Delayed onset of neuropsychiatric symptoms after apparent recovery from acute carbon monoxide (CO) poisoning has been described as delayed neuropsychiatric sequelae (DNS). No previous study has determined whether early use of diffusion-weighted magnetic resonance imaging (DWI) can predict which patients will develop DNS in the acute CO poisoning. This retrospective observational study was performed on adult patients with acute CO poisoning consecutively treated over a 17-month period. All included patients with acute CO poisoning underwent DWI to evaluate brain injury within 72 h after CO exposure. DWI was evaluated as follows: (1) presence of pathology, (2) number of pathologies, (3) asymmetry, and (4) location of pathology. Patients were divided into two groups. The DNS group was composed of patients with delayed sequelae, while the non-DNS group included patients with no sequelae. A total of 102 patients with acute CO poisoning were finally enrolled in this study. DNS developed in 10 patients (9.8%). Between the DNS group and the non-DNS group, presence of pathology on DWI and initial Glasgow Coma Scale (GCS) showed significant difference. There was also a statistical difference between the non-DNS group and DNS group in terms of CO exposure time, troponin I, rhabdomyolysis, acute kidney injury, and pneumonia. The presence of pathology in DWI and initial GCS (cutoff: <12) at the emergency department served as an early predictors of DNS.

Digital health-based lifestyle interventions (e.g., mobile applications, short messaging service, wearable devices, social media, and interactive websites) are widely used to manage metabolic syndrome (MetS). This study aimed to confirm the utility of self-care for prevention or management of MetS. We recruited 106 participants with one or more MetS risk factors from December 2019 to September 2020. Participants were provided five healthcare devices and applications. Characteristics were compared at baseline and follow-up to examine changes in risk factors, engagement, persistence, and physical activity (analyzed through device use frequency and lifestyle interventions performed). Participants with 1–2 MetS risk factors showed statistically significant reductions in waist circumference (WC) and blood pressure (BP). Participants with ≥3 MetS risk factors showed statistically significant reductions in risk factors including weight, body mass index, WC, BP, and fasting blood sugar (FBS). The prevention and improvement groups used more healthcare devices than the other groups. Smartwatch was the most frequently used device (5 times/week), and physical activity logged more than 7000 steps/week. WC, BP, and FBS of the improvement group were reduced by more than 40%. Based on engagement, persistence, and physical activity, digital health-based lifestyle interventions could be helpful for MetS prevention and management.

Background: Research on whether wearable devices and app-based interventions can effectively prevent metabolic syndrome (MetS) by increasing physical activity (PA) among middle-aged people living in the rural areas of South Korea remains insufficient. Objective: The aim of this study was to determine whether mobile and wearable device interventions can improve health indicators, including PA, in MetS risk groups in rural South Korea. Methods: In this clinical trial, performed from December 2019 to June 2020, participants were asked to use a wearable device (GalaxyWatch Active1) alone (standard intervention) or the wearable device and mobile app (Yonsei Health Korea) (enhanced intervention). Clinical measures and International Physical Activity Questionnaire (IPAQ) scores were evaluated initially and after 6 months. The number of steps was monitored through the website. The primary outcome was the difference in PA and clinical measures between the enhanced intervention and standard intervention groups. The secondary outcome was the decrease in MetS factors related to the change in PA. Results: A total of 267 participants were randomly selected, 221 of whom completed the 6-month study. Among the 221 participants, 113 were allocated to the enhanced intervention group and 108 were allocated to the standard intervention group. After 6 months, the body weight and BMI for the enhanced intervention group decreased by 0.6 (SD 1.87) and 0.21 (SD 0.76), respectively (P<.001). In both groups, systolic blood pressure, diastolic blood pressure, waist circumference, and glycated hemoglobin A1c (HbA1c) decreased (P<.001). The total PA was approximately 2.8 times lower in the standard intervention group (mean 44.47, SD 224.85) than in the enhanced intervention group (mean 124.36, SD 570.0). Moreover, the enhanced intervention group achieved the recommended level of moderate to vigorous physical activity (MVPA), whereas the standard intervention group did not (188 minutes/week vs 118 minutes/week). Additionally, the number of participants in the enhanced intervention group (n=113) that reached 10,000 daily steps or more after the intervention increased from 9 (8.0%) to 26 (23.1%) (P=.002), whereas this number did not increase significantly in the standard intervention group (n=108), from 8 (7.4%) to 16 (14.8%) (P=.72). The number of participants without any MetS factors increased by 12 (11%) and 8 (7%) in the enhanced and standard intervention group, respectively. Conclusions: PA monitoring and an intervention using wearable devices were effective in preventing MetS in a rural population in Korea. Blood pressure, waist circumference, and HbA1c were improved in both intervention groups, which were effective in reducing MetS factors. However, only the participants in the enhanced intervention group continuously increased their MVPA and step counts above the recommended level to prevent MetS. Body weight and BMI were further improved, and a higher number of participants with zero MetS factors was attained from the enhanced intervention. Trial Registration: Clinical Research Information Service KCT0005783; https://cris.nih.go.kr/cris/search/detailSearch.do/16123

Lee DK, et al. Yonsei Med J. 2016 Jan;57(1):254-259. https://doi.org/10.3349/ymj.2016.57.1.254

Ankle fractures are common and, compared to other injuries, tend to be overlooked in the emergency department. We aim to develop a deep learning algorithm that can detect not only definite fractures but also obscure fractures. We collected the data of 1226 patients with suspected ankle fractures and performed both X-rays and CT scans. With anteroposterior (AP) and lateral ankle X-rays of 1040 patients with fractures and 186 normal patients, we developed a deep learning model. The training, validation, and test datasets were split in a 3/1/1 ratio. Data augmentation and under-sampling techniques were administered as part of the preprocessing. The Inception V3 model was utilized for the image classification. Performance of the model was validated using a confusion matrix and the area under the receiver operating characteristic curve (AUC-ROC). For the AP and lateral trials, the best accuracy and AUC values were 83%/0.91 in AP and 90%/0.95 in lateral. Additionally, the mean accuracy and AUC values were 83%/0.89 for the AP trials and 83%/0.9 for the lateral trials. The reliable dataset resulted in the CNN model providing higher accuracy than in past studies.

Lee KH, et al. Yonsei Med J. 2022 Jan;63(Suppl):S84-S92. https://doi.org/10.3349/ymj.2022.63.S84