Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: Happiness is an important factor in life, and food literacy (FL) has been emphasized as a core concept for a happy and healthy life. This study examined the level of happiness of Seoul citizens according to their sociodemographic factors and their association with FL. SUBJECTS/METHODS: This study used the data from the Seoul Food Survey, a cross-sectional study conducted on 4,039 Seoul citizens from September to October 2021. FL was measured using a validated questionnaire consisting of 33 items from 3 sub-domains: 14 items in the nutrition and safety domain, 8 items in the cultural and relational domain, and 11 items in the socio-ecological domain. Statistical analysis involved descriptive statistics and multivariate regression analysis. RESULTS: Various sociodemographic factors, such as household income, subjective health status, and food insecurity, were found to be associated with the level of happiness. The level of FL was also associated with the happiness scores. After adjusting for variables associated with happiness, the participants with the highest quartile FL scores were 7.32 times more likely to respond that they were happy than those with the lowest FL score. Three FL domains and total FL showed linear increases in overall happiness after controlling for subjective health status and sociodemographic factors (P < 0.001). CONCLUSIONS After adjusting for the related covariates, higher levels of FL were associated with higher scores in happiness. Based on this study, it would be meaningful to evaluate ways to intervene in FL to improve the level of happiness among the general population.

BACKGROUND/OBJECTIVES: The growing aging population has led to an increased utilization of senior daycare centers. This study was conducted to design a program to enhance the health of older adults in senior daycare centers in Chuncheon City, South Korea. SUBJECTS/METHODS: The study explored the health conditions and dietary patterns of older adults in senior daycare centers. Participants included staff and older adults from senior daycare centers in Chuncheon City. A mixed methods research design was used to obtain both qualitative and quantitative data. Qualitative insights were obtained through in-depth interviews with 26 staff members and older adults, coupled with observations made at 10 senior daycare centers. The quantitative component comprised structured questionnaires and physical measurements of 204 older adults at these centers. RESULTS: Many of the older adults relied on the meals provided by the center due to their limited cooking abilities. Dental health issues and dysphagia were common. Interviews highlighted the budgetary constraints of the centers in providing wholesome meals and the need for government support to alleviate meal expenses and enhance quality. A structured survey of older adults showed that the average age was 83.3 yrs, with an average of 2 chronic conditions per participant. Frailty analysis of the participants revealed that 56.2% were prefrail and 32.0% were frail. Almost half of the participants (47.0%) used dentures. Based on these findings, a preventive intervention program was proposed, addressing the specific needs and challenges of older adults while promoting overall well-being and preventing frailty. CONCLUSION Tailored health promotion strategies are crucial in senior daycare centers.Recommended interventions include staff nutrition education, improved dietary plans, and cost-effective strength training programs. These interventions aim to reduce frailty and enhance the quality of life of older adults in the community via interventions in daycare centers.

BACKGROUND/OBJECTIVES: Mukbang, a trend originating in South Korea and gaining global popularity, could influence children’s food choices and eating habits. This study analyzed the correlation between Mukbang viewing time in children and adolescents, their meal consumption frequency, nutrition quotient (NQ), and frequency of food intake. SUBJECTS/METHODS: From July to August 2022, this cross-sectional study investigated upper elementary students (ages 9–11 yrs) and adolescents (aged 12–18 yrs) using an online survey. The survey items included key demographic factors, Mukbang viewing frequency and duration, frequency of main meal consumption, commonly consumed foods, and the validated NQ, which was used to assess food intake quality. Multiple linear regression analysis was used to explore the link between Mukbang viewing and nutritional habits. RESULTS: Weekly Mukbang viewing time was significantly correlated with eating habits after adjusting for gender, age, physical activity frequency past week, household income, and primary caregiver’s level of education. Increased Mukbang viewing time correlated with reduced frequency of breakfast (P for trend < 0.001) and dinner (P for trend = 0.012), while the frequency of eating out (P for trend < 0.001) and late-night snacking (P for trend = 0.008) increased. Higher Mukbang viewing time notably decreased scores in the moderation domain (P for trend < 0.001), in the practice domain (P for trend = 0.031), and overall NQ (P for trend < 0.001). It also significantly elevated intake of sweets (P for trend = 0.001), Koreanstyle street food, Western-style fast food, instant noodles, sweetened beverages, caffeinated beverages, and fruit and vegetable juices (P for trend < 0.001). CONCLUSION This study identifies a negative correlation between Mukbang viewing and eating habits among Korean children and adolescents. The results indicate the importance of incorporating children and adolescents’ media usage and environmental factors on dietary education and the development of policy programs.