Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: To assess the quality of life (QoL) and treatment satisfaction with intermittently-scanned continuous glucose monitoring (isCGM) in women with gestational diabetes mellitus (GDM). Methods: This prospective observational study included 189 women with GDM who completed the Korean version of the Audit of Diabetes-Dependent Quality of Life Questionnaire (K-ADDQoL). Among them, 25 women who utilized isCGM between gestational weeks 30 and 34 completed the Korean version of the Diabetes Treatment Satisfaction Questionnaire change version (K-DTSQc) to evaluate their satisfaction with isCGM during pregnancy. Results: GDM had a negative impact on the perceived QoL in 89.4% of the women. All 19 domains of the K-ADDQoL were adversely influenced by GDM, with the most significant impact on the freedom to eat (weighted impact score, −6.98 ± 2.49, P < 0.001) and the least impact on the sex life (−0.25 ± 0.80, P = 0.008). Younger women and those treated with insulin perceived themselves as being more affected in their QoL due to GDM. Women perceived to have less effect on their QoL attributed to GDM exhibited higher ΔHbA1c one year after delivery (ΔHbA1c, 0.3 ± 0.4% vs. 0.0 ± 0.4% in less affected vs. more affected women). The utilization of isCGM improved treatment satisfaction (overall satisfaction score, 10.36 ± 9.21, P < 0.001), independent of glycemic control during pregnancy. Conclusion Although GDM negatively affects the perceived QoL during pregnancy, attentiveness to GDM management may have a positive impact on long-term glycemic control.Moreover, employing isCGM can enhance treatment satisfaction in women with GDM.

Background: To assess the quality of life (QoL) and treatment satisfaction with intermittently-scanned continuous glucose monitoring (isCGM) in women with gestational diabetes mellitus (GDM). Methods: This prospective observational study included 189 women with GDM who completed the Korean version of the Audit of Diabetes-Dependent Quality of Life Questionnaire (K-ADDQoL). Among them, 25 women who utilized isCGM between gestational weeks 30 and 34 completed the Korean version of the Diabetes Treatment Satisfaction Questionnaire change version (K-DTSQc) to evaluate their satisfaction with isCGM during pregnancy. Results: GDM had a negative impact on the perceived QoL in 89.4% of the women. All 19 domains of the K-ADDQoL were adversely influenced by GDM, with the most significant impact on the freedom to eat (weighted impact score, −6.98 ± 2.49, P < 0.001) and the least impact on the sex life (−0.25 ± 0.80, P = 0.008). Younger women and those treated with insulin perceived themselves as being more affected in their QoL due to GDM. Women perceived to have less effect on their QoL attributed to GDM exhibited higher ΔHbA1c one year after delivery (ΔHbA1c, 0.3 ± 0.4% vs. 0.0 ± 0.4% in less affected vs. more affected women). The utilization of isCGM improved treatment satisfaction (overall satisfaction score, 10.36 ± 9.21, P < 0.001), independent of glycemic control during pregnancy. Conclusion Although GDM negatively affects the perceived QoL during pregnancy, attentiveness to GDM management may have a positive impact on long-term glycemic control.Moreover, employing isCGM can enhance treatment satisfaction in women with GDM.

Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: To assess the quality of life (QoL) and treatment satisfaction with intermittently-scanned continuous glucose monitoring (isCGM) in women with gestational diabetes mellitus (GDM). Methods: This prospective observational study included 189 women with GDM who completed the Korean version of the Audit of Diabetes-Dependent Quality of Life Questionnaire (K-ADDQoL). Among them, 25 women who utilized isCGM between gestational weeks 30 and 34 completed the Korean version of the Diabetes Treatment Satisfaction Questionnaire change version (K-DTSQc) to evaluate their satisfaction with isCGM during pregnancy. Results: GDM had a negative impact on the perceived QoL in 89.4% of the women. All 19 domains of the K-ADDQoL were adversely influenced by GDM, with the most significant impact on the freedom to eat (weighted impact score, −6.98 ± 2.49, P < 0.001) and the least impact on the sex life (−0.25 ± 0.80, P = 0.008). Younger women and those treated with insulin perceived themselves as being more affected in their QoL due to GDM. Women perceived to have less effect on their QoL attributed to GDM exhibited higher ΔHbA1c one year after delivery (ΔHbA1c, 0.3 ± 0.4% vs. 0.0 ± 0.4% in less affected vs. more affected women). The utilization of isCGM improved treatment satisfaction (overall satisfaction score, 10.36 ± 9.21, P < 0.001), independent of glycemic control during pregnancy. Conclusion Although GDM negatively affects the perceived QoL during pregnancy, attentiveness to GDM management may have a positive impact on long-term glycemic control.Moreover, employing isCGM can enhance treatment satisfaction in women with GDM.

Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: To assess the quality of life (QoL) and treatment satisfaction with intermittently-scanned continuous glucose monitoring (isCGM) in women with gestational diabetes mellitus (GDM). Methods: This prospective observational study included 189 women with GDM who completed the Korean version of the Audit of Diabetes-Dependent Quality of Life Questionnaire (K-ADDQoL). Among them, 25 women who utilized isCGM between gestational weeks 30 and 34 completed the Korean version of the Diabetes Treatment Satisfaction Questionnaire change version (K-DTSQc) to evaluate their satisfaction with isCGM during pregnancy. Results: GDM had a negative impact on the perceived QoL in 89.4% of the women. All 19 domains of the K-ADDQoL were adversely influenced by GDM, with the most significant impact on the freedom to eat (weighted impact score, −6.98 ± 2.49, P < 0.001) and the least impact on the sex life (−0.25 ± 0.80, P = 0.008). Younger women and those treated with insulin perceived themselves as being more affected in their QoL due to GDM. Women perceived to have less effect on their QoL attributed to GDM exhibited higher ΔHbA1c one year after delivery (ΔHbA1c, 0.3 ± 0.4% vs. 0.0 ± 0.4% in less affected vs. more affected women). The utilization of isCGM improved treatment satisfaction (overall satisfaction score, 10.36 ± 9.21, P < 0.001), independent of glycemic control during pregnancy. Conclusion Although GDM negatively affects the perceived QoL during pregnancy, attentiveness to GDM management may have a positive impact on long-term glycemic control.Moreover, employing isCGM can enhance treatment satisfaction in women with GDM.

Objectives: Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be actively used in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide range of research and subsequent social consensus on requirements for trustworthy AI. Methods: This review divided the requirements for trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trends in the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field. Results: Explainability provides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires the further development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary task is to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologies is needed, especially in defending training data or AI algorithms against adversarial attacks. Conclusions In the future, detailed standards need to be established according to the issues that medical AI would solve or the clinical field where medical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI development guidelines and approval processes for medical devices.

Aged people are challenged by serious complications from chronic diseases, such as mood disorder, diabetes, heart disease, and infectious diseases, which are also the most common causes of death in older people. Therefore, elderly care facilities are more important than ever. The most common causes of death in elderly care facilities were reported to be diabetes, cardiovascular disease, and pneumonia. Recently, the coronavirus disease 2019 (COVID-19) pandemic have a great impact on blind spots of safety where aged people were isolated from society. Elderly care facilities were one of the blind spots in the midst of the pandemic, where major casualties were reported from COVID-19 complications because most people had one or two mortality risk factors, such as diabetes or cardiovascular disease. Therefore, medical governance of public health center and hospital, and elderly care facility is becoming important issue of priority. Thus, remote health monitoring service by the Internet of Medical Things (IoMT) sensors is more important than ever. Recently, technological breakthroughs have enabled healthcare professionals to have easy access to patients in medical blind spots through the use of IoT sensors. These sensors can detect medically urgent situations in a timely fashion and make medical decisions for aged people in elderly care facilities. Real-time electrocardiograms and blood sugar monitoring sensors are approved by the medical insurance service. Real-time monitoring services in medical blind spots, such as elderly care facilities, has been suggested. Heart rhythm monitoring could play a role in detecting early cardiovascular disease events and monitoring blood glucose levels in the management of chronic diseases, such as diabetes, in aged people in elderly care facilities. This review presents the potential usefulness of remote monitoring with IoMT sensors in medical blind spots and clinical suggestions for applications.