Purpose: This study aimed to evaluate the effects of an intervention combining warm therapy (via a thermoelectric-element tourniquet) and a distraction-based approach (via an augmented reality-based app known as TWINKLE) on pain, stress, and satisfaction during intravenous catheterization in adults. Methods: A randomized controlled trial was conducted in South Korea with 93 healthy adults who were randomly assigned to one of three groups: the experimental group (TWINKLE app with warm therapy), the comparison group (warm therapy only), and the control group (no treatment). Participants’ pain, stress, and satisfaction, as well as practitioner satisfaction, were measured after the intervention. Results: Pain scores differed significantly among the three groups (F = 5.68, p = .005), with the experimental group showing significantly lower scores than the control group (p = .003). Stress levels were also significantly lower in the experimental group than in the other groups (F = 9.42, p < .001). Participant satisfaction was highest in the experimental group (F = 17.65, p < .001), while nurse satisfaction was significantly higher in the comparison group than in the experimental and control groups (F = 67.91, p < .001), suggesting that the additional distraction intervention may have increased nurses’ workload. Conclusion Combining digital distraction with warm therapy using a thermoelectric-element tourniquet effectively reduces pain and stress while improving patient satisfaction during intravenous catheterization. Further research is needed to optimize this approach, with a particular focus on targeting digital distraction interventions to patients with higher levels of procedural anxiety and finding ways to minimize practitioner workload.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study aimed to evaluate the effects of an intervention combining warm therapy (via a thermoelectric-element tourniquet) and a distraction-based approach (via an augmented reality-based app known as TWINKLE) on pain, stress, and satisfaction during intravenous catheterization in adults. Methods: A randomized controlled trial was conducted in South Korea with 93 healthy adults who were randomly assigned to one of three groups: the experimental group (TWINKLE app with warm therapy), the comparison group (warm therapy only), and the control group (no treatment). Participants’ pain, stress, and satisfaction, as well as practitioner satisfaction, were measured after the intervention. Results: Pain scores differed significantly among the three groups (F = 5.68, p = .005), with the experimental group showing significantly lower scores than the control group (p = .003). Stress levels were also significantly lower in the experimental group than in the other groups (F = 9.42, p < .001). Participant satisfaction was highest in the experimental group (F = 17.65, p < .001), while nurse satisfaction was significantly higher in the comparison group than in the experimental and control groups (F = 67.91, p < .001), suggesting that the additional distraction intervention may have increased nurses’ workload. Conclusion Combining digital distraction with warm therapy using a thermoelectric-element tourniquet effectively reduces pain and stress while improving patient satisfaction during intravenous catheterization. Further research is needed to optimize this approach, with a particular focus on targeting digital distraction interventions to patients with higher levels of procedural anxiety and finding ways to minimize practitioner workload.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study aimed to evaluate the effects of an intervention combining warm therapy (via a thermoelectric-element tourniquet) and a distraction-based approach (via an augmented reality-based app known as TWINKLE) on pain, stress, and satisfaction during intravenous catheterization in adults. Methods: A randomized controlled trial was conducted in South Korea with 93 healthy adults who were randomly assigned to one of three groups: the experimental group (TWINKLE app with warm therapy), the comparison group (warm therapy only), and the control group (no treatment). Participants’ pain, stress, and satisfaction, as well as practitioner satisfaction, were measured after the intervention. Results: Pain scores differed significantly among the three groups (F = 5.68, p = .005), with the experimental group showing significantly lower scores than the control group (p = .003). Stress levels were also significantly lower in the experimental group than in the other groups (F = 9.42, p < .001). Participant satisfaction was highest in the experimental group (F = 17.65, p < .001), while nurse satisfaction was significantly higher in the comparison group than in the experimental and control groups (F = 67.91, p < .001), suggesting that the additional distraction intervention may have increased nurses’ workload. Conclusion Combining digital distraction with warm therapy using a thermoelectric-element tourniquet effectively reduces pain and stress while improving patient satisfaction during intravenous catheterization. Further research is needed to optimize this approach, with a particular focus on targeting digital distraction interventions to patients with higher levels of procedural anxiety and finding ways to minimize practitioner workload.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.