PURPOSE: This study was to identify the stages of change in smoking cessation behavior and factors associated with the stages of smoking cessation behavior according to the trans-theoretical model. METHODS: The subjects were 154 industrial workers working at H Industry in N City, Chonnam Province who were currently smoking and had smoked in the past. Data were analyzed by descriptive statistics, ANOVA, and Duncan's multiple comparison test using SAS Version 10.0. RESULTS: The subjects were distributed among the stages of change in smoking cessation behavior: there were 28 subjects (18.2%) in the precontemplation stage, 71 (46.1%) in the contemplation stage, 21 (13.6%) in the preparation stage, 8 (5.2%) in the action stage, and 26 (16.9%) in the maintenance stage. The amount of smoking per day, self-efficacy, and advantages (pros) of smoking were significantly associated with the stage of change in smoking cessation behavior. CONCLUSION: This study suggested that the stage of change in smoking cessation behavior of the subject should be identified prior to the application of intervention programs, nursing intervention strategies should be considered to reduce the amount of smoking per day, and the disadvantages of smoking should be perceived.
Jeollanam-do ; Nursing ; Smoke* ; Smoking Cessation* ; Smoking*

Objective: The clozapine/N-desmethylclozapine (NDMC) ratio is proposed to be used as a predictor of cognitive performance in clozapine-treated patients, as its principal metabolite, NDMC, has an opposite action with clozapine on the cholinergic system. The aim of this study is to determine whether clozapine has influence on cognitive performance in accordance with changes in the clozapine/NDMC in patients with schizophrenia. Methods: The data of fifteen patients with schizophrenia, who had initial and follow-up assessments after starting clozapine treatment, were retrospectively collected. The assessments included clinical scale, cognitive battery, and pharmacological data including plasma concentrations of clozapine and NDMC. The data were analyzed with Pearson correlation and stepwise multiple regression analyses. Results: ΔAttention/vigilance, Δsocial cognition, and Δcomposite score had a significant correlation with Δclozapine/NDMC ratio, while ΔWorking memory had correlation with Δclozapine concentration and ΔNDMC concentration, and Δsocial cognition had association with Δclozapine concentration. Multiple regression analysis showed that Δattention/vigilance had negative association with Δclozapine/NDMC ratio, Δworking memory had negative relation with Δclozapine concentration, and that Δsocial cognition had negative association with Δclozapine concentration. Conclusion This finding implicates that lowering the clozapine/NDMC ratio could enhance cognition in patients with schizophrenia treated with clozapine.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

PURPOSE: Massive blood transfusios are uncommon. The goal of this study was to propose an ideal ratio for the blood component of massive hemorrhage treatment after review of five years of massive transfusion practice, in order to have the best possible clinical outcomes. MATERIALS AND METHODS: We defined a 'massive transfusion' as receiving 10 or more units of red blood cells in one day. A list of patients receiving a massive transfusion from 2004 to 2008 was generated using the electronic medical records. For each case, we calculated the ratio of blood components and examined its relationship to their survival. RESULTS: Three hundred thirty four patients underwent massive transfusion during the five years of the study. The overall seven-day hospital mortality for massive transfusion patients was 26.1%. Factors independently predictive of survival were a fresh-frozen plasma (FFP)/packed red blood cell (pRBC) ratio> or =1.1 with an odds ratio (OR) of 1.96 (1.03-3.70), and elective admission with an OR of 2.6 (1.52-4.40). The receiver operation characteristic (ROC) curve suggest that a 1 : 1 : 1 ratio of pRBCs to FFP to platelets is the best ratio for survival. CONCLUSION: Fixing blood-component ratios during active hemorrhage shows improved outcomes. Thus, the hospital blood bank and physician hypothesized that a fixed blood component ratio would help to reduce mortality and decrease utilization of the overall blood component.
Adult ; Blood Cell Count ; Blood Transfusion/*methods ; Female ; Hospitals ; Humans ; Male ; Middle Aged ; Republic of Korea ; Retrospective Studies ; Treatment Outcome

The purpose of this study was to examine restaurant customers' use of menu labeling and their perception of menu labeling attributes. Further, the study investigated relations of menu labeling use behavior, and perception of menu labeling attributes with behavioral intentions toward menu labeling. Using a self-administered survey conducted for 2 weeks from the 2nd week of October, 2015, data were collected from restaurant customers who were exposed to menu labeling over 3 months at the time of the survey. A total of 426 respondents completed the survey. Respondents were asked about use of menu labeling, usefulness, ease of understanding, accuracy, and demographic information. There was a difference in menu labeling use behavior according to age, whereas respondents aged 50 years or over showed significantly higher use of menu labeling than those in 20s (P<0.001). Perceptions of menu labeling attributes positively affected behavioral intentions towards menu labeling. While all three menu labeling attributes, ‘usefulness’, ‘ease of understanding’, and ‘accuracy’, were positive factors for behavioral intentions towards menu labeling, usefulness was the biggest attribute explaining behavioral intentions (P<0.001). The study findings offer implications that can be applied to academics, the foodservice industry, and government in an attempt to nurture a healthy eating environment through provision of nutritional information at restaurants.
Eating ; Intention ; Restaurants* ; Surveys and Questionnaires

OBJECTIVES: This research was conducted to identify the consumers' food choice factors that affect the consumers' replacement of soft drinks with carbonated water. METHODS: The present study used secondary data from a consumer panel survey conducted by the Rural Development Administration of Korea, and the data included the panel members' purchase records based on their monthly spending receipts. The survey asked the participants about their food choice factors and their personal responsibility for their health. This survey included independent variables for the consumers' food purchase factors. As a dependent variable, two types of groups were defined. The replacement group included those people who increased their purchase of carbonated water and decreased their purchase of soft drinks. The non-replacement group included those people who did not change their purchase patterns or they increased their purchase of soft drinks and they decreased their purchase of carbonated water. Logistic regression analysis was conducted to determine the consumers' food choice factors that were associated with replacing soft drinks with carbonated water. RESULTS: The replacement group was significantly associated with (1) a younger age (OR=0.953), (2) being a housewife (OR=2.03), (3) higher income (OR=1.001) and (4) less concern about price (OR=0.819) when purchasing food. This group also showed (5) higher enjoyment (OR=1.328) when choosing food and (6) they took greater responsibly for their personal health (OR=1.233). CONCLUSIONS: This research is the first study to mainly focus on soft drinks and carbonated water. The result of this research showed that young, health-conscious consumers with a higher income and who are more interested in food have more possibilities to replace soft drinks with carbonated water. These research findings may be applied to consumers who have characteristics that are similar to the young health-conscious consumers and the results can help to suggest ways to reduce sugar intake and improve public health. However, this research has a limitation due to the application of secondary data. Therefore, a future study is needed to develop detailed survey questions about food choice factors and to extend these factors to all beverages, including soft drinks made with sugar substitutes, so as to reflect the growth of alternative industries that use artificial sweeteners or different types of sugar to make commercially available drinks.
Beverages ; Carbon ; Carbonated Beverages ; Carbonated Water ; Consumer Behavior ; Humans ; Korea ; Logistic Models ; Public Health ; Social Planning ; Sweetening Agents