Objectives: The aim of this study was to assess the impact of a multi-component program, including partially hydrolyzed guar gum (PHGG, Sunfiber®) supplementation, on glycemic control, gut health, and nutritional status to support diabetes prevention and management among Korean adults. Methods: A single-arm trial was conducted with 29 adults (aged 20-55 years) with fasting plasma glucose (FPG) ≥ 100 mg/dL. Over a six-week period, participants engaged in a multi-component program that incorporated the supplementation of PHGG (Sunfiber®, 12.5 g/day), weekly nutritional coaching, and the use of continuous glucose monitoring devices. The program’s effectiveness was evaluated by measuring FPG and glycated hemoglobin (HbA1c) levels through blood tests conducted before and after the intervention. Improvements in gut health were gauged using the Korean Gut Quotient Measurement Scales, while enhancements in nutritional status were assessed using the Nutrition Quotient (NQ) and surveys that evaluated improvements in gut health and nutritional status. Results: Participants’ average age was 43.89 years, with approximately 80% being male. Most participants (about 75%) were classified as overweight or obese. After six-weeks, 17 participants who adhered closely to the program (meeting certification criteria) exhibited significant reductions in key blood glucose markers. FPG levels decreased from 113.06 ± 23.16 mg/dL to 106.24 ± 16.33 mg/dL (P < 0.05), and HbA1c levels decreased from 6.08% ± 0.81% to 5.87% ± 0.53% (P < 0.05). The NQ evaluation revealed significant increases in comprehensive nutrition scores, and in the balance and practice domain scores for all participants (P < 0.05). Furthermore, in the gut health survey, approximately 82.1% of all participants reported experiencing positive changes. Conclusion Among adults with elevated FPG levels, a multi-component intervention program that included PHGG (Sunfiber®) supplementation, structured dietary management, and the use of health-monitoring devices showed significant benefits in improving glycemic control, overall nutritional status, and gut health.Trial Registration: Clinical Research Information Service Identifier: KCT0010049.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: Neonatal nurses are expected to have clinical competency to provide qualified and safe care for high-risk infants. An educational intervention to enhance nurses’ clinical competence is often a priority in the nursing field. This study was conducted to explore nurses’ perceived importance and performance confidence of nursing care activities in neonatal intensive care units. Methods: One hundred forty-one neonatal nurses from seven hospitals across South Korea participated in the online survey study. The scale of neonatal nursing care activity consisted of 8 subdomains including professional practice (assessment, diagnosis, planning, intervention, evaluation, education, research, and leadership). The Importance-Performance Matrix was used to analyze the importance of and confident performance in each of the nursing subdomains. Results: Both importance and performance confidence increased as nurses’ age (p=.042 and p<.001) and clinical experience (p=.004 and p<.001). Participants scored relatively higher in importance and performance confidence in the professional practice subdomains (assessment, intervention, evaluation), but scored lower in the education and research subdomains. Conclusion To provide evidence-based nursing care for high-risk infants in neonatal intensive care units, educational interventions should be developed to support nurses based on the findings of the research.

PURPOSE: The aim of the study is to evaluate the current and prospective status of nursing in Korea and develop a strategic framework and plan to accommodate the increased demands on nurses in the changing health-care system. METHODS: This study used a mixed-methods approach including a literature review, an online survey with health-care consumers, expert panel interviews, and an analysis of strengths, weaknesses, opportunities, and threats to develop the strategic plans and framework. RESULTS: The vision of the strategic framework involved improving health and quality of life, and its mission was to elevate the status of Korea's nursing sector as a key health-care profession through high-quality and innovative nursing education, research, and practice. The five values in accordance with the mission and vision were innovation, creation, collaboration, excellence, and authenticity. Three strategic goals, namely, education, research, and practice, were identified, and 31 related strategic tasks were developed. CONCLUSION: In response to the rising social demand for a paradigm shift in nursing care services, there is a need for advancements in nursing education, research, and practice in Korea. This study provide some recommendations to achieve these aims.
Cooperative Behavior ; Education ; Education, Nursing ; Health Policy ; Korea ; Nurse's Role ; Nursing Care ; Nursing ; Prospective Studies ; Quality of Life

PURPOSE: This study compared the pain-relieving effects of human milk, sucrose, and distilled water during examinations for retinopathy of prematurity. METHODS: Forty-five preterm infants were randomly assigned to receive a pacifier dipped in one of three solutions: human milk (n=14), 24% sucrose (n=15), or distilled water (n=16), 2 minutes before an eye examination. Their pain score, pulse rate, and oxygen saturation were measured at three time points: 5 minutes before the examination, 30 seconds after speculum introduction, and 2 minutes after the examination. RESULTS: The infants' mean gestational age and weight at birth were 33.1±2.1 weeks and 1,842±470 g, respectively. There were no between-group differences in pain relief during the eye examination. The pain score significantly increased both during (p<.001) and after the examinations (p=.003). Oxygen saturation decreased during the examinations (p<.001); however, the infants in the 24% sucrose group showed higher oxygen saturation (p=.047) during the examinations than the infants in the other groups. CONCLUSION: Sucking on a pacifier dipped in human milk or 24% sucrose did not reduce the pain associated with eye examinations in preterm infants. Pacifiers dipped in sucrose can be used to maintain better oxygen saturation during these examinations.
Analgesia ; Gestational Age ; Heart Rate ; Humans ; Infant ; Infant, Newborn ; Infant, Premature ; Milk, Human ; Oxygen ; Pacifiers ; Parturition ; Retinopathy of Prematurity ; Sucrose ; Surgical Instruments ; Water

BACKGROUND: Medical device adverse event reporting is an essential activity for mitigating device-related risks. Reporting of adverse events can be done by anyone like healthcare workers, patients, and others. However, for an individual to determine the reporting, he or she should recognize the current situation as an adverse event. The objective of this report is to share observed individual differences in the perception of a medical device adverse event, which may affect the judgment and the reporting of adverse events. METHODS: We trained twenty-three participants from twelve Asia-Pacific Economic Cooperation (APEC) member economies about international guidelines for medical device vigilance. We developed and used six virtual cases and six questions. We divided participants into six groups and compared their opinions. We also surveyed the country's opinion to investigate the beginning point of ‘patient use’. The phases of ‘patient use’ are divided into: 1) inspecting, 2) preparing, and 3) applying medical device. RESULTS: As for the question on the beginning point of ‘patient use,’ 28.6%, 35.7%, and 35.7% of participants provided answers regarding the first, second, and third phases, respectively. In training for applying international guidelines to virtual cases, only one of the six questions reached a consensus between the two groups in all six virtual cases. For the other five questions, different judgments were given in at least two groups. CONCLUSION: From training courses using virtual cases, we found that there was no consensus on ‘patient use’ point of view of medical devices. There was a significant difference in applying definitions of adverse events written in guidelines regarding the medical device associated incidents. Our results point out that international harmonization effort is needed not only to harmonize differences in regulations between countries but also to overcome diversity in perspectives existing at the site of medical device use.
Consensus ; Delivery of Health Care ; Education ; Humans ; Individuality ; Judgment ; Social Control, Formal