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

eHealth Literacy / Digital Literacy (562) Diabetes (280) Diabetes Self-Management (615)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77856, first published .
Association of Electronic Health Literacy With Self-Care and Health Outcomes Among Patients With Type 2 Diabetes Mellitus: Cross-Sectional Study

Association of Electronic Health Literacy With Self-Care and Health Outcomes Among Patients With Type 2 Diabetes Mellitus: Cross-Sectional Study

Association of Electronic Health Literacy With Self-Care and Health Outcomes Among Patients With Type 2 Diabetes Mellitus: Cross-Sectional Study

Authors of this article:

Phoenix Kit-Han Mo1 Author Orcid Image ;   Alice PS Kong2 Author Orcid Image ;   Luyao Xie1 Author Orcid Image ;   Virginia WY Chan1, 3 Author Orcid Image ;   Joseph TF Lau4, 5 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    1Centre of Health Behaviours Research, Jockey Club School of Public Health and Primary Care, Prince of Wales Hospital, Chinese University of Hong Kong, 202D, Shatin, N.T., Hong Kong, China

    2Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China

    3Community Mental Health Association, Hong Kong, China

    4Public Mental Health Center, School of Mental Health, Wenzhou Medical University, Wenzhou, China

    5Zhejiang Provincial Clinical Research Center for Mental Disorders, The Affiliated Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, China

    Corresponding Author:

    Phoenix Kit-Han Mo, PhD


    Background: Diabetes mellitus (DM) continues to be a critical public health issue in Hong Kong. Although self-care behaviors help promote health among patients with DM, adherence remains suboptimal. More attention should be paid to eHealth literacy with the development of modern technologies.

    Objective: This study aims to assess the level of eHealth literacy among patients with DM and examine its association with self-care and health outcomes.

    Methods: A cross-sectional study was conducted among patients with type 2 DM from the DM clinic of a public hospital in Hong Kong. Data on eHealth literacy, self-care, self-care self-efficacy, diabetes distress, glycated hemoglobin (HbA1c) control, and sociodemographic information were collected. Multivariable regression analyses were performed, adjusting for relevant sociodemographic and medical variables.

    Results: Among the 427 patients with DM recruited, around two-thirds (65.1%) were classified as having a high level of eHealth literacy. Compared to those with lower eHealth literacy, participants with higher eHealth literacy demonstrated significantly higher levels of self-care (P<.001) and self-care self-efficacy (P<.001) and lower levels of diabetes distress (P<.001). Higher eHealth literacy was also associated with greater odds of achieving ideal HbA1c control (<7%) in unadjusted analyses (odds ratio 1.90, 95% CI 1.15-2.81); however, this association was not statistically significant after adjustment for sociodemographic and medical covariates (adjusted odds ratio 1.57, 95% CI 0.99-2.52; P=.07).

    Conclusions: This study evaluated eHealth literacy levels among patients with DM and examined the associations between eHealth literacy and health outcomes (eg, self-care, self-care self-efficacy, diabetes distress, and HbA1c control). Assessing eHealth literacy in patients with DM could be useful in identifying those who are vulnerable to poorer health outcomes. Promoting eHealth literacy among patients with DM may be important.

    J Med Internet Res 2026;28:e77856

    doi:10.2196/77856

    Keywords

    eHealth literacy; diabetes mellitus; patients; health outcomes; self-care 


    Globally, there were 537 million people living with diabetes mellitus (DM) in 2021 [1]. In Hong Kong, the prevalence of DM has increased sharply by 35.1% since 1995 [2]. By 2021, an estimated 7.8% of adults in Hong Kong were affected by DM [1]. It is projected that the prevalence of DM will rise to 9.4% in 2030. DM poses a significant burden to the health care system due to its chronic nature.

    There is no known cure for DM, but it is highly controllable. Self-care disease management, including having a healthy diet, exercising, medication adherence, and regular glucose monitoring, is an essential element of diabetes care [3]. Self-care behaviors are strongly associated with positive health outcomes, including lowering blood glucose level, reducing insulin resistance, and reducing the risk of cardiovascular disease [3]; however, adherence to self-care remains suboptimal. A study in Hong Kong among older adults with DM reported that 41.4% had unsatisfactory metabolic control (ie, glycated hemoglobin [HbA1c] exceeding the threshold of 7%) [4]. It has also been suggested that patients with DM require support and health information to help with disease management, but these needs have not always been met [5-7].

    Online platforms are, and will continue to be, an important resource for obtaining health information, as patients with DM are increasingly playing a larger role in managing their own health [8-10]. The widespread use of digital technologies has facilitated access to information previously available primarily through health professionals [11]. Digital health resources can help individuals with chronic diseases share their experiences and gather information and may help them overcome traditional barriers to self-care [12,13]. Moreover, easy access to health resources provided by digital approaches can increase individuals’ knowledge and awareness regarding medical and health issues, thus empowering them to actively participate in their own health care by making informed and well-considered decisions [14].

    Health literacy is defined as “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” [15]. With the advent of digital technologies, the concept of eHealth literacy (also known as digital health literacy) was introduced to extend health literacy in the digital context [16]. The importance of eHealth literacy for patients to properly use online health information and to participate in informed health care decision-making has been increasingly emphasized [17]. Due to the complexity and amount of information available online, it can be challenging for individuals with little experience in searching for online health information to locate the relevant information. In most cases, information retrieved from the internet is not properly understood by all groups of people [18]. There is also evidence that people have difficulty discerning between trustworthy and untrustworthy health information online [19,20]. Poor-quality, incorrect, and confusing health information online could also be harmful to the patients [21]. It is therefore highly important that individuals are able to effectively process and understand medical information available on the internet.

    Increasing attention to eHealth literacy has been observed in recent years. For example, studies in various populations, such as older people [22,23], underserved populations [18], or patients with chronic diseases [17,24,25], have suggested that there is, in general, a low level of eHealth literacy among the study samples. Various sociodemographic factors have been shown to affect the prevalence and extent of online health information seeking, including age, gender, education, socioeconomic status, health status, and internet use [26-31]. Research has further shown that higher eHealth literacy is associated with better health outcomes across different populations, such as better subjective health status [32], health-related quality of life [33-35], lower risk of chronic disease [36], healthy behaviors [37,38], and lower psychological distress [39,40].

    Several theoretical models, such as the Transactional Model of eHealth Literacy [41] and the Integrative Model of eHealth Use [42], provide frameworks for understanding how eHealth literacy may influence health outcomes. These models suggest that eHealth literacy can directly or indirectly affect health knowledge, patient–provider communication, as well as health outcomes such as proactive health behaviors and health-related quality of life [41,42]. Among patients with diabetes, higher eHealth literacy has been related to better self-care and other health outcomes through multiple psychosocial and behavioral pathways. For example, a study of 453 adults with diabetes in South Korea found that eHealth literacy was positively linked to self-management via self-efficacy and social support [43]. In a Chinese study, eHealth literacy influenced self-care behaviors indirectly through diabetes knowledge and self-efficacy [44]. Additional studies in South Korea and Taiwan also reported that eHealth literacy was associated with health-promoting behaviors and directly influenced self-care behaviors [45,46]. While previous studies have shown that eHealth literacy is associated with health-related behaviors in diabetes, evidence from patients in Hong Kong remains limited.

    Given the substantial health needs of individuals with DM, examining their ability to effectively use high-quality digital health resources and make informed decisions to improve their health would be particularly useful. The purposes of this study were to assess the level of eHealth literacy among patients with DM and to examine the association between eHealth literacy and behavioral (self-care), psychosocial (self-care self-efficacy and diabetes distress), and clinical (HbA1c control) outcomes among patients with type 2 DM in Hong Kong.


    Study Design and Participants

    A cross-sectional study was conducted among patients with DM. Target participants were patients with type 2 DM in Hong Kong who fulfilled the following criteria: (1) diagnosed with type 2 DM; (2) age between 35 and 65 years; (3) ability to speak and understand Cantonese (the primary language used in Hong Kong). Participants who were receiving psychiatric care at the time of the study were excluded.

    Sample Size Calculation

    Based on a previous study by Kim et al [45] conducted among patients with type 2 diabetes, which reported a correlation of r=0.15 between eHealth literacy and health-promoting behaviors. The sample size was estimated using G*Power version 3.1, assuming a correlation between eHealth literacy and self-care (r=0.15), with a 2-sided significance level of α=.05, and 80% statistical power, the minimum required sample size was 346 participants.

    Data Collection

    Eligible patients with type 2 DM attending a diabetes management clinic at a public hospital in Hong Kong were invited to participate in this cross-sectional survey using a clinic-based convenience sampling approach. For recruitment, informational materials (eg, posters and leaflets) were distributed to patients visiting the clinic. Trained research assistants approached eligible individuals who were waiting to receive routine follow-up consultations. Prospective participants received a detailed explanation of the study objectives and procedures. Those who expressed interest in joining the study received an information sheet, which contained detailed information about the purposes and logistics of the study. Written informed consent and contact information were obtained from participants who agreed to participate, and interview appointments were arranged on site. Telephone surveys were administered by trained research assistants within 1 week of the participants’ clinic visit. Participants received a gift coupon valued at HKD 50 (~US $6.5) as a token of appreciation for the time they spent on the study.

    Ethical Considerations

    Ethical approval was obtained from the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee (reference number T6080016). Written informed consent was obtained from all participants prior to data collection. Participant information was deidentified to ensure privacy and confidentiality, and all data were handled in accordance with institutional ethical guidelines. No compensation was provided to participants.

    Measures

    eHealth Literacy

    eHealth literacy was measured using the 8-item eHealth Literacy Scale (eHEALS), which was developed to measure individuals’ perceived skills of internet use for health information [47]. Items are rated on a 5-point Likert scale ranging from “1=Strongly disagree” to “5=Strongly agree.” Total scores of the eHEALS range from 8 to 40, with higher scores representing higher levels of eHealth literacy. The scale has been formally translated and validated in Chinese populations [48] and in the Hong Kong context [49-51]. Using the cutoff proposed by Milne et al [52], participants who scored 4 or above on at least 5 out of the 8 eHEALS items were classified as having high level of eHealth literacy. Internal consistency of the scale was satisfactory in the present study (Cronbach α=0.98), which is comparable to that reported in previous studies conducted among general adults (Cronbach α=0.95) [50] and older adults (Cronbach α=0.939) in Hong Kong [51].

    Level of Self-Care

    Self-care was measured by the 15-item Summary of Diabetes Self-Care Activity scale [53], a self-report questionnaire assessing diabetes self-management behaviors across domains including general diet, specific diet, foot care, self-monitoring of blood glucose, smoking, and exercise. Participants were asked to indicate the frequency (days per week) they engaged in each of these activities, with higher scores indicating higher levels of self-care. A validated Chinese version was used [54]. Internal consistency of the scale was satisfactory in the present study (Cronbach α=0.70).

    Self-Care Self-Efficacy

    Self-care self-efficacy was measured using the 20-item Chinese Diabetes Empowerment Scale [55]. Items were adapted and translated from the original 37-item Diabetes Empowerment Scale [56]. The Chinese Diabetes Empowerment Scale measures the degree of psychosocial self-efficacy in carrying out diabetes care in 5 domains: achieving goals, overcoming barriers, coping, suitable methods, and obtaining support. Participants were asked to rate their level of agreement on their perceived ability to handle diabetes-related psychosocial problems on a 5-point Likert scale, from “1=Strongly disagree” to “5=Strongly agree,” with higher scores indicating a greater level of self-care self-efficacy. The internal consistency of the scale was satisfactory in the present study (Cronbach α=0.94).

    Diabetes Distress

    Diabetes-related distress was assessed using the 15-item Chinese Diabetes Distress Scale [57], which measures emotional burden distress, regimen- and social support-related distress, and physician-related distress. Participants were asked to rate the degree to which each item may have distressed or bothered them in the past month on a 6-point Likert Scale from “1=Not a problem” to “6=A very serious problem,” with higher scores indicating a higher level of diabetes distress. The internal consistency of the scale was satisfactory in the present study (Cronbach α=0.93).

    Sociodemographic Characteristics

    Sociodemographic characteristics, including gender, age, education level, relationship status, employment status, monthly income, were obtained. Participants were also asked to report the average time spent on the internet per week. Medical information, such as duration of diagnosis, number of complications, types of treatment, and HbA1c control, was retrieved from participants’ medical records.

    Data Analysis

    Descriptive statistics were used to summarize participants’ sociodemographic and medical characteristics and levels of eHealth literacy. Correlations between participants’ sociodemographic and background characteristics, eHealth literacy, and outcome variables were also examined. To evaluate the association between eHealth literacy and outcome variables, participants were classified into high and low eHealth literacy groups based on the cutoff proposed by Milne et al [52]. Independent-sample t tests were used to examine the differences in self-care, self-care self-efficacy, and diabetes distress between groups. One-way analysis of covariance (ANCOVA) was also conducted with covariates being adjusted for in each analysis. Sociodemographic and medical variables that were significantly correlated with the respective outcome variables would be included as covariates. To evaluate the association between eHealth literacy and HbA1c control, participants with HbA1c less than 7% were first defined as having ideal HbA1c control. Univariate logistic regression was conducted for obtaining odds ratio (OR) of eHealth literacy on health-related outcomes. Adjusted logistic regression, adjusting for significant sociodemographic and medical variables, was also conducted; adjusted OR for eHealth literacy was then derived. All analyses were performed using SPSS version 21 (IBM Corp). Statistical significance was defined as P<.05.


    Participant Characteristics

    A total of 427 patients with type 2 DM completed the survey. The achieved sample size was deemed sufficient for the planned analyses. Slightly more than half of the participants (223/427, 52.1%) were male, and their mean age was 56.53 (SD 7.05) years. About half of them (218/427, 51%) had received a senior secondary level of education or above. The majority of participants (364/427, 85.2%) were married, and more than half of them (240/427, 56.2%) were unemployed. The mean duration of DM diagnosis was 10.69 (SD 8.14) years. Slightly less than two-thirds (261/427, 61.2%) of them had at least one DM-related complication. One hundred ninety out of 427 (44.5%) and 185 out of 427 (43.3%) participants were on oral treatment and on both oral treatment and injections, respectively. Around two-thirds (278/427, 65.1%) of the participants were classified as having high levels of eHealth literacy (Table 1).

    Table 1. Sociodemographic and medical characteristics of participants (N=427).
    CharacteristicSample
    Sociodemographic characteristics
    Gender, n (%)
    Male221 (52.1)
    Female203 (47.9)
    Age (y), mean (SD)56.53 (7.05)
    Education level, n (%)
    Primary or below113 (26.5)
    Junior secondary (Year 7 to 9)96 (22.5)
    Senior secondary (Year 10 to 13)166 (39)
    College or university51 (12)
    Relationship status, n (%)
    Single32 (7.5)
    Married364 (85.2)
    Separated/divorced/widowed29 (6.8)
    Others2 (0.5)
    Employment, n (%)
    Employed240 (56.2)
    Housewife66 (15.5)
    Unemployed15 (3.5)
    Retired102 (23.9)
    Others4 (0.9)
    Monthly income (HKD)a, n (%)
    999968 (16.5)
    10,000-19,999159 (38.7)
    20,000-29,999117 (28.5)
    ≥30,00067 (16.3)
    Medical characteristics
    Duration of diagnosis (y), mean (SD)10.69 (8.14)
    Number of complications, n (%)
    None165 (38.8)
    1162 (38.1)
    278 (18.4)
    320 (4.7)
    Types of treatment, n (%)
    No prescribed medication21 (4.9)
    Oral only190 (44.5)
    Injection only27 (6.3)
    Both oral and injection185 (43.3)
    Others4 (0.9)
    Internet-related variables
    Time spent on the internet per week, n (%)
    0 h64 (15)
    <1 h178 (41.6)
    Between 1 and 2 h121 (28.3)
    >2 h64 (14.9)
    eHEALSb score, mean (SD)23.68 (11.61)
    Level of eHealth literacyc, n (%)
    Low149 (34.9)
    High278 (65.1)

    a1 HKD=0.13 USD.

    beHEALS: the 8-item eHealth literacy scale.

    cA high level of eHealth literacy was defined as those who scored 4 or above with at least 5 eHEALS items.

    Correlation Between Variables

    The correlation between variables is presented in Table 2. Among sociodemographic and medical variables, male gender (r=−0.26, P<.01), younger age (r=−0.43, P<.01), higher educational level (r=0.50, P<.01), being married (r=−0.11, P<.01), being employed (r=−0.24, P<.01), higher monthly income (r=0.30, P<.01), and shorter duration of diagnosis (r=−0.14, P<.01) had significant correlation with higher level of eHealth literacy. Higher level of eHealth literacy was also significantly correlated with higher level of self-care (r=0.15, P<.01) and higher level of self-care self-efficacy (r=.32, P<.01), as well as lower level of diabetes distress (r=−0.11, P<.05) and lower level of HbA1c (r=−0.11, P<.05).

    Table 2. Correlation between variables.
    Variable1234567891011121314
    Gendera—
    Age0.12b—
    Education level−0.30c−0.43c—
    Relationship statusd0.040.18c−0.13c—
    Employment statuse0.17c0.39c−0.20c0.06—
    Monthly income−0.10b−0.29c0.40c−0.09−0.27c—
    Duration of diagnosis0.13b0.24c−0.21c0.020.16c−0.06—
    Number of complications−0.10b0.19c−0.13c0.070.05−0.10b0.24c—
    Type of treatment0.040.06−0.06−0.010.10b−0.040.34c0.12b—
    eHealth literacy−0.26c−0.43c0.50c−0.11b−0.24c0.30c−0.14c−0.09−0.06—
    Self-care0.080.010.05−0.10b0.13c0.040.10b−0.050.090.15c—
    Self-care self-efficacy−0.17c−0.16c0.25c−0.04−0.050.21c−0.05−0.05−0.050.32c0.30c—
    Diabetes distress0.07−0.03−0.050.01−0.02-0.20c0.10b0.030.02−0.11b−0.23c−0.46c—
    HbA1cf0.080.01−0.11b0.060.06−0.070.34c0.18c0.21c−0.11b0.09−0.10b0.08—

    aDummy variable was created as 1: male; 2: female.

    bP<.05.

    cP<.01.

    dDummy variable was created as 1: married; 2: single/separate/divorced/widowed.

    eDummy variable was created as 1: employed; 2: housewife/unemployed/retired.

    fHbA1c: glycated hemoglobin.

    Group Difference in Self-Care, Self-Care Self-Efficacy, and Diabetes Distress

    Results of independent-samples t tests showed that participants classified as having a high level of eHealth literacy had significantly higher levels of self-care (P<.001) and self-care self-efficacy (P<.001), as well as significantly lower levels of diabetes distress (P<.001).

    Results of the ANCOVA showed that such differences remained statistically significant after controlling for significant sociodemographic and medical variables. The adjusted mean differences between the high and low eHealth literacy groups were 0.442 for self-care, 3.875 for self-care self-efficacy, and −0.237 for diabetes distress (all P<.001) (Table 3).

    Table 3. Independent sample t test and analysis of covariance analyses of group difference in self-care, self-care self-efficacy, and diabetes distress.
    VariableLow eHealth literacy (n=149), mean (SD)High eHealth literacy (n=278), mean (SD)Low eHealth literacy (n=149), adjusted mean (95% CI)High eHealth literacy (n=278), adjusted mean (95% CI)Mean difference
    Self-care1.78 (1.03)2.07 (1.16)a1.71 (1.56–1.87)b2.16 (2.02–2.23)b0.442c
    Self-care self-efficacy72.46 (7.87)77.08 (4.51)c73.37 (72.37‐74.37)d77.25 (76.45‐78.05)d3.875c
    Diabetes distress2.74 (0.56)2.44 (0.47)c2.68 (2.60‐2.76)e2.44 (2.38‐2.50)e−0.237c

    aP<.01.

    bAdjusted for relationship status, employment, duration of diagnosis, and types of treatment.

    cP<.001.

    dAdjusted for sex, age, education level, and monthly family income.

    eAdjusted for monthly family income and duration of diagnosis.

    Logistic Regression on Ideal HbA1c Control

    There were 38.7% (96/248) and 26.0% (38/146) ideal HbA1c in participants with low level and high level of eHealth literacy, respectively. Results from the univariate logistic regression analysis showed that participants with a high level of eHealth literacy were more likely to report ideal HbA1c control (unadjusted OR 1.90, 95% CI 1.15-2.81; P<.01). After adjusting for significant sociodemographic and medical information, the association was not statistically significant (adjusted OR 1.57, 95% CI 0.99-2.52, P=0.070).


    Main Findings and Implications

    DM is a chronic disease that requires good knowledge and self-management skills to optimize glycemic control and health outcomes. As digital health technologies are now widely used to support patients, eHealth literacy is of great importance because seeking online health information reflects a complex issue involving information awareness, consciousness, reasoning, and knowledge. It is important to assess patients with DM eHealth literacy, as health care professionals should have a good understanding of the patients’ ability to use electronic resources before recommending the use of online health information [47].

    Few studies in Hong Kong have reported the eHealth literacy levels of the population, especially in patients. Several local studies reported mean eHEALS scores of 26.10 (SD 7.70) in 1501 general adults [50] and 27.34 (SD 6.44) among 306 adults aged 60 years or older [51]. This study expands the evidence base among patients in Hong Kong and shows that the mean eHEALS score among 427 patients with type 2 DM was 23.68 (SD 11.61). In addition, using the cutoff proposed by Milne et al [52], approximately two-thirds (278/427, 65.1%) of the patients with DM were considered as having a high level of eHealth literacy. Compared with other studies using the same cutoff, the proportion of patients having a high level of eHealth literacy in the present study was significantly higher than that reported among lung cancer survivors (33.7%) in Canada [52]. This difference may be partly attributable to age, as participants in the present study were younger (mean age 56.5, SD 7.05 y) than those in Milne et al’s study (median age 71 y). This interpretation is further supported by the negative association between age and eHealth literacy observed in the present study and reported consistently in the literature [11,31]. The patients with DM in the present study were likely confident in using the internet for health information and advice; they also demonstrated reasonable skills and self-efficacy required to achieve maximum benefit from the use.

    In addition to age, the present study also revealed that among the sociodemographic and medical variables, male gender, higher level of education, currently married, employed, higher monthly income, and shorter duration of diagnosis were associated with higher eHealth literacy. These findings are consistent with the literature showing that eHealth literacy is generally lower among individuals with lower socioeconomic status [14,58-60]. Because the eHEALS was designed to measure an individual’s confidence in their ability to locate and access online health information, it is conceivable that individuals with better socioeconomic status would possess higher ability in understanding and discerning the quality of the health information available on the internet. Individuals who were married and employed may also be more likely to receive a higher level of social support in searching for health information, including those from the online sources.

    Understanding the association between eHealth literacy and health-related outcomes is essential for developing effective health communication strategies in the future. It has been found that the use of online health information and eHealth literacy might affect health behaviors [37,45,61,62]. In the present study, higher eHealth literacy was significantly associated with higher levels of self-care and self-care self-efficacy and with lower levels of diabetes distress after controlling for significant sociodemographic and medical variables. These findings are consistent with recent evidence showing that eHealth literacy significantly influences diabetes self-care behaviors among patients with type 2 DM in Saudi Arabia [63]. Although this study also revealed eHealth literacy was significantly associated with better glycemic outcomes in their population [63], the present study did not find a statistically significant association between eHealth literacy and HbA1c among patients with diabetes in Hong Kong after adjustment for covariates. Patients with higher eHealth literacy are not only more inclined to use the internet for health-related information but also are able to understand, evaluate the information obtained, and use quality information to make informed decisions about health. They may therefore be more motivated to adopt healthier lifestyles based on the information that they obtain online [38], more directly leading to better behavioral and psychosocial outcomes. The integrated Model of eHealth Use model also supports the motivational pathways through which eHealth literacy may influence health behavior [42]. Individuals with a higher ability to understand online health information may also be more likely to access health care and interact with health care professionals, therefore more likely to benefit from their health care utilization. In the conceptual model proposed by Paasche-Orlov and Wolf [64], self-efficacy is considered an important mediator linking health literacy and health outcomes. In digital health contexts, self-efficacy has also been found to be a significant mediator in the relationship between eHealth literacy and health outcomes in patients with DM [43,44]. Individuals with higher eHealth literacy are likely to have greater confidence in their ability to effectively utilize online health resources and engage in self-care behaviors, which in turn, is associated with overall behavioral and mental health in patients with DM. However, HbA1c is a more distal biomedical indicator and reflects average glycemic control over the preceding two to three months, which may be less directly associated with eHealth literacy. Future studies are warranted to further elucidate the pathways linking eHealth literacy to glycemic outcomes in this population. Findings of this study suggest that eHealth literacy is an important concept associated with the health of patients with DM. Improving eHealth literacy of patients with DM would potentially be an effective means in improving the health status of the population.

    Implications for Practice

    The eHEALS is currently one of the most common tools designed to measure an individual’s confidence in their ability to locate and access online health information [65,66]. It demonstrated high internal consistency and predictive validity in Chinese chronic patients [67], and is associated with a range of health-related outcomes among various populations [22,30,67,68]. Findings of the present study further confirmed its utility in Chinese patients with DM and suggested that it could be an effective screening tool for screening patients’ level of eHealth literacy in clinical settings.

    The higher levels of self-care and self-care self-efficacy, and lower levels of diabetes distress documented among participants with higher eHealth literacy also provided important insights that promoting eHealth literacy can have the potential to support patients with DM with self-management behaviors and may result in better DM outcomes. Recent studies have shown that patients with DM who used DM apps had higher eHealth literacy than nonusers and perceived the app to have a greater effectiveness on their health behaviors [68-70]. Health care providers should therefore empower patients with DM with the skills in utilizing online resources. Improving eHealth literacy in patients with type 2 diabetes has important practical implications for self-care and health outcomes. Evidence suggests that improving diabetes knowledge and attitudes toward online health information are likely to be effective strategies for enhancing eHealth literacy in this population [71]. However, it is also noted that participant engagement in eHealth technologies might wane over time. Given the chronic nature of DM, interventions to increase eHealth literacy should make use of content and features that can be personalized and adapted to the individual over time to achieve a sustainable positive effect [72]. Continuous support, training, and monitoring by health care providers are essential to help patients acquire and apply accurate health information effectively, ultimately improving self-care and health outcomes.

    Limitations

    There are several limitations that should be noted. First, the present study was cross-sectional in nature; therefore, no causality between the variables could be assumed. Second, self-perceived eHealth literacy was measured in the present study as there was a lack of objective, validated measure in eHealth literacy in the current literature. The current literature seems to have revealed mixed findings on the association between subjective and objective measures of eHealth literacy on health-related internet use [25,36]. However, it is important to note that the eHEALS scale is one of the most used validated scales to measure eHealth literacy. This study was also limited by self-selection bias, that those who agreed to take part in the study might have more favorable views towards internet use and thus a higher level of eHealth literacy. Finally, as the present sample of patients with DM was relatively young and recruited from only one public DM clinic, cautions should be taken when generalizing the results to the general DM population in Hong Kong.

    Conclusion

    The internet plays an increasingly important part in disease management among patients with DM. Findings of the present study showed that higher level of eHealth literacy was associated with their better self-care, self-care self-efficacy, lower level of diabetes distress, and having ideal HbA1c control. Assessing the level of eHealth literacy of patients with DM could be useful for identifying patients who are more vulnerable to poorer health. Promoting the eHealth literacy of patients with DM could potentially be associated with better health outcomes.

    Funding

    This work was supported by Health and Medical Research Fund of Hong Kong Food and Health Bureau (12130751).

    Data Availability

    The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

    Authors' Contributions

    Conceptualization: PKHM, APSK, VWYC, JTFL

    Data curation: PKHM, APSK, VWYC

    Formal analysis: PKHM

    Methodology: PKHM, APSK, VWYC, JTFL

    Project administration: PKHM

    Supervision: PKHM

    Writing – original draft: PKHM, LX

    Conflicts of Interest

    None declared.

    1. IDF Diabetes Atlas. 10th ed. International Diabetes Federation; 2021. URL: https://diabetes.org.br/wp-content/uploads/2022/02/IDF_Atlas_10th_Edition_2021-.pdf [Accessed 2026-02-03] ISBN: 978-2-930229-98-0
    2. Cheung B, Man Y, Lam K, Wat N, Lo J, Chau D, et al. Prevalence of diabetes mellitus (DM) in the Hong Kong Cardiovascular Risk Factor Prevalence Study cohort. Hong Kong Med J. 2003;9(Supp 1). URL: https://www.hkmj.org/system/files/2003-S1.pdf [Accessed 2026-02-03]
    3. American Diabetes Association. Standards of medical care in diabetes--2011. Diabetes Care. Jan 2011;34 Suppl 1(Suppl 1):S11-S61. [CrossRef] [Medline]
    4. Cheng LY, Leung SY, Leung MKW. The association of glycemic control and fall risk in diabetic elderly: a cross-sectional study in Hong Kong. BMC Prim Care. Aug 1, 2022;23(1):192. [CrossRef] [Medline]
    5. Carolan M, Holman J, Ferrari M. Experiences of diabetes self-management: a focus group study among Australians with type 2 diabetes. J Clin Nurs. Apr 2015;24(7-8):1011-1023. [CrossRef] [Medline]
    6. Song Y, Song HJ, Han HR, Park SY, Nam S, Kim MT. Unmet needs for social support and effects on diabetes self-care activities in Korean Americans with type 2 diabetes. Diabetes Educ. 2012;38(1):77-85. [CrossRef] [Medline]
    7. Timmers T, Janssen L, Kool RB, Kremer JA. Educating patients by providing timely information using smartphone and tablet apps: systematic review. J Med Internet Res. Apr 13, 2020;22(4):e17342. [CrossRef] [Medline]
    8. Desveaux L, Shaw J, Saragosa M, et al. A mobile app to improve self-management of individuals with type 2 diabetes: qualitative realist evaluation. J Med Internet Res. Mar 16, 2018;20(3):e81. [CrossRef] [Medline]
    9. Kruse CS, Beane A. Health information technology continues to show positive effect on medical outcomes: systematic review. J Med Internet Res. Feb 5, 2018;20(2):e41. [CrossRef] [Medline]
    10. Shahbazi M, Bagherian H, Sattari M, Saghaeiannejad-Isfahani S. The opportunities and challenges of using mobile health in elderly self-care. J Educ Health Promot. 2021;10:80. [CrossRef] [Medline]
    11. Tennant B, Stellefson M, Dodd V, et al. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res. Mar 17, 2015;17(3):e70. [CrossRef] [Medline]
    12. Perakslis E, Ginsburg GS. Digital health—the need to assess benefits, risks, and value. JAMA. Jan 12, 2021;325(2):127-128. [CrossRef] [Medline]
    13. McBride D. Web forums allow patients with colorectal cancer to share their real-world experiences. ONS Connect. Dec 2013;28(4):46. [Medline]
    14. Yoon H, Jang Y, Vaughan PW, Garcia M. Older adults’ internet use for health information: digital divide by race/ethnicity and socioeconomic status. J Appl Gerontol. Jan 2020;39(1):105-110. [CrossRef] [Medline]
    15. Parker RM, Ratzan SC, Lurie N. Health literacy: a policy challenge for advancing high-quality health care. Health Aff (Millwood). 2003;22(4):147-153. [CrossRef] [Medline]
    16. Norman CD, Skinner HA. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res. Jun 16, 2006;8(2):e9. [CrossRef] [Medline]
    17. Wu Y, Wen J, Wang X, et al. Associations between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: a mediation analysis. BMC Public Health. Nov 29, 2022;22(1):2226. [CrossRef] [Medline]
    18. Chesser A, Burke A, Reyes J, Rohrberg T. Navigating the digital divide: a systematic review of eHealth literacy in underserved populations in the United States. Inform Health Soc Care. 2016;41(1):1-19. [CrossRef] [Medline]
    19. Nguyen A, Mosadeghi S, Almario CV. Persistent digital divide in access to and use of the Internet as a resource for health information: results from a California population-based study. Int J Med Inform. Jul 2017;103:49-54. [CrossRef] [Medline]
    20. Tonsaker T, Bartlett G, Trpkov C. Health information on the Internet: gold mine or minefield? Can Fam Physician. May 2014;60(5):407-408. [Medline]
    21. Valizadeh-Haghi S, Rahmatizadeh S. Evaluation of the quality and accessibility of available websites on kidney transplantation. Urol J. Sep 26, 2018;15(5):261-265. [CrossRef] [Medline]
    22. Xie L, Zhang S, Xin M, Zhu M, Lu W, Mo PKH. Electronic health literacy and health-related outcomes among older adults: a systematic review. Prev Med. Apr 2022;157:106997. [CrossRef] [Medline]
    23. Wang Y, Song Y, Zhu Y, Ji H, Wang A. Association of eHealth literacy with health promotion behaviors of community‑dwelling older people: the chain mediating role of self‑efficacy and self‑care ability. Int J Environ Res Public Health. May 17, 2022;19(10):6092. [CrossRef] [Medline]
    24. Robinson C, Graham J. Perceived Internet health literacy of HIV-positive people through the provision of a computer and Internet health education intervention. Health Info Libr J. Dec 2010;27(4):295-303. [CrossRef] [Medline]
    25. van der Vaart R, Drossaert CHC, de Heus M, Taal E, van de Laar MAFJ. Measuring actual eHealth literacy among patients with rheumatic diseases: a qualitative analysis of problems encountered using Health 1.0 and Health 2.0 applications. J Med Internet Res. Feb 11, 2013;15(2):e27. [CrossRef] [Medline]
    26. Oh YS, Cho Y. Examining the relationships between resources and online health information seeking among patients with chronic diseases and healthy people. Soc Work Health Care. 2015;54(2):83-100. [CrossRef] [Medline]
    27. Li J, Theng YL, Foo S. Predictors of online health information seeking behavior: changes between 2002 and 2012. Health Informatics J. Dec 2016;22(4):804-814. [CrossRef] [Medline]
    28. Moreland J, French TL, Cumming GP. The prevalence of online health information seeking among patients in Scotland: a cross‑sectional exploratory study. JMIR Res Protoc. Jul 15, 2015;4(3):e85. [CrossRef] [Medline]
    29. Li N, Orrange S, Kravitz RL, Bell RA. Reasons for and predictors of patients’ online health information seeking following a medical appointment. Fam Pract. Oct 2014;31(5):550-556. [CrossRef] [Medline]
    30. Bailey CE, Kohler WJ, Makary C, Davis K, Sweet N, Carr M. eHealth literacy in otolaryngology patients. Ann Otol Rhinol Laryngol. Nov 2019;128(11):1013-1018. [CrossRef] [Medline]
    31. Halwas N, Griebel L, Huebner J. eHealth literacy, Internet and eHealth service usage: a survey among cancer patients and their relatives. J Cancer Res Clin Oncol. Nov 2017;143(11):2291-2299. [CrossRef] [Medline]
    32. Kim H, Yang E, Ryu H, Kim HJ, Jang SJ, Chang SJ. Psychometric comparisons of measures of eHealth literacy using a sample of Korean older adults. Int J Older People Nurs. May 2021;16(3):e12369. [CrossRef] [Medline]
    33. Li S, Cui G, Yin Y, Wang S, Liu X, Chen L. Health-promoting behaviors mediate the relationship between eHealth literacy and health-related quality of life among Chinese older adults: a cross-sectional study. Qual Life Res. Aug 2021;30(8):2235-2243. [CrossRef] [Medline]
    34. Brørs G, Wentzel-Larsen T, Dalen H, et al. Psychometric properties of the Norwegian version of the electronic Health Literacy Scale (eHEALS) among patients after percutaneous coronary intervention: cross‑sectional validation study. J Med Internet Res. Jul 28, 2020;22(7):e17312. [CrossRef] [Medline]
    35. Xie L, Mo PKH. A 3‑wave longitudinal study of eHealth literacy and older people’s health‑related quality of life in China: the mediating role of general self‑efficacy. J Am Med Dir Assoc. Dec 2024;25(12):105310. [CrossRef] [Medline]
    36. Neter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res. Jan 27, 2012;14(1):e19. [CrossRef] [Medline]
    37. Kim K, Shin S, Kim S, Lee E. The relation between eHealth literacy and health‑related behaviors: systematic review and meta‑analysis. J Med Internet Res. Jan 30, 2023;25:e40778. [CrossRef] [Medline]
    38. Xie L, Mo PKH. Prospective relationship between electronic health literacy and health-promoting lifestyle among Chinese older adults: a three-wave longitudinal study. Soc Sci Med. Jan 2025;364:117166. [CrossRef] [Medline]
    39. Lin CY, Broström A, Griffiths MD, Pakpour AH. Psychometric evaluation of the Persian eHealth Literacy Scale (eHEALS) among elder Iranians with heart failure. Eval Health Prof. Dec 2020;43(4):222-229. [CrossRef] [Medline]
    40. Leung AYM, Parial LL, Tolabing MC, et al. Sense of coherence mediates the relationship between digital health literacy and anxiety about the future in aging population during the COVID-19 pandemic: a path analysis. Aging Ment Health. Mar 2022;26(3):544-553. [CrossRef] [Medline]
    41. Paige SR, Stellefson M, Krieger JL, Anderson-Lewis C, Cheong J, Stopka C. Proposing a transactional model of eHealth literacy: concept analysis. J Med Internet Res. Oct 2, 2018;20(10):e10175. [CrossRef] [Medline]
    42. Bodie GD, Dutta MJ. Understanding health literacy for strategic health marketing: eHealth literacy, health disparities, and the digital divide. Health Mark Q. 2008;25(1-2):175-203. [CrossRef] [Medline]
    43. Lee EH, Lee YW, Kang EH, Kang HJ. Relationship between electronic health literacy and self‑management in people with type 2 diabetes using a structural equation modeling approach. J Nurs Res. Jan 1, 2024;32(1):e315. [CrossRef] [Medline]
    44. Ji X, Chi J. Exploring the relationship between eHealth literacy and diabetes knowledge, self‑efficacy, and self‑care behaviors in Chinese diabetic patients: a cross‑sectional study. J Nurs Res. Dec 1, 2024;32(6):e359. [CrossRef] [Medline]
    45. Kim KA, Kim YJ, Choi M. Association of electronic health literacy with health‑promoting behaviors in patients with type 2 diabetes: a cross‑sectional study. Comput Inform Nurs. Sep 2018;36(9):438-447. [CrossRef] [Medline]
    46. Guo SHM, Hsing HC, Lin JL, Lee CC. Relationships between mobile eHealth literacy, diabetes self‑care, and glycemic outcomes in Taiwanese patients with type 2 diabetes: cross‑sectional study. JMIR mHealth uHealth. Feb 5, 2021;9(2):e18404. [CrossRef] [Medline]
    47. Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. Nov 14, 2006;8(4):e27. [CrossRef] [Medline]
    48. Xu RH, Zhou L, Lu SY, Wong EL, Chang J, Wang D. Psychometric validation and cultural adaptation of the simplified Chinese eHealth literacy scale: cross‑sectional study. J Med Internet Res. Dec 7, 2020;22(12):e18613. [CrossRef] [Medline]
    49. Wong DKK, Cheung MK. Online health information seeking and eHealth literacy among patients attending a primary care clinic in Hong Kong: a cross‑sectional survey. J Med Internet Res. Mar 27, 2019;21(3):e10831. [CrossRef] [Medline]
    50. Guo Z, Zhao SZ, Guo N, et al. Socioeconomic disparities in eHealth literacy and preventive behaviors during the COVID‑19 pandemic in Hong Kong: cross‑sectional study. J Med Internet Res. Apr 14, 2021;23(4):e24577. [CrossRef] [Medline]
    51. Kim S, Chow BC, Park S, Liu H. The usage of digital health technology among older adults in Hong Kong and the role of technology readiness and eHealth literacy: path analysis. J Med Internet Res. Apr 12, 2023;25(1):e41915. [CrossRef] [Medline]
    52. Milne RA, Puts MTE, Papadakos J, et al. Predictors of high eHealth literacy in primary lung cancer survivors. J Cancer Educ. Dec 2015;30(4):685-692. [CrossRef] [Medline]
    53. Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. Jul 2000;23(7):943-950. [CrossRef] [Medline]
    54. Tang YH, Pang SMC, Chan MF, Yeung GSP, Yeung VTF. Health literacy, complication awareness, and diabetic control in patients with type 2 diabetes mellitus. J Adv Nurs. Apr 2008;62(1):74-83. [CrossRef] [Medline]
    55. Shiu ATY, Wong RYM, Thompson DR. Development of a reliable and valid Chinese version of the diabetes empowerment scale. Diabetes Care. Oct 2003;26(10):2817-2821. [CrossRef] [Medline]
    56. Anderson RM, Funnell MM, Fitzgerald JT, Marrero DG. The diabetes empowerment scale: a measure of psychosocial self‑efficacy. Diabetes Care. Jun 2000;23(6):739-743. [CrossRef] [Medline]
    57. Ting RZW, Nan H, Yu MWM, et al. Diabetes-related distress and physical and psychological health in chinese type 2 diabetic patients. Diabetes Care. May 2011;34(5):1094-1096. [CrossRef] [Medline]
    58. Verma R, Saldanha C, Ellis U, Sattar S, Haase KR. eHealth literacy among older adults living with cancer and their caregivers: a scoping review. J Geriatr Oncol. Jun 2022;13(5):555-562. [CrossRef] [Medline]
    59. Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: internet use patterns, eHealth literacy, and attitudes toward computer/internet use. J Med Internet Res. May 2, 2013;15(5):e93. [CrossRef] [Medline]
    60. Hung LY, Lyons JG, Wu CH. Health information technology use among older adults in the United States, 2009-2018. Curr Med Res Opin. May 2020;36(5):789-797. [CrossRef] [Medline]
    61. Li SJ, Yin YT, Cui GH, Xu HL. The associations among health‑promoting lifestyle, eHealth literacy, and cognitive health in older Chinese adults: a cross‑sectional study. Int J Environ Res Public Health. Mar 27, 2020;17(7):2263. [CrossRef] [Medline]
    62. Kim SH, Son YJ. Relationships between eHealth literacy and health behaviors in Korean adults. Comput Inform Nurs. Feb 2017;35(2):84-90. [CrossRef] [Medline]
    63. AlShehri WS, Khojah YY. Impact of e‑health literacy on diabetes self‑care activities among people with type 2 diabetes attending primary healthcare centers in Makkah City, Saudi Arabia: a cross‑sectional study, 2025. Cureus. Jul 2025;17(7):e87968. [CrossRef] [Medline]
    64. Paasche-Orlow MK, Wolf MS. The causal pathways linking health literacy to health outcomes. Am J Health Behav. 2007;31 Suppl 1:S19-S26. [CrossRef] [Medline]
    65. Griebel L, Enwald H, Gilstad H, Pohl AL, Moreland J, Sedlmayr M. eHealth literacy research-quo vadis? Inform Health Soc Care. Dec 2018;43(4):427-442. [CrossRef] [Medline]
    66. Lee J, Lee EH, Chae D. eHealth literacy instruments: systematic review of measurement properties. J Med Internet Res. Nov 15, 2021;23(11):e30644. [CrossRef] [Medline]
    67. Chang A, Schulz PJ. The measurements and an elaborated understanding of Chinese eHealth literacy (C‑eHEALS) in chronic patients in China. Int J Environ Res Public Health. Jul 23, 2018;15(7):1553. [CrossRef] [Medline]
    68. Ernsting C, Stühmann LM, Dombrowski SU, Voigt-Antons JN, Kuhlmey A, Gellert P. Associations of health app use and perceived effectiveness in people with cardiovascular diseases and diabetes: population‑based survey. JMIR mHealth uHealth. Mar 28, 2019;7(3):e12179. [CrossRef] [Medline]
    69. Rollo ME, Aguiar EJ, Williams RL, et al. eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management. Diabetes Metab Syndr Obes. 2016;9:381-390. [CrossRef] [Medline]
    70. Kebede MM, Pischke CR. Popular diabetes apps and the impact of diabetes app use on self‑care behaviour: a survey among the digital community of persons with diabetes on social media. Front Endocrinol (Lausanne). 2019;10:135. [CrossRef] [Medline]
    71. Lee M, Shim J. Effects of diabetes knowledge and attitudes toward internet health information on e‑health literacy in middle‑aged patients with diabetes. Healthcare (Basel). Feb 27, 2025;13(5):512. [CrossRef] [Medline]
    72. Pereira K, Phillips B, Johnson C, Vorderstrasse A. Internet delivered diabetes self-management education: a review. Diabetes Technol Ther. Jan 2015;17(1):55-63. [CrossRef] [Medline]

    ‎
    ANCOVA: analysis of covariance
    DM: diabetes mellitus
    eHEALS: the 8-item eHealth Literacy Scale
    HbA1c: glycated hemoglobin
    OR: odds ratio

    Edited by Amy Schwartz, Matthew Balcarras; submitted 02.Jun.2025; peer-reviewed by Cheong Lieng Teng, Chun Wai Chan, Mohamad Adam Bujang; final revised version received 30.Dec.2025; accepted 02.Jan.2026; published 09.Mar.2026.

    Copyright

    © Phoenix Kit-Han Mo, Alice PS Kong, Luyao Xie, Virginia WY Chan, Joseph TF Lau. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 9.Mar.2026.

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