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The availability of health information through media has raised public awareness of health literacy (HL). HL is essential for medication adherence and self-management in individuals with chronic diseases, and for those without chronic conditions, HL is important for promoting health and engaging in preventive behaviors. This study examined the role of having a regular physician in improving HL among Korean adults, both with and without chronic diseases.
Methods
We conducted a retrospective, cross-sectional analysis using data from 8,322 participants in the 2021 Korea Health Panel Study. HL was measured with the 16-item European Health Literacy Survey Questionnaire. To identify factors associated with HL categories and to calculate adjusted mean HL scores, we used multiple logistic regression and weighted linear regression.
Results
Among participants with chronic diseases (n=4,627), 56.6% reported having a regular physician, with the largest group (42.7%) showing inadequate HL. After adjustment, age 75 years or older and lower education were significantly linked to lower HL levels, regardless of chronic disease status. Having a regular physician was significantly associated with higher HL levels in participants with chronic diseases (adjusted odds ratio, 1.94; 95% confidence interval, 1.42–2.63), but not in those without chronic diseases. Participants with chronic diseases who had a regular physician showed higher mean HL scores across all HL competencies and domains.
Conclusion
Although access to health information has increased, the risk of encountering conflicting or misleading content has grown. These findings emphasize the importance of regular physicians in guiding patients and improving HL, especially among individuals with chronic diseases.
Health information significantly influences individuals’ perceptions, attitudes, and behaviors regarding health promotion and disease prevention [1]. Health literacy (HL) refers to the capacity to access, understand, and apply health information to maintain and promote health [2]. The World Health Organization states that improving HL enables individuals to gain the knowledge, skills, and confidence needed to change lifestyles and living conditions, which supports both personal and community health [3].
Adequate HL is widely recognized as necessary for adopting healthy lifestyles and behaviors, while limited HL is consistently associated with adverse health outcomes. Previous studies have shown that limited HL leads to increased hospitalizations and emergency visits [4], higher readmission rates [5], poorer self-perceived health [6], lower engagement in preventive activities such as cancer screening and vaccination [7,8], and higher mortality among older adults [9]. For individuals with chronic disease, adequate HL is essential for medication adherence and self-care [10,11]. In this population, limited HL is associated with higher medical costs [12], reduced self-management [13], and poorer health outcomes [9,11]. Interventions aimed at improving HL have been effective in enhancing self-management in patients with diabetes [14], chronic obstructive pulmonary disease [15], and chronic kidney disease [16].
Despite its importance, HL faces new challenges in an era of abundant and easily accessible health information. Individuals are often exposed to inaccurate or commercially driven content [17], and conflicting research findings and inconsistent recommendations from professional organizations are increasingly common [18,19]. Prolonged exposure to this environment can cause confusion and distrust toward health information, which may lead to resistance to clinical guidance [20]. Addressing these challenges requires individuals to strengthen HL competencies to critically evaluate and apply information relevant to their circumstances, and to seek appropriate professional support when needed. Although healthcare providers may help improve patients’ HL, most evidence comes from specific clinical settings [10,11]. However, individuals also encounter health information outside of what healthcare providers deliver, and patients with chronic conditions often seek additional information [1,10]. Healthcare provider support is essential in helping patients assess the validity of this information and interpret messages. By using the trust in the patient-provider relationship, healthcare providers can help patients reconcile conflicting information and apply it in clinically meaningful ways [1,21].
In South Korea, there is no formal primary care physician system, and individuals may consult specialists based on their health needs. While this approach provides patients with flexibility, it also makes continuity of care and effective medical guidance more difficult. Having a regular physician whom patients trust and consult regularly may improve access to accurate health information and help patients understand, filter, and apply health knowledge more effectively [1,22]. Given South Korea’s information environment, where health information is widely available but often fragmented or inconsistent, it is important to examine the role of regular physicians in improving HL and to develop empirical evidence for this relationship.
This study aimed to evaluate HL levels and related factors in the general Korean population, stratified into subgroups with and without chronic diseases, and to examine the association between having a regular physician and HL levels, thereby assessing whether regular physicians influence patients’ HL. In this study, “regular physician” refers to a physician whom individuals visit regularly or prefer for ongoing care, rather than a formally designated primary care provider.
Methods
Data sources
This study used data from the Korea Health Panel Study (KHPS), provided by the Korea Institute for Health and Social Affairs and the National Health Insurance Service (https://www.khp.re.kr:444/eng/main.do). Established in 2008, the KHPS is a public database that uses two-stage stratified cluster sampling with probability proportional to size, based on the Korea Population and Housing Census. The data set aims to inform policy by providing evidence on health-related perceptions and behaviors, health status, healthcare use, and medical expenses among the South Korean population. Data were collected through computer-assisted face-to-face interviews conducted by trained interviewers in participants’ homes.
Study design and study population
A retrospective, cross-sectional analysis used the 2021 KHPS database because the HL questionnaire was included only in 2021. The initial data set contained 13,779 individuals from 5,907 households. Participants missing age or gender information (n=925), those younger than 19 years (n=1,683), and those who did not complete all 16 HL questionnaire items (n=2,561) were excluded. The final sample included 8,630 individuals, with 4,935 participants having chronic diseases and 3,695 without chronic diseases.
Participants with chronic diseases were those who reported at least one physician-diagnosed chronic condition from a list of over 30 common diseases in South Korea. Major categories included hypertension, diabetes, chronic hepatitis and liver cirrhosis, cancers, heart diseases, cerebrovascular diseases, spinal disc disorders, osteoarthritis, rheumatoid arthritis, chronic respiratory diseases, thyroid diseases, depression or bipolar disorders, dementia, chronic renal failure, and other conditions. The study excluded participants with dementia or depression or bipolar disorders because these conditions could affect HL levels. The final study population included 4,627 participants with chronic diseases and 3,695 participants without chronic diseases.
Ethics statement
This study followed the ethical principles of the Declaration of Helsinki. Because it used publicly accessible and anonymized databases, it was exempt from Institutional Review Board (IRB) oversight. The relevant ethics committee granted this exemption after a preliminary review (NMC IRB 2024-09-003).
Measurement of the HL
The KHPS assessed HL using the 16-item European Health Literacy Survey Questionnaire (HLS-EU-Q16), which was developed to measure HL in general populations across Europe and is suitable for clinical and public health settings [23]. This study used the validated Korean version of the HLS-EU-Q16, as shown in Supplement 1 [24,25]. The questionnaire includes 16 items that assess four information processing competencies—accessing (four items), understanding (six items), appraising (three items), and applying (three items) health-related information—across three domains of the health continuum: healthcare (seven items), disease prevention (five items), and health promotion (four items). Participants responded to each item using a Likert-type scale with options “very easy,” “easy,” “I don’t know,” “difficult,” and “very difficult.” Responses of “very easy” or “easy” received a score of 1, while “difficult” or “very difficult” received a score of 0.
Statements answered “I don’t know” were treated as missing data [26]. Individual item scores were combined to produce total, competency, and domain scores, which were analyzed as both categorical and continuous variables. The total score ranged from 0 to 16 points and was divided into three levels, as in previous HLSEU studies: inadequate (0–8 points), marginal (9–12 points), and adequate (13–16 points) [23,25,27]. The four competency scores were structured as follows: accessing (0–4 points), understanding (0–6 points), appraising (0–3 points), and applying (0–3 points). The three domain scores were healthcare (0–7 points), disease prevention (0–5 points), and health promotion (0–4 points).
Variables
The analysis included the following sociodemographic and health-related variables. Sociodemographic factors were gender (man or woman), age (19–29, 30–44, 45–59, 60–74, and 75+ years), education level (lower than elementary school, middle school, high school, and college or higher), marital status (divorced, widowed, unmarried, or currently married), residential area (urban or rural), and annual household income tertiles (low, middle, and high, adjusted for the square root of household size). Health-related factors included the number of physician-diagnosed chronic diseases (0, 1, 2, or ≥3), tertiles of annual outpatient clinic visits (low, middle, and high), and the presence of a regular clinic or hospital and a regular physician (yes or no). Because South Korea does not have a formal primary care physician system and individuals can choose their healthcare providers freely, “regular clinic or hospital” and “regular physician” refer to clinics, hospitals, or physicians that participants prefer or visit regularly. Sources of health information were grouped into in-person contacts (family, friends, colleagues, acquaintances, and healthcare professionals), traditional mass media (television, radio, newspapers, magazines, and books), and internet-based online platforms (webpages, social network services, YouTube, and so forth).
To examine the reasons for selecting a regular doctor among the available variables, we also assessed whether the regular physician provided comprehensive services (yes or no) and whether the regular physician coordinated or referred necessary healthcare services and personnel (yes or no) among patients who reported having a regular physician.
Statistical analysis
Categorical variables related to sociodemographic and health-related characteristics are summarized using frequency and percentage distributions, and the Rao-Scott chi-square test was used to compare groups with sampling weights. Continuous variables, including total HL scores and scores for the four competencies and three domains, are reported as means with standard errors. Multiple logistic regression models assessed the relationships among HL levels, presence of a primary care physician, and other contributing factors. The categorized HL level was the dependent variable. The SAS PROC SURVEYLOGISTIC procedure with a cumulative logit link function estimated adjusted odds ratios (AORs) for higher HL levels. Model 1 adjusted for sociodemographic variables (age, gender, education level, marital status, household income, and residential area). Model 2 included both sociodemographic and health-related variables (number of chronic diseases, annual outpatient clinic visits, presence of a primary care facility and primary care physician, and sources of health information). Effect sizes are reported as AORs and 95% confidence intervals (CIs) after adjustment. Additional ordinal logistic regression analyses used the same modeling approach to examine the association between whether the regular physician provided comprehensive services and HL levels, stratified by chronic disease status. Adjusted means were estimated using a weighted regression approach, controlling for age and education level. A t-test compared the least squares means between the two groups. Because complex survey data were used, sampling weights represented the inverse probability of selection in calculating means, adjusted means, and adjusted ORs. P-values <0.05 were considered significant. All analyses were performed using R software ver. 4.4.1 (R Foundation for Statistical Computing) and SAS ver. 9.4 (SAS Institute).
Results
Table 1 presents the general characteristics of participants by the presence of chronic diseases. Among those with chronic diseases, 82.2% were older than 60 years, and 53.3% had an education level of middle school or lower. The largest proportion in this group was in the lowest tertile of household income. Most participants with chronic diseases (78.9%) reported having a regular clinic, and 56.6% had a regular physician. About half primarily obtained health information through online platforms. Regarding HL levels, 42.68% of participants with chronic diseases were in the inadequate category. In contrast, among participants without chronic diseases, only 20.9% had a regular physician, and most (79.1%) obtained health information mainly from online platforms. The majority (69.9%) of this group had an adequate HL level.
Table 2 presents the weighted mean values of the total HL score, as well as scores for the four competencies and three domains, among participants with and without diseases. Supplements 2 and 3 provide detailed distributions of participants with or without chronic diseases by HL levels, respectively.
In the multivariate logistic regression, age 75 years or older and lower education levels were significantly associated with lower HL levels in both groups, with and without chronic diseases (Table 3). Among participants with chronic diseases, the AOR for those aged 75 years or older was 0.32 (95% CI, 0.16–0.62) in model 2 compared with those aged 19 to 44 years. The effect of higher education on HL level was smaller in participants with chronic diseases than in those without chronic diseases. Among participants with chronic diseases, the AOR for individuals with a college education or higher was more than 4 times that of those with the lowest education level. Having a regular physician was significantly associated with higher HL levels (AOR, 1.94; 95% CI, 1.42–2.63). Obtaining health information primarily through mass media and online platforms was also significantly associated with higher HL levels compared to relying mainly on in-person contacts. In the group with chronic diseases, a broader range of services provided by the regular physician was negatively associated with higher HL (Supplement 4). In the group without chronic diseases, having a regular physician was not significantly related to higher HL levels.
Table 4 presents the adjusted means of HL-related scores by the presence of a regular physician among participants with and without chronic diseases. Among participants with chronic diseases, those with a regular physician had significantly higher mean HL scores across all HL competencies and domains compared to those without a regular physician. In contrast, among participants without chronic diseases, there was no significant difference in HL scores between those with and without a regular physician.
Discussion
This study found that two-thirds of participants with chronic diseases had inadequate or marginal HL levels, and overall HL levels were lower in this group than in those without chronic diseases. Among individuals with chronic diseases, having a regular physician was significantly associated with higher HL levels, indicating the physician’s role in supporting appropriate interpretation and use of health information. Education level showed the largest effect size on HL, while income and multimorbidity were not significantly associated with HL levels.
Patients with chronic diseases encounter extensive health information beyond the medical guidance from healthcare institutions; however, professional support may be necessary to use this information in clinically meaningful ways [22]. The positive association found in this study between having a regular physician and higher HL likely reflects this need, as it appeared consistently across all HL competencies and domains. Previous studies have also shown that patients with chronic diseases consider their physicians their most trusted source of health information [22,28], suggesting that ongoing communication with a regular physician may help improve HL. Conversely, patients with higher HL are more likely to choose a preferred physician and visit them regularly, as evidence shows that patients with higher HL make more efficient use of healthcare services [29,30]. In the Korean healthcare system, which does not have a formal primary care physician system, both mechanisms may occur. Patients with higher HL may be more likely to select and regularly visit a trusted physician, and close interactions may further reinforce their HL, creating a reciprocal relationship. Although the relationship between HL and sustained communication with physicians may be more complex in practice, the positive association between HL and ongoing physician–patient communication remains clear.
Patients often find it difficult to discuss health information they encounter in daily life during clinical consultations. These challenges may result from physicians’ indifference, patients’ concerns about their relationship with physicians, or limited consultation time [10,31,32]. Shortened consultation time is a structural issue in South Korea, which primarily uses a fee-for-service reimbursement model. A previous study found that although physicians recognize some value in patients’ use of online health information, they also express concerns about inaccurate or misleading advice [33], viewing such information as undermining the doctor–patient relationship and their professional authority [34]. Patients value a positive relationship with healthcare providers and prefer to be treated as respected partners rather than judged for shortcomings in self-management or information-seeking activities [10,35]. These findings suggest that a high-quality physician– patient relationship is essential for effective disease management. From the physicians’ perspective, respecting patients’ active involvement in disease management, including informationseeking behaviors, and improving communication skills should not be seen as reducing professional authority. Instead, these strategies can increase patient engagement and support improvements in HL. When concerns arise about inaccurate information, guiding patients to resources from professional organizations or other reputable institutions is effective [33]. Furthermore, physicians’ active participation on platforms such as social media, based on their professional expertise, can help share reliable health information. This is a professional responsibility to ensure patients have easy access to high-quality information and trustworthy interpretations [36].
The role of a regular physician is important in addressing conflicting or inaccurate information [37]. Such information often results from contradictory scientific findings, conflicting recommendations across disciplines, commercial interests, or deliberate manipulation [37,38]. Moreover, the variety of online platforms has increased the likelihood of inconsistent or inaccurate health information [28,39], which may require more expert guidance and higher HL levels among patients [10]. The findings of this study showed that about half of patients with chronic diseases primarily used online platforms as their main source of health information, and this use was significantly associated with higher HL levels. Although there are some interpretative limitations, these results suggest that patients engage in active heuristic learning and interpersonal communication with medical professionals through online platforms [40], which may help reduce the influence of inaccurate information. However, the effect of health information from online platforms likely varies depending on users’ HL levels, usage patterns, and support from professionals.
This study showed that HL scores among patients with chronic diseases were significantly lower than those without chronic diseases, after adjusting for age and education attainment. Although HL levels have specific meanings and interpretations in different populations, which requires caution in cross-group comparisons, our findings align with previous studies reporting that chronic conditions are linked to lower HL levels [41,42]. Recently, Gille et al. [1] provided insight into these results by identifying a temporal pattern in the health information interest of patients with chronic disease. Specifically, patients’ interest in health information increases immediately after diagnosis but decreases over time, resulting in lower HL levels. This suggests that, over time, patients may have more difficulty understanding and applying health information in daily life, which could lead to frustration or prompt them to assess their HL levels more critically. The finding shows that patients’ perception and understanding of health information is dynamic and changes over time, with their needs shifting accordingly. Previous results show that HL scores from self-reported measures are generally higher than those from direct assessments of health information comprehension [43,44], indicating that self-reported HL tends to overestimate actual HL levels. Because this study also used self-reported questionnaires, the HL measured may not reflect patients’ actual status, and this discrepancy may have been smaller among patients with chronic diseases.
This study has several key limitations in its research design that require further investigation. First, its cross-sectional design prevents determination of causal relationships between HL and having a regular physician, highlighting the need for longitudinal analyses. The next wave of the KHPS may help address this issue because it has included the HLS-EU-Q16 since 2021.
Second, more qualitative studies are needed to clarify the significance of HL and the role of regular physicians in providing health information. Because the presence of a regular physician was based on self-reported responses, individuals who reported having a regular physician may have been more likely to make extra efforts to understand and follow medical advice, which could lead to higher HL levels. Therefore, in addition to the physician’s role, patient adherence may also explain the observed association and requires further qualitative investigation.
Third, further research should examine whether self-assessed HL declines after the onset of chronic diseases. Patients with chronic conditions differ from those without such conditions in age, life experience, disease perception, and how illness affects daily life. This variation indicates the need to track HL levels over time in patients with chronic diseases. In addition, mood or cognitive changes may influence HL measurement, although the KHPS aimed to reduce this by using face-to-face surveys, and our study excluded patients with dementia or major depression.
Finally, because this study relied on self-reported survey data, potential inaccuracies may exist in the reporting of HL levels and chronic disease diagnoses, and residual confounding cannot be entirely excluded.
In conclusion, although patients with chronic diseases access large amounts of health information through online platforms, they often have difficulty using this information to manage their health. Our results indicate that the involvement of regular physicians is closely related to patients’ HL. This finding suggests that physicians may help improve patients’ HL and should pay greater attention to the information patients obtain.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
We would like to express our gratitude to the National Medical Center for supporting the publication fees.
Funding
None.
Data availability
The data analyzed in this study are available from the Korea Health Panel Study upon reasonable request and approval through the official website (https://www.khp.re.kr:444/).
Author contribution
Conceptualization: HSM, JL. Methodology: HSM, KSL, DHR. Data curation: HSM. Validation: KSL, DHR. Writing–original draft: HSM, JL. Writing–review & editing: KSL, DHR. Final approval of the manuscript: all authors.
a)After excluding cases with missing values, a total of 4,597 and 3,378 cases with and without chronic diseases, respectively, were included in the analysis.
b)After excluding cases with missing values, a total of 1,888 and 2,103 cases with and without chronic diseases, respectively, were included.
Table 2.
Measurement of health literacy in the study participants according to the presence of chronic disease
Dimension
Variable (maximum score)
Participants with chronic disease
Participants without chronic disease
Overall
Total score (16)
10.95±0.09
13.73±0.06
Competency
Access (4)
2.27±0.03
3.32±0.02
Understand (6)
4.99±0.03
5.59±0.02
Appraise (3)
1.69±0.02
2.30±0.02
Apply (3)
2.00±0.02
2.53±0.02
Domain
Healthcare (7)
4.82±0.04
6.05±0.03
Disease prevention (5)
3.30±0.03
4.16±0.03
Health promotion (4)
2.84±0.03
3.52±0.02
Values are presented as weighted mean±standard error.
Table 3.
AORs and 95% CIs for higher health literacy levels by chronic disease status
Variable
Participants with chronic disease (n=1,880)
Participants without chronic disease (n=1,954)
Model 1
Model 2
Model 1
Model 2
Gender
Man
Ref
Ref
Ref
Ref
Woman
0.72 (0.54–0.94)
0.75 (0.57–1.00)
0.89 (0.67–1.16)
0.90 (0.68–1.19)
Age (y)
19–44
Ref
Ref
Ref
Ref
45–59
0.94 (0.50–1.77)
0.94 (0.49–1.78)
1.01 (0.74–1.37)
1.01 (0.74–1.37)
60–74
0.72 (0.39–1.32)
0.78 (0.42–1.46)
0.77 (0.51–1.15)
0.79 (0.52–1.21)
≥75
0.27 (0.14–0.51)
0.32 (0.16–0.62)
0.42 (0.19–0.89)
0.44 (0.21–0.94)
Education
≤Elementary
Ref
Ref
Ref
Ref
Middle school
1.85 (1.25–2.73)
1.85 (1.30–2.75)
2.52 (1.02–6.27)
2.42 (0.98–5.98)
High school
3.54 (2.44–5.14)
3.31 (2.25–4.87)
5.58 (2.59–12.01)
5.36 (2.51–11.43)
≥College
4.55 (2.94–7.05)
4.14 (2.62–6.55)
6.75 (3.04–14.98)
6.54 (2.98–14.34)
Marriage status
Divorced/widowed/unmarried
Ref
Ref
Ref
Ref
Currently married
0.95 (0.72–1.27)
0.98 (0.73–1.30)
0.77 (0.56–1.05)
0.78 (0.56–1.07)
Household income
Low (lowest tertile)
Ref
Ref
Ref
Ref
Middle
1.07 (0.79–1.46)
1.05 (0.77–1.43)
1.46 (0.94–2.28)
1.47 (0.94–2.30)
High
1.19 (0.85–1.67)
1.18 (0.84–1.66)
1.41 (0.91–2.19)
1.43 (0.92–2.22)
Residential area
Urban
Ref
Ref
Ref
Ref
Rural
1.12 (0.84–1.49)
1.10 (0.83–1.47)
1.67 (1.09–2.55)
1.64 (1.06–2.53)
No. of chronic conditions
1
Ref
-
-
2
1.12 (0.80–1.56)
1.07 (0.76–1.52)
-
-
≥3
0.88 (0.64–1.21)
0.91 (0.65–1.29)
-
-
Annual outpatient clinic visits
Low
Ref
Ref
Middle
0.94 (0.65–1.36)
0.88 (0.65–1.20)
High
0.71 (0.48–1.04)
1.11 (0.73–1.71)
Presence of regular clinic/hospital
No
Ref
Ref
Yes
0.88 (0.61–1.27)
0.78 (0.54–1.13)
Presence of regular physician
No
Ref
Ref
Yes
1.94 (1.42–2.63)
1.23 (0.81–1.86)
Sources of health information
In-person contact
Ref
Ref
Traditional mass media
1.42 (1.02–1.97)
1.19 (0.69–2.03)
Online (internet-based)
1.80 (1.29–2.51)
1.28 (0.86–1.91)
AORs and 95% CIs were estimated using ordinal logistic regression across ordered levels of health literacy.
AOR, adjusted odds ratio; CI, confidence interval; Ref, reference.
Table 4.
Comparison of adjusted health literacy means in participants according to chronic diseases and primary care physician
Dimension
Participants with chronic disease
Participants without chronic disease
Presence of regular physician
P-value
Presence of regular physician
P-value
No
Yes
No
Yes
Overall
Total score
10.48±0.12
11.12±0.13
<0.001
11.43±0.16
11.36±0.20
0.675
Competency
Access
2.14±0.04
2.31±0.04
0.003
2.46±0.05
2.47±0.07
0.854
Understand
4.89±0.04
5.00±0.04
0.027
5.02±0.06
4.96±0.07
0.301
Appraise
1.55±0.04
1.76±0.04
<0.001
1.84±0.04
1.85±0.05
0.772
Apply
1.90±0.03
2.06±0.03
<0.001
2.11±0.04
2.07±0.05
0.361
Domain
Healthcare
4.62±0.06
4.89±0.06
<0.001
4.97±0.07
4.96±0.09
0.859
Disease prevention
3.15±0.05
3.36±0.05
<0.001
3.44±0.06
3.44±0.07
0.843
Health promotion
2.71±0.04
2.87±0.04
<0.001
3.01±0.05
2.97±0.06
0.378
Values are presented as mean±standard error.
References
1. Gille S, Griese L, Schaeffer D. Preferences and experiences of people with chronic illness in using different sources of health information: results of a mixed-methods study. Int J Environ Res Public Health 2021;18:13185.
4. Baker DW, Gazmararian JA, Williams MV, Scott T, Parker RM, Green D, et al. Functional health literacy and the risk of hospital admission among Medicare managed care enrollees. Am J Public Health 2002;92:1278-83.
5. Mitchell SE, Sadikova E, Jack BW, Paasche-Orlow MK. Health literacy and 30-day postdischarge hospital utilization. J Health Commun 2012;17 Suppl 3:325-38.
6. Toci E, Burazeri G, Jerliu N, Sorensen K, Ramadani N, Hysa B, et al. Health literacy, self-perceived health and self-reported chronic morbidity among older people in Kosovo. Health Promot Int 2015;30:667-74.
7. Adams RJ, Piantadosi C, Ettridge K, Miller C, Wilson C, Tucker G, et al. Functional health literacy mediates the relationship between socio-economic status, perceptions and lifestyle behaviors related to cancer risk in an Australian population. Patient Educ Couns 2013;91:206-12.
8. Goto E, Ishikawa H, Okuhara T, Kiuchi T. Relationship of health literacy with utilization of health-care services in a general Japanese population. Prev Med Rep 2019;14:100811.
9. Bostock S, Steptoe A. Association between low functional health literacy and mortality in older adults: longitudinal cohort study. BMJ 2012;344:e1602.
10. Kjærulff EM, Andersen TH, Kingod N, Nexo MA. When people with chronic conditions turn to peers on social media to obtain and share information: systematic review of the implications for relationships with health care professionals. J Med Internet Res 2023;25:e41156.
11. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med 2011;155:97-107.
12. Vandenbosch J, Van den Broucke S, Vancorenland S, Avalosse H, Verniest R, Callens M. Health literacy and the use of healthcare services in Belgium. J Epidemiol Community Health 2016;70:1032-8.
13. van der Heide I, Uiters E, Rademakers J, Struijs JN, Schuit AJ, Baan CA. Associations among health literacy, diabetes knowledge, and self-management behavior in adults with diabetes: results of a Dutch cross-sectional study. J Health Commun 2014;19 Suppl 2:115-31.
14. Leong CM, Lee TI, Chien YM, Kuo LN, Kuo YF, Chen HY. Social media- delivered patient education to enhance self-management and attitudes of patients with type 2 diabetes during the COVID-19 pandemic: randomized controlled trial. J Med Internet Res 2022;24:e31449.
15. Kiser K, Jonas D, Warner Z, Scanlon K, Shilliday BB, DeWalt DA. A randomized controlled trial of a literacy-sensitive self-management intervention for chronic obstructive pulmonary disease patients. J Gen Intern Med 2012;27:190-5.
17. Min HS, Yun EH, Park J, Kim YA. Cancer news coverage in Korean newspapers: an analytic study in terms of cancer awareness. J Prev Med Public Health 2020;53:126-34.
18. Borges do Nascimento IJ, Pizarro AB, Almeida JM, Azzopardi-Muscat N, Gonçalves MA, Bjorklund M, et al. Infodemics and health misinformation: a systematic review of reviews. Bull World Health Organ 2022;100:544-61.
19. Terada M, Okuhara T, Nishiie Y, Kiuchi T, Murakami K. Misinformation surrounding sodium reduction for blood pressure: content analysis of Japanese posts on X. Health Promot Int 2024;39:daae073.
20. Nagler RH, Vogel RI, Gollust SE, Yzer MC, Rothman AJ. Effects of prior exposure to conflicting health information on responses to subsequent unrelated health messages: results from a population-based longitudinal experiment. Ann Behav Med 2022;56:498-511.
22. Oedekoven M, Herrmann WJ, Ernsting C, Schnitzer S, Kanzler M, Kuhlmey A, et al. Patients’ health literacy in relation to the preference for a general practitioner as the source of health information. BMC Fam Pract 2019;20:94.
23. Pelikan JM, Ganahl K. Measuring health literacy in general populations: primary findings from the HLS-EU consortium’s health literacy assessment effort. Stud Health Technol Inform 2017;240:34-59.
24. Chun H, Lee JY. Factors associated with health literacy among older adults: results of the HLS-EU-Q16 measure. Korean J Health Educ Promot 2020;37:1-13.
26. Lorini C, Santomauro F, Grazzini M, Mantwill S, Vettori V, Lastrucci V, et al. Health literacy in Italy: a cross-sectional study protocol to assess the health literacy level in a population-based sample, and to validate health literacy measures in the Italian language. BMJ Open 2017;7:e017812.
27. Mekhail KT, Burstrom B, Marttila A, Wangdahl J, Lindberg L. Psychometric qualities of the HLS-EU-Q16 instrument for parental health literacy in Swedish multicultural settings. BMC Public Health 2022;22:293.
28. Chen X, Hay JL, Waters EA, Kiviniemi MT, Biddle C, Schofield E, et al. Health literacy and use and trust in health information. J Health Commun 2018;23:724-34.
29. Akakpo MG, Neuerer M. The relationship between health literacy and health-seeking behavior amongst university students in Ghana: a cross-sectional study. Health Sci Rep 2024;7:e2153.
30. Franzen J, Mantwill S, Rapold R, Schulz PJ. The relationship between functional health literacy and the use of the health system by diabetics in Switzerland. Eur J Public Health 2014;24:997-1003.
32. Wong DK, 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 2019;21:e10831.
34. Murray E, Lo B, Pollack L, Donelan K, Catania J, Lee K, et al. The impact of health information on the Internet on health care and the physician-patient relationship: national U.S. survey among 1.050 U.S. physicians. J Med Internet Res 2003;5:e17.
35. Oh J, Hwang J. Usual source of care and communication between physician and patients: patient experiences in the Korea Health Panel Survey (KHPS). Health Soc Welf Rev 2021;41:234-50.
37. Nagler RH, Vogel RI, Rothman AJ, Yzer MC, Gollust SE. Vulnerability to the effects of conflicting health information: testing the moderating roles of trust in news media and research literacy. Health Educ Behav 2023;50:224-33.
38. Wang Y, McKee M, Torbica A, Stuckler D. Systematic literature review on the spread of health-related misinformation on social media. Soc Sci Med 2019;240:112552.
39. Kelley MS, Su D, Britigan DH. Disparities in health information access: results of a county-wide survey and implications for health communication. Health Commun 2016;31:575-82.
41. Garcia-Codina O, Juvinya-Canal D, Amil-Bujan P, Bertran-Noguer C, Gonzalez-Mestre MA, Masachs-Fatjo E, et al. Determinants of health literacy in the general population: results of the Catalan health survey. BMC Public Health 2019;19:1122.
43. Schulz PJ, Pessina A, Hartung U, Petrocchi S. Effects of objective and subjective health literacy on patients’ accurate judgment of health information and decision-making ability: survey study. J Med Internet Res 2021;23:e20457.
44. Voigt-Barbarowicz M, Brutt AL. The agreement between patients’ and healthcare professionals’ assessment of patients’ health literacy: a systematic review. Int J Environ Res Public Health 2020;17:2372.
Potential role of regular physicians in improving health literacy in patients with chronic diseases: a retrospective cross-sectional study in Korea
Graphical abstract
Graphical abstract
Potential role of regular physicians in improving health literacy in patients with chronic diseases: a retrospective cross-sectional study in Korea
Characteristic
Participants with chronic disease (%)
Participants without chronic disease (%)
Gender
Man
1,991 (43.0)
1,711 (46.3)
Woman
2,636 (57.0)
1,984 (53.7)
Age (y)
19–44
151 (3.3)
1,445 (39.1)
45–59
669 (14.5)
1,259 (34.1)
60–74
2,017 (43.6)
794 (21.5)
≥75
1,790 (38.7)
197 (5.3)
Education
≤Elementary
1,597 (34.5)
188 (5.1)
Middle school
870 (18.8)
255 (6.9)
High school
1,361 (29.4)
1,138 (30.8)
≥College
799 (17.3)
2,114 (57.2)
Marriage status
Divorced/widowed/unmarried
1,268 (27.4)
1,144 (31.0)
Currently married
3,359 (72.6)
2,551 (69.0)
Annual household income
Low (lowest tertile)
2,075 (44.8)
584 (15.8)
Middle
1,461 (31.6)
1,319 (35.7)
High
1,091 (23.6)
1,792 (48.5)
Residential area
Urban
3,219 (69.6)
3,047 (82.5)
Rural
1,408 (30.4)
648 (17.5)
No. of chronic conditions
1
1,121 (24.2)
-
2
1,407 (30.4)
-
≥3
2,099 (45.4)
-
Annual outpatient clinic visitsa)
Low
864 (18.8)
2,009 (59.5)
Middle
1,646 (35.8)
931 (27.6)
High
2,087 (45.4)
438 (13.0)
Presence of regular clinic/hospital
No
978 (21.1)
2,410 (65.2)
Yes
3,649 (78.9)
1,285 (34.8)
Presence of regular physician
No
2,008 (43.4)
2,924 (79.1)
Yes
2,619 (56.6)
771 (20.9)
Sources of health informationb)
In-person contact
445 (23.6)
217 (10.3)
Traditional mass media
492 (26.1)
222 (10.6)
Online (internet-based)
951 (50.4)
1,664 (79.1)
Level of health literacy
Inadequate
1,975 (42.7)
454 (12.3)
Marginal
1,056 (22.8)
659 (17.8)
Adequate
1,596 (34.5)
2,582 (69.9)
Total
4,627 (100.0)
3,695 (100.0)
Dimension
Variable (maximum score)
Participants with chronic disease
Participants without chronic disease
Overall
Total score (16)
10.95±0.09
13.73±0.06
Competency
Access (4)
2.27±0.03
3.32±0.02
Understand (6)
4.99±0.03
5.59±0.02
Appraise (3)
1.69±0.02
2.30±0.02
Apply (3)
2.00±0.02
2.53±0.02
Domain
Healthcare (7)
4.82±0.04
6.05±0.03
Disease prevention (5)
3.30±0.03
4.16±0.03
Health promotion (4)
2.84±0.03
3.52±0.02
Variable
Participants with chronic disease (n=1,880)
Participants without chronic disease (n=1,954)
Model 1
Model 2
Model 1
Model 2
Gender
Man
Ref
Ref
Ref
Ref
Woman
0.72 (0.54–0.94)
0.75 (0.57–1.00)
0.89 (0.67–1.16)
0.90 (0.68–1.19)
Age (y)
19–44
Ref
Ref
Ref
Ref
45–59
0.94 (0.50–1.77)
0.94 (0.49–1.78)
1.01 (0.74–1.37)
1.01 (0.74–1.37)
60–74
0.72 (0.39–1.32)
0.78 (0.42–1.46)
0.77 (0.51–1.15)
0.79 (0.52–1.21)
≥75
0.27 (0.14–0.51)
0.32 (0.16–0.62)
0.42 (0.19–0.89)
0.44 (0.21–0.94)
Education
≤Elementary
Ref
Ref
Ref
Ref
Middle school
1.85 (1.25–2.73)
1.85 (1.30–2.75)
2.52 (1.02–6.27)
2.42 (0.98–5.98)
High school
3.54 (2.44–5.14)
3.31 (2.25–4.87)
5.58 (2.59–12.01)
5.36 (2.51–11.43)
≥College
4.55 (2.94–7.05)
4.14 (2.62–6.55)
6.75 (3.04–14.98)
6.54 (2.98–14.34)
Marriage status
Divorced/widowed/unmarried
Ref
Ref
Ref
Ref
Currently married
0.95 (0.72–1.27)
0.98 (0.73–1.30)
0.77 (0.56–1.05)
0.78 (0.56–1.07)
Household income
Low (lowest tertile)
Ref
Ref
Ref
Ref
Middle
1.07 (0.79–1.46)
1.05 (0.77–1.43)
1.46 (0.94–2.28)
1.47 (0.94–2.30)
High
1.19 (0.85–1.67)
1.18 (0.84–1.66)
1.41 (0.91–2.19)
1.43 (0.92–2.22)
Residential area
Urban
Ref
Ref
Ref
Ref
Rural
1.12 (0.84–1.49)
1.10 (0.83–1.47)
1.67 (1.09–2.55)
1.64 (1.06–2.53)
No. of chronic conditions
1
Ref
-
-
2
1.12 (0.80–1.56)
1.07 (0.76–1.52)
-
-
≥3
0.88 (0.64–1.21)
0.91 (0.65–1.29)
-
-
Annual outpatient clinic visits
Low
Ref
Ref
Middle
0.94 (0.65–1.36)
0.88 (0.65–1.20)
High
0.71 (0.48–1.04)
1.11 (0.73–1.71)
Presence of regular clinic/hospital
No
Ref
Ref
Yes
0.88 (0.61–1.27)
0.78 (0.54–1.13)
Presence of regular physician
No
Ref
Ref
Yes
1.94 (1.42–2.63)
1.23 (0.81–1.86)
Sources of health information
In-person contact
Ref
Ref
Traditional mass media
1.42 (1.02–1.97)
1.19 (0.69–2.03)
Online (internet-based)
1.80 (1.29–2.51)
1.28 (0.86–1.91)
Dimension
Participants with chronic disease
Participants without chronic disease
Presence of regular physician
P-value
Presence of regular physician
P-value
No
Yes
No
Yes
Overall
Total score
10.48±0.12
11.12±0.13
<0.001
11.43±0.16
11.36±0.20
0.675
Competency
Access
2.14±0.04
2.31±0.04
0.003
2.46±0.05
2.47±0.07
0.854
Understand
4.89±0.04
5.00±0.04
0.027
5.02±0.06
4.96±0.07
0.301
Appraise
1.55±0.04
1.76±0.04
<0.001
1.84±0.04
1.85±0.05
0.772
Apply
1.90±0.03
2.06±0.03
<0.001
2.11±0.04
2.07±0.05
0.361
Domain
Healthcare
4.62±0.06
4.89±0.06
<0.001
4.97±0.07
4.96±0.09
0.859
Disease prevention
3.15±0.05
3.36±0.05
<0.001
3.44±0.06
3.44±0.07
0.843
Health promotion
2.71±0.04
2.87±0.04
<0.001
3.01±0.05
2.97±0.06
0.378
Table 1. General characteristics of study population
After excluding cases with missing values, a total of 4,597 and 3,378 cases with and without chronic diseases, respectively, were included in the analysis.
After excluding cases with missing values, a total of 1,888 and 2,103 cases with and without chronic diseases, respectively, were included.
Table 2. Measurement of health literacy in the study participants according to the presence of chronic disease
Values are presented as weighted mean±standard error.
Table 3. AORs and 95% CIs for higher health literacy levels by chronic disease status
AORs and 95% CIs were estimated using ordinal logistic regression across ordered levels of health literacy.
AOR, adjusted odds ratio; CI, confidence interval; Ref, reference.
Table 4. Comparison of adjusted health literacy means in participants according to chronic diseases and primary care physician
