Purpose: Psychiatric conditions are common in children and adolescents with diabetes and can hinder disease management. In this study, we examined whether mental health status at diagnosis predicts glycemic control at 1 year. Methods: We included 57 patients aged 6–18 years diagnosed with type 1 or type 2 diabetes between 2019 and 2023 at Seoul National University Bundang Hospital. Mental health was assessed within 3 months of diagnosis using the Eating Disorder Inventory-2, Children’s Depression Inventory, and Child Behavior Checklist (CBCL) for ages 6–18. Poor glycemic control was defined as glycated hemoglobin >6.5% at 1 year. Associations between screening results and glycemic control were analyzed using Fisher exact test and multivariate logistic regression. Results: Of the 57 patients, 32 (56.1%) had type 1 diabetes, and the mean age at diagnosis was 12.9±3.1 years; 31 (54.4%) were male. Poor glycemic control at 1 year was observed in 16 patients (28.1%). Although individual subscale positivity was not significantly associated with glycemic control, borderline somatic complaints on the CBCL were significantly associated with poor control (p=0.022). In multivariate analysis, having 2 or more positive CBCL subscales showed a trend toward association with poor glycemic control (adjusted odds ratio=21.47, p=0.054). Conclusion Early psychological screening, especially for somatic symptoms or multiple psychological problems, may help identify those at risk for poor glycemic control in pediatric diabetes. These findings underscore the importance of early detection and intervention in optimizing diabetes management.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Background: The use of helmet treatment for positional plagiocephaly has increased recently; however, its effect is unknown in Korea.Purpose: This study aimed to investigate the effectiveness of helmet therapy and identify its influencing factors. Methods: Ninety pediatric patients diagnosed with moderate to severe positional plagiocephaly received helmet therapy. Severity of moderate to severe positional plagiocephaly was defined as cranial vault asymmetry (CVA) >10 mm or CVA index (CVAI) >6%. Patients were categorized by age, severity, and daily helmet wear. Multiple regression analysis controlled for factors like sex and prematurity. Treatment success was assessed by comparing pre/post-helmet theray CVA and CVAI, considering normalization or decrease to mild plagiocephaly (CVA ≤10 mm or CVAI ≤6%). Results: A total of 90 participants were enrolled (mean age, 5.6±1.6 months; male, 53 [58.9%]). The mean helmet therapy duration was 6.4±2.7 months, while the mean daily wear time was 18.4±2.7 hours. Among the 90 patients, 66 (73.3%) had moderate disease and 24 (26.7%) had severe disease. The mean CVA and CVAI decreased by 6.3±2.7 mm and 4.3%±1.8% after versus before treatment (P<0.001). Treatment was successful in 76 infants (84.4%). The most effective changes in CVA and CVAI were noted in those who began treatment before 9 months of age (6.2±2.5 mm and 5.0%±1.9%, P<0.001), had high compliance (6.2±2.4 mm and 4.9%±1.9%, P<0.001), and had high severity (8.0±2.3 mm and 6.6%±1.7%, P<0.001). Conclusion Starting helmet treatment before 9 months and wearing it over 15 hours daily yielded better outcomes.

Background: The use of helmet treatment for positional plagiocephaly has increased recently; however, its effect is unknown in Korea.Purpose: This study aimed to investigate the effectiveness of helmet therapy and identify its influencing factors. Methods: Ninety pediatric patients diagnosed with moderate to severe positional plagiocephaly received helmet therapy. Severity of moderate to severe positional plagiocephaly was defined as cranial vault asymmetry (CVA) >10 mm or CVA index (CVAI) >6%. Patients were categorized by age, severity, and daily helmet wear. Multiple regression analysis controlled for factors like sex and prematurity. Treatment success was assessed by comparing pre/post-helmet theray CVA and CVAI, considering normalization or decrease to mild plagiocephaly (CVA ≤10 mm or CVAI ≤6%). Results: A total of 90 participants were enrolled (mean age, 5.6±1.6 months; male, 53 [58.9%]). The mean helmet therapy duration was 6.4±2.7 months, while the mean daily wear time was 18.4±2.7 hours. Among the 90 patients, 66 (73.3%) had moderate disease and 24 (26.7%) had severe disease. The mean CVA and CVAI decreased by 6.3±2.7 mm and 4.3%±1.8% after versus before treatment (P<0.001). Treatment was successful in 76 infants (84.4%). The most effective changes in CVA and CVAI were noted in those who began treatment before 9 months of age (6.2±2.5 mm and 5.0%±1.9%, P<0.001), had high compliance (6.2±2.4 mm and 4.9%±1.9%, P<0.001), and had high severity (8.0±2.3 mm and 6.6%±1.7%, P<0.001). Conclusion Starting helmet treatment before 9 months and wearing it over 15 hours daily yielded better outcomes.

Background: The use of helmet treatment for positional plagiocephaly has increased recently; however, its effect is unknown in Korea.Purpose: This study aimed to investigate the effectiveness of helmet therapy and identify its influencing factors. Methods: Ninety pediatric patients diagnosed with moderate to severe positional plagiocephaly received helmet therapy. Severity of moderate to severe positional plagiocephaly was defined as cranial vault asymmetry (CVA) >10 mm or CVA index (CVAI) >6%. Patients were categorized by age, severity, and daily helmet wear. Multiple regression analysis controlled for factors like sex and prematurity. Treatment success was assessed by comparing pre/post-helmet theray CVA and CVAI, considering normalization or decrease to mild plagiocephaly (CVA ≤10 mm or CVAI ≤6%). Results: A total of 90 participants were enrolled (mean age, 5.6±1.6 months; male, 53 [58.9%]). The mean helmet therapy duration was 6.4±2.7 months, while the mean daily wear time was 18.4±2.7 hours. Among the 90 patients, 66 (73.3%) had moderate disease and 24 (26.7%) had severe disease. The mean CVA and CVAI decreased by 6.3±2.7 mm and 4.3%±1.8% after versus before treatment (P<0.001). Treatment was successful in 76 infants (84.4%). The most effective changes in CVA and CVAI were noted in those who began treatment before 9 months of age (6.2±2.5 mm and 5.0%±1.9%, P<0.001), had high compliance (6.2±2.4 mm and 4.9%±1.9%, P<0.001), and had high severity (8.0±2.3 mm and 6.6%±1.7%, P<0.001). Conclusion Starting helmet treatment before 9 months and wearing it over 15 hours daily yielded better outcomes.

Background: The use of helmet treatment for positional plagiocephaly has increased recently; however, its effect is unknown in Korea.Purpose: This study aimed to investigate the effectiveness of helmet therapy and identify its influencing factors. Methods: Ninety pediatric patients diagnosed with moderate to severe positional plagiocephaly received helmet therapy. Severity of moderate to severe positional plagiocephaly was defined as cranial vault asymmetry (CVA) >10 mm or CVA index (CVAI) >6%. Patients were categorized by age, severity, and daily helmet wear. Multiple regression analysis controlled for factors like sex and prematurity. Treatment success was assessed by comparing pre/post-helmet theray CVA and CVAI, considering normalization or decrease to mild plagiocephaly (CVA ≤10 mm or CVAI ≤6%). Results: A total of 90 participants were enrolled (mean age, 5.6±1.6 months; male, 53 [58.9%]). The mean helmet therapy duration was 6.4±2.7 months, while the mean daily wear time was 18.4±2.7 hours. Among the 90 patients, 66 (73.3%) had moderate disease and 24 (26.7%) had severe disease. The mean CVA and CVAI decreased by 6.3±2.7 mm and 4.3%±1.8% after versus before treatment (P<0.001). Treatment was successful in 76 infants (84.4%). The most effective changes in CVA and CVAI were noted in those who began treatment before 9 months of age (6.2±2.5 mm and 5.0%±1.9%, P<0.001), had high compliance (6.2±2.4 mm and 4.9%±1.9%, P<0.001), and had high severity (8.0±2.3 mm and 6.6%±1.7%, P<0.001). Conclusion Starting helmet treatment before 9 months and wearing it over 15 hours daily yielded better outcomes.

Background and Objectives: It is well recognized that early detection and intervention are most important for the prevention of neonatal hearing loss. The national support policy in Korea for newborn hearing screening has been changed since October 2018; however, parent awareness of the change still needs to be increased. This study investigated how well parents, who have underwent national infant checkups of their children, were aware of the Korean national policy for neonatal and infant hearing loss in parents.Subjects and Method A survey of neonatal hearing tests and national support policies conducted for 353 parents was analyzed. The survey included questions about parent awareness of the neonatal hearing screening test period, confirmatory test period, national support for these expenses, and hearing aid support. Also evaluated were the necessity of national guidance and management system for neonatal hearing. Results: The test time of neonatal hearing screening was correctly recognized by 82.2% of the parents. The percentage increased after the national insurance coverage started in October 2018 in Korea, and the rate was higher for parents who visited an otolaryngology clinic rather than a pediatric clinic. The test time of the confirmatory test was correctly recognized only by 20.4%. National support policy for neonatal hearing screening tests, confirmatory hearing tests, and hearing aids were acknowledged by 50.7%, 43.1%, and 56.1% of the parents, respectively. Conclusion These results indicate the necessity of efforts to increase the awareness of neonatal hearing tests and relevant support policies in Korea to ultimately achieve early hearing detection and intervention of neonates and infants in Korea.

Background: This study aimed to differentiate video nystagmography (VNG) characteristics, including the video head impulse test (vHIT), in patients with idiopathic rapid eye movement behavior disorder (RBD) from healthy controls, which is considered a precursor to degenerative diseases. Methods: One hundred eighty-five patients underwent overnight polysomnography (PSG) and VNG. Based on overnight PSG, 27 patients with RBD or REM sleep without atonia (RWA) and AHI<15 were categorized into the RBD group, 34 patients with RBD/RWA and AHI≥15 were grouped into the combined group. Sixty patients with AHI≥15 and no RBD/RWA were included in the obstructive sleep apnea (OSA) group, and 64 negative participants were assigned to the control group. In VNG, we measured the gain of vHIT in each canal, with the latency, amplitude, and velocity of horizontal saccades and smooth pursuit. We compared the results between groups using ANOVA, after normalization and adjustment for age and sex. Results: The gain of vHIT in the left horizontal canal was decreased in the RBD group, but it was more pronounced in the OSA group. Elevated gain of the left posterior canal was seen in the RBD group, but technical errors were attributable. The RBD group displayed prolonged latency of saccade on the left side and slowed saccade on the right side, but these were statistically insignificant. Conclusions The VNG study revealed differences between the sleep disorders, potentially reflecting brainstem function in each disorder. However, these differences lacked statistical significance. We anticipate that significant results could be obtained with more controlled conditions.