The Science of Diabetes Self-Management and Care 2025, Vol. 51(5) 476 –486 © The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/26350106251361372 journals.sagepub.com/home/tde
Abstract
Purpose: The purpose of the study was to examine the psychometric properties of the Korean version of the Self-Efficacy for Appropriate Medication Use Scale (SEAMS-K).
Methods: A cross-sectional design was used with 130 adults with type 2 diabetes taking medications from an outpatient clinic at a university hospital in Korea. Structured questionnaires were used for psychometric evaluation. The SEAMS-K validity was examined using exploratory factor analysis, and reliability was assessed using Cronbach’s alpha and intraclass correlation coefficient. The original 13-item SEAMS was forwardtranslated and back-translated to ensure the translation equivalence of the SEAMS-K.
Results: Factor analysis for structural validity identified 3 dimensions of the SEAMS-K, explaining 71.2% of the total variance. The SEAMS-K showed significant associations with refilling and taking medication (r = −.58, P < .001), depressive symptoms (r = −.27, P = .002), and diabetes self-efficacy (r = .38, P < .001), thus, validating the construct validity hypotheses. As evidence of known groups’ validity, there was a significant association between the SEAMS-K score according to A1C level (P = .042). The intraclass correlation coefficient for testretest reliability was .91, and the alpha for internal consistency reliability was .92.
Conclusions: These results suggest that the SEAMS-K may be used clinically to assess the self-efficacy of appropriate medication use among Korean patients with type 2 diabetes.
As the prevalence of diabetes increases globally, it poses a critical challenge.1,2 According to Fast Facts on Diabetes, 11.6% of the U.S. population and 38.1 million adults ages 18 years or older—or 14.7% of all US adults—had diabetes.1,3 Therapeutic lifestyle modifications (ie, diet and physical activity) and medication for diabetes are essential for managing glycemic control4 and preventing microvascular and macrovascular complications.5 Lower participation in treatment regimens, including diet, physical activity, and medication, is associated with poor glycemic control, which is related to worsening complications and rising medical costs.6 With the increase in diabetes, patients taking medication have increased; 16.7% of Korean adults ages 30 years or older have diabetes, and 61.8% of them have been taking diabetes medications.7 Medication taking plays a key role in the management of diabetes and its complications.1
Medication is a critical factor for symptom control and treatment of chronic diseases, including diabetes. Moreover, people with diabetes (PWD) and/or metabolic syndrome with complex complications commonly take many medications to manage their diseases and symptoms, which can result in polypharmacy and poor medication-taking behaviors. Various dosage times depend on the characteristics of the medications; therefore, the risk of poor medication use is high.8 Poor medication taking has a negative impact on glycemic control and clinical outcomes, leading to worsening complications and an increased risk of mortality.9,10 Lin et al11 reported that an increase in “taking medication” by 10% lowers glycosylated hemoglobin (A1C) by 0.16%.
According to the 2024 American Diabetes Association guidelines for pharmacologic therapy for PWD, combination medication therapy is recommended, focusing on good blood sugar control, especially in cases where A1C is not controlled and comorbidities exist.10 Oral hypoglycemic agents (OHAs) are administered in various ways (ie, before, with or without, or immediately after a meal) depending on the pharmacodynamics of each medication.12 However, taking medication for diabetes has common attributes of physical and psychological factors, such as social, family, and health team support factors and the unique characteristics of diabetes.4 In particular, taking medication is recognized as 1 of the 7 essential self-care behaviors that contribute to effective diabetes selfcare through improved behavior changes.13 According to the Association of Diabetes Care and Education Specialists13 (ADCES), the ADCES7 self-care behaviors (healthy eating, being active, monitoring, taking medication, problem-solving, healthy coping, and reducing risks) provide a comprehensive framework to guide individuals with diabetes in achieving optimal health outcomes. Therefore, medication taking plays a pivotal role in maintaining glycemic control and is closely linked with the ability to manage one’s condition effectively.
Self-efficacy motivates positive cognition and is related to behavioral practices that can improve clinical outcomes; it can play a key role in diabetes self-management.14 Self-efficacy theory has guided research on the influence of people’s belief in their ability to control their self-care behaviors,15 particularly in people with chronic illnesses, including diabetes. In people with diabetes, self-efficacy is essential for initiating or participating in recommended self-care activities and predicting individuals’ behaviors and outcomes. Strategies to enhance self-efficacy are related to positive behavioral changes. Self-efficacy refers to the belief or confidence that one can successfully perform a specific action required to attain a desired outcome,16 the most important prerequisite for behavioral change and a critical aspect of taking medication.17 Therefore, identifying a patient’s level of self-efficacy for medication use when pharmaceutical care is provided is a way to maximize the effect of taking medication.
The Self-Efficacy for Appropriate Medication Use Scale (SEAMS) is a brief, reliable, and valid instrument that evaluates self-efficacy for medication use among patients with a variety of chronic diseases. Initially, the English version of SEAMS was developed for individuals with coronary artery disease and other comorbidities.16 It has been translated into Chinese to evaluate its psychometric properties when used to evaluate people with stroke and PWD17,18 and into Portuguese to evaluate outpatients with coronary heart disease.19 However, a valid and reliable version of the SEAMS has not yet been adapted for use by PWD taking medications for metabolic syndrome in Korea. Therefore, this study aimed to evaluate the psychometric properties of the Korean version of the SEAMS (SEAMS-K) for PWD who are taking medication for metabolic syndrome so that it can be used to assess medication self-efficacy in a clinical setting.
The first step in expanding the use of the SEAMS to Koreans was to assess the validity and reliability of the existing version for measuring the self-efficacy of PWD taking medication for appropriate medication use for various reasons. This study aimed to examine the psychometric properties of the SEAMS-K with PWD taking medication for metabolic syndrome, specifically, (1) structural validity, (2) hypothesis testing for construct validity, (3) reliability, and (4) responsiveness.
A cross-sectional survey was conducted to evaluate the psychometric properties of SEAMS-K. A convenience sample of 130 PWD was recruited from a diabetes outpatient clinic at a university-affiliated hospital in Suwon, South Korea. The sample size was satisfactory because the instrument comprised 13 items. For exploratory factor analysis (EFA), a sample of 5 to 10 participants per item was considered adequate.20 The Kaiser–Meyer–Olkin (KMO) measure of sample adequacy supported this sample size. The inclusion criteria were being age 19 years or older, being diagnosed with diabetes, and taking 1 or more OHAs for the past 6 months and/or taking medication for metabolic syndrome according to The National Cholesterol Education Program Adult Treatment Panel III criteria.21 Patients with severe cardiovascular disease and cognitive impairment were excluded. Electronic medical records provided clinical information, such as the duration of diabetes, the number of OHAs administered, the A1C level within 1 week before the interview, and the presence of diabetic complications.
On August 8, 2018, written permission was obtained from the developer of the original scale to translate the SEAMS and verify the psychometric properties of the Korean version. Based on prior research,22-24 the original English version of the SEAMS was independently forward-translated from English into Korean by 2 bilingual nursing professors who were native speakers of Korean and proficient in English. An expert panel committee reviewed the 2 primary versions and reached a consensus on the Korean draft. A bilingual expert, who was a doctoral candidate, translated the draft back into English. To confirm the cross-cultural appropriateness of the final draft of the SEAMS-K, translators and researchers compared the backward-translated English version with the original version in terms of conceptual equivalence; both instruments were deemed equivalent. The translated version was submitted to a committee to review for content validity to assess the level of comprehensibility, applicability, and cultural appropriateness of the SEAMS-K. A pilot study involving 11 persons with PWD was conducted at the data collection site to assess the comprehension and feasibility of the SEAMS-K in the Korean context. The participants verbally indicated that they understood all the questions and had no difficulty answering them. The pretest version of the SEAMS-K was adopted for psychometric testing.
SEAMS. The SEAMS16 is a 13-item self-efficacy instrument used to assess patients’ confidence in their ability to take medication appropriately. SEAMS was developed based on Bandura’s self-efficacy theory.16 This instrument was developed for patients with low literacy and chronic diseases. A multidisciplinary team with expertise in medication taking and health literacy developed this instrument and tested its validity and reliability in patients with coronary artery disease and additional comorbidities. The response scale is a 3-point Likert scale where 1 = not confident about taking medications correctly, 2 = somewhat confident, and 3 = very confident.16 The lowest possible score for the 13-item questionnaire was 13, and the highest possible score was 39; a high score indicated that the patients were highly confident about taking medication. Although Cronbach’s alpha was .89 for 436 adults (the mean age was 63.8 years) in a previous study,16 in this study, it was .92, indicating satisfactory internal consistency.
Adherence to Refills and Medications Scale-Korean. Medication taking was measured using the Korean version of the Adherence to Refills and Medications Scale (ARMS-K).25,26 The 12-item questionnaire consisted of 2 subscales: adherence with taking medications (8 items) and adherence to refilling prescriptions (4 items). The instrument uses a 4-point Likert scale ranging from 1 = none of the time to 4 = all the time. Possible scores range from 12 to 48, with lower scores indicating better refilling and medication-taking behavior.
Center for Epidemiologic Studies Depression Scale. Depressive symptoms were assessed using the Korean version of the Center for Epidemiologic Studies Depression (CES-D) Scale.27,28 This self-report scale consists of 20 items that assess patients’ current depressive state by asking them the frequency of depression-related symptoms they have experienced over the past 7 days. The instrument uses a 4-point Likert scale ranging from 0 = not at all to 3 = very much. Three items (5, 10, and 15) are inverse items that are recoded for scoring purposes. The total score ranged from 0 to 60, with higher scores indicating higher levels of depressive symptoms.
Diabetes self-efficacy. Diabetes self-efficacy is a 7-item self-efficacy instrument used to assess self-efficacy in diabetes self-management.29 Participants rated their self-efficacy on a 100-point scale (each statement was scored on a 100-point percentage scale where 0 indicated not at all confident and 100 indicated highly confident), with higher scores indicating greater perceived self-efficacy for self-management.
Glycemic control. A1C was measured using an ADAMS A1C HA-8190V (ARKRAY Inc, Kyoto, Japan). The level of A1C <7.0% (54 mmol/mol) was considered good glycemic control.30
Cognitive function. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE-K). Possible scores range from 0 to 30 (≥24 = normal, 20-23 = suspected cognitive decline, <19 = cognitive impairment), with higher scores indicating better cognitive function.31
The data were analyzed using SPSS (Version 23.0; IBM SPSS Statistics and AMOS for Windows, IBM Corporation, Armonk, NY, USA) and STATA (Version 14.2; College Station, TX, USA). The demographic and clinical characteristics were recorded as frequencies (percentages) or means (standard deviations). After 12 weeks, a reliability test-retest interview was conducted face-to-face. The distribution of scores on the 13-item SEAMS-K was examined using means, standard deviations, and floor and ceiling effects. The floor or ceiling effect was examined using the highest or lowest score frequency, and a frequency of <30% was considered acceptable.32 The floor effect was acceptable at less than 30%, whereas the ceiling effect was over 30%. The floor effect occurs when many respondents select the lowest possible score, which may indicate limited sensitivity in detecting lower levels of the construct. In contrast, the ceiling effect arises when a large proportion of participants choose the highest score.32 The item-total correlations ranged from .52 to .78. Because items with values higher than .30 are deemed acceptable,33 all items in the SEAMS-K met the structural validity criterion.
The structure of the SEAMS-K was investigated using EFA, principal component analysis, and a correlation matrix. The obtained KMO measure of .903, which met the KMO criterion (≥.50), supported the adequacy of the data for factor analysis. Bartlett’s test of sphericity, a test of the overall significance of correlations within a matrix, was statistically significant (P < .001), thus supporting the use of factor analysis.34 Principal component analysis used orthogonal varimax rotation to create the factors. The rotated factor pattern was reported, and the factor-loading criterion was set to ≥.60. The number of factors extracted was determined by performing parallel analyses and, considering the cutoff criterion of an eigenvalue >1.0, using the scree plot.35
Hypothesis testing for construct validity examined whether the direction and magnitude of the correlation were similar to or different from what was expected based on the measured construct. Spearman’s and Pearson’s correlation coefficients were used to examine the a priori hypothesis that SEAMS-K scores would be higher in participants with higher medication taking (ie, lower scores indicate better medication taking), fewer depressive symptoms, and higher self-efficacy in diabetes self-management. Known-groups validity involves demonstrating that a scale can differentiate individuals in one group from those in another based on their scores.36 The known-groups validity was assessed by comparing the level of SEAMS-K to the group with A1C levels (≥7.0% vs <7.0%) using an independent t test.
Cronbach’s alpha coefficients, a measure of internal consistency, were calculated to determine the reliability of the SEAMS-K. The test-retest reliability was assessed using the intraclass correlation coefficient (ICC), with adequate test-retest reliability at ICC >.70.37 Responsiveness was assessed using the standardized response mean (SRM) to evaluate whether the SEAMS-K could sensitively reflect changes in the participants’ self-efficacy for appropriate medication use. The SRM of 0.2, 0.5, or 0.80 indicates responsiveness to change of a small, medium, or large magnitude, respectively.38
This study was approved by the institutional review board of the authors’ institution regarding the protocol, survey instruments, and consent documents (IRB No. SUR-2017-349). All the participants voluntarily provided signed informed consent and were informed that they could withdraw from the study at any time. A total of 130 participants completed the questionnaire. Confidentiality of the information provided was maintained, and all data were encoded for analysis and only used for research purposes.
The general characteristics of the 130 participants are presented in Table 1. The mean (SD) age ranged from 35 to 79 years (61.15 ± 9.61), and most were male (n = 86, 66.2%). Of the participants, 102 (78.5%) had an education of or beyond high school, and 28 (21.5%) had less than a high school education. Additionally, 104 (80.0%) of the participants were self-perceived as having a moderate economic status, 119 (91.5%) had a job, and 97 (74.7%) reported a duration of type 2 diabetes for 11 or more years, with a mean (SD) prevalence duration of 16.0 (±7.58).
There was no floor effect, ranging from 0% to 13.1% for the SEAMS-K. However, there was a ceiling effect of over 30% for all items, ranging from 36.2% to 86.2%. The ceiling effects of Item 2 (“When you take medicines more than once a day?”) and Item 6 (“When no one reminds you to take the medicine?”) were the highest, at 86.2% and 80.8%, respectively (Table 2). The mean score for the SEAMS-K was 34.0 (SD, 4.7; median, 36) out of 39. The highest score was 2.86 (out of 3) for Item 2, followed by 2.81 for Item 6; the lowest was 2.23 for Item 5, followed by 2.32 for Item 8 (Table 2).
Structural validity. The EFA results for SEAMS-K are presented in Table 2. The factor loading criterion was set at ≥.60, which was met by factor loadings of .606 to .824 for 12 of the 13 items. The factor analysis for structural validity revealed 3 dimensions of the SEAMS-K, explaining 71.2% of the total variance; Factors 1 (self-efficacy for medication in management challenges), 2 (self-efficacy for medication with complex routines), and 3 (self-efficacy for medication during disrupting barriers) accounted for 31.8%, 20.9%, and 18.6% of the variance, respectively (Table 2).
Hypothesis testing for construct validity. The SEAMS-K scores were significantly correlated with medication taking (ARMS-K; r = −.58, P < .001; ie, lower scores indicating better medication taking), depressive symptoms (r = −.27, P = .002), and diabetes self-efficacy (r = .38, P < .001; Table 3). There was a significant difference in the SEAMS-K score between the glycemic control (P = .042) for known-groups validity (Table 4). The good glycemiccontrolled group (A1C < 7.0) had a significantly higher rate of confidence about taking medication than the poor glycemic-controlled group (A1C ≥ 7.0).
Reliability. The internal consistency reliability of the SEAMS-K was .92. The Cronbach’s alphas for the three factors were .90, .83, and .77 (Table 2), indicating good reliability. Test-retest reliability was measured 12 weeks after the first survey with 98 participants, showing an ICC of .91.
Responsiveness. The SRM of SEAMS-K was 0.511, indicating responsiveness to medium-magnitude changes.
In this study, the 13-item SEAMS-K was validated to measure self-efficacy for appropriate medication use by PWD taking medications. Three factors were extracted and labeled: self-efficacy for medication in management challenges, self-efficacy for medication with complex routines, and self-efficacy for medication during disrupting barriers. These factors accounted for 71.2% of the total variance. The 3-factor model also demonstrated a satisfactory fit. Cronbach’s alphas ranged from .77 to .90, indicating adequate reliability. The validation of this tool is significant for nursing practitioners with the increasing prevalence of PWD. The SEAMS-K measures self-efficacy for appropriate medication use and was shown to be effective in assessing the level of self-efficacy by PWD taking medication.
The ceiling effect was over 30% and did not satisfy the criterion.31 The original SEAMS study16 and 3 previous studies18,19,39 did not test the floor and ceiling effects of the items. Floor and ceiling effects are defined as the proportion of respondents scoring the highest (ceiling) or lowest (floor) possible scores across any given domain, measuring the sensitivity and coverage of the questionnaire at each end of the scale. High floor and ceiling effects may also suggest a limited instrument range, measurement inaccuracy, and response bias.40 In this tool, patients are asked to indicate their level of confidence in taking medication correctly across 3 categories (not confident = 1, somewhat confident = 2, very confident = 3). Likert scales are usually 5 to 7 response alternatives ranging from “strongly disagree” on one end to “strongly agree” on the other, with “neither agree nor disagree” in the middle. These scales typically consist of items that require respondents to rate their degree of agreement or disagreement with various declarative statements. Although there is no difference between the 3 scaling formats in terms of the parameters, the forms with 5 and 7 response categories are more advantageous in terms of test information and reliability functions.41 Therefore, to measure the participants’ self-efficacy for appropriate medication use using a Likert scale, it is preferable to use 5 rather than 3 response categories.
The SEAMS-K 3 factors explained 71.2% of the total variance. The first factor is self-efficacy for medication in management challenges for adults with diabetes, hyperlipidemia, and metabolic syndrome, which explained 31.8% of the variance. As such, medication taking is expected to be dependent on PWD’s perceived needs and management of the medication,42 which reflects the maintenance of taking medication. In PWD, medications should be taken throughout life. Therefore, assessing PWD’s self-efficacy for taking medication in management challenges is needed to establish medication intervention strategies with them.
The second factor is related to self-efficacy for medication with complex routines and explained 20.9% of the variance. In addition to lifestyle changes for glycemic control in persons with type 2 diabetes, an effective strategy to reduce blood glucose levels is with prescribed OHAs, which essentially differ in their mechanism of action and method of administration.10 Despite the various OHAs, adequate glycemic control (A1C < 7.0%) by PWD is difficult to achieve. Poor medication taking is one of the most common causes of emergency room visits, hospitalizations, increased morbidity and mortality, and increased costs of care to PWD.5 Accordingly, it is vital to assess self-efficacy before changing medications or prescribing complex medications because strategies to address medication routines and control blood sugar levels may be needed in those with low self-efficacy for taking medication with complex routines.
The third factor is related to self-efficacy for medication during disrupting barriers—taking medication under difficult circumstances. It explained 18.6% of the variance. Medication is the basis of treatment for chronic diseases, and barriers to taking medication are issues that need to be addressed with PWD requiring long-term medication. In a study of the barriers to PWD taking medication, perceived lack of support, lack of a medication routine or regimen, medication problems (adverse events), and lack of knowledge were found to be factors influencing taking medication.4 Assessing self-efficacy for these barriers could suggest strategies to promote taking medication despite disrupting barriers.
Regarding hypothesis testing, self-efficacy for appropriate medication use was significantly correlated with refilling and taking medication. Self-efficacy is a key concept in explaining the maintenance of specific health behaviors such as medication taking.43 High self-efficacy enables one to overcome difficulties and successfully change one’s behavior.17 In PWD on long-term medication, successful medication taking depends more on self-motivation than on the insistence of others. Thus, nurses should actively seek methods to increase patient self-efficacy.
Furthermore, the fewer the depressive symptoms, the greater the self-efficacy for appropriate medication use is, which matches with previous findings suggesting that patients with chronic diseases and depression tend to have poor medication taking.44 Patients with depression usually have an outlook of hopelessness and lack self-efficacy and motivation, which may contribute to poor medication taking, especially in the long term.45 Therefore, it is possible to increase confidence and reduce depression by assessing self-efficacy in taking medication using the SEAMS-K and recommending appropriate medication use interventions.
Self-efficacy for diabetes self-management refers to confidence in self-management, including taking medications, following dietary requirements, engaging in regular exercise, and monitoring blood glucose.29 Therefore, the SEAMS-K is an important construct that can explain medication-taking behavior in PWD. It is potentially modifiable and can serve as a basis for developing interventions related to behavior change.
For known-groups validity, a significant association between the SEAMS-K scores and glycemic control indicated that the scale was able to differentiate whether diabetes was well controlled in a person with diabetes using A1C levels. Research has shown that confidence in taking medications appropriately is associated with a better diet and regular exercise, which contribute to A1C control.4
In measuring responsiveness, the SEAMS-K showed medium reactivity to self-efficacy for appropriate medication use by PWD, making it a helpful intervention-monitoring instrument. Furthermore, this study identified 3 factors for assessing self-efficacy for appropriate medication use by PWD. Therefore, the experience of self-efficacy for appropriate medication use may contribute to more positive lifestyle changes in addition to taking medication, where participation in treatment develops behaviors that motivate further positive health behaviors. For example, if taking medication meant that the person could exercise more, this behavior could lead to other positive health behaviors. In a previous study, a positive attitude toward the benefits of medication among PWD was associated with improved self-care and attendance for diabetic screening.46
This study has some limitations. First, the study participants were recruited from a single hospital. Thus, caution should be exercised when generalizing the results beyond large academic medical centers. Second, the participants recruited in this study were outpatients; therefore, future research should test the psychometric properties of the SEAMS-K using larger and more diverse samples of PWD. Third, there was a 12-week interval between tests, which influenced the test-retest reliability. Hence, it is suggested that in future studies, the interval between tests should be within 2 weeks, with a sample size of >100 participants.36
The 13-item SEAMS-K has good validity, high reliability, and moderate responsiveness. The questionnaire may be an effective instrument for nurses to prospectively evaluate the impact of self-efficacy for appropriate medication use on complications and various health outcomes by PWD taking medication, which may help in designing and delivering effective psychosocial interventions to improve medication use and health outcomes.
The SEAMS-K scale is aimed at measuring the self-efficacy for appropriate medication use in PWD. Diabetes care and education specialists can use the findings of the scale to help participants reflect on the benefits or importance of appropriate medication use for the prevention or reduction of the risk of diabetes complications and hyperglycemic episodes. Thus, the 13-item SEAMS-K can be used as a diabetes educational instrument in clinical settings to screen self-efficacy for appropriate medication use regarding the benefits of taking medication for diabetes. In particular, the SEAMS-K showed medium responsiveness to self-efficacy for appropriate medication use of PWD; it has the potential to be a helpful intervention-monitoring instrument.
All authors declare that they have no conflicts of interest related to this research study.
This work was partly supported by the National Research Foundation of Korea grant (No. 2021R1A2C2007858) funded by the Korea government (MSIT) and a 2024 research grant (No. M-2024-08-0110) from the Department of Nursing Science, Graduate School, Ajou University.
Jung-Suk Kim https://orcid.org/0000-0001-9169-4906
Chun-Ja Kim https://orcid.org/0000-0002-7594-5418
Dae Jung Kim https://orcid.org/0000-0003-1025-2044
Elizabeth A. Schlenck https://orcid.org/0000-0001-7361-1951
American Diabetes Association. Statistics about diabetes. 2025. Accessed February 6, 2025. https://diabees.org/aboutdiabetes/statistics
World Health Organization. Diabetes fact sheet. 2024. Accessed December 12, 2024. https://www.who.int/news-room/fact-sheets
Centers for Disease Control and Prevention. National diabetes statistics report. 2024. Accessed February 6, 2025. https://www.cdc.gov/diabetes/php/data-research/?CDC_AAref_Val=https://www.cdc.gov/diabetes/data/statistics-report/index.html
Gow K, Rashidi A, Whitehead L. Factors influencing medication adherence among adults living with diabetes and comorbidities: a qualitative systematic review. Cur Diab Rep. 2024;24(2):19-25. doi:10.1007/s11892-023-01532-0
Wexler DJ. Patient education: type 2 diabetes: treatment (beyond the basics). UpToDate. 2024. Accessed July 29, 2025. https://www.uptodate.com/contents/type-2-diabetes-overview-beyond-the-basics
Davies MJ, Aroda VR, Collins BS, et al. Management of hyperglycemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2022;45(11):2753-2786. doi:10.1007/s00125-022-05787-2
Korean Diabetes Association. Diabetes fact sheet in Korea 2022. 2023. Accessed February 6, 2025. https://www.diabetes.or.kr/bbs/?code=fact_sheet&mode=view&number=2500&page=1&code=fact_sheet
Al-Qerem W, Jarab AS, Badinjki M, Hyassat D, Qarqaz R. Exploring variables associated with medication non-adherence in patients with type 2 diabetes mellitus. PLoS One. 2021;16(8):e0256666. doi:10.1371/journal.pone.0256666
Burnier M. The role of adherence in patients with chronic diseases. Eur J Intern Med. 2023;119:1-5. doi:10.1016/j .ejim.2023.07.008
ElSayed NA, Aleppo G, Bannuru RR, et al. 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes—2024. Diabetes Care. 2024;47(1):S158-S178. doi:10.2337/dc24-er07a
Lin LK, Sun Y, Heng BH, Chew DEK, Chong PN. Medication adherence and glycemic control among newly diagnosed diabetes patients. BMJ Open Diabetes Re Care. 2017;5(1):e000429. doi:10.1136/bmjdrc-2017-000429
Dey AK, Gokalani R, Kale M, et al. Expert consensus recommendations on the evaluation and management of hypoglycemia in diabetes: DGENius Group Diabetes India. J Diabetol. 2024;15(1):38-62. doi:10.4103/jod.jod_121_23
Association of Diabetes Care and Education Specialists, Kolb L. An effective model of diabetes care and education: the ADCES7 Self-Care Behaviors™. Sci Diabetes Self Manag Care. 2021;47(1):30-53. doi:10.1177/0145721720978154
Jang GY, Chang SJ, Noh JH. Relationships among health literacy, self-efficacy, self-management, and HbA1c levels in older adults with diabetes in South Korea: a cross-sectional study. J Multidiscip Healthc. 2024;17:409-418. doi:10.2147/JMDH.S448056
Bandura A. Self-Efficacy: The Exercise of Control. WH Freeman and Company; 1997.
Risser J, Jacobson TA, Kripalani S. Development and psychometric evaluation of the Self-efficacy for Appropriate Medication Use Scale (SEAMS) in low-literacy patients with chronic disease. J Nurs Meas. 2007;15(3):203-219. doi:10.1891/106137407783095757
Wu J, Shen J, Tao Z, Song Z, Chen ZL. Self-efficacy as moderator and mediator between medication beliefs and adherence in elderly patients with type 2 diabetes. Patient Prefer Adherence. 2023;17:217-226. doi:10.2147/PPA.S382362
Dong XF, Liu YJ, Wang AX, Lv PH. Psychometric properties of the Chinese version of the Self-Efficacy for Appropriate Medication Use Scale in patients with stroke. Patient Prefer Adherence. 2016;10:321-327. doi:10.2147/PPA.S101844
Pedrosa RBDS, Rodrigues RCM. Adaptation and evaluation of the measurement properties of the Brazilian version of the Self-Efficacy for Appropriate Medication Adherence Scale. Rev Lat-Am Enfermagem. 2016;24:e2692. doi:10.1590/1518-8345.0167.2692
Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess. 1995;7(3):286-299. doi:10.1037/1040-3590.7.3.286
Lipsy RJ. The National Cholesterol Education Program Adult Treatment Panel III guidelines. J Manag Care Pharm. 2003;9(suppl 1):2-5. doi:10.18553/jmcp.2003.9.s1.2
Brislin RW. The wording and translation of research instruments. In: Lonner W, Berry J, eds. Field Methods in Cross Cultural Research. Sage Publications; 1986:137-164.
Jones PS, Lee JW, Phillips LR, Zhang XE, Jaceldo KB. An adaptation of Brislin’s translation model for cross-cultural research. Nurs Res. 2001;50(5):300-304. doi:10.1097/00006199-200109000-00008
Wild D, Grove A, Martin M, et al. Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: report of the ISPOR task force for translation and cultural adaptation. Value Health. 2005;8(2):94-104. doi:10.1111/j.1524-4733.2005.04054.x
Kim CJ, Park E, Schlenk EA, Kim M, Kim DJ. Psychometric evaluation of a Korean version of the Adherence to Refills and Medications Scale (ARMS) in adults with type 2 diabetes. Diabetes Educ. 2016;42(2):188-198. doi:10.1177/0145721716632062
Kripalani S, Risser J, Gatti ME, Jacobson TA. Development and evaluation of the Adherence to Refills and Medications Scale (ARMS) among low-literacy patients with chronic disease. Value Health. 2009;12(1):118-123. doi:10.1111/j.1524-4733.2008.00400.x
Chon KK, Choi SC, Yang BC. Integrated adaptation of CES-D in Korea. Korean J Health Psychol. 2001;6(1):59-76.
Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385-401.
Littlefield CH, Craven JL, Rodin GM, Daneman D, Murray MA, Rydall AC. Relationship of self-efficacy and binging to adherence to diabetes regimen among adolescents. Diabetes Care. 1992;15(1):90-94.
American Diabetes Association Professional Practice Committee. Summary of revisions: standards of medical care in diabetes-2022. Diabetes Care. 2022;45(suppl 1):S4-S7. doi:10.2337/dc22-SREV
Park JH. Standardization of Korean version of the Mini-Mental State Examination (MMSE-K) for use in the elderly. Part II. Diagnostic validity. Korean J Neuropsych Assoc. 1989;28:125-135.
Kane RL. Understanding Health Care Outcomes Research. 2nd ed. Jones & Bartlett Publishers Learning; 2006.
Brown J. Choosing the right number of components or factors in PCA and EFA. JALT TEval SIG. 2009;13(2):19-23.
Seo W, Lee S, Kim M, Kim J. Exploratory factor analysis in psychological research: current status and suggestions for methodological improvements. J Soc Sci. 2018;29(1):177-193. doi:10.16881/jss.2018.01.29.1.177
Shrestha N. Factor analysis as a tool for survey analysis. Am J Appl Math Stat. 2021;9(1):4-11. doi:10.12691/ajams-9-1-2
DeVellis RF, Thorpe CT. Scale Development: Theory and Applications. Sage Publications; 2021.
Prinsen CA, Mokkink LB, Bouter LM, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res. 2018;27(5):1147-1157. doi:10.1007/s11136-018-1798-3
Gwadry-Sridhar F, Guyatt GH, Arnold JMO, et al. Instruments to measure acceptability of information and acquisition of knowledge in patients with heart failure. Eur J Heart Fail. 2003;5(6):783-791. doi:10.1016/s1388-9842(03)00158-2
Wu J, Tao Z, Song Z, et al. Validation and psychometric properties of the self-efficacy for Appropriate Medication Use Scale in elderly Chinese patients. Int J Clin Pharm. 2021;43:586-594. doi:10.1007/s11096-020-01167-1
Bernstein DN, Houck JR, Hammert WC. A comparison of PROMIS UE versus PF: correlation to PROMIS PI and depression, ceiling and floor effects, and time to completion. J Hand Sur Br. 2019;44(10):901.e1-901.e7. doi:10.1016/j .jhsa.2018.12.006
Aybek EC, Toraman C. How many response categories are sufficient for Likert-type scales? An empirical study based on the item response theory. Int J Assess Tools Educ. 2022;9(2):534-547. doi:10.21449/ijate.1132931
Peh KQE, Kwan YH, Goh H, et al. An adaptable framework for factors contributing to medication adherence: results from a systematic review of 102 conceptual frameworks. J Gen Intern Med. 2021;36:2784-2795. doi:10.1007/s11606-021-06648-1
Park S, Kang Y. Symptom experience, self-efficacy, depression, and medication adherence in patients with chronic obstructive pulmonary disease. J Korean Clin Nurs Res. 2017;23(2):170-178. doi:10.22650/JKCNR.2017.23.2.170
Son YJ, Won MH. Depression and medication adherence among older Korean patients with hypertension: mediating role of self-efficacy. Int J Nurs Pract. 2017;23(3):e12525. doi:10.1111/ijn.12525C
Zhou Y, Huo Q, Du S, et al. Social support and self-efficacy as mediating factors affecting the association between depression and medication adherence in older patients with coronary heart disease: a multiple mediator model with a crosssectional study. Patient Prefer Adherence. 2022;16:285-295. doi:10.2147/PPA.S337634
Jiang L, Liu S, Li H, Xie L, Jiang Y. The role of health beliefs in affecting patients’ chronic diabetic complication screening: a path analysis based on the health belief model. J Clin Nurs. 2021;30(19-20):2948-2959. doi:10.1111/jocn.15802
From Department of Nursing, Hyejeon College, Hongseong, Korea (Dr Kim); College of Nursing and Research Institute of Nursing Science, Ajou University, Suwon, Korea (Dr Kim); Department of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, Korea (Dr Kim); and Graduate Clinical Education, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania (Dr Schlenck).
Corresponding Author: Chun-Ja Kim, College of Nursing and the Research Institute of Nursing Science, Ajou University, 164 Worldcup-Ro, Yeongtong-Gu, Suwon 16499, South Korea. Email: ckimha@ajou.ac.kr
