Purpose: This study examined the associations of self-determination (SDT), premenstrual syndrome (PMS), health-promoting lifestyle (HPL), and satisfaction-related factors with quality of life (QoL) among nursing students. Methods: This cross-sectional descriptive study included 206 nursing students engaged in clinical practice, recruited from nine nursing colleges in Jeollabuk-do, Korea. Data were collected between July 1 and August 19, 2024. Descriptive statistics, independent t-tests, one-way analysis of variance, Pearson correlation analysis, and multiple regression analysis were performed using SPSS version 26.0. SDT, PMS, HPL, satisfaction with economic status, and QoL were assessed using validated self-report questionnaires. Results: Factors significantly associated with QoL included HPL (β = .37, p < .001), SDT (β = .24, p < .001), PMS (β = −.21, p < .001), menstrual quantity (average vs. large) (β = .13, p = .028), satisfaction with nursing major (satisfied vs. dissatisfied) (β = −.11, p = .035), and satisfaction with economic status (satisfied vs. dissatisfied) (β = .16, p = .001). The overall regression model was statistically significant (F = 27.56, p < .001) and explained 61% of the variance in QoL. Conclusion From a biological nursing perspective, physiological factors such as PMS and menstrual quantity, together with HPL and SDT, were significant correlates of QoL among nursing students, alongside satisfaction with academic major and economic status. These findings suggest that nursing education programs should promote HPL and SDT while providing systematic support for PMS management and economic stress reduction to enhance students’ QoL.

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.

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: 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: 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.

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.

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.

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.