Objectives: Cognitive behavioral therapy for insomnia (CBT-I) is the first line treatment for insomnia. However, many patients remain with sleep disturbances even after undergoing CBT-I, and those with short sleep durations have shown fewer gains. Acceptance and commitment therapy (ACT) is one of the third wave of behavioral therapies, and it is useful in helping patients get flexibility of mind. Therefore, we incorporated its components into CBT-I, came up with an advanced CBT-I program that involves cognitive behavior therapy based on ACT, and examined its efficacy in comparison to that of CBT-I. Methods: Patients with chronic primary insomnia were recruited at the memory center of CHA University Hospital from June to August 2020. To examine the efficacy of advanced CBT-I compared to that of CBT-I, the patients (n=16) were assigned to two groups (CBT-I: n=6; advanced CBT-I: n=10). The patients in each group were treated for 4 weeks (8 sessions). The quality of sleep, severity of insomnia, sleepiness, depression, anxiety, acceptance, efforts to sleep, and dysfunctional beliefs concerning sleep were assessed with self-report questionnaires. Results: The severity of insomnia, quality of sleep, depression, anxiety, acceptance, efforts to sleep, and dysfunctional beliefs concerning sleep improved after both CBT-I and advanced CBT-I treatment. Conclusions This study examined the efficacy of advanced CBT-I in improving the severity of insomnia, sleep quality, and other symptoms related to sleep. The results suggest that components of ACT were useful for insomnia.

Objectives: Cognitive behavioral therapy for insomnia (CBT-I) is the first line treatment for insomnia. However, many patients remain with sleep disturbances even after undergoing CBT-I, and those with short sleep durations have shown fewer gains. Acceptance and commitment therapy (ACT) is one of the third wave of behavioral therapies, and it is useful in helping patients get flexibility of mind. Therefore, we incorporated its components into CBT-I, came up with an advanced CBT-I program that involves cognitive behavior therapy based on ACT, and examined its efficacy in comparison to that of CBT-I. Methods: Patients with chronic primary insomnia were recruited at the memory center of CHA University Hospital from June to August 2020. To examine the efficacy of advanced CBT-I compared to that of CBT-I, the patients (n=16) were assigned to two groups (CBT-I: n=6; advanced CBT-I: n=10). The patients in each group were treated for 4 weeks (8 sessions). The quality of sleep, severity of insomnia, sleepiness, depression, anxiety, acceptance, efforts to sleep, and dysfunctional beliefs concerning sleep were assessed with self-report questionnaires. Results: The severity of insomnia, quality of sleep, depression, anxiety, acceptance, efforts to sleep, and dysfunctional beliefs concerning sleep improved after both CBT-I and advanced CBT-I treatment. Conclusions This study examined the efficacy of advanced CBT-I in improving the severity of insomnia, sleep quality, and other symptoms related to sleep. The results suggest that components of ACT were useful for insomnia.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Purpose: A total of 39 seafood menus were prepared according to the Korean standard recipe, and analyzed for vitamin B 9 (folate) and B 12 (cobalamins) contents, using validated applied analytical methods. The menus included Guk/Tang/Jjigae (boiled or stewed dishes, n = 10), Bokkeum (stir-fried dishes, n = 10), Jjim/Jorim (braised or steamed dishes, n = 7), Gui (baked or grilled dishes, n = 7), Twigim (deep-fried dishes, n = 2) and Muchim (dried or blanchedseasoned dishes, n = 3). Methods: The contents of vitamin B 9 and B12 in all food samples were determined by the trienzyme extraction-Lactobacillus casei and immunoaffinity-high-performance liquid chromatography/photodiode array detection methods. Analytical quality control was performed in order to assure reliability of the analysis. Results: Accuracy (97.4–100.6% recoveries) and precision (< 6% relative standard deviations for repeatability and reproducibility) of vitamin B 9 and B12 analyses were determined to be excellent. The vitamin B 9 and B 12 contents of the 39 seafood menus evaluated, varied in the range of 1.83–523.08 μg/100 g and 0.11–38.30 μg/100 g, respectively, depending on the ingredients and cooking methods. The vitamin B 9 content was highest in Jomi-gim (523.08 μg/100 g), followed by Geonsaeu-bokkeum (128.34 μg/100 g) and Janmyeolchi-bokkeum (121.53 μg/100 g). Vitamin B12 was detected in all seafood menus, with highest level obtained in Kkomack-jjim (41.58 μg/100 g). The seaweed dish was found to have high levels of both vitamin B 9 and B12 . All assays were performed under strict quality control. Conclusion Guk and Tang menus, which contain a large amount of water, were relatively lower in the vitamin B 9 and B12 contents than the other menus. Bokkeum menus containing various vegetables were high in the vitamin B 9 content, but the vitamin B12 content was dependent on the type of seafood used in the menu.

OBJECTIVES: We aimed to study the effects of the coronavirus disease 2019 (COVID-19) pandemic on adolescents’ mental health in Korea. METHODS: We used data from the Korea Youth Risk Behavior Survey 2018-2021 with 227,139 students aged 12-18 years. We estimated the differences in depressive symptoms, suicidal ideation, and stress perception before (2018-2019) and during (2020-2021) the pandemic, as well as before (2019), the first year (2020) of, and the second year (2021) of the pandemic. We also examined whether COVID-19 is statistically associated with mental health. RESULTS: In both male and female adolescents, the prevalence of depressive symptoms, suicidal ideation, and stress perception was higher in the “not living with family,” “low household economic status,” and “self-rated unhealthy status” subgroups. The prevalence of depressive symptoms and stress perception was higher in middle school students. Adolescents were less likely to experience depressive symptoms (adjusted odds ratio [aOR], 0.86; 95% confidence interval [CI], 0.83 to 0.89), suicidal ideation (aOR, 0.80; 95% CI, 0.76 to 0.83), and stress perception (aOR, 0.76; 95% CI, 0.74 to 0.79) in 2020 than in 2019. However, there were more depressive symptoms (aOR, 1.06; 95% CI, 1.02 to 1.09), suicidal ideation (aOR, 1.15; 95% CI, 1.10 to 1.21), and stress perception (aOR, 1.19; 95% CI, 1.16 to 1.23) in 2021 than in 2020. CONCLUSIONS The COVID-19 pandemic had positive impacts on mental health of adolescents in its early stages but has had negative impacts as the pandemic continues. Attention should be paid to adolescents who are particularly vulnerable to the mental health effects of the pandemic.

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BACKGROUND: Projection of future trends in disease burden can facilitate setting of priorities for health policies and resource allocation. We report here projections of disease-specific mortality and the burdens of various diseases in Korea from 2016 to 2030. METHODS: Separate age- and sex-specific projection models for 21 major cause clusters from 2016 to 2030 were developed by applying coherent functional data models based on historical trends from 2002 to 2015. The age- and sex-specific years of life lost (YLL) for each cause cluster were projected based on the projected number of deaths. Years lived with disability (YLD) projections were derived using the 2015 age- and sex-specific YLD to YLL ratio. The disability-adjusted life years (DALYs) was the sum of YLL and YLD. RESULTS: The total number of deaths is projected to increase from 275,777 in 2015 to 421,700 in 2030, while the age-standardized death rate is projected to decrease from 586.9 in 2015 to 447.3 in 2030. The largest number of deaths is projected to be a result of neoplasms (75,758 deaths for males; 44,660 deaths for females), followed by cardiovascular and circulatory diseases (34,795 deaths for males; 48,553 deaths for females). The three leading causes of DALYs for both sexes are projected to be chronic respiratory diseases, musculoskeletal disorders, and other non-communicable diseases (NCDs). CONCLUSION We demonstrate that NCDs will continue to account for the majority of the disease burden in Korea in the future.