This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
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.
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.
MethodsStudy 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.
MeasureseHealth 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.
ResultsParticipant 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).
Sociodemographic and medical characteristics of participants (N=427).
Characteristic
Sample
Sociodemographic characteristics
Gender, n (%)
Male
221 (52.1)
Female
203 (47.9)
Age (y), mean (SD)
56.53 (7.05)
Education level, n (%)
Primary or below
113 (26.5)
Junior secondary (Year 7 to 9)
96 (22.5)
Senior secondary (Year 10 to 13)
166 (39)
College or university
51 (12)
Relationship status, n (%)
Single
32 (7.5)
Married
364 (85.2)
Separated/divorced/widowed
29 (6.8)
Others
2 (0.5)
Employment, n (%)
Employed
240 (56.2)
Housewife
66 (15.5)
Unemployed
15 (3.5)
Retired
102 (23.9)
Others
4 (0.9)
Monthly income (HKD)a, n (%)
≤9999
68 (16.5)
10,000-19,999
159 (38.7)
20,000-29,999
117 (28.5)
≥30,000
67 (16.3)
Medical characteristics
Duration of diagnosis (y), mean (SD)
10.69 (8.14)
Number of complications, n (%)
None
165 (38.8)
1
162 (38.1)
2
78 (18.4)
≥3
20 (4.7)
Types of treatment, n (%)
No prescribed medication
21 (4.9)
Oral only
190 (44.5)
Injection only
27 (6.3)
Both oral and injection
185 (43.3)
Others
4 (0.9)
Internet-related variables
Time spent on the internet per week, n (%)
0 h
64 (15)
<1 h
178 (41.6)
Between 1 and 2 h
121 (28.3)
>2 h
64 (14.9)
eHEALSb score, mean (SD)
23.68 (11.61)
Level of eHealth literacyc, n (%)
Low
149 (34.9)
High
278 (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).
Correlation between variables.
Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Gendera
—
Age
0.12b
—
Education level
−0.30c
−0.43c
—
Relationship statusd
0.04
0.18c
−0.13c
—
Employment statuse
0.17c
0.39c
−0.20c
0.06
—
Monthly income
−0.10b
−0.29c
0.40c
−0.09
−0.27c
—
Duration of diagnosis
0.13b
0.24c
−0.21c
0.02
0.16c
−0.06
—
Number of complications
−0.10b
0.19c
−0.13c
0.07
0.05
−0.10b
0.24c
—
Type of treatment
0.04
0.06
−0.06
−0.01
0.10b
−0.04
0.34c
0.12b
—
eHealth literacy
−0.26c
−0.43c
0.50c
−0.11b
−0.24c
0.30c
−0.14c
−0.09
−0.06
—
Self-care
0.08
0.01
0.05
−0.10b
0.13c
0.04
0.10b
−0.05
0.09
0.15c
—
Self-care self-efficacy
−0.17c
−0.16c
0.25c
−0.04
−0.05
0.21c
−0.05
−0.05
−0.05
0.32c
0.30c
—
Diabetes distress
0.07
−0.03
−0.05
0.01
−0.02
-0.20c
0.10b
0.03
0.02
−0.11b
−0.23c
−0.46c
—
HbA1cf
0.08
0.01
−0.11b
0.06
0.06
−0.07
0.34c
0.18c
0.21c
−0.11b
0.09
−0.10b
0.08
—
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).
Independent sample t test and analysis of covariance analyses of group difference in self-care, self-care self-efficacy, and diabetes distress.
Variable
Low 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-care
1.78 (1.03)
2.07 (1.16)a
1.71 (1.56–1.87)b
2.16 (2.02–2.23)b
0.442c
Self-care self-efficacy
72.46 (7.87)
77.08 (4.51)c
73.37 (72.37‐74.37)d
77.25 (76.45‐78.05)d
3.875c
Diabetes distress
2.74 (0.56)
2.44 (0.47)c
2.68 (2.60‐2.76)e
2.44 (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 HbA<sub>1c</sub> 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).
DiscussionMain 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.
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
None declared.
AbbreviationsANCOVA
analysis of covariance
DM
diabetes mellitus
eHEALS
the 8-item eHealth Literacy Scale
HbA1c
glycated hemoglobin
OR
odds ratio
ReferencesIDF Diabetes Atlas20212026-02-0310International Diabetes Federationhttps://diabetes.org.br/wp-content/uploads/2022/02/IDF_Atlas_10th_Edition_2021-.pdf978-2-930229-98-0CheungBManYLamKWatNLoJChauDPrevalence of diabetes mellitus (DM) in the Hong Kong Cardiovascular Risk Factor Prevalence Study cohortHong Kong Med J20032026-02-039Supp 1https://www.hkmj.org/system/files/2003-S1.pdfAmerican Diabetes AssociationStandards of medical care in diabetes--2011Diabetes Care20110134 Suppl 1Suppl 1S11S6110.2337/dc11-S01121193625ChengLYLeungSYLeungMKWThe association of glycemic control and fall risk in diabetic elderly: a cross-sectional study in Hong KongBMC Prim Care202208123119210.1186/s12875-022-01807-735915395CarolanMHolmanJFerrariMExperiences of diabetes self-management: a focus group study among Australians with type 2 diabetesJ Clin Nurs201504247-81011102310.1111/jocn.1272425363710SongYSongHJHanHRParkSYNamSKimMTUnmet needs for social support and effects on diabetes self-care activities in Korean Americans with type 2 diabetesDiabetes Educ2012381778510.1177/014572171143245622222514TimmersTJanssenLKoolRBKremerJAEducating patients by providing timely information using smartphone and tablet apps: systematic reviewJ Med Internet Res20200413224e1734210.2196/1734232281936DesveauxLShawJSaragosaMA mobile app to improve self-management of individuals with type 2 diabetes: qualitative realist evaluationJ Med Internet Res20180316203e8110.2196/jmir.871229549070KruseCSBeaneAHealth information technology continues to show positive effect on medical outcomes: systematic reviewJ Med Internet Res2018025202e4110.2196/jmir.879329402759ShahbaziMBagherianHSattariMSaghaeiannejad-IsfahaniSThe opportunities and challenges of using mobile health in elderly self-careJ Educ Health Promot2021108010.4103/jehp.jehp_871_2034084827TennantBStellefsonMDoddVeHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adultsJ Med Internet Res20150317173e7010.2196/jmir.399225783036PerakslisEGinsburgGSDigital health—the need to assess benefits, risks, and valueJAMA20210112325212712810.1001/jama.2020.2291933369627McBrideDWeb forums allow patients with colorectal cancer to share their real-world experiencesONS Connect2013122844624400584YoonHJangYVaughanPWGarciaMOlder adults’ internet use for health information: digital divide by race/ethnicity and socioeconomic statusJ Appl Gerontol20200139110511010.1177/073346481877077229661052ParkerRMRatzanSCLurieNHealth literacy: a policy challenge for advancing high-quality health careHealth Aff (Millwood)200322414715310.1377/hlthaff.22.4.14712889762NormanCDSkinnerHAeHealth literacy: essential skills for consumer health in a networked worldJ Med Internet Res2006061682e910.2196/jmir.8.2.e916867972WuYWenJWangXAssociations between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: a mediation analysisBMC Public Health20221129221222610.1186/s12889-022-14695-436447176ChesserABurkeAReyesJRohrbergTNavigating the digital divide: a systematic review of eHealth literacy in underserved populations in the United StatesInform Health Soc Care201641111910.3109/17538157.2014.94817125710808NguyenAMosadeghiSAlmarioCVPersistent digital divide in access to and use of the Internet as a resource for health information: results from a California population-based studyInt J Med Inform201707103495410.1016/j.ijmedinf.2017.04.00828551001TonsakerTBartlettGTrpkovCHealth information on the Internet: gold mine or minefield?Can Fam Physician20140560540740824828994Valizadeh-HaghiSRahmatizadehSEvaluation of the quality and accessibility of available websites on kidney transplantationUrol J2018092615526126510.22037/uj.v0i0.425230058062XieLZhangSXinMZhuMLuWMoPKHElectronic health literacy and health-related outcomes among older adults: a systematic reviewPrev Med20220415710699710.1016/j.ypmed.2022.10699735189203WangYSongYZhuYJiHWangAAssociation of eHealth literacy with health promotion behaviors of community‑dwelling older people: the chain mediating role of self‑efficacy and self‑care abilityInt J Environ Res Public Health202205171910609210.3390/ijerph1910609235627627RobinsonCGrahamJPerceived Internet health literacy of HIV-positive people through the provision of a computer and Internet health education interventionHealth Info Libr J20101227429530310.1111/j.1471-1842.2010.00898.x21050372van der VaartRDrossaertCHCde HeusMTaalEvan de LaarMAFJMeasuring actual eHealth literacy among patients with rheumatic diseases: a qualitative analysis of problems encountered using Health 1.0 and Health 2.0 applicationsJ Med Internet Res20130211152e2710.2196/jmir.242823399720OhYSChoYExamining the relationships between resources and online health information seeking among patients with chronic diseases and healthy peopleSoc Work Health Care20155428310010.1080/00981389.2014.98794025674723LiJThengYLFooSPredictors of online health information seeking behavior: changes between 2002 and 2012Health Informatics J20161222480481410.1177/146045821559585126261218MorelandJFrenchTLCummingGPThe prevalence of online health information seeking among patients in Scotland: a cross‑sectional exploratory studyJMIR Res Protoc2015071543e8510.2196/resprot.401026177562LiNOrrangeSKravitzRLBellRAReasons for and predictors of patients’ online health information seeking following a medical appointmentFam Pract20141031555055610.1093/fampra/cmu03424963151BaileyCEKohlerWJMakaryCDavisKSweetNCarrMeHealth literacy in otolaryngology patientsAnn Otol Rhinol Laryngol201911128111013101810.1177/000348941985637731195809HalwasNGriebelLHuebnerJeHealth literacy, Internet and eHealth service usage: a survey among cancer patients and their relativesJ Cancer Res Clin Oncol201711143112291229910.1007/s00432-017-2475-628699035KimHYangERyuHKimHJJangSJChangSJPsychometric comparisons of measures of eHealth literacy using a sample of Korean older adultsInt J Older People Nurs202105163e1236910.1111/opn.1236933527701LiSCuiGYinYWangSLiuXChenLHealth-promoting behaviors mediate the relationship between eHealth literacy and health-related quality of life among Chinese older adults: a cross-sectional studyQual Life Res2021083082235224310.1007/s11136-021-02797-233661455BrørsGWentzel-LarsenTDalenHPsychometric properties of the Norwegian version of the electronic Health Literacy Scale (eHEALS) among patients after percutaneous coronary intervention: cross‑sectional validation studyJ Med Internet Res20200728227e1731210.2196/1731232720900XieLMoPKHA 3‑wave longitudinal study of eHealth literacy and older people’s health‑related quality of life in China: the mediating role of general self‑efficacyJ Am Med Dir Assoc202412251210531010.1016/j.jamda.2024.10531039426796NeterEBraininEeHealth literacy: extending the digital divide to the realm of health informationJ Med Internet Res20120127141e1910.2196/jmir.161922357448KimKShinSKimSLeeEThe relation between eHealth literacy and health‑related behaviors: systematic review and meta‑analysisJ Med Internet Res2023013025e4077810.2196/4077836716080XieLMoPKHProspective relationship between electronic health literacy and health-promoting lifestyle among Chinese older adults: a three-wave longitudinal studySoc Sci Med20250136411716610.1016/j.socscimed.2024.11716639586136LinCYBroströmAGriffithsMDPakpourAHPsychometric evaluation of the Persian eHealth Literacy Scale (eHEALS) among elder Iranians with heart failureEval Health Prof20201243422222910.1177/016327871982799730744419LeungAYMParialLLTolabingMCSense of coherence mediates the relationship between digital health literacy and anxiety about the future in aging population during the COVID-19 pandemic: a path analysisAging Ment Health20220326354455310.1080/13607863.2020.187020633438448PaigeSRStellefsonMKriegerJLAnderson-LewisCCheongJStopkaCProposing a transactional model of eHealth literacy: concept analysisJ Med Internet Res20181022010e1017510.2196/1017530279155BodieGDDuttaMJUnderstanding health literacy for strategic health marketing: eHealth literacy, health disparities, and the digital divideHealth Mark Q2008251-217520310.1080/0735968080212630118935884LeeEHLeeYWKangEHKangHJRelationship between electronic health literacy and self‑management in people with type 2 diabetes using a structural equation modeling approachJ Nurs Res2024011321e31510.1097/jnr.000000000000058838128065JiXChiJExploring the relationship between eHealth literacy and diabetes knowledge, self‑efficacy, and self‑care behaviors in Chinese diabetic patients: a cross‑sectional studyJ Nurs Res2024121326e35910.1097/jnr.000000000000064239593226KimKAKimYJChoiMAssociation of electronic health literacy with health‑promoting behaviors in patients with type 2 diabetes: a cross‑sectional studyComput Inform Nurs20180936943844710.1097/CIN.000000000000043829742548GuoSHMHsingHCLinJLLeeCCRelationships between mobile eHealth literacy, diabetes self‑care, and glycemic outcomes in Taiwanese patients with type 2 diabetes: cross‑sectional studyJMIR mHealth uHealth202102592e1840410.2196/1840433544088NormanCDSkinnerHAeHEALS: the eHealth literacy scaleJ Med Internet Res2006111484e2710.2196/jmir.8.4.e2717213046XuRHZhouLLuSYWongELChangJWangDPsychometric validation and cultural adaptation of the simplified Chinese eHealth literacy scale: cross‑sectional studyJ Med Internet Res20201272212e1861310.2196/1861333284123WongDKKCheungMKOnline health information seeking and eHealth literacy among patients attending a primary care clinic in Hong Kong: a cross‑sectional surveyJ Med Internet Res20190327213e1083110.2196/1083130916666GuoZZhaoSZGuoNSocioeconomic disparities in eHealth literacy and preventive behaviors during the COVID‑19 pandemic in Hong Kong: cross‑sectional studyJ Med Internet Res20210414234e2457710.2196/2457733784240KimSChowBCParkSLiuHThe usage of digital health technology among older adults in Hong Kong and the role of technology readiness and eHealth literacy: path analysisJ Med Internet Res20230412251e4191510.2196/4191537043274MilneRAPutsMTEPapadakosJPredictors of high eHealth literacy in primary lung cancer survivorsJ Cancer Educ20151230468569210.1007/s13187-014-0744-525355524ToobertDJHampsonSEGlasgowREThe summary of diabetes self-care activities measure: results from 7 studies and a revised scaleDiabetes Care20000723794395010.2337/diacare.23.7.94310895844TangYHPangSMCChanMFYeungGSPYeungVTFHealth literacy, complication awareness, and diabetic control in patients with type 2 diabetes mellitusJ Adv Nurs200804621748310.1111/j.1365-2648.2007.04526.x18352966ShiuATYWongRYMThompsonDRDevelopment of a reliable and valid Chinese version of the diabetes empowerment scaleDiabetes Care20031026102817282110.2337/diacare.26.10.281714514585AndersonRMFunnellMMFitzgeraldJTMarreroDGThe diabetes empowerment scale: a measure of psychosocial self‑efficacyDiabetes Care20000623673974310.2337/diacare.23.6.73910840988TingRZWNanHYuMWMDiabetes-related distress and physical and psychological health in chinese type 2 diabetic patientsDiabetes Care2011053451094109610.2337/dc10-161221398526VermaRSaldanhaCEllisUSattarSHaaseKReHealth literacy among older adults living with cancer and their caregivers: a scoping reviewJ Geriatr Oncol20220613555556210.1016/j.jgo.2021.11.00834810146ChoiNGDinittoDMThe digital divide among low-income homebound older adults: internet use patterns, eHealth literacy, and attitudes toward computer/internet useJ Med Internet Res2013052155e9310.2196/jmir.264523639979HungLYLyonsJGWuCHHealth information technology use among older adults in the United States, 2009-2018Curr Med Res Opin20200536578979710.1080/03007995.2020.173478232096650LiSJYinYTCuiGHXuHLThe associations among health‑promoting lifestyle, eHealth literacy, and cognitive health in older Chinese adults: a cross‑sectional studyInt J Environ Res Public Health20200327177226310.3390/ijerph1707226332230935KimSHSonYJRelationships between eHealth literacy and health behaviors in Korean adultsComput Inform Nurs201702352849010.1097/CIN.000000000000025527258808AlShehriWSKhojahYYImpact 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, 2025Cureus202507177e8796810.7759/cureus.8796840666264Paasche-OrlowMKWolfMSThe causal pathways linking health literacy to health outcomesAm J Health Behav200731 Suppl 1S192610.5555/ajhb.2007.31.supp.S1917931132GriebelLEnwaldHGilstadHPohlALMorelandJSedlmayrMeHealth literacy research-quo vadis?Inform Health Soc Care20181243442744210.1080/17538157.2017.136424729045164LeeJLeeEHChaeDeHealth literacy instruments: systematic review of measurement propertiesJ Med Internet Res202111152311e3064410.2196/3064434779781ChangASchulzPJThe measurements and an elaborated understanding of Chinese eHealth literacy (C‑eHEALS) in chronic patients in ChinaInt J Environ Res Public Health20180723157155310.3390/ijerph1507155330041420ErnstingCStühmannLMDombrowskiSUVoigt-AntonsJNKuhlmeyAGellertPAssociations of health app use and perceived effectiveness in people with cardiovascular diseases and diabetes: population‑based surveyJMIR mHealth uHealth2019032873e1217910.2196/1217930920383RolloMEAguiarEJWilliamsRLeHealth technologies to support nutrition and physical activity behaviors in diabetes self-managementDiabetes Metab Syndr Obes2016938139010.2147/DMSO.S9524727853384KebedeMMPischkeCRPopular 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 mediaFront Endocrinol (Lausanne)20191013510.3389/fendo.2019.0013530881349LeeMShimJEffects of diabetes knowledge and attitudes toward internet health information on e‑health literacy in middle‑aged patients with diabetesHealthcare (Basel)2025022713551210.3390/healthcare1305051240077074PereiraKPhillipsBJohnsonCVorderstrasseAInternet delivered diabetes self-management education: a reviewDiabetes Technol Ther201501171556310.1089/dia.2014.015525238257