Objectives: The purpose of this study was to investigate the perception of nutrition teachers and the factors influencing their intention toward sustainable dietary education utilizing the theory of planned behavior (TPB). Methods: The self-administered online survey was completed by nutrition teachers in Jeollanam-do, South Korea. A total of 151 valid questionnaires were analyzed. Factor analysis and multiple regressions were employed to test the research model. Results: The study findings demonstrated that all TPB variables significantly influenced the sustainable dietary educational intention, with the degree of influence ranking as follows: external perceived behavioral control (β = 0.417), attitude (β = 0.240), internal perceived behavioral control (β = 0.207), and subjective norms (β = 0.181). For external perceived behavioral control, nutrition teachers and elementary schools exhibited higher levels compared to dietitians and middle/high schools, respectively. The participants in sustainable dietary education training programs exhibited a higher level of internal perceived behavioral control compared to those who did not participate. The highest perception levels were reported for attitude (4.26), followed by subjective norms (4.02), internal perceived behavioral control (3.67), and external perceived behavioral control (3.20). Conclusions This study affirmed that the TPB variables elucidated the sustainable dietary educational intentions of nutrition teachers. The significant impacts of external and internal perceived behavioral control, attitude, and subjective norms on educational intentions were confirmed. Consequently, proactive support from schools and governments is essential to enhance the facilitating factors and mitigate the barriers toward sustainable dietary education in schools.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Three-dimensional (3D) printing technology, with additive manufacturing, can aid in the production of various kinds of patient-specific medical devices and implants in medical fields, which cannot be covered by mass production systems for producing conventional devices/implants. The simulator-based medical image demonstrates the anatomical structure of the disease, which can be used for education, diagnosis, preparation of treatment plan and preoperative surgical guide, etc. The surgical guide is used as a patient-specific medical device for guiding incision, resection, insertion, and marking. As 3D printers can output materials that can be inserted into the human body, the patient-specific implant device that reflects the patient's anatomy and surgical plan could be of relevance. In addition, patient-specific aids, including gibs, splints, prostheses, and epitheses, could be used for a better outcome. Finally, bio-printing is also used to cultivate cells to produce functional artificial tissues.