Purpose: This secondary data analysis study examined adults’ levels and networks of obesity-related health behaviors according to the life cycle stage. Methods: Participants included 5,203 adults aged 19–79 years who participated in the third year of the eighth Korea National Health and Nutrition Examination Survey (2021). Life cycle stages were divided into young, middle-aged, and older adult groups. Obesity status was classified based on a body mass index of 25 kg/m2. Selected obesity-related health behaviors included alcohol abstinence, not smoking, proper sleep, eating breakfast, fruit intake, vegetable intake, not eating out, aerobic physical activity, walking, and weight training. Obesity-related health behavior networks were analyzed for density, inclusiveness, degree, and degree/closeness/betweenness centrality using social network analysis. Results: Participants’ obesity rate was 37.6%, with the highest rate observed in the older adult group (39.2%). In all life cycle stages, the non-obese group had a higher density and average degree in the obesity-related health behavior network than the obese group. The young adult group showed higher centrality for vegetable intake, not smoking, alcohol abstinence, and proper sleep. The middle-aged group generally had higher centrality for health behaviors, whereas the older adult group had lower overall centrality for health behaviors, especially proper sleep and physical activity-related behaviors. Conclusion There were differences in the levels and network structures of obesity-related health behaviors according to the life cycle stage, indicating a need for differentiated obesity-management strategies according to the life cycle stage.

Purpose: This secondary data analysis study examined adults’ levels and networks of obesity-related health behaviors according to the life cycle stage. Methods: Participants included 5,203 adults aged 19–79 years who participated in the third year of the eighth Korea National Health and Nutrition Examination Survey (2021). Life cycle stages were divided into young, middle-aged, and older adult groups. Obesity status was classified based on a body mass index of 25 kg/m2. Selected obesity-related health behaviors included alcohol abstinence, not smoking, proper sleep, eating breakfast, fruit intake, vegetable intake, not eating out, aerobic physical activity, walking, and weight training. Obesity-related health behavior networks were analyzed for density, inclusiveness, degree, and degree/closeness/betweenness centrality using social network analysis. Results: Participants’ obesity rate was 37.6%, with the highest rate observed in the older adult group (39.2%). In all life cycle stages, the non-obese group had a higher density and average degree in the obesity-related health behavior network than the obese group. The young adult group showed higher centrality for vegetable intake, not smoking, alcohol abstinence, and proper sleep. The middle-aged group generally had higher centrality for health behaviors, whereas the older adult group had lower overall centrality for health behaviors, especially proper sleep and physical activity-related behaviors. Conclusion There were differences in the levels and network structures of obesity-related health behaviors according to the life cycle stage, indicating a need for differentiated obesity-management strategies according to the life cycle stage.

Purpose: This secondary data analysis study examined adults’ levels and networks of obesity-related health behaviors according to the life cycle stage. Methods: Participants included 5,203 adults aged 19–79 years who participated in the third year of the eighth Korea National Health and Nutrition Examination Survey (2021). Life cycle stages were divided into young, middle-aged, and older adult groups. Obesity status was classified based on a body mass index of 25 kg/m2. Selected obesity-related health behaviors included alcohol abstinence, not smoking, proper sleep, eating breakfast, fruit intake, vegetable intake, not eating out, aerobic physical activity, walking, and weight training. Obesity-related health behavior networks were analyzed for density, inclusiveness, degree, and degree/closeness/betweenness centrality using social network analysis. Results: Participants’ obesity rate was 37.6%, with the highest rate observed in the older adult group (39.2%). In all life cycle stages, the non-obese group had a higher density and average degree in the obesity-related health behavior network than the obese group. The young adult group showed higher centrality for vegetable intake, not smoking, alcohol abstinence, and proper sleep. The middle-aged group generally had higher centrality for health behaviors, whereas the older adult group had lower overall centrality for health behaviors, especially proper sleep and physical activity-related behaviors. Conclusion There were differences in the levels and network structures of obesity-related health behaviors according to the life cycle stage, indicating a need for differentiated obesity-management strategies according to the life cycle stage.

Purpose: This secondary data analysis study examined adults’ levels and networks of obesity-related health behaviors according to the life cycle stage. Methods: Participants included 5,203 adults aged 19–79 years who participated in the third year of the eighth Korea National Health and Nutrition Examination Survey (2021). Life cycle stages were divided into young, middle-aged, and older adult groups. Obesity status was classified based on a body mass index of 25 kg/m2. Selected obesity-related health behaviors included alcohol abstinence, not smoking, proper sleep, eating breakfast, fruit intake, vegetable intake, not eating out, aerobic physical activity, walking, and weight training. Obesity-related health behavior networks were analyzed for density, inclusiveness, degree, and degree/closeness/betweenness centrality using social network analysis. Results: Participants’ obesity rate was 37.6%, with the highest rate observed in the older adult group (39.2%). In all life cycle stages, the non-obese group had a higher density and average degree in the obesity-related health behavior network than the obese group. The young adult group showed higher centrality for vegetable intake, not smoking, alcohol abstinence, and proper sleep. The middle-aged group generally had higher centrality for health behaviors, whereas the older adult group had lower overall centrality for health behaviors, especially proper sleep and physical activity-related behaviors. Conclusion There were differences in the levels and network structures of obesity-related health behaviors according to the life cycle stage, indicating a need for differentiated obesity-management strategies according to the life cycle stage.

Purpose: This secondary data analysis study examined adults’ levels and networks of obesity-related health behaviors according to the life cycle stage. Methods: Participants included 5,203 adults aged 19–79 years who participated in the third year of the eighth Korea National Health and Nutrition Examination Survey (2021). Life cycle stages were divided into young, middle-aged, and older adult groups. Obesity status was classified based on a body mass index of 25 kg/m2. Selected obesity-related health behaviors included alcohol abstinence, not smoking, proper sleep, eating breakfast, fruit intake, vegetable intake, not eating out, aerobic physical activity, walking, and weight training. Obesity-related health behavior networks were analyzed for density, inclusiveness, degree, and degree/closeness/betweenness centrality using social network analysis. Results: Participants’ obesity rate was 37.6%, with the highest rate observed in the older adult group (39.2%). In all life cycle stages, the non-obese group had a higher density and average degree in the obesity-related health behavior network than the obese group. The young adult group showed higher centrality for vegetable intake, not smoking, alcohol abstinence, and proper sleep. The middle-aged group generally had higher centrality for health behaviors, whereas the older adult group had lower overall centrality for health behaviors, especially proper sleep and physical activity-related behaviors. Conclusion There were differences in the levels and network structures of obesity-related health behaviors according to the life cycle stage, indicating a need for differentiated obesity-management strategies according to the life cycle stage.

Purpose: Awareness of environmental control is considered a major influence on the performance of asthma self-management behaviors that are involved in maintaining effective control of asthma. The aim of this study was to investigate whether immersive virtual reality (VR) education is effective in environmental control education for asthmatic children. Methods: Thirty asthmatic children aged 9 to 13 years with aeroallergen sensitization were enrolled. Environmental control education for asthmatic subjects were performed using either immersive VR (VR group) or conventional leaflets provided by asthma specialists (control group). Five questionnaires, such as awareness of environmental control, memory, assessment of intent to act, satisfaction test, and asthma control test (ACT) questionnaires were used for estimating the effects of education. Results: Awareness of environmental control, memory, and intent to act scores were significantly increased after education in both groups and the scores were maintained high until 4 weeks after education. In both group, ACT scores were maintained high scores before and 4 weeks after education. Satisfaction scores were very high in the VR group. Conclusion The increased scores in awareness of environmental control and intent to act indicate that the environmental control education using VR is worthy of attention as an effective educational tool for asthma management. Application of further developed techniques, including active environmental intervention by participants in VR, could be applied to effective asthma management.

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-Seq) is a powerful technology to profile the location of proteins of interest on a whole-genome scale. To identify the enrichment location of proteins, many programs and algorithms have been proposed. However, none of the commonly used peak calling programs could accurately explain the binding features of target proteins detected by ChIP-Seq. Here, publicly available data on 12 histone modifications, including H3K4ac/me1/me2/me3, H3K9ac/me3, H3K27ac/me3, H3K36me3, H3K56ac, and H3K79me1/me2, generated from a human embryonic stem cell line (H1), were profiled with five peak callers (CisGenome, MACS1, MACS2, PeakSeq, and SISSRs). The performance of the peak calling programs was compared in terms of reproducibility between replicates, examination of enriched regions to variable sequencing depths, the specificity-to-noise signal, and sensitivity of peak prediction. There were no major differences among peak callers when analyzing point source histone modifications. The peak calling results from histone modifications with low fidelity, such as H3K4ac, H3K56ac, and H3K79me1/me2, showed low performance in all parameters, which indicates that their peak positions might not be located accurately. Our comparative results could provide a helpful guide to choose a suitable peak calling program for specific histone modifications.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-Seq) is a powerful technology to profile the location of proteins of interest on a whole-genome scale. To identify the enrichment location of proteins, many programs and algorithms have been proposed. However, none of the commonly used peak calling programs could accurately explain the binding features of target proteins detected by ChIP-Seq. Here, publicly available data on 12 histone modifications, including H3K4ac/me1/me2/me3, H3K9ac/me3, H3K27ac/me3, H3K36me3, H3K56ac, and H3K79me1/me2, generated from a human embryonic stem cell line (H1), were profiled with five peak callers (CisGenome, MACS1, MACS2, PeakSeq, and SISSRs). The performance of the peak calling programs was compared in terms of reproducibility between replicates, examination of enriched regions to variable sequencing depths, the specificity-to-noise signal, and sensitivity of peak prediction. There were no major differences among peak callers when analyzing point source histone modifications. The peak calling results from histone modifications with low fidelity, such as H3K4ac, H3K56ac, and H3K79me1/me2, showed low performance in all parameters, which indicates that their peak positions might not be located accurately. Our comparative results could provide a helpful guide to choose a suitable peak calling program for specific histone modifications.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.