Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Gestational diabetes mellitus (GDM) poses significant health risks to both mothers and newborns, requiring rigorous self-management and frequent medical consultations. Advances in information and communications technology (ICT) have shown promising results in reducing the number of in-person visits for GDM management. ICT enhances patient self-care engagement, with some studies reporting reductions in average blood glucose and HbA1c levels. ICT for GDM management has demonstrated benefits such as fewer in-person visits, improved adherence to self-monitoring of blood glucose, increased global user satisfaction, and maintenance of blood glucose control and perinatal outcomes. Common barriers to ICT for GDM include technological literacy, inadequate education, limited technical support, the additional burden of non-customized applications, and restricted interoperability. Further research is needed on the impact of technology on GDM management to optimize digital health solutions.