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: The purpose of this study was to identify the characteristics of and risk factors for medical-device-related pressure ulcer (MDRPU) development in intensive care units. METHODS: A prospective cohort study design was used, and the participants were 253 adult patients who had stayed in medical and surgical intensive care units. Data were collected regarding the application of medical devices and MDRPU-related characteristics over a period of six months from June to November, 2017. Data were analyzed using independent t-test, χ2-test, Fisher's exact test, and binary logistic regression analysis with the SPSS 21.0 program. RESULTS: Among the 253 participants, MDRPUs occurred in 51 (19.8%) participants. The results of the logistic regression analysis showed that the risk factors for MDRPUs were the use of endotracheal tubes (OR=5.79, 95% CI: 1.66~20.20), having had surgery (OR=2.95, 95% CI: 1.11~7.77), being in a semi-coma/coma (OR=5.79, 95% CI: 1.04~32.05), and sedation (OR=5.54, 95% CI: 1.39~22.19). CONCLUSION On the basis of the study results, it is effectively facilitated by nurses when they care for patients with MDRPUs in intensive care units and the results are expected to be of help in preventive education for MDRPU development as well as preparing the base data for intervention studies.

PURPOSE: The purpose of this study was to identify the characteristics of and risk factors for medical-device-related pressure ulcer (MDRPU) development in intensive care units. METHODS: A prospective cohort study design was used, and the participants were 253 adult patients who had stayed in medical and surgical intensive care units. Data were collected regarding the application of medical devices and MDRPU-related characteristics over a period of six months from June to November, 2017. Data were analyzed using independent t-test, χ2-test, Fisher's exact test, and binary logistic regression analysis with the SPSS 21.0 program. RESULTS: Among the 253 participants, MDRPUs occurred in 51 (19.8%) participants. The results of the logistic regression analysis showed that the risk factors for MDRPUs were the use of endotracheal tubes (OR=5.79, 95% CI: 1.66~20.20), having had surgery (OR=2.95, 95% CI: 1.11~7.77), being in a semi-coma/coma (OR=5.79, 95% CI: 1.04~32.05), and sedation (OR=5.54, 95% CI: 1.39~22.19). CONCLUSION: On the basis of the study results, it is effectively facilitated by nurses when they care for patients with MDRPUs in intensive care units and the results are expected to be of help in preventive education for MDRPU development as well as preparing the base data for intervention studies.
Adult ; Cohort Studies ; Critical Care ; Education ; Humans ; Intensive Care Units ; Logistic Models ; Pressure Ulcer ; Prospective Studies ; Risk Factors

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.

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.

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.