Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has led to a decrease in face-to-face classes worldwide, affecting the mental health of children and their parents. The global pandemic has increased children’s overall use of electronic media. This study analyzed the effect of children’s screen time on problematic behaviors during the COVID-19 pandemic. Methods: A total of 186 parents from Suwon, South Korea, were recruited to participate in an online survey. The mean age of the children was 10.14 years old, and 44.1% were females. The questionnaire included questions on children’s screen time, problematic behaviors, and parental stress. Children’s behavioral problems were evaluated using the Behavior Problem Index, whereas the Parental Stress Scale was used to estimate parental stress. Results: The mean smartphone usage frequency of the children was 5.35 days per week, and the mean smartphone screen time was 3.52 hours per day. Smartphone screen time (Z=4.49, p<0.001) and usage frequency (Z=2.75, p=0.006) were significantly correlated with children’s behavioral problem scores. The indirect effect of parental stress on this relationship was also statistically significant (p=0.049, p=0.045, respectively). Conclusion This study suggests that children’s smartphone screen time has affected problematic behaviors during the COVID-19 pandemic. Furthermore, parental stress is related to the relationship between children’s screen time and problematic behaviors.

Background: Postoperative pain is affected by preoperative depression. If the risk of postoperative pain associated with depression can be predicted preoperatively, anesthesiologists and/or surgeons can better manage it with personalized care. The objective of this study was to determine the efficacy of Patient Health Questionnaire-2 (PHQ-2) depression screening tool as a predictor of postoperative pain. Methods: A total of 50 patients scheduled for elective laparoscopic cholecystectomy with an American Society of Anesthesiologists grade of 1 or 2 were enrolled. They answered the PHQ-2, which consists of two questions, under the supervision of a researcher on the day before the surgery. The numerical rating scale (NRS) scores were assessed at post-anesthesia care unit (PACU), at 24, and 48 postoperative hours, and the amount of intravenous patient-controlled analgesia (IV-PCA) administered was documented at 24, 48, and 72 postoperative hours. At 72 h, the IV-PCA device was removed and the final dosage was recorded. Results: The NRS score in PACU was not significantly associated with the PHQ-2 score (correlation coefficients: 0.13 [P = 0.367]). However, the use of analgesics after surgery was higher in patients with PHQ-2 score of 3 or more (correlation coefficients: 0.33 [P = 0.018]). Conclusions We observed a correlation between the PHQ-2 score and postoperative pain. Therefore, PHQ-2 could be useful as a screening test for preoperative depression. Particularly, when 3 points were used as the cut-off score, the PHQ-2 score was associated with the dosage of analgesics, and the analgesic demand could be expected to be high with higher PHQ-2 scores.

BACKGROUND/AIMS: The optimal strategy for anticoagulation treatment in patients with atrial fibrillation (AF) and end-stage renal disease (ESRD) has not been established. We evaluated the efficacy and bleeding risk of warfarin and antiplatelet agents in patients with AF and ESRD. METHODS: We retrospectively reviewed the medical records of 256 patients with AF and ESRD and included 158 patients (age, 63.7 ± 12.2 years; male sex, n = 103) with a CHA2DS2-VASc score ≥ 1 who were taking warfarin (n = 53) or an antiplatelet agent (n = 105). RESULTS: During the follow-up period (31.0 ± 29.4 months), 10 ischemic events and 29 major bleeding events occurred. The thromboembolic event rate did not significantly differ between the warfarin and antiplatelet groups (1.9% and 8.6%, respectively; p = 0.166). However, the rate of major bleeding events was significantly higher in the warfarin group than it was in the antiplatelet group (32.1% and 11.4%, respectively; p = 0.002). Cox's regression analysis indicated that warfarin was related to an increased risk of major bleeding events (hazard ratio [HR], 3.44; 95% confidence interval [CI], 1.60–7.36; p = 0.001). Conversely, warfarin was not related to a decreased risk of thromboembolic events (HR, 0.34; 95% CI, 0.04–2.70; p = 0.306). CONCLUSIONS: In patients with AF and ESRD, warfarin use was associated with an increased risk of bleeding events, compared with antiplatelet agents.
Anticoagulants ; Atrial Fibrillation ; Follow-Up Studies ; Hemorrhage ; Humans ; Kidney Failure, Chronic ; Male ; Medical Records ; Platelet Aggregation Inhibitors ; Retrospective Studies ; Warfarin

BACKGROUND/AIMS: The optimal strategy for anticoagulation treatment in patients with atrial fibrillation (AF) and end-stage renal disease (ESRD) has not been established. We evaluated the efficacy and bleeding risk of warfarin and antiplatelet agents in patients with AF and ESRD. METHODS: We retrospectively reviewed the medical records of 256 patients with AF and ESRD and included 158 patients (age, 63.7 ± 12.2 years; male sex, n = 103) with a CHA2DS2-VASc score ≥ 1 who were taking warfarin (n = 53) or an antiplatelet agent (n = 105). RESULTS: During the follow-up period (31.0 ± 29.4 months), 10 ischemic events and 29 major bleeding events occurred. The thromboembolic event rate did not significantly differ between the warfarin and antiplatelet groups (1.9% and 8.6%, respectively; p = 0.166). However, the rate of major bleeding events was significantly higher in the warfarin group than it was in the antiplatelet group (32.1% and 11.4%, respectively; p = 0.002). Cox's regression analysis indicated that warfarin was related to an increased risk of major bleeding events (hazard ratio [HR], 3.44; 95% confidence interval [CI], 1.60–7.36; p = 0.001). Conversely, warfarin was not related to a decreased risk of thromboembolic events (HR, 0.34; 95% CI, 0.04–2.70; p = 0.306). CONCLUSIONS In patients with AF and ESRD, warfarin use was associated with an increased risk of bleeding events, compared with antiplatelet agents.

BACKGROUND: Sedation by dexmedetomidine, like natural sleep, often causes bradycardia. We explored the nature of heart rate (HR) changes as they occur during natural sleep versus those occurring during dexmedetomidine sedation. METHODS: The present study included 30 patients who were scheduled to undergo elective surgery with spinal anesthesia. To assess HR and sedation, a pulse oximeter and bispectral index (BIS) monitor were attached to the patient in the ward and the operating room. After measuring HR and BIS at baseline, as the patients slept and once their BIS was below 70, HR and BIS were measured at 5-minute intervals during sleep. Baseline HR and BIS were also recorded before spinal anesthesia measured at 5-minute intervals after dexmedetomidine injection. RESULTS: During natural sleep, HR changes ranged from 2 to 19 beats/min (13.4 ± 4.4 beats/min), while in dexmedetomidine sedation, HR ranged from 9 to 40 beats/min (25.4 ± 8.5 beats/min). Decrease in HR was significantly correlated between natural sleep and dexmedetomidine sedation (R2 = 0.41, P < 0.001). The lowest HR was reached in 66 min during natural sleep (59 beats/min) and in 13 min with dexmedetomidine sedation (55 beats/min). The time to reach minimum HR was significantly different (P < 0.001), but there was no difference in the lowest HR obtained (P = 0.09). CONCLUSIONS: There was a correlation between the change in HR during natural sleep and dexmedetomidine sedation. The bradycardia that occurs when using dexmedetomidine may be a normal physiologic change, that can be monitored rather than corrected.
Anesthesia, Spinal ; Bradycardia ; Dexmedetomidine ; Heart Rate ; Heart ; Humans ; Hypnotics and Sedatives ; Operating Rooms