Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

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.

Although many wearable devices are used to assess sleep, their accuracy remains controversial. This study aimed to investigate the accuracy of the Actiwatch, a research-grade device, and the Fitbit, a consumer-grade device, against sleep diaries to assess sleep patterns. Methods: Twenty participants wore Fitbit and Actiwatch for two weeks and tracked their sleep patterns using sleep diaries. Total sleep time (TST), time-in-bed (TIB), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) from the two devices and sleep diaries were analyzed using analysis of variance and Bland-Altman analysis. Results: The TIB measured by the sleep log, Fitbit, and Actiwatch were 420.9 minutes, 417.3 minutes, and 567.4 minutes, respectively. Compared to the sleep log, the Fitbit underestimated TST, TIB, and SE, with significant differences observed for TST (p<0.001) and SE (p<0.001), but not for TIB. The Actiwatch overestimated TIB (p<0.001) and TST (p=0.02) and underestimated SE (p<0.001) compared to the sleep log. The difference between the Fitbit and Actiwatch was significant for TST, TIB, and SE (all p<0.001). Conclusions: The Fitbit showed a smaller difference than the Actiwatch when compared with the sleep logs. The Fitbit could be used as a tool to assess sleep patterns in the clinic as well as in daily life.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

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.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

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.

Although many wearable devices are used to assess sleep, their accuracy remains controversial. This study aimed to investigate the accuracy of the Actiwatch, a research-grade device, and the Fitbit, a consumer-grade device, against sleep diaries to assess sleep patterns. Methods: Twenty participants wore Fitbit and Actiwatch for two weeks and tracked their sleep patterns using sleep diaries. Total sleep time (TST), time-in-bed (TIB), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) from the two devices and sleep diaries were analyzed using analysis of variance and Bland-Altman analysis. Results: The TIB measured by the sleep log, Fitbit, and Actiwatch were 420.9 minutes, 417.3 minutes, and 567.4 minutes, respectively. Compared to the sleep log, the Fitbit underestimated TST, TIB, and SE, with significant differences observed for TST (p<0.001) and SE (p<0.001), but not for TIB. The Actiwatch overestimated TIB (p<0.001) and TST (p=0.02) and underestimated SE (p<0.001) compared to the sleep log. The difference between the Fitbit and Actiwatch was significant for TST, TIB, and SE (all p<0.001). Conclusions: The Fitbit showed a smaller difference than the Actiwatch when compared with the sleep logs. The Fitbit could be used as a tool to assess sleep patterns in the clinic as well as in daily life.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

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.