We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

Purpose: This study aimed to develop a Korean version of the frailty model that reflects a holistic perspective. There were three phases of the study: a literature review, a two-round Delphi study, and a public hearing. Methods: The model was developed based on the middle-range theory generation approach proposed by Roy. A literature search was conducted, and a review of 36 studies on frailty involving Koreans led to the development of an initial frailty model. A two-round Delphi study was then conducted with nine experts to evaluate the appropriateness of the model. The revised model was presented at a public hearing to achieve consensus. Based on feedback indicating the need for improved visualization, a finalized diagrammatic model was developed. Results: The final frailty model comprised four domains, seven subdomains, and 30 items. It included specific items reflecting the distinctive characteristics of Korean culture, such as relationships with adult children (filial piety), nutritional status (consumption of red meat), and type of residential building. Conclusion The final frailty model provides a comprehensive perspective on the factors contributing to frailty, their interactions, and the potential interventions that healthcare providers can implement to prevent frailty.

We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

Purpose: This study aimed to develop a Korean version of the frailty model that reflects a holistic perspective. There were three phases of the study: a literature review, a two-round Delphi study, and a public hearing. Methods: The model was developed based on the middle-range theory generation approach proposed by Roy. A literature search was conducted, and a review of 36 studies on frailty involving Koreans led to the development of an initial frailty model. A two-round Delphi study was then conducted with nine experts to evaluate the appropriateness of the model. The revised model was presented at a public hearing to achieve consensus. Based on feedback indicating the need for improved visualization, a finalized diagrammatic model was developed. Results: The final frailty model comprised four domains, seven subdomains, and 30 items. It included specific items reflecting the distinctive characteristics of Korean culture, such as relationships with adult children (filial piety), nutritional status (consumption of red meat), and type of residential building. Conclusion The final frailty model provides a comprehensive perspective on the factors contributing to frailty, their interactions, and the potential interventions that healthcare providers can implement to prevent frailty.

We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

Purpose: This study aimed to develop a Korean version of the frailty model that reflects a holistic perspective. There were three phases of the study: a literature review, a two-round Delphi study, and a public hearing. Methods: The model was developed based on the middle-range theory generation approach proposed by Roy. A literature search was conducted, and a review of 36 studies on frailty involving Koreans led to the development of an initial frailty model. A two-round Delphi study was then conducted with nine experts to evaluate the appropriateness of the model. The revised model was presented at a public hearing to achieve consensus. Based on feedback indicating the need for improved visualization, a finalized diagrammatic model was developed. Results: The final frailty model comprised four domains, seven subdomains, and 30 items. It included specific items reflecting the distinctive characteristics of Korean culture, such as relationships with adult children (filial piety), nutritional status (consumption of red meat), and type of residential building. Conclusion The final frailty model provides a comprehensive perspective on the factors contributing to frailty, their interactions, and the potential interventions that healthcare providers can implement to prevent frailty.