Factors associated with glycemic control in Korean older adults with diabetes living alone: A secondary analysis
Article information
Abstract
Purpose
Older adults living alone face challenges in managing diabetes, yet research on glycemic control in this group is limited. This study analyzed data from the Korean National Health and Nutrition Examination Survey (2016~2021) to identify factors associated with glycemic control in 453 older adults with diabetes living alone.
Methods
Glycemic control was categorized as good (glycated hemoglobin [HbA1c]<7.0%) or poor (HbA1c≥7.0%). Complex sample logistic regression examined demographic, disease and health-related, behavioral, and psychological factors associated with glycemic control.
Results
Older adults aged ≥80 years had better glycemic control than those aged 65~69 years, while a diabetes duration of ≥15 years and higher body mass index were linked to poorer control. Strength training 5~7 days per week was associated with better control, whereas non-adherence to a healthy diet was unexpectedly linked to better outcomes.
Conclusion
These findings highlight the need for tailored interventions to improve diabetes self-management and support healthy aging among older adults living alone. They also offer practical insights into shaping community-based health programs and social support systems for this population.
INTRODUCTION
Diabetes is a common chronic condition among older adults, and is an important focus of age-inclusive healthcare strategies. As the number of older adults increases globally, managing chronic conditions such as diabetes has become an essential aspect of promoting health and well-being in later life [1]. In South Korea, the prevalence of diabetes among adults aged 65 and older reached 29.3% by 2022 [2]. Since inadequate glycemic control may result in serious complications such as cardiovascular conditions, renal disease, and neuropathy, effective diabetes management is essential for preserving the health and quality of life of older adults [3]. Nevertheless, managing diabetes can be particularly challenging for older adults largely because of the functional decline associated with aging, multiple comorbidities, and difficulties in adhering to self-management routines [4]. Therefore, continuous social and national support is needed to promote effective glycemic control.
Parallel to this trend, the aging population in South Korea has led to a rapid increase in the number of older adults living alone. Data from Statistics Korea show that the number of single-person households among those aged 65 and older has increased by more than 3.4 times, from 543,787 in 2000 to 1,875,270 in 2022 [5]. Older individuals living alone represent a socially vulnerable group and are at increased risk of social isolation and loneliness [6], both of which have been consistently associated with adverse physical and mental health outcomes, including poorer adherence to chronic disease self-management behaviors that are important for glycemic control [3]. Social isolation may adversely influence diabetes self-management and, in turn, be associated with glycemic control through pathways such as poorer adherence to a healthy diet, reduced physical activity, and decreased engagement in self-care behaviors [7]. In addition, older adults living alone may experience difficulties accessing timely healthcare services or receiving assistance with daily health-related activities [8].
Despite the increasing number of older adults living alone, little is known about how their living situation affects glycemic control and the specific factors that contribute to poor diabetes management in this population. Previous studies have shown that glycemic control in older adults with diabetes is associated with clinical and lifestyle determinants, including diabetes duration and psychosocial and behavioral factors such as self-efficacy and self-care behaviors [9,10]. However, prior research—including studies based on the Korean National Health and Nutrition Examination Survey (KNHANES) [9] and other population-based studies [10,11]—has largely focused on older adults as a whole and has not specifically examined older adults living alone. Consequently, little is known about glycemic control and its associated factors in this subgroup. In this study, living alone is conceptualized as a form of social vulnerability, with demographic, disease and health-related, behavioral, and psychological factors positioned as key pathways through which this vulnerability may affect glycemic control.
Recent clinical guidelines emphasize that glycemic targets for older adults may be individualized according to clinical context, including comorbidities, functional status, and life expectancy [12]. However, good glycemic control (GGC) has traditionally been defined as a glycated hemoglobin (HbA1c) level below 7.0%, a threshold that remains widely used in epidemiological and population-based studies [3,9]. Since glycemic control remains an important and widely accepted indicator of diabetes management in population-based research, more in-depth research is needed to understand glycemic control patterns among older adults with diabetes living alone (OADL). Therefore, this study aimed to identify demographic, disease and health-related, behavioral, and psychological factors associated with glycemic control among OADL, based on nationally representative data from the 2016~2021 KNHANES, to inform future diabetes self-management programs and policy recommendations for this population.
METHODS
Ethic statement: KNHANES is conducted by the Korea Disease Control and Prevention Agency (KDCA), and all participants provide written informed consent. The survey protocol is annually approved by the KDCA Institutional Review Board (IRB). This study is a secondary analysis of publicly available, de-identified KNHANES data. The IRB of the Seoul National University confirmed that additional ethical approval and informed consent were not required and granted an exemption from IRB review (IRB No. E2308/001-016).
1. Study Design
This study employed secondary data analysis based on data collected from the 2016~2021 KNHANES. The KNHANES is a nationwide survey administered by the Korea Disease Control and Prevention Agency (KDCA) under the framework of the National Health Promotion Act [13]. It collects comprehensive data on health conditions, health-related behaviors, and dietary and nutritional intake in the South Korean population. The survey is composed of three major components: health interviews, health examinations, and nutrition surveys, and provides reliable data on chronic diseases, including diabetes [14]. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (https://www.strobe-statement.org).
We selected the KNHANES for this study because it provides a large-scale, nationally representative dataset that enables a detailed analysis of sociodemographic characteristics, disease- and health-related factors, health behaviors, psychological factors, and glycemic control levels of OADL in South Korea. The data also allow for the assessment of diabetes control and can provide insights into the development of health and welfare policies for this population. The survey employs a stratified multistage probability sampling design based on the Population and Housing Census to ensure national representativeness [13]. Raw data were retrieved from the official KNHANES website (https://knhanes.kdca.go.kr), where the KDCA provides access to public health datasets.
2. Study Sample
The study sample was extracted from the 2016~2021 KNHANES dataset. Specific inclusion criteria were established to achieve the study objectives: (1) individuals aged ≥65 years living alone, and (2) individuals diagnosed with diabetes. Diabetes was defined based on at least one of the following criteria: a) fasting blood glucose level ≥126 mg/dL, b) a documented diabetes diagnosis, c) current use of oral hypoglycemic agents or insulin, or d) HbA1c level ≥6.5%. Of the 46,828 participants initially surveyed, those aged <65 years (n=36,587) and those without diabetes (n=7,473) were excluded, resulting in a total of 2,768 participants aged ≥65 years with diabetes. Individuals living with family members (n=2,025) and those with missing data (n=290) were excluded. Consequently, the final analytical sample comprised 453 OADL (Figure 1).
Flowchart of study samples. KNHANES=Korean National Health and Nutrition Examination Survey.
3. Measures
All variables were obtained from the 2016~2021 KNHANES [14]. Detailed information on the survey design, data collection, and validation processes is publicly accessible on the official KNHANES website. For this study, the selected variables included glycemic control levels as the dependent variable, disease- and health-related factors, behavioral factors, and psychological factors as independent variables, and sociodemographic characteristics as demographic factors. Each variable is described in detail below.
1) Demographic Factors
General characteristics included sex, age, educational attainment, and household income. Age was categorized as 65~69 years, 70~74 years, 75~79 years, and ≥80 years. Educational attainment was categorized as elementary school or lower, middle school graduate, high school graduate, and college graduate or higher. Household income was stratified into four quartiles (1 to 4) and adjusted for the number of household members.
2) Disease and Health-Related Factors
We incorporated disease and health-related factors, including obesity, waist circumference, comorbidities (excluding diabetes), duration of diabetes, diabetes treatment, and self-rated health into the study. Obesity status was categorized according to body mass index (BMI) into six groups: underweight (BMI, <18.5 kg/m2), normal weight (BMI 18.5~22.9 kg/m2), overweight (BMI, 23.0~24.9 kg/m2), obesity class 1 (BMI, 25.0~29.9 kg/m2), obesity class 2 (BMI, 30.0~34.9 kg/m2), and obesity class 3 (BMI, ≥35.0 kg/m2). Waist circumference was categorized according to the national criteria for abdominal obesity (≥90 cm for males and ≥85 cm for females). Comorbidities (excluding diabetes) were classified into four groups according to the number of chronic diseases: none, one, two, and three or more. Duration of diabetes was categorized as <5 years, 5~9 years, 10~14 years, and ≥15 years. Diabetes treatment was classified as oral hypoglycemic agents alone, a combination of oral hypoglycemic agents and insulin injections, and nonpharmacological management (diet and exercise therapy alone). Self-rated health was based on the participants’ self-perception and categorized as very good, good, moderate, poor, or very poor.
3) Behavioral Factors
We explored behavioral factors, including smoking, drinking, physical activity (aerobic and strength training), and adherence to a healthy diet. To assess smoking habits, participants were asked, “Do you currently smoke conventional cigarettes or use electronic cigarettes?” Responses to both types of cigarettes were recorded. Drinking was categorized into three groups based on the frequency of alcohol consumption over the past year: non-drinkers, individuals who drank less than twice per week, and those who drank twice or more per week. Physical activity was evaluated based on the frequency of walking per week for aerobic exercise and the frequency of strength training per week. The frequency of both aerobic and strength exercises was categorized as follows: none, 1~2 days, 3~4 days, and 5 or more days per week. Adherence to a healthy diet was assessed using KNHANES items asking whether participants were controlling their diet and whether this was for disease management. Participants reporting dietary control for disease management were classified as adherent. Detailed dietary composition or intake patterns were not assessed.
4) Psychological Factors
We examined psychological factors, including perceived stress. Participants’ perceived stress was categorized based on responses to the question, “How much stress do you usually experience?” Response options included “extreme stress,” “a lot,” “a little,” and “almost none.”
5) Glycemic Control Level
Glycemic control levels were determined according to the 2025 guidelines for blood glucose management issued by the Korean Diabetes Association [12]. Glycemic control was classified as GGC (HbA1c<7.0%) or poor glycemic control (PGC; HbA1c≥7.0%).
4. Data Analysis
We analyzed the complex sample data using SPSS 28.0 (IBM Corp.). The KNHANES employs a multistage, stratified, probability-cluster sampling design. To ensure national representativeness, strata (Kstrata) and primary sampling units were specified as the sampling design variables, and the integrated weights (wt_tot) were adjusted by dividing them by the number of survey years included. Accounting for the complex sampling design is essential in KNHANES analyses because complex sample procedures prevent biased estimates that may arise from unequal selection probabilities. Frequencies, weighted percentages, means, and standard errors (SEs) were calculated using complex sample descriptive statistics to assess demographic, disease- and health-related, behavioral, and psychological characteristics, as well as glycemic control levels. Differences in study variables according to glycemic control levels were examined using complex sample cross-tabulation analysis for categorical variables and general linear modeling for continuous variables. Factors associated with glycemic control were examined using complex sample logistic regression. Multicollinearity was assessed using variance inflation factor, and all values (1.02~1.06) indicated no multicollinearity. Glycemic control status (GGC or PGC) was the dependent variable, and independent variables were selected based on their significant associations with glycemic control in OADL as well as their theoretical and clinical relevance. Model discrimination was assessed using the C-statistic (area under the curve [AUC]). All analyses were conducted with a significance threshold of α=.05.
RESULTS
1. Sample Descriptives
The study included 332 female participants (71.2%), outnumbering males, with the 75~79 years age group comprising the largest group with 142 individuals (29.3%). The majority of the participants had an educational level of elementary school graduation or below (n=330, 70.8%), and the predominant income level was low (n=356, 77.5%). A total of 225 participants (48.6%) had three or more comorbidities, excluding diabetes. The mean BMI was 24.99 kg/m2 (SE=0.17), and 281 participants (62.4%) were classified as having abdominal obesity. The duration of diabetes was 15 years or more in 151 participants (33.8%). The primary diabetes treatment method was oral hypoglycemic agents alone (n=410, 90.8%). Regarding self-rated health status, 208 participants (45.7%) reported average health status. In terms of behavioral factors, 413 participants (90.8%) were non-smokers, and 284 participants (61.7%) reported not consuming alcohol. Regarding physical activity, 151 participants (34.4%) engaged in aerobic exercise 5~7 days per week, while 391 participants (85.1%) reported no engagement in strength training. Regarding healthy dietary habits, 281 participants (62.5%) reported non-adherence. Regarding psychological factors, 191 participants (42.8%) reported experiencing mild stress. Regarding glycemic control, 265 individuals (57.1%) demonstrated GGC, whereas 188 individuals (42.9%) exhibited PGC. Table 1 presents an overview of the descriptive statistics of the study sample.
2. Comparison of Study Variables by Glycemic Control Levels
Several significant differences in the study variables were identified according to glycemic control levels. Among the demographic factors, age group (p<.001) and educational level (p=.028) were significantly associated with glycemic control. Specifically, the proportion of individuals aged 80 years and older was higher in the GGC group (25.7%) compared to the PGC group (15.9%), whereas individuals aged 75~79 years were more prevalent in the PGC group (33.4%) than in the GGC group (26.3%). Regarding education level, a greater proportion of high school graduates was observed in the PGC group (13.0%) than in the GGC group (6.7%). Among disease- and health-related factors, BMI (p=.029) was significantly higher in the PGC group, with a mean BMI of 25.45±0.28 kg/m2, compared to 24.65±0.22 kg/m2 in the GGC group. Duration of diabetes (p<.001) was also significantly longer in the PGC group, with a higher proportion with diabetes for 15 years or more (46.8%) compared to the GGC group (24.1%). Moreover, diabetes treatment methods (p=.005) differed significantly between the groups. Insulin use combined with oral hypoglycemic agents was more common in the PGC group (6.2%) than in the GGC group (1.1%). Regarding behavioral factors, a lack of strength training (p=.020) was significantly more frequent among individuals in the PGC group (89.5%) than in the GGC group (81.8%). Interestingly, adherence to a healthy diet (p=.004) was paradoxically higher in the PGC group (45.8%) than in the GGC group (31.3%). With respect to psychological factors, perceived stress did not differ significantly between the GGC and PGC groups (p=.803). These findings are shown in Table 2.
Comparison of Study Variables by Glycemic Control Levels (n=453, N=341,898)
3. Factors Associated With Glycemic Control
We performed a complex sample logistic regression analysis to identify the factors associated with glycemic control. Glycemic control level (GGC vs. PGC) was used as the dependent variable. Independent variables were selected to represent demographic (age, education level), disease and health-related (duration of diabetes, diabetes treatment, BMI), and behavioral factors (strength training, adherence to a healthy diet) that showed significant differences between the glycemic control groups.
Logistic regression analysis revealed that age was significantly associated with glycemic control. Compared to individuals aged 65~69 years, those aged 80 years and older had significantly higher odds of achieving GGC (odds ratio [OR]=2.15, 95% confidence interval [CI]=1.09~4.26, p=.027). Diabetes duration was strongly associated with PGC. Compared to participants with a diabetes duration of <5 years, those with diabetes for ≥15 years had significantly lower odds of achieving GGC (OR=0.24, 95% CI=0.13~0.46, p<.001). Strength training was significantly and positively associated with GGC. Participants who engaged in strength training 5~7 days per week had higher odds of achieving GGC compared to those who did not perform strength training (OR=3.68, 95% CI=1.26~10.83, p=.018). Adherence to a healthy diet was unexpectedly associated with PGC. Individuals who did not adhere to a healthy diet had significantly higher odds of achieving GGC compared to those who adhered to a healthy diet (OR=1.93, 95% CI=1.22~3.05, p=.005). BMI was negatively associated with glycemic control, indicating that a higher BMI was linked to PGC (OR=0.93, 95% CI=0.88~0.99, p=.031). In contrast, educational level and diabetes treatment methods were not significantly associated with glycemic control. These results are shown in Table 3, and the model showed acceptable discrimination (AUC=0.726).
Factors Associated with Glycemic Control (n=453, N=341,898)
DISCUSSION
This study examined factors associated with glycemic control among OADL in South Korea using 2016~2021 KNHANES data. Age, duration of diabetes, strength training, and BMI were significantly associated with glycemic control in this population.
In this study, 42.9% of participants had PGC, indicating that nearly half of OADL remain at risk of hyperglycemia and related complications. This proportion was higher than those reported in nationally representative studies from Japan (25.6%) [15] and the United States (28.5%) [16]. Despite recent recommendations emphasizing early and intensive treatment [12], many older adults living alone continue to face challenges in achieving adequate glycemic control. Given the challenges associated with self-management among older adults living independently, there is a need to strengthen education and support systems that help them monitor their glycemic status and prevent diabetes-related complications.
Considering demographic factors, the predominance of females among older adults living alone in this study contrasts with national statistics showing a higher diabetes prevalence among males in South Korea [2]. This discrepancy likely reflects the demographic structure of the older adult population living alone, in which females constitute the majority [17]. In contrast, adults aged 80 years and older were more likely to achieve GGC than those aged 65~69 years, consistent with U.S. National Health and Nutrition Examination Survey findings [11]. Among older adults living alone, younger-old individuals may face greater challenges in diabetes self-management due to higher daily responsibilities, whereas older-old individuals may adopt more structured self-care behaviors. This pattern may also reflect differences in disease duration across age groups, as some oldest-old individuals may have been diagnosed later in life, whereas younger-old individuals may have lived longer with diabetes. In addition, survival bias may partly explain this finding, as individuals with PGC may have died earlier, leaving relatively healthier survivors in the oldest age group. These findings highlight the importance of considering demographic and lifestyle contexts when interpreting glycemic control in this population.
With respect to disease and health-related factors, individuals with diabetes for more than 15 years were less likely to achieve GGC. This finding aligns with previous research indicating that a longer diabetes duration is associated with a higher likelihood of uncontrolled blood glucose levels [18]. A prolonged duration of diabetes is linked to progressive β-cell dysfunction, making blood glucose management increasingly challenging [19]. This highlights the difficulties faced by both healthcare providers and patients in maintaining glycemic control as the disease progresses. Early education on the long-term effects of hyperglycemia is essential, particularly for OADL. Ensuring that patients fully understand the impact of prolonged hyperglycemia on β-cell function from the time of diagnosis may help improve their long-term diabetes management and glycemic outcomes.
In addition, we found that individuals with a higher BMI were more likely to have PGC, which is consistent with previous research [20]. BMI is strongly associated with insulin resistance, particularly in obesity, thereby exacerbating diabetes management challenges [21]. Notably, more than half of the participants were overweight or obese, indicating a high risk of PGC. Among older adults living alone, excess body weight may be particularly difficult to manage due to reduced physical activity and the need to independently maintain diet and exercise routines. Given that weight loss is recognized as a key factor in improving glycemic control [22], implementation of targeted weight management programs is essential in OADL. In contrast, waist circumference, a commonly used indicator of abdominal obesity, was not significantly associated with glycemic control in this analysis. Taken together, these findings suggest that BMI remains a relevant indicator of glycemic control in older adults living alone but should be interpreted in the context of age-related changes in body composition.
Among behavioral factors, strength training was associated with better glycemic control in OADL. Participants who engaged in resistance exercise 5~7 days per week demonstrated superior glycemic control compared to those who did not perform strength training, consistent with previous studies in individuals with diabetes [23]. Skeletal muscle plays a critical role in glucose uptake and utilization [24], and muscle loss progresses more rapidly in older adults with diabetes than in the general older population [23]. Resistance training may be particularly suitable for older adults with diabetes, as it can be performed easily in a home setting and is feasible for sedentary individuals [23], making it a practical strategy for older adults living alone with high self-management demands. Considering the physical variability among OADL, personalized strength training programs with appropriate safety guidelines are needed to minimize injury risk and maximize effectiveness. However, this finding should be interpreted with caution due to the small sample size and wide CI, and because individuals with better glycemic control may have been more capable of engaging in regular strength training.
Surprisingly, individuals who did not adhere to a healthy diet demonstrated better glycemic control than those who followed a dietary regimen, which differs from findings reported in a previous systematic review [25]. This paradox may stem from reverse causation, whereby individuals with poorer glycemic control were more likely to receive stricter dietary advice, a hypothesis that warrants further examination in longitudinal studies. In addition, perceived adherence to a healthy diet may not necessarily reflect optimal food choices, particularly among older adults living alone, for whom limited social support or challenges related to meal preparation may affect dietary practices. Misconceptions regarding dietary management for diabetes—such as focusing primarily on sugar avoidance without considering overall nutritional balance—may contribute to ineffective dietary practices. Limited access to structured nutritional education may further hinder appropriate dietary self-management in this population [26]. Furthermore, self-reported dietary adherence may not adequately reflect actual dietary quality or nutritional balance. These findings should be interpreted with caution, as dietary adherence was assessed using a single self-reported item.
In contrast to a previous study conducted in individuals with diabetes that reported significant associations between educational level, diabetes treatment methods, and glycemic control [27], no such associations were observed in this study of OADL. This discrepancy may be partially explained by findings from prior research in individuals with type 2 diabetes with a mean age of approximately 70 years, suggesting that regular hospital visits play an important role in diabetes management [28]. In this study, most participants were undergoing pharmacological treatment (oral hypoglycemic agents or insulin), and regular hospital visits are essential for continued medication prescriptions and disease monitoring. In this context, the effects of education level and diabetes treatment methods on glycemic control may have been attenuated in this population. Nevertheless, further research is needed to determine the precise effects of regular hospital visits on glycemic control.
Although perceived stress was not significantly associated with glycemic control in this study, psychological factors have been widely recognized as important influences on diabetes self-management and metabolic outcomes [29], and their potential role among older adults living alone warrants further investigation.
This study has several limitations. First, although this study used an HbA1c threshold of <7.0% to define glycemic control, recent clinical guidelines emphasize that glycemic targets for older adults should be individualized based on comorbidities, frailty, functional status, and life expectancy, with HbA1c targets of up to 8.0% considered reasonable for those with significant comorbidities or functional impairment, and reliance on HbA1c potentially inappropriate for those with limited life expectancy [12]. Accordingly, the HbA1c cut-off used in this study should be interpreted as an operational definition for analytical purposes rather than a clinical target for older adults. Because the KNHANES lacks detailed clinical assessments needed to individualize glycemic goals, this study was unable to adjust glycemic control definitions according to geriatric considerations. Future research should incorporate individualized glycemic targets in line with current guideline recommendations for older adults. Second, adherence to a healthy diet was assessed using a single binary self-reported item, which limited the ability to fully capture dietary practices and interpret its association with glycemic control. Future studies should incorporate more detailed dietary assessments to better capture dietary quality and its relationship with glycemic control. Third, psychological factors were assessed using a single stress-related item, without including other indicators such as depression or loneliness. Future research should incorporate more comprehensive psychological measures to clarify their role in glycemic control. Fourth, this analysis focused on community-dwelling older adults living alone and did not include individuals residing in long-term care facilities or hospitals [16]. As a result, the participants in this study may have been in relatively better health than the institutionalized older adults. Future studies should include institutionalized older adults to provide a more comprehensive understanding of glycemic control across diverse care settings. Fifth, although the KNHANES provides extensive general health information, it does not distinguish between type 1 and type 2 diabetes. Consequently, this study could not account for important differences in disease etiology, treatment strategies, insulin dependence, and duration-related metabolic characteristics. Given that glycemic control patterns and management needs differ substantially between type 1 and type 2 diabetes, the inability to classify diabetes types may have limited the interpretation of glycemic outcomes. Future studies using datasets with detailed clinical indicators that allow differentiation between diabetes types are warranted. Finally, the cross-sectional design of the 2016~2021 KNHANES limits causal inference between glycemic control and associated factors. Longitudinal studies are needed to clarify the temporal and causal pathways underlying glycemic control in older adults living alone.
Despite these limitations, this study is significant in using nationally representative data to identify key factors associated with glycemic control among OADL, a vulnerable population. These findings can inform future research and guide the development of tailored interventions that address both clinical and psychosocial needs. Additionally, the results offer practical insights for nursing education by highlighting the importance of preparing future healthcare providers to support the self-management of diabetes in older populations. At the policy level, these findings may assist in shaping community-based health programs, such as community nurse visiting services and digital health support, for older adults who live alone. Finally, sustainable home-based self-management may contribute to health equity and resource-efficient models of care for aging populations.
CONCLUSION
In conclusion, this study identified key factors associated with glycemic control among OADL in South Korea using 2016~2021 KNHANES data. The findings highlight that age, diabetes duration, strength training, and BMI were significantly associated with glycemic control in this population. Older adults aged 80 years and above were more likely to achieve GGC, whereas those with a longer diabetes duration (≥15 years) had a significantly lower likelihood of maintaining optimal glucose levels. Engaging in strength training for 5~7 days per week was associated with better glycemic control, underscoring the importance of regular strength training in diabetes management. Interestingly, those who did not adhere to a healthy diet appeared to have better glycemic control. However, this finding may be limited by factors such as reverse causation and limitations in the measurement of dietary adherence. Additionally, higher BMI was linked to poorer glycemic control, reinforcing the need for weight management strategies in this population. These findings support the need for tailored, community-based interventions to improve diabetes self-management among OADL and promote healthy aging.
Notes
Authors' contribution
Conceptualization - HJK; Data curation - HJK, YP, JRK, YWJ, and GWC; Formal analysis - HJK, YP, JRK, YWJ, and GWC; Funding acquisition - HJK; Investigation - HJK, SJC, YP, JRK, YWJ, and GWC; Methodology - HJK, YP, JRK, YWJ, and GWC; Supervision - SJC; Validation - HJK, SJC, YP, JRK, YWJ, and GWC; Visualization - HJK; Writing–original draft - HJK, YP, JRK, YWJ, and GWC; Writing–review and editing - HJK, SJC, YP, JRK, YWJ, and GWC
Conflict of interest
No existing or potential conflict of interest relevant to this article was reported.
Funding
This study was supported by the 2023 Graduate Student Research Grant from the Research Institute of Nursing Science, Seoul National University. Also, Yujin Park received a scholarship from the BK21 education program (Center for World-leading Human-care Nurse Leaders for the Future).
Data availability
This study used the 2016~2021 data from the Korean National Health and Nutrition Examination Survey (KNHANES).
Acknowledgements
None.