Background: This study aimed to compare the effects of applying a vibrating foam roller before resistance exercise versus after resistance exercise on changes in serum muscle damage markers, muscle stiffness, and range of motion. This study also aimed to provide foundational data for optimizing the timing of vibrating foam roller application to enhance recovery after resistance exercise in practical settings. Methods: Twelve healthy adult males were recruited as participants. Each participant was subjected to three interventions in a random order with a washout period of at least 5 days: vibration foam rolling before resistance exercise, vibration foam rolling after resistance exercise, and resistance exercise without vibration foam rolling. Blood creatine kinase, knee flexion range of motion, and muscle stiffness were measured before, immediately after, 24 hours after, and 48 hours after exercise and foam rolling protocols. Results: Creatine kinase levels in vibration foam rolling after resistance exercise were significantly lower than those in vibration foam rolling before resistance exercise at 24 and 48 hours post-exercise. Muscle stiffness was significantly lower immediately and 24 hours post-exercise in vibration foam rolling after resistance exercise than in vibration foam rolling before resistance exercise and resistance exercise without vibration foam rolling. Knee flexion range of motion was significantly lower in resistance exercise without vibration foam rolling than in vibration foam rolling after resistance exercise at 24 and 48 hours post-exercise. Conclusions The application of vibration foam rolling after resistance exercise was more effective than that before exercise in decreasing muscle damage markers, reducing muscle stiffness, and improving the range of motion.

Purpose: This study examines the effectiveness of personalized goal setting and smartphonebased nutrition counseling among adults in their 20s and 30s. Methods: Nutrition counseling was conducted for a total of 30 adults through a 1:1 chat room of a mobile instant messenger, once a week for 8 weeks. The first week of counseling included a preliminary online questionnaire survey and a dietary intake survey. Based on the results of the preliminary survey, 2 dietary goals were set in the second week and the participants were asked to record their achievements on a daily checklist. From the third week onwards, counselors sent feedback messages based on the checklist and provided information on dietary guidelines in a card news format every week. Post-counseling questionnaires and dietary intake surveys were conducted in the seventh week. Changes in dietary habits during the counseling were reviewed in the eighth week, followed by a questionnaire survey on the evaluation of the counseling process. Results: The nutrition quotient (NQ) scores and self-efficacy scores were significantly higher after nutrition counseling. The NQ scores of consumption frequencies of fruits, milk and dairy products, nuts, fast food, Ramyeon, sweet and greasy baked products, sugarsweetened beverages, the number of vegetable dishes at meals, and breakfast frequency were significantly higher after nutrition counseling. The intake of protein, vitamin A, thiamin, riboflavin, folate, calcium, and iron, and the index of nutritional quality of vitamin A, riboflavin, folate, calcium, and iron were higher after nutrition education. The participants were satisfied with the nutrition counseling program and the provided nutrition information. Conclusion Personalized goal setting and smartphone-based nutrition counseling were found to be effective in improving the quality of diet and self-efficacy in young adults. Similar results were obtained in both the underweightormal weight and the overweight/obese groups.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.