test and univariate analysis of variance, statistically significant differences were found in self-management behaviours among rural elderly patients with chronic diseases in terms of age, education, type of preretirement work, pension, duration of mobile phone internet usage, number of illnesses, duration of illnesses, and disease burden ( all P < 0.05). Table 1 Demographic and disease characteristics of participants (N = 202) Variable N (%) Self-management behaviours \(\:\stackrel{-}{\varvec{x}}\) (s) T/F P Gender Male 113(55.9) 28.88(9.90) 1.785 0.076 Female 89(44.1) 26.47(8.97) Age <70 119(58.9) 29.37(9.54) 2.833 0.005 ≥ 70 83(41.1) 25.55(9.18) Marital status Married 174(86.1) 28.31(9.74) 1.840 0.067 Not married 28(13.9) 24.75(7.84) Education level Illiterate 47(23.3) 22.68(8.01) 19.998 < 0.001 Elementary school 93(46.0) 26.87(8.60) Junior high school and above 62(30.7) 33.13(9.51) Type of work before retirement Nonagricultural households 71(35.1) 30.70(9.83) 3.235 0.001 Farmer household 131(64.9) 26.25(9.0) Pension ≤ 500 RMB 105(52.0) 25.28(8.74) 12.326 < 0.001 500 ~ 1000 34(16.8) 27.15(9.44) ≥ 1000 RMB 63(31.2) 32.41(9.37) Medical payment methods Medical insurance for urban and rural residents 171(84.7) 27.50(9.38) 2.698 0.07 Urban employee resident medical insurance 22(10.9) 31.77(9.23) Self-paid/other 9(4.5) 24.11(11.84) Duration of Mobile internet use ≤ 3 hours 131(64.9) 26.64(8.62) -2.403 0.017 >3 hours 71(35.1) 29.98(10.81) Number of chronic diseases 1 60(29.7) 30.77(11.12) 2.614 0.01 ≥ 2 142(70.3) 26.57(8.55) Years of illness <10 139(68.8) 26.87(8.95) -2.108 0.036 ≥ 10 63(31.2) 29.90(10.56) Disease burden Yes 92(45.5) 24.80(8.69) -4.269 < 0.001 No 110(54.4) 30.34(9.55) SD: standard deviation Common method bias This study used scales and self-report methods to collect data. After the data were retrieved, Harman’s single-factor test was used to test for common method bias for all the items associated with the study variables. The Harman’s single-factor test is a statistical method for detecting common method bias through exploratory factor analysis, and the method is valuable as an initial screening tool in cross-sectional studies[ 42 , 43 ]. A significant common method bias is considered to exist if the proportion of variance explained by a single factor exceeds 40% [ 44 , 45 ].The results of exploratory factor analysis revealed that the first factor explained 16.347% of the variation, which was lower than the critical standard of 40%; this indicates that the data in our study did not have serious common method bias and that the effect was within the acceptable range. Descriptive statistics and correlation analysis Table 2 lists the results of the descriptive analysis and the Pearson correlation analysis of the core variables. As shown in Table 2 , the mean score for the digital health literacy scale was 39.25 ± 9.00, the mean score for the SSRS was 34.40 ± 4.46, the mean score for the SDS was 46.28 ± 6.23, and the mean score for the CDMSM was 27.82 ± 9.56. These findings indicate that the rural elderly group with chronic diseases had lower levels of digital health literacy, social support, and chronic disease self-management behaviour and that rural elderly patients with chronic diseases tended to experience slight depression. Additionally, chronic disease self-management behaviour was significantly positively correlated with digital health literacy ( r = 0.391, P < 0.01) and social support ( r = 0.336, P < 0.01) and was negatively correlated with depression ( r = -0.456, P < 0.01). Digital health literacy was significantly and positively correlated with social support ( r = 0.0.316, P < 0.01) and was negatively correlated with depression ( r = -0.394, P < 0.01). A significant negative correlation was observed between social support and depression ( r = -0.342, P < 0.01). The findings of these correlation analyses support the subsequent hypothesis tests. Table 2 Correlation analysis and description of the core variables ( r ) Variable Mean ± SD 1.DHL 2.SRSS 3.SDS 4.CDSMS 1. DHL 39.25 ± 9.00 1 2. SSRS 34.40 ± 4.46 0.316** 1 3. SDS 46.28 ± 6.23 -0.394** -0.342** 1 4. CDSMS 27.82 ± 9.56 0.391** 0.336** -0.456** 1 DHL: Digital Health Literacy; SSRS: Social Support Rating Scale; SDS: Self-Rating Depression Scale; CDSMS: Chronic Disease Self-Management Behaviour Scale; *: P < 0.05; **: P < 0.01; ***: P < 0.001 Mediation analysis To further investigate the correlations among digital health literacy, social support, depression, and chronic disease self-management behaviour among rural elderly patients with chronic diseases and to test the mediating effects of social support and depression, structural equation modelling was used to construct a relationship model among the four core variables. Statistically significant variables were controlled for in the model. The fit indices of the initial model were as follows: CMIN/DF = 1.805, RMSEA = 0.063, GFI = 0.932, IFI = 0.929, CFI = 0.927, TLI = 0.904, SRMR = 0.084. The fitting results showed that, overall, the data fit the theoretical model well. Although the SRMR is slightly greater than 0.08, the model in this study is more complex, and the sample size is relatively small, and thus this value needs to be combined with other indicators to make a comprehensive judgement. Moreover, many studies in the literature have shown that an SRMR < 0.1 is acceptable in the field of social sciences[ 46 – 49 ]. First, as shown in Fig. 2 A, a significant positive correlation was observed between digital health literacy and chronic disease self-management behaviour among rural elderly patients with chronic diseases ( β = 0.30, p < 0.001), which supports Hypothesis H1. A significant positive correlation was found between digital health literacy and social support ( β = 0.30, p < 0.001) and between social support and chronic disease self-management behaviour ( β = 0.18, p < 0.01), which supports both the H2a and H2b hypotheses. Additionally, digital health literacy and depression were significantly negatively correlated ( β =-0.36, p < 0.001), and depression was significantly negatively correlated with chronic disease self-management behaviour ( β =-0.26, p < 0.001), which supports the H3a and H3b hypotheses. A significant negative correlation was observed between social support and depression ( β = -0.23, p < 0.001), which supports Hypothesis H4. Mediating effect test Next, the indirect effects of social support and depression on the relationship between digital health literacy and chronic disease self-management behaviour were further explored. A bootstrap test was used to test the mediating effect. The number of repeated samples was set to 5000, and the confidence interval was set to 95%. The 95% confidence interval of each path coefficient did not include 0, which indicates that the mediating effect was significant. The results are shown in Table 3 . The total indirect effect value of social support and depression was 0.167, which accounted for 36.07% of the total effect. The indirect effects included the following three paths: (1) Digital health literacy → social support → chronic disease self-management behaviour. The indirect effect value was 0.055, and the corresponding confidence interval was [0.012, 0.127]. This confidence interval did not include 0, thus supporting Hypothesis H2. (2) Digital health literacy → depression → chronic disease self-management behaviour. The indirect effect value was 0.094, and the corresponding confidence interval was [0.024, 0.201]. This confidence interval did not include 0, thus supporting Hypothesis H3. (3) Digital health literacy → social support → depression → chronic disease self-management behaviour. The indirect effect value was 0.018, and the corresponding confidence interval was [0.004, 0.055]. This confidence interval did not include 0, thus supporting Hypothesis H4. Table 3 Bootstrap mediation effects test Category β SE 95% CI Effect size (%) Lower Upper Total effect 0.463 0.096 0.262 0.642 100 Direct effect 0.296 0.116 0.070 0.522 63.93 Total indirect effect 0.167 0.049 0.086 0.279 36.07 DHL → SSRS→ CDSMS 0.055 0.028 0.012 0.127 11.88 DHL → SDS → CDSMS 0.094 0.043 0.024 0.201 20.30 DHL → SSRS→ SDS → CDSMS 0.018 0.012 0.004 0.055 3.89 DHL: Digital Health Literacy; SSRS: Social Support Rating Scale; SDS: Self-Rating Depression Scale; CDSMS: Chronic Disease Self-Management Behaviour Scale Discussion This study focused on the self-management behaviour of rural elderly patients with chronic diseases in the digital age. On the basis of the biopsychosocial medicine model, which focuses on social support and depression, a chain mediation model was constructed to explore the mechanism underlying the effect of digital health literacy on chronic disease self-management behaviours among rural elderly patients with chronic diseases. The results revealed that rural elderly patients with chronic diseases had lower scores for chronic disease self-management and that patients had multiple opportunities for improvement. Significant differences were observed in the self-management behaviour scores of rural elderly patients with chronic diseases who differed in terms of age, literacy level, number of illnesses, duration of illnesses, and disease burden. These findings are consistent with the results of related domestic and international studies [ 50 – 57 ]. Rural elderly individuals comprise a group that deserves more attention. We should adopt targeted health promotion measures according to the disease characteristics, lifestyle and economic status of this population. Moreover, the rapid development of digital technology has accelerated the integrated development of the internet and medical services, and new medical systems and models, such as systems medicine, precision medicine, and intelligent medicine, continue to emerge. Considering the positive role of the internet in the management of chronic diseases, the effect of digital health literacy on chronic disease self-management behaviour among rural elderly patients is worthy of attention. The results of this study revealed that the digital health literacy of rural elderly patients with chronic diseases can positively predict chronic disease self-management behaviour. This finding is consistent with the results reported by Lee[ 9 ], who studied patients with type 2 diabetes, and those reported by Chuang[ 8 ], who studied patients with chronic heart failure. The rapid development of information technology has expanded the accessibility of healthcare resource services, especially for rural areas, thus providing many health e-resources for the management of chronic diseases among rural older adults. Older adults with higher levels of digital health literacy are more confident in accessing, understanding, and applying health information, which enables them to participate in more behaviours that are conducive to managing their own health[ 10 , 58 ]. This study also confirmed both the independent and chain mediating effects of social support and depression on the relationship between digital health literacy and chronic disease self-management behaviour. On the one hand, digital health literacy can indirectly exert a positive effect on chronic disease self-management behaviour through the partial mediating effect of social support. The results revealed a significant positive correlation between digital health literacy and social support, which indicates that rural elderly patients with chronic diseases can continuously improve their ability to access digital health technologies and obtain more social support through the internet regardless of time and space boundaries. Social support has been widely used in the field of chronic disease management as an extrinsic protective factor. Similarly, the present study revealed that social support positively predicts chronic disease self-management behaviours, a finding that is consistent with earlier findings on self-management behaviour in elderly patients with hypertension[ 59 ], obstructive sleep apnoea-hypopnea syndrome[ 28 ] and kidney disease[ 60 ]. Therefore, rural elderly patients with chronic diseases can take full advantage of the internet and social support to improve their self-management ability. On the other hand, digital health literacy can also indirectly affect chronic disease self-management behaviour through the partial mediating effect of depression, as expected. Previous studies have shown that higher levels of e-health literacy are associated with better health outcomes, including stronger medication adherence, higher quality of life, and mental health[ 33 , 61 ]. Moreover, mental health status plays an important role in chronic disease self-management behaviours, as most studies have confirmed a strong association between mental health status and self-management ability[ 11 , 62 ]. Therefore, higher levels of digital health literacy can improve the mental health status of older adults and reduce their tendency towards depression, thereby reducing the psychological burden of self-health management. Additionally, the study results emphasize that the association between digital health literacy and chronic disease self-management behaviour can be partially explained by the chain mediating role of social support and depression. While rural elderly individuals use the internet to obtain more health information, they can also obtain more external social support, including that from patients and doctors, thereby increasing their level of social activity and alleviating mental health problems caused by their experience with long-term chronic diseases. These effects include reducing the risk of depression[ 63 ], enhancing active health awareness, and promoting the development of chronic disease self-management behaviour in patients. By analysing the chain mediating effects of social support and depression, this study investigated the mechanisms underlying the effect of digital health literacy on the self-management behaviour of chronic diseases among rural elderly patients with chronic diseases. This study may contribute to the existing knowledge of this field in several ways. First, rural elderly individuals with chronic diseases have rarely been examined in previous research. The results of the current study not only expand the sample range for studies on chronic disease self-management behaviour but also enrich the existing data on the factors that influence chronic disease self-management behaviour among rural elderly patients with chronic diseases. Second, considering the urgency and importance of chronic disease management for rural elderly individuals in the digital age, this study, which is based on the biopsychosocial medicine model, introduces social support and depression as mediator variables. This study also reveals the three influencing mechanisms by which digital health literacy affects the self-management behaviours of rural elderly patients with chronic diseases from a new perspective, opening the ‘dark box’ of how digital health literacy affects the self-management behaviours of these patients. Moreover, this study also fills the research gap in the field of digital health in the rural population studied. This study provides not only a theoretical basis for improving the health self-management level of rural elderly patients with chronic diseases but also a theoretical framework for the self-management of these patients. The research model established herein and the results also provide scientific data and guidance for follow-up studies. This study has certain practical implications for the management of chronic diseases among elderly individuals in rural areas. Currently, China has a variety of health management models for elderly individuals, each with its own characteristics. However, most health management models rely on external policy support, and management models that give full play to the active health awareness and behaviour of elderly people are lacking. The chronic disease self-management model is a typical model of chronic disease management that can effectively intervene in the occurrence of diseases and can improve the physical and mental health of patients. Guiding residents’ health philosophy from “passive health” to “active health” has always been a focus of the prevention and management of chronic diseases in China. The results of this study not only emphasized the importance of digital health literacy for the self-management behaviour of rural elderly patients with chronic diseases but also emphasized the indirect effects of social support and depression on the relationship between digital health literacy and chronic disease self-management behaviour. Therefore, relevant health departments should strengthen digital health education for rural elderly individuals, emphasize the importance of self-health management from the beginning of “not ill”, and establish positive awareness of digital health literacy education. Moreover, in the process of providing digital health literacy education, special attention should be given to the social support and mental health status of rural elderly patients with chronic diseases. It is necessary to take relevant measures to provide more social support, thereby amplifying the positive impact of digital health literacy on the self-management behaviours of rural elderly patients with chronic diseases and enhancing active health awareness and active health behaviours among rural elderly individuals. Limitations However, this study still has certain limitations. First, this study used convenience sampling, and thus the external validity of the study results may be reduced. In the future, more scientifically rigorous methods and more representative samples can be selected to verify the conclusions of the study. Second, like many related studies, this study used a self-report questionnaire to collect data. Although the Harman single-factor test was used to show a lack of serious common method bias, self-reported assessments of individuals' abilities and performance may still be susceptible to the Dunning-Kruger effect[ 64 ]. Specifically, such evaluations may be biased, with lower-ability individuals potentially overestimating their capabilities and higher-ability individuals possibly underestimating theirs.Therefore, future studies could use more standardized tests or explore other nonself-report methods to further validate the findings. Third, because this study was a cross-sectional survey, inferences about the causal relationships among variables cannot be made. A longitudinal study should be conducted to further validate the conclusions of this study. Additionally, the participants in this study were residents of Anhui Province; therefore, the research sample has certain regional limitations. Future studies should continue to expand the geographic scope of the research to provide more effective guidance for the future management of chronic diseases in elderly individuals in rural areas. Conclusion The main conclusions of this study are as follows: first, the self-management level of rural elderly patients with chronic diseases is relatively low and can be largely improved in the future. Second, digital health literacy, social support, and depression are three important factors that affect the self-management behaviour of rural elderly patients with chronic diseases. Third, the digital health literacy level of rural elderly patients with chronic diseases not only directly affects their chronic disease self-management behaviour but also indirectly affects this behaviour through the direct mediating effects of social support and depression as well as through the chain mediating effect of social support and depression. These findings enrich the existing research findings related to digital chronic disease management in elderly individuals, and the internal mechanisms revealed also provide scientific and practical insights for promoting self-health management behaviours in rural elderly chronic disease patients. Abbreviations DHL: Digital health literacy; SSRS: Social Support Rating Scale; SDS: Self-Rating Depression Scale; CDSMS: Chronic Disease Self-Management Behaviour Scale. Declarations Acknowledgments The authors would like to thank village committees in rural areas of Anhui Province for their cooperation in providing samples. Authors’ contributions Theme: X.L and X.G. Methodology: X.L and X.G. Software: X.L and J.W. Data Curation: X.L, X.G, G.R, Z.M, J.H and C.S. Original draft: X.L. Review and editing: X.L and J.W. Supervision and funding acquisition: J.W. All authors have read and agreed to the published version of the manuscript. Funding This work was funded by the Ministry of Education Humanities and Social Sciences Youth Project (Grant number 22YJCZH188) and Anhui Provincial Colleges and Universities Outstanding Youth Scientific Research Project (Grant number 2023AH030062). Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All experimental protocols of this study were approved by the Ethics Committee of Anhui Medical University(No.83243452), and all methods were conducted according to the guidelines of the Declaration of Helsinki and relevant Chinese laws and regulations. We confirm that informed consent was obtained from all participants and/or their legal guardians. Considering that the study participants were all rural residents with generally low literacy levels, we used plain language to prepare the informed consent form, avoiding jargon and ensuring the clarity of the content. At the same time, before signing the informed consent form, the investigator explained the study purpose, procedures, potential risks and rights and benefits to the participants line by line, and for participants with limited comprehension, their family members or members of the village committee were invited to assist in the explanations to ensure that they fully understood the content of the study. Written informed consent was obtained from all participants before any study procedures were performed. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Collaborators GDAH. 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The relationship between coping strategies and psychological distress in Chinese older adults with multiple chronic conditions. AUSTRALAS J AGEING 2021, 40(4):397-405. Scheffer MM, Menting J, Boeije HR. Self-management of social well-being in a cross-sectional study among community-dwelling older adults: The added value of digital participation. BMC GERIATR. 2021;21(1):1-7. Ding W, Li T, Su Q, Yuan M, Lin A. Integrating factors associated with hypertensive patients' self-management using structural equation modeling: a cross-sectional study in Guangdong, China. PATIENT PREFER ADHER. 2018;12:2169-2178. Kim KA, Kim YJ, Choi M. Association of Electronic Health Literacy With Health-Promoting Behaviors in Patients With Type 2 Diabetes. CIN: Computers, Informatics, Nursing. 2018; 36(9):438-447. Zhang X, Zheng Y, Qiu C, Zhao Y, Zang X. Well-being mediates the effects of social support and family function on self-management in elderly patients with hypertension. 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Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2025 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 31 Mar, 2025 Reviews received at journal 31 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 22 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5647182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433611800,"identity":"e1ac94ec-0fd7-457f-b86b-d28a9dec9b98","order_by":0,"name":"Xuefang Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xuefang","middleName":"","lastName":"Liu","suffix":""},{"id":433611801,"identity":"c4003ce6-3911-4dd1-a0a0-13f3e8aa2e0e","order_by":1,"name":"Xiaomin Gan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Gan","suffix":""},{"id":433611802,"identity":"55d2f2c8-b521-4d76-9572-9fae25e8e18a","order_by":2,"name":"Guangqin Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guangqin","middleName":"","lastName":"Ren","suffix":""},{"id":433611803,"identity":"b4c240f3-da44-484e-97e6-f8352837dab5","order_by":3,"name":"Zhongrui Mao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhongrui","middleName":"","lastName":"Mao","suffix":""},{"id":433611804,"identity":"19d22c82-7553-4d37-88e1-fdff769dd0fd","order_by":4,"name":"Jiuying Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiuying","middleName":"","lastName":"Hu","suffix":""},{"id":433611805,"identity":"b740337e-ee45-4dc8-b9cb-90ac0c2d2b6e","order_by":5,"name":"Chengcheng Sha","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chengcheng","middleName":"","lastName":"Sha","suffix":""},{"id":433611806,"identity":"4d381599-c289-483b-90ad-19510b196aec","order_by":6,"name":"Juan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACfmbGxsc/DCR4+BkOH2CGiCXg1yLZ3nzYmKHCRk6y8VgCcVoMzhxLk2Y4k2ZscPiMAXFaGG7kGBsXth1ObDh25uPnwpzDDPzsOQYMP3fg1sE4I8fw8Uyglsaes5ulZ247zCDZ88aAsfcMbi3MEjnGBrxALc0SZzdI8wK1GNzIMWBmbMOthU0ix0wCpKVN/s3j3yAt9oS08PAAvc8D9D4Pwxk2iC0SBLRIsDcfNpwBDGQJhmNm1rzb0nkkzjwrONiLR4v9YcbGBx+AUWl/4PDj27zbrOX425M3PviJRwumS0HEARI0jIJRMApGwSjAAgAGdFdip/HCIAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-12-15 11:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5647182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5647182/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-025-05952-3","type":"published","date":"2025-04-29T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79319586,"identity":"f4f04617-557b-4809-b7a3-8298cf3bb344","added_by":"auto","created_at":"2025-03-27 04:19:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40129,"visible":true,"origin":"","legend":"\u003cp\u003eshows the theoretical framework.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5647182/v1/1a6e45de3fe570b265691c0e.jpg"},{"id":79318778,"identity":"e273b6f6-a771-4eb7-b4f2-3808c2a9a3e6","added_by":"auto","created_at":"2025-03-27 04:11:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43433,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the standardized path coefficients of the model\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5647182/v1/63b628f98fb09a1178c10144.jpg"},{"id":81987981,"identity":"cf0bc963-3ee8-4212-8bae-c63af6928fb6","added_by":"auto","created_at":"2025-05-05 16:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1127481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5647182/v1/d7fc54ba-dcb7-420d-87a5-51d1d31f5fa1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Path analysis of the influence of digital health literacy on self-management behaviour among elderly patients with chronic diseases in rural China","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global population is ageing rapidly, and thus the population of elderly patients with chronic diseases continues to increase. According to a 2018 report from the World Health Organization, approximately 41.1\u0026nbsp;million deaths worldwide were caused by chronic noncommunicable diseases, which accounted for 71% of all deaths, and it is estimated that this figure may reach 52\u0026nbsp;million by 2030[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. More than 180\u0026nbsp;million elderly individuals in China have chronic noncommunicable diseases that account for 75% of all cases of chronic disease, 88.5% of the total deaths and 70% of the total disease burden[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, medical service systems face enormous challenges. Rural elderly individuals are most strongly impacted by chronic diseases, thereby constituting a public health issue in China that urgently requires resolution. Chronic disease self-management refers to the undertaking of preventive or therapeutic health care activities by individuals with the assistance of health care professionals[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For rural elderly individuals with low educational levels, low economic income, and low availability of medical and health resources, the self-health management model is an affordable and highly efficient model for improving quality of life and preventing disease. In recent years, ageing and digitalization have become mainstream trends worldwide. However, due to the limitations of physiological and psychological factors, elderly individuals have limited levels of acceptance and application of digital media[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which hinders the digital-based, precise, and long-acting development of smart elder care systems. Digital health literacy is a skill that should be mastered by elderly patients with chronic diseases[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many scholars have noted that the current self-management behaviour among individuals with chronic diseases in a digital environment is limited by low levels of digital health literacy[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, few studies have explored the mechanism underlying the influence of digital health literacy on chronic disease self-management behaviour; furthermore, most of the relevant studies have focused on a single chronic disease, such as hypertension[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], diabetes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], or chronic heart failure[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Few studies have examined chronic disease groups as a whole[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and even fewer studies have focused on chronic disease groups among rural elderly individuals. Therefore, the current study examined two important antecedent variables of chronic disease self-management behaviour, i.e., social support and depression, on the basis ofaccording to the biopsychosocial medicine model, and constructed a chain mediation model. The aim of this study was to elucidate the mechanism underlying the effect of digital health literacy on chronic disease self-management behaviour among rural elderly patients with chronic diseases and to provide a scientific basis for improving active health behaviours among rural elderly patients with chronic diseases in China and worldwide.\u003c/p\u003e \u003cp\u003eDigital health literacy refers to the ability to use digital technology to search, select, evaluate, and apply online health information and to interact with doctors or service organizations online[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In recent years, the rapid development of the internet and the extensive integration of intelligent digital technologies have yielded considerable changes in the management model of chronic diseases, which has led to the integration of chronic disease medicine and prevention characterized by digitization, intelligence, and informatization[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this context, the role of digital health literacy in improving individuals\u0026rsquo; self-health management ability and improving health outcomes has become increasingly important[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients can not only consult a doctor who can evaluate their condition online, but they can also search for a large amount of health information with the aim of improving their health status[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Especially during the COVID-19 pandemic, the attention to the importance of self-health care has been widely raised[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and digital tools (e.g., online consultation, and health apps) became an effective way to manage the health of patients with chronic diseases.The self-management behaviour of patients with chronic diseases may be limited according to their level of e-health literacy. Patients with chronic diseases who have higher levels of e-health literacy are more aware of the utility of internet health resources for health management and are more willing to use health applications[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, many domestic studies of patients with a single chronic disease, such as diabetes, stroke, or chronic heart failure, have confirmed the positive correlation between digital health literacy and health management behaviour [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, Hypothesis H1 is proposed as follows: Digital health literacy is positively associated with the self-management behaviour of rural elderly patients with chronic diseases.\u003c/p\u003e \u003cp\u003eThe concept of social support comes from psychiatric research in the 1960s and refers to the material and spiritual support that an individual can obtain from family, friends, and colleagues when facing stress; this support includes the establishment of good intimate relationships with others, social participation, helping others, recognition of self-worth and receiving help from others[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Many previous studies have confirmed the close relationship between higher levels of e-health literacy and higher levels of social support[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It has been suggested that a high level of digital health literacy could lead to higher levels of social support. Additionally, as a social determinant of individual health, social support strongly affects individuals\u0026rsquo; emotions, quality of life, and health outcomes. Many previous studies have confirmed the significant positive correlation between social support and self-management behaviour in elderly patients with chronic diseases[\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In other words, higher levels of social support can better enable individuals to manage their own disease, thereby leading to the maintenance of good physical health. Therefore, Hypothesis H2 is proposed as follows: Social support mediates the relationship between digital health literacy and self-management behaviour among rural elderly patients with chronic diseases (H2a: Digital health literacy is positively correlated with social support among rural elderly patients with chronic diseases; H2b: Social support is positively associated with self-management behaviour among rural elderly patients with chronic diseases).\u003c/p\u003e \u003cp\u003eDepression is one of the most common mental health problems among the elderly. Numerous studies have shown that mental health status is correlated with self-management ability among patients with chronic diseases. Chen[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] noted that mental health was significantly associated with self-management ability in patients with hypertensive kidney disease and emphasized that mental health is the most important explanatory variable in the self-management of these patients. Zhang[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] also confirmed that well-being has an important effect on the self-management behaviour of elderly hypertensive patients and that well-being is positively correlated with disease self-management and lifestyle management. Additionally, the mental health status of an individual is related to the individual\u0026rsquo;s level of digital health literacy. Studies have shown a significant negative correlation between e-health literacy and psychological problems such as insomnia, anxiety and depression, which indicates that improvements in e-health literacy can help mitigate these psychological problems[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, Hypothesis H3 is proposed as follows: Depression mediates the relationship between digital health literacy and self-management behaviour among rural elderly patients with chronic diseases (including H3a: Digital literacy is negatively associated with depression among rural elderly patients with chronic diseases; H3b: Depression is negatively associated with self-management behaviours among rural elderly patients with chronic diseases).\u003c/p\u003e \u003cp\u003eIn summary, individuals with higher levels of digital health literacy are prone to receiving more social support from different groups and are thus more likely to perceive the benefits of social support. According to the social support theory, good social support can promote physical and mental health, and strong evidence suggests that social support can effectively alleviate patients' psychological distress; i.e., a relatively high level of social support can effectively alleviate depression, anxiety, loneliness and other psychological problems among elderly patients with chronic diseases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This alleviation of psychological problems in turn improves the overall health and quality of life of individuals. Therefore, Hypothesis H4 is proposed as follows: Social support and depression play a chain mediating role in the relationship between digital health literacy and self-management behaviour among rural elderly patients with chronic diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eUsing a convenience sampling method, the group recruited 20 students from a medical university in Anhui Province, who were from the rural areas of six cities in Anhui Province: Hefei, Huangshan, Fuyang, Wuhu, Chuzhou, Bengbu, and Anqing. The researchers were trained to conduct questionnaire surveys from July to September 2024 to the elderly in their respective villages. Since rural elderly people generally have a low level of education and limited literacy, the survey process was based on collecting data by asking questions one by one by the researcher, responding by the respondents, and filling in the questions after the researcher confirmed the answers.The inclusion criteria were as follows: aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years; diagnosed with one or more chronic diseases (based on definitions provided by the International Classification of Diseases (ICD-10)), including diabetes, hypertension, coronary heart disease, and chronic obstructive pulmonary disease, by a secondary or one of the above medical institutions; rural residents; access to the internet via smart devices; no severe cognitive impairment; good communication skills; and voluntarily participated in this study and signed an informed consent form. The exclusion criterion was diagnosis of a critical illness, such as hearing impairment, serious vital organ or somatic diseases, and advanced malignant tumours, which may have inhibited participants from cooperating with the investigation. A total of 237 rural elderly people were surveyed by 20 researchers, of whom 35 were unwilling to continue answering and terminated early, which led to 35 questionnaires with missing data, and finally 202 valid questionnaires were obtained in this study, with an effective recovery rate of 85.23%. This study was ethically approved by the Biomedical Ethics Committee of Anhui Medical University (No. 83243452).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information system (GIS) questionnaire\u003c/h2\u003e \u003cp\u003eThe following general demographic data were collected: sex, age, educational level, retirement income, marital status, preretirement occupation, and medical payment methods. The following disease-related data were collected: years of illness, number of illnesses, and disease burden.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDigital Health Literacy Assessment Scale\u003c/h3\u003e\n\u003cp\u003eThe Digital Health Literacy Assessment Scale was developed by Siqi Liu[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This scale includes 15 items across 3 dimensions: digital health information acquisition and assessment ability, digital health information interaction ability, and digital health information application ability. Each item was scored on a 5-point Likert scale, and the maximum score was 75 points. Higher scores indicate higher levels of digital health literacy. In this study, the Cronbach\u0026rsquo;s α coefficient for the Digital Health Literacy Assessment Scale was 0.880.\u003c/p\u003e\n\u003ch3\u003eSocial Support Rating Scale (SSRS)\u003c/h3\u003e\n\u003cp\u003eThe Social Support Rating Scale [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was developed by Shuiyuan Xiao to assess the degree of perceived social support among individuals. The SSRS includes 10 items across three dimensions: subjective support, objective support, and the utilization of social support. Due to the basic situation of the population included in the current study, Question 4 was deleted; thus, 9 items appeared on the questionnaire. Each item was scored on a 4-point Likert scale, and the maximum score was 62 points. Higher scores indicate a higher level of perceived social support. A total score\u0026thinsp;\u0026le;\u0026thinsp;18 indicates a low level of social support, a score between 18 and 40 indicates a moderate level of social support, and a score\u0026thinsp;\u0026gt;\u0026thinsp;40 indicates a high level of social support. The Cronbach\u0026rsquo;s α coefficient of this scale in this study was 0.727.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSelf-Rating Depression Scale (SDS)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe Self-Rating Depression Scale (SDS) was developed by WKZung[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in 1965 and was derived from the Zung Depression Scale (1965), which was used to measure the severity of depression and its changes in response to treatment. The SDS includes 20 items, among which 10 items are positively scored and 10 items are negatively scored. Each item is scored on a 4-point Likert scale. The items assessed the respondent\u0026rsquo;s depressive symptoms within the past week. The maximum total score is 80 points and can be categorized as follows: no depression risk (0\u0026ndash;41), mild depression risk (42\u0026ndash;50), moderate depression risk (51\u0026ndash;57), or severe depression risk (58\u0026ndash;80). The Cronbach\u0026rsquo;s α coefficient for the SDS in this study was 0.803.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChronic Disease Self-Management Behaviour Scale (CDSMS)\u003c/h3\u003e\n\u003cp\u003eThe Chronic Disease Self-management Behaviour Scale was developed by Dr. Lorig of the Chronic Disease Education Research Center of Stanford University[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This scale can be used by patients with various chronic diseases according to the framework of the self-efficacy theory. Fu Dongbo[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] developed a localized chronic disease self-management questionnaire based on Lorig\u0026rsquo;s scale. The CDSMS used herein includes three dimensions: physical exercise, the management of cognitive symptoms, and communication with doctors. Each item in the exercise dimension (6 items) was scored on a 5-point Likert scale. Each item in the management of cognitive symptoms (6 items) and communication with the doctor (3 items) dimensions was scored on a 6-point Likert scale. The total score of the scale ranged from 0\u0026ndash;69. Higher scores indicate stronger self-management ability. The Cronbach\u0026rsquo;s α coefficient for the CDSMS in this study was 0.843.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 29.0 and AMOS 27.0 were used for the statistical analysis. First, the common method bias test, descriptive statistical analysis, and correlation analysis were performed via SPSS 29.0. Next, AMOS 27.0 was used to perform structural equation modelling to analyse the chain mediating role of social support and depression in the relationship between digital health literacy and chronic disease self-management behaviour among rural elderly patients with chronic diseases. Statistical significance was indicated by \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eThe study included 202 rural elderly patients with chronic diseases, including 113 (55.9%) males and 89 (44.1%) females. Additional data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the independent samples t test and univariate analysis of variance, statistically significant differences were found in self-management behaviours among rural elderly patients with chronic diseases in terms of age, education, type of preretirement work, pension, duration of mobile phone internet usage, number of illnesses, duration of illnesses, and disease burden (\u003cem\u003eall P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and disease characteristics of participants (N\u0026thinsp;=\u0026thinsp;202)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-management behaviours \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\varvec{x}}\\)\u003c/span\u003e\u003c/span\u003e(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eT/F\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.88(9.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.47(8.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119(58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.37(9.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.55(9.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174(86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.31(9.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.75(7.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.68(8.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e19.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.87(8.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62(30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.13(9.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of work before retirement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonagricultural households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.70(9.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131(64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.25(9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;500 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.28(8.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026thinsp;~\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.15(9.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.41(9.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical payment methods\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance for urban and rural residents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171(84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.50(9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban employee resident medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.77(9.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-paid/other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.11(11.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of Mobile internet use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131(64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.64(8.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.98(10.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of chronic diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.77(11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142(70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.57(8.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of illness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139(68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.87(8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.90(10.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease burden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92(45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.80(8.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-4.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110(54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.34(9.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCommon method bias\u003c/h2\u003e \u003cp\u003eThis study used scales and self-report methods to collect data. After the data were retrieved, Harman\u0026rsquo;s single-factor test was used to test for common method bias for all the items associated with the study variables. The Harman\u0026rsquo;s single-factor test is a statistical method for detecting common method bias through exploratory factor analysis, and the method is valuable as an initial screening tool in cross-sectional studies[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A significant common method bias is considered to exist if the proportion of variance explained by a single factor exceeds 40% [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].The results of exploratory factor analysis revealed that the first factor explained 16.347% of the variation, which was lower than the critical standard of 40%; this indicates that the data in our study did not have serious common method bias and that the effect was within the acceptable range.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and correlation analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the results of the descriptive analysis and the Pearson correlation analysis of the core variables. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the mean score for the digital health literacy scale was 39.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00, the mean score for the SSRS was 34.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46, the mean score for the SDS was 46.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23, and the mean score for the CDMSM was 27.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56. These findings indicate that the rural elderly group with chronic diseases had lower levels of digital health literacy, social support, and chronic disease self-management behaviour and that rural elderly patients with chronic diseases tended to experience slight depression. Additionally, chronic disease self-management behaviour was significantly positively correlated with digital health literacy (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.391, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and social support (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.336, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and was negatively correlated with depression (\u003cem\u003er\u003c/em\u003e = -0.456, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Digital health literacy was significantly and positively correlated with social support (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0.316, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and was negatively correlated with depression (\u003cem\u003er\u003c/em\u003e = -0.394, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A significant negative correlation was observed between social support and depression (\u003cem\u003er\u003c/em\u003e = -0.342, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The findings of these correlation analyses support the subsequent hypothesis tests.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis and description of the core variables (\u003cem\u003er\u003c/em\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.DHL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.SRSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.SDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.CDSMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. \u003cb\u003eDHL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e39.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. \u003cb\u003eSSRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e34.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.316**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. \u003cb\u003eSDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e46.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.394**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.342**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. \u003cb\u003eCDSMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.391**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.336**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.456**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDHL: Digital Health Literacy; SSRS: Social Support Rating Scale; SDS: Self-Rating Depression Scale; CDSMS: Chronic Disease Self-Management Behaviour Scale; *: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis\u003c/h2\u003e \u003cp\u003eTo further investigate the correlations among digital health literacy, social support, depression, and chronic disease self-management behaviour among rural elderly patients with chronic diseases and to test the mediating effects of social support and depression, structural equation modelling was used to construct a relationship model among the four core variables. Statistically significant variables were controlled for in the model. The fit indices of the initial model were as follows: CMIN/DF\u0026thinsp;=\u0026thinsp;1.805, RMSEA\u0026thinsp;=\u0026thinsp;0.063, GFI\u0026thinsp;=\u0026thinsp;0.932, IFI\u0026thinsp;=\u0026thinsp;0.929, CFI\u0026thinsp;=\u0026thinsp;0.927, TLI\u0026thinsp;=\u0026thinsp;0.904, SRMR\u0026thinsp;=\u0026thinsp;0.084. The fitting results showed that, overall, the data fit the theoretical model well. Although the SRMR is slightly greater than 0.08, the model in this study is more complex, and the sample size is relatively small, and thus this value needs to be combined with other indicators to make a comprehensive judgement. Moreover, many studies in the literature have shown that an SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is acceptable in the field of social sciences[\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, a significant positive correlation was observed between digital health literacy and chronic disease self-management behaviour among rural elderly patients with chronic diseases (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which supports Hypothesis H1. A significant positive correlation was found between digital health literacy and social support (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between social support and chronic disease self-management behaviour (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which supports both the H2a and H2b hypotheses. Additionally, digital health literacy and depression were significantly negatively correlated (\u003cem\u003eβ\u003c/em\u003e =-0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and depression was significantly negatively correlated with chronic disease self-management behaviour (\u003cem\u003eβ\u003c/em\u003e =-0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which supports the H3a and H3b hypotheses. A significant negative correlation was observed between social support and depression (\u003cem\u003eβ\u003c/em\u003e = -0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which supports Hypothesis H4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMediating effect test\u003c/h2\u003e \u003cp\u003eNext, the indirect effects of social support and depression on the relationship between digital health literacy and chronic disease self-management behaviour were further explored. A bootstrap test was used to test the mediating effect. The number of repeated samples was set to 5000, and the confidence interval was set to 95%. The 95% confidence interval of each path coefficient did not include 0, which indicates that the mediating effect was significant. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The total indirect effect value of social support and depression was 0.167, which accounted for 36.07% of the total effect. The indirect effects included the following three paths: (1) Digital health literacy \u0026rarr; social support \u0026rarr; chronic disease self-management behaviour. The indirect effect value was 0.055, and the corresponding confidence interval was [0.012, 0.127]. This confidence interval did not include 0, thus supporting Hypothesis H2. (2) Digital health literacy \u0026rarr; depression \u0026rarr; chronic disease self-management behaviour. The indirect effect value was 0.094, and the corresponding confidence interval was [0.024, 0.201]. This confidence interval did not include 0, thus supporting Hypothesis H3. (3) Digital health literacy \u0026rarr; social support \u0026rarr; depression \u0026rarr; chronic disease self-management behaviour. The indirect effect value was 0.018, and the corresponding confidence interval was [0.004, 0.055]. This confidence interval did not include 0, thus supporting Hypothesis H4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBootstrap mediation effects test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect size (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal indirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHL \u0026rarr; SSRS\u0026rarr; CDSMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHL \u0026rarr; SDS \u0026rarr; CDSMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHL \u0026rarr; SSRS\u0026rarr; SDS \u0026rarr; CDSMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDHL: Digital Health Literacy; SSRS: Social Support Rating Scale; SDS: Self-Rating Depression Scale; CDSMS: Chronic Disease Self-Management Behaviour Scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focused on the self-management behaviour of rural elderly patients with chronic diseases in the digital age. On the basis of the biopsychosocial medicine model, which focuses on social support and depression, a chain mediation model was constructed to explore the mechanism underlying the effect of digital health literacy on chronic disease self-management behaviours among rural elderly patients with chronic diseases. The results revealed that rural elderly patients with chronic diseases had lower scores for chronic disease self-management and that patients had multiple opportunities for improvement. Significant differences were observed in the self-management behaviour scores of rural elderly patients with chronic diseases who differed in terms of age, literacy level, number of illnesses, duration of illnesses, and disease burden. These findings are consistent with the results of related domestic and international studies [\u003cspan additionalcitationids=\"CR51 CR52 CR53 CR54 CR55 CR56\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Rural elderly individuals comprise a group that deserves more attention. We should adopt targeted health promotion measures according to the disease characteristics, lifestyle and economic status of this population.\u003c/p\u003e \u003cp\u003eMoreover, the rapid development of digital technology has accelerated the integrated development of the internet and medical services, and new medical systems and models, such as systems medicine, precision medicine, and intelligent medicine, continue to emerge. Considering the positive role of the internet in the management of chronic diseases, the effect of digital health literacy on chronic disease self-management behaviour among rural elderly patients is worthy of attention. The results of this study revealed that the digital health literacy of rural elderly patients with chronic diseases can positively predict chronic disease self-management behaviour. This finding is consistent with the results reported by Lee[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], who studied patients with type 2 diabetes, and those reported by Chuang[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who studied patients with chronic heart failure. The rapid development of information technology has expanded the accessibility of healthcare resource services, especially for rural areas, thus providing many health e-resources for the management of chronic diseases among rural older adults. Older adults with higher levels of digital health literacy are more confident in accessing, understanding, and applying health information, which enables them to participate in more behaviours that are conducive to managing their own health[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study also confirmed both the independent and chain mediating effects of social support and depression on the relationship between digital health literacy and chronic disease self-management behaviour. On the one hand, digital health literacy can indirectly exert a positive effect on chronic disease self-management behaviour through the partial mediating effect of social support. The results revealed a significant positive correlation between digital health literacy and social support, which indicates that rural elderly patients with chronic diseases can continuously improve their ability to access digital health technologies and obtain more social support through the internet regardless of time and space boundaries. Social support has been widely used in the field of chronic disease management as an extrinsic protective factor. Similarly, the present study revealed that social support positively predicts chronic disease self-management behaviours, a finding that is consistent with earlier findings on self-management behaviour in elderly patients with hypertension[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], obstructive sleep apnoea-hypopnea syndrome[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and kidney disease[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Therefore, rural elderly patients with chronic diseases can take full advantage of the internet and social support to improve their self-management ability.\u003c/p\u003e \u003cp\u003eOn the other hand, digital health literacy can also indirectly affect chronic disease self-management behaviour through the partial mediating effect of depression, as expected. Previous studies have shown that higher levels of e-health literacy are associated with better health outcomes, including stronger medication adherence, higher quality of life, and mental health[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Moreover, mental health status plays an important role in chronic disease self-management behaviours, as most studies have confirmed a strong association between mental health status and self-management ability[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Therefore, higher levels of digital health literacy can improve the mental health status of older adults and reduce their tendency towards depression, thereby reducing the psychological burden of self-health management. Additionally, the study results emphasize that the association between digital health literacy and chronic disease self-management behaviour can be partially explained by the chain mediating role of social support and depression. While rural elderly individuals use the internet to obtain more health information, they can also obtain more external social support, including that from patients and doctors, thereby increasing their level of social activity and alleviating mental health problems caused by their experience with long-term chronic diseases. These effects include reducing the risk of depression[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], enhancing active health awareness, and promoting the development of chronic disease self-management behaviour in patients.\u003c/p\u003e \u003cp\u003eBy analysing the chain mediating effects of social support and depression, this study investigated the mechanisms underlying the effect of digital health literacy on the self-management behaviour of chronic diseases among rural elderly patients with chronic diseases. This study may contribute to the existing knowledge of this field in several ways. First, rural elderly individuals with chronic diseases have rarely been examined in previous research. The results of the current study not only expand the sample range for studies on chronic disease self-management behaviour but also enrich the existing data on the factors that influence chronic disease self-management behaviour among rural elderly patients with chronic diseases. Second, considering the urgency and importance of chronic disease management for rural elderly individuals in the digital age, this study, which is based on the biopsychosocial medicine model, introduces social support and depression as mediator variables. This study also reveals the three influencing mechanisms by which digital health literacy affects the self-management behaviours of rural elderly patients with chronic diseases from a new perspective, opening the \u0026lsquo;dark box\u0026rsquo; of how digital health literacy affects the self-management behaviours of these patients. Moreover, this study also fills the research gap in the field of digital health in the rural population studied. This study provides not only a theoretical basis for improving the health self-management level of rural elderly patients with chronic diseases but also a theoretical framework for the self-management of these patients. The research model established herein and the results also provide scientific data and guidance for follow-up studies.\u003c/p\u003e \u003cp\u003eThis study has certain practical implications for the management of chronic diseases among elderly individuals in rural areas. Currently, China has a variety of health management models for elderly individuals, each with its own characteristics. However, most health management models rely on external policy support, and management models that give full play to the active health awareness and behaviour of elderly people are lacking. The chronic disease self-management model is a typical model of chronic disease management that can effectively intervene in the occurrence of diseases and can improve the physical and mental health of patients. Guiding residents\u0026rsquo; health philosophy from \u0026ldquo;passive health\u0026rdquo; to \u0026ldquo;active health\u0026rdquo; has always been a focus of the prevention and management of chronic diseases in China. The results of this study not only emphasized the importance of digital health literacy for the self-management behaviour of rural elderly patients with chronic diseases but also emphasized the indirect effects of social support and depression on the relationship between digital health literacy and chronic disease self-management behaviour. Therefore, relevant health departments should strengthen digital health education for rural elderly individuals, emphasize the importance of self-health management from the beginning of \u0026ldquo;not ill\u0026rdquo;, and establish positive awareness of digital health literacy education. Moreover, in the process of providing digital health literacy education, special attention should be given to the social support and mental health status of rural elderly patients with chronic diseases. It is necessary to take relevant measures to provide more social support, thereby amplifying the positive impact of digital health literacy on the self-management behaviours of rural elderly patients with chronic diseases and enhancing active health awareness and active health behaviours among rural elderly individuals.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eHowever, this study still has certain limitations. First, this study used convenience sampling, and thus the external validity of the study results may be reduced. In the future, more scientifically rigorous methods and more representative samples can be selected to verify the conclusions of the study. Second, like many related studies, this study used a self-report questionnaire to collect data. Although the Harman single-factor test was used to show a lack of serious common method bias, self-reported assessments of individuals' abilities and performance may still be susceptible to the Dunning-Kruger effect[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Specifically, such evaluations may be biased, with lower-ability individuals potentially overestimating their capabilities and higher-ability individuals possibly underestimating theirs.Therefore, future studies could use more standardized tests or explore other nonself-report methods to further validate the findings. Third, because this study was a cross-sectional survey, inferences about the causal relationships among variables cannot be made. A longitudinal study should be conducted to further validate the conclusions of this study. Additionally, the participants in this study were residents of Anhui Province; therefore, the research sample has certain regional limitations. Future studies should continue to expand the geographic scope of the research to provide more effective guidance for the future management of chronic diseases in elderly individuals in rural areas.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe main conclusions of this study are as follows: first, the self-management level of rural elderly patients with chronic diseases is relatively low and can be largely improved in the future. Second, digital health literacy, social support, and depression are three important factors that affect the self-management behaviour of rural elderly patients with chronic diseases. Third, the digital health literacy level of rural elderly patients with chronic diseases not only directly affects their chronic disease self-management behaviour but also indirectly affects this behaviour through the direct mediating effects of social support and depression as well as through the chain mediating effect of social support and depression. These findings enrich the existing research findings related to digital chronic disease management in elderly individuals, and the internal mechanisms revealed also provide scientific and practical insights for promoting self-health management behaviours in rural elderly chronic disease patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDHL: Digital health literacy; SSRS: Social Support Rating Scale; SDS: Self-Rating Depression Scale; CDSMS: Chronic Disease Self-Management Behaviour Scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank village committees in rural areas of Anhui Province for their cooperation in providing samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTheme: X.L and X.G. Methodology: X.L and X.G. Software: X.L and J.W. Data Curation: X.L, X.G, G.R, Z.M, J.H and C.S. Original draft: X.L. Review and editing: X.L and J.W. Supervision and funding acquisition: J.W. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Ministry of Education Humanities and Social Sciences Youth Project (Grant number 22YJCZH188) and Anhui Provincial Colleges and Universities Outstanding Youth Scientific Research Project (Grant number 2023AH030062).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental protocols of this study were approved by the Ethics Committee of Anhui Medical University(No.83243452), and all methods were conducted according to the guidelines of the Declaration of Helsinki and relevant Chinese laws and regulations. We confirm that informed consent was obtained from all participants and/or their legal guardians.\u0026nbsp;Considering that the study participants were all rural residents with generally low literacy levels, we used plain language to prepare the informed consent form, avoiding jargon and ensuring the clarity of the content. At the same time, before signing the informed consent form, the investigator explained the study purpose, procedures, potential risks and rights and benefits to the participants line by line, and for participants with limited comprehension, their family members or members of the village committee were invited to assist in the explanations to ensure that they fully understood the content of the study.\u0026nbsp;Written informed consent was obtained from all participants before any study procedures were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators GDAH. 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CIN: Computers, Informatics, Nursing. 2018; 36(9):438-447.\u003c/li\u003e\n\u003cli\u003eZhang X, Zheng Y, Qiu C, Zhao Y, Zang X. Well-being mediates the effects of social support and family function on self-management in elderly patients with hypertension. Psychology, health \u0026amp; medicine. 2020;25(5):559-571.\u003c/li\u003e\n\u003cli\u003eNoviana CM, Zahra AN. Social support and self-management among end-stage renal disease patients undergoing hemodialysis in Indonesia. J PUBLIC HEALTH RES. 2022;11(2):jphr-2021.\u003c/li\u003e\n\u003cli\u003eFilabadi ZR, Estebsari F, Milani AS, Feizi S, Nasiri M. Quick Response Code: Relationship between electronic health literacy, quality of life, and self-efficacy in Tehran, Iran: A community-based study. J EDUC HEALTH PROMOT. 2020;9(1):175.\u003c/li\u003e\n\u003cli\u003eCramm JM, Nieboer AP. The importance of health behaviours and especially broader self-management abilities for older Turkish immigrants. EUR J PUBLIC HEALTH. 2018; 28(6):1087-1092.\u003c/li\u003e\n\u003cli\u003eKempen GIJM, Jelicic M, Ormel J. Personality, chronic medical conditions and health-related quality of life among older persons. HEALTH PSYCHOL. 1997;16(6):539-546.\u003c/li\u003e\n\u003cli\u003eDunning D. The Dunning-Kruger Effect: On Being Ignorant of One\u0026apos;s Own Ignorance. ADV EXP SOC PSYCHOL. 2011; 44:247-296.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital health literacy, Self-management behaviour of chronic diseases, Social support, Depression, Chain mediation analysis, Rural elderly patients with chronic diseases","lastPublishedDoi":"10.21203/rs.3.rs-5647182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5647182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Chronic disease self-management is very important for the progression and treatment of diseases worldwide. The management of chronic diseases among elderly individuals in rural areas is an urgent public health concern in China. The purpose of this study was to investigate the relationship between digital health literacy and chronic disease self-management behaviour in elderly Chinese patients with chronic diseases in rural areas, as well as the chain mediating effects of social support and depression. The objective was to provide a scientific basis for improving the active health behaviour of rural elderly patients with chronic diseases in China and worldwide.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Using convenience sampling, the survey subjects were elderly patients with chronic diseases in rural areas of Anhui Province, China. A self-designed questionnaire was used to collect general survey data, digital health literacy scale scores, social support scale scores, depression scale scores, and chronic disease self-management behaviour scale scores. Common method bias tests, descriptive statistics and correlation analyses were performed via SPSS 29.0. The structural equation model was constructed and tested via AMOS 27.0. Differences for which \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In all, 202 elderly patients with chronic diseases who resided in rural areas were enrolled. The digital health literacy score was 39.25±9.00 points, and the chronic disease self-management behaviour score was 27.82±9.56 points. The self-management behaviours of rural elderly patients with chronic diseases were positively correlated with digital health literacy and social support and were negatively correlated with depression (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). After the mediating effect test, the total indirect effect value of social support and depression was 0.167, which accounted for 36.07% of the total effect. Among them, social support and depression were partial mediators of digital health literacy and chronic disease self-management behaviour, with effect values of 0.055 (95% CI: 0.012, 0.127) and 0.094 (95% CI: 0.024, 0.201), which accounted for 11.88% and 20.3% of the total effect, respectively. Social support and depression were chain mediators of digital health literacy and chronic disease self-management behaviour, with an effect value of 0.018 (95% CI: 0.004, 0.055) and an effect share of 3.89%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe self-management level of elderly patients with chronic diseases in rural China is low. Digital health literacy not only directly affects the chronic disease self-management behaviour of elderly individuals but also indirectly predicts chronic disease self-management behaviour through the mediating effects of social support and depression.\u003c/p\u003e","manuscriptTitle":"Path analysis of the influence of digital health literacy on self-management behaviour among elderly patients with chronic diseases in rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 04:11:23","doi":"10.21203/rs.3.rs-5647182/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-01T02:48:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T00:13:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T08:12:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202474870524494615186083568752096523609","date":"2025-03-25T03:30:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62261640708457342908294183174357164695","date":"2025-03-25T02:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T02:37:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T02:10:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-03-22T13:52:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"567a150f-0c44-4999-a44a-f8af265cae07","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:04:18+00:00","versionOfRecord":{"articleIdentity":"rs-5647182","link":"https://doi.org/10.1186/s12877-025-05952-3","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-04-29 15:57:44","publishedOnDateReadable":"April 29th, 2025"},"versionCreatedAt":"2025-03-27 04:11:23","video":"","vorDoi":"10.1186/s12877-025-05952-3","vorDoiUrl":"https://doi.org/10.1186/s12877-025-05952-3","workflowStages":[]},"version":"v1","identity":"rs-5647182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5647182","identity":"rs-5647182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}