OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic had direct effects on population health through infection and morbidity, as well as indirect effects on population mental health. We estimated the network structure of post-traumatic stress symptoms (PTSS) and depressive symptoms throughout the pandemic in Korea and aimed to identify the most central and bridging symptoms. METHODS: Participants aged 30–64 years completed mental health surveys across 3 phases of the COVID-19 pandemic: March 2020 (n=1,925), February–March 2021 (n=1,754), and December 2021–January 2022 (n=1,595). Using PTSS and depressive symptom data, we conducted network analyses, and the primary measures of symptom importance (centrality) were expected influence and bridge expected influence. RESULTS: In the comorbidity network, although the most central symptoms fluctuated over the course of the pandemic, sleep problems were consistently identified as the most influential bridge symptoms throughout. The symptom network structure differed between the subacute and chronic phases of the pandemic. CONCLUSIONS We found evidence of changes in the network structure of PTSS and depressive symptoms, even as sleep problems retained a consistent role as a bridging symptom. Although overall network structures varied across phases of the pandemic, the bridging role of sleep-related symptoms remained consistently strong, suggesting that sleep problems may represent a general and enduring mechanism underlying PTSS–depression comorbidity. During future pandemics, prompt screening for sleep problems may help prevent the development of comorbidity between PTSS and depressive symptoms.

Objectives: Adhering to management guidelines, including routine follow-up and education, is crucial for the effective management of chronic diseases such as diabetes. Uncontrolled diabetes is linked to poor sleep quality. We used nationally representative data from community-dwelling Koreans to investigate potential associations between non-adherence to diabetes management and poor sleep quality. Methods: Of the 228 340 participants in the 2018 Korea Community Health Survey, we analyzed data from 68 246 participants aged 65 years and older. Based on their diabetes status and adherence to diabetes-related tests (glycated hemoglobin, fundus examination, and urine microalbuminuria) and management education, study participants were divided into 3 groups: no diabetes, adherence, and non-adherence. Outcome variables included poor overall sleep quality and sleep disturbances, as measured by the Pittsburgh Sleep Quality Index. Results: The study included 934 participants in the adherence group (2.0%) and 13 420 in the non-adherence group (20.2%). Participants displaying non-adherence were significantly more likely to report poor overall sleep quality (adjusted odds ratio [aOR], 1.09; 95% confidence interval [CI], 1.04 to 1.13) and to experience sleep fragmentation, nocturia, pain during sleep, and difficulty falling asleep compared to participants without diabetes. Even in the earlier stage of diabetes, non-adherent participants were more likely to report poor sleep quality (aOR, 1.06; 95% CI, 1.00 to 1.18). Conclusions Patients with diabetes who do not follow management guidelines are more likely to experience poor sleep quality than those without diabetes. Emphasizing diabetes management and increasing awareness of management strategies may improve sleep quality among patients with this disease.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Objectives: Adhering to management guidelines, including routine follow-up and education, is crucial for the effective management of chronic diseases such as diabetes. Uncontrolled diabetes is linked to poor sleep quality. We used nationally representative data from community-dwelling Koreans to investigate potential associations between non-adherence to diabetes management and poor sleep quality. Methods: Of the 228 340 participants in the 2018 Korea Community Health Survey, we analyzed data from 68 246 participants aged 65 years and older. Based on their diabetes status and adherence to diabetes-related tests (glycated hemoglobin, fundus examination, and urine microalbuminuria) and management education, study participants were divided into 3 groups: no diabetes, adherence, and non-adherence. Outcome variables included poor overall sleep quality and sleep disturbances, as measured by the Pittsburgh Sleep Quality Index. Results: The study included 934 participants in the adherence group (2.0%) and 13 420 in the non-adherence group (20.2%). Participants displaying non-adherence were significantly more likely to report poor overall sleep quality (adjusted odds ratio [aOR], 1.09; 95% confidence interval [CI], 1.04 to 1.13) and to experience sleep fragmentation, nocturia, pain during sleep, and difficulty falling asleep compared to participants without diabetes. Even in the earlier stage of diabetes, non-adherent participants were more likely to report poor sleep quality (aOR, 1.06; 95% CI, 1.00 to 1.18). Conclusions Patients with diabetes who do not follow management guidelines are more likely to experience poor sleep quality than those without diabetes. Emphasizing diabetes management and increasing awareness of management strategies may improve sleep quality among patients with this disease.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Objectives: Adhering to management guidelines, including routine follow-up and education, is crucial for the effective management of chronic diseases such as diabetes. Uncontrolled diabetes is linked to poor sleep quality. We used nationally representative data from community-dwelling Koreans to investigate potential associations between non-adherence to diabetes management and poor sleep quality. Methods: Of the 228 340 participants in the 2018 Korea Community Health Survey, we analyzed data from 68 246 participants aged 65 years and older. Based on their diabetes status and adherence to diabetes-related tests (glycated hemoglobin, fundus examination, and urine microalbuminuria) and management education, study participants were divided into 3 groups: no diabetes, adherence, and non-adherence. Outcome variables included poor overall sleep quality and sleep disturbances, as measured by the Pittsburgh Sleep Quality Index. Results: The study included 934 participants in the adherence group (2.0%) and 13 420 in the non-adherence group (20.2%). Participants displaying non-adherence were significantly more likely to report poor overall sleep quality (adjusted odds ratio [aOR], 1.09; 95% confidence interval [CI], 1.04 to 1.13) and to experience sleep fragmentation, nocturia, pain during sleep, and difficulty falling asleep compared to participants without diabetes. Even in the earlier stage of diabetes, non-adherent participants were more likely to report poor sleep quality (aOR, 1.06; 95% CI, 1.00 to 1.18). Conclusions Patients with diabetes who do not follow management guidelines are more likely to experience poor sleep quality than those without diabetes. Emphasizing diabetes management and increasing awareness of management strategies may improve sleep quality among patients with this disease.

OBJECTIVES: To test the hypothesis that tobacco exposure is associated with elevated blood pressure (EBP) in Korean adolescents, and that the association is dose dependent. METHODS: This cross-sectional study used data from the 2011-2020 Korea National Health and Nutrition Survey (KNHANES). Subjects were eligible if they were 13-18 years at the time of participation in KNHANES. Tobacco exposure was defined by urine cotinine level. The main outcomes were EBP and hypertension. Statistical analyses were conducted using SAS version 9.4 with appropriate sampling weights to account for the complex survey design, stratification, and cluster variable. RESULTS: A total of 2,518 adolescents was included in the analysis, representing 2.5 million Korean adolescents. The mean± standard deviation participant age was 15.3±1.7 years, and 55.3% were male. The number of participants with active tobacco smoke exposure was 283 (11.2%), passive tobacco smoke exposure was 145 (5.8%), and no smoke exposure was 2,090 (83.0%). Analysis of the 2,518 urine-cotinine-verified participants showed that tobacco smoke exposure had a significant effect on EBP: with an odds of elevated blood pressure of 3.00 (95% confidence interval [CI], 1.14 to 7.89). The odds of hypertension were 3.61 (95% CI, 1.13 to 11.49) in the active smoking group compared with the no tobacco exposure group after adjustment for potential confounders. CONCLUSIONS It is necessary to present a range of public health plans to reduce tobacco exposure that affects adolescents’ blood pressure, and further research with a larger number of participants using urine cotinine as a biomarker is needed.