OBJECTIVE: We investigated the relationship between the complexity of the glucose time series index (CGI) during pregnancy and adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM).
METHODS: In this retrospective cohort study, 388 singleton pregnant women with GDM underwent continuous glucose monitoring (CGM) at a median of 26.86 gestational weeks. CGI was calculated using refined composite multiscale entropy based on CGM data. The participants were categorized into tertiles according to their baseline CGI (CGI <2.32, 2.32-3.10, ≥3.10). Logistic regression was used to assess the association between CGI and composite adverse outcomes or large for gestational age (LGA). The discrimination performance of CGI was estimated using receiver operating characteristic analysis.
RESULTS: Of the 388 participants, 71 (18.3%) had LGA infants and 63 (16.2%) had composite adverse outcomes. After adjustments were made for confounders, compared with those with a high CGI (CGI ≥3.10), participants with a low CGI (CGI <2.32) had a higher risk of composite adverse outcomes (odds ratio: 12.10, 95% confidence interval: 4.41-33.18) and LGA (odds ratio: 12.68, 95% confidence interval: 4.04-39.75). According to the receiver operating characteristic analysis, CGI was significantly better than glycated haemoglobin and conventional CGM indicators for the prediction of adverse pregnancy outcomes (all p < .05).
CONCLUSIONS: A lower CGI during pregnancy was associated with composite adverse outcomes and LGA. CGI, a novel glucose homeostasis predictor, seems to be superior to conventional glucose indicators for the prediction of adverse pregnancy outcomes in women with GDM.

BACKGROUND: With the advent of the smart phone era, managing blood glucose at home through apps will become more common for older individuals with diabetes. Adult children play important roles in glucose management of older parents. Few studies have explored how adult children really feel about engaging in the glucose management of their older parents with type 2 diabetes mellitus (T2DM) through mobile apps. This study provides insights into the role perceptions and experiences of adult children of older parents with T2DM participating in glucose management through mobile apps.
METHODS: In this qualitative study, 16 adult children of older parents with T2DM, who had used mobile apps to manage blood glucose for 6 months, were recruited through purposive sampling. Semi-structured, in-depth, face-to-face interviews to explore their role perceptions and experiences in remotely managing their older parents\' blood glucose were conducted. The Consolidated Criteria for Reporting Qualitative Research (COREQ) were followed to ensure rigor in the study. The data collected were analyzed by applying Colaizzi\'s seven-step qualitative analysis method.
RESULTS: Six themes and eight sub-themes were identified in this study. Adult children\'s perceived roles in glucose management of older parents with T2DM through mobile apps could be categorized into four themes: health decision-maker, remote supervisor, health educator and emotional supporter. The experiences of participation could be categorized into two themes: facilitators to participation and barriers to participation.
CONCLUSIONS: Some barriers existed for adult children of older parents with T2DM participating in glucose management through mobile apps; however, the findings of this study were generally positive. It was beneficial and feasible for adult children to co-manage the blood glucose of older parents. Co-managing blood glucose levels in older parents with T2DM can enhance both adherence rates and confidence in managing blood glucose effectively.

UNASSIGNED: Patients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D.
UNASSIGNED: Data provided by the First People\'s Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients\' PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs.
UNASSIGNED: The experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model.
UNASSIGNED: The proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

Continuous glucose monitors are crucial for diabetes management, but invasive sampling, signal drift and frequent calibrations restrict their widespread usage. Microneedle sensors are emerging as a minimally-invasive platform for real-time monitoring of clinical parameters in interstitial fluid. Herein, a painless and flexible microneedle sensing patch is constructed by a mechanically-strong microneedle base and a thin layer of fluorescent hydrogel sensor for on-site, accurate, and continuous glucose monitoring. The Förster resonance energy transfer (FRET)-based hydrogel sensors are fabricated by facile photopolymerizations of acryloylated FRET pairs and glucose-specific phenylboronic acid. The optimized hydrogel sensor enables quantification of glucose with reversibility, high selectivity, and signal stability against photobleaching. Poly (ethylene glycol diacrylate)-co-polyacrylamide hydrogel is utilized as the microneedle base, facilitating effective skin piercing and biofluid extraction. The integrated microneedle sensor patch displays a sensitivity of 0.029 mM-1 in the (patho)physiological range, a low detection limit of 0.193 mM, and a response time of 7.7 min in human serum. Hypoglycemia, euglycemia and hyperglycemia are continuously monitored over 6 h simulated meal and rest activities in a porcine skin model. This microneedle sensor with high transdermal analytical performance offers a powerful tool for continuous diabetes monitoring at point-of-care settings.

BACKGROUND: Type 1 diabetes mellitus (T1DM) is the most common chronic autoimmune disease among children and adolescents. Telemedicine has been widely used in the field of chronic disease management and can benefit patients with T1DM. However, existing studies lack high-level evidence related to the effectiveness of telemedicine for glycemic control in children and adolescents with T1DM.
OBJECTIVE: This study aims to systematically review the evidence on the effectiveness of telemedicine interventions compared with usual care on glycemic control among children and adolescents with T1DM.
METHODS: In this systematic review and meta-analysis, we searched PubMed, Cochrane Library, Embase, Web of Science (all databases), and CINAHL Complete from database inception to May 2023. We included randomized controlled trials (RCTs) that evaluated the effectiveness of a telemedicine intervention on glycemic control in children and adolescents with T1DM. In total, 2 independent reviewers performed the study selection and data extraction. Study quality was assessed using the Cochrane Risk of Bias 2 tool. Our primary outcome was glycated hemoglobin (HbA1c) levels. Secondary outcomes were quality of life, self-monitoring of blood glucose, the incidence of hypoglycemia, and cost-effectiveness. A random-effects model was used for this meta-analysis.
RESULTS: Overall, 20 RCTs (1704 participants from 12 countries) were included in the meta-analysis. Only 5% (1/20) of the studies were at high risk of bias. Compared to usual care, telemedicine was found to reduce HbA1c levels by 0.22 (95% CI -0.33 to -0.10; P<.001; I2=35%). There was an improvement in self-monitoring of blood glucose (mean difference [MD] 0.54, 95% CI -0.72 to 1.80; P=.40; I2=67.8%) and the incidence of hypoglycemia (MD -0.15, 95% CI -0.57 to 0.27; P=.49; I2=70.7%), although this was not statistically significant. Moreover, telemedicine had no convincing effect on the Diabetes Quality of Life for Youth score (impact of diabetes: P=.59; worries about diabetes: P=.71; satisfaction with diabetes: P=.68), but there was a statistically significant improvement in non-youth-specific quality of life (MD -0.24, 95% CI -0.45 to -0.02; P=.04; I2=0%). Subgroup analyses revealed that the effect of telemedicine on HbA1c levels appeared to be greater in studies involving children (MD -0.41, 95% CI -0.62 to -0.20; P<.001), studies that lasted <6 months (MD -0.32, 95% CI -0.48 to -0.17; P<.001), studies where providers used smartphone apps to communicate with patients (MD -0.37, 95% CI -0.53 to -0.21; P<.001), and studies with medication dose adjustment (MD -0.25, 95% CI -0.37 to -0.12; P<.001).
CONCLUSIONS: Telemedicine can reduce HbA1c levels and improve quality of life in children and adolescents with T1DM. Telemedicine should be regarded as a useful supplement to usual care to control HbA1c levels and a potentially cost-effective mode. Meanwhile, researchers should develop higher-quality RCTs using large samples that focus on hard clinical outcomes, cost-effectiveness, and quality of life.

Continuous glucose monitoring (CGM)-derived metrics have been used to accurately assess glycemic variability (GV) to facilitate management of diabetes mellitus, yet their relationship with diabetic peripheral neuropathy (DPN) is not fully understood. We performed a systematic review and meta-analysis to evaluate the association between GV metrics and the risk of developing DPN. Nine studies totaling 3,649 patients with type 1 and type 2 diabetes mellitus were included. A significant association was found between increased GV, as indicated by metrics including standard deviation (SD) with OR and 95% CI of 2.58 (1.45-4.57), mean amplitude of glycemic excursions (MAGE) with OR and 95% CI of 1.90 (1.01-3.58), mean of daily difference (MODD) with OR and 95% CI of 2.88 (2.17-3.81) and the incidence of DPN. Our findings support a link between higher GV and an increased risk of DPN in patients with diabetes. These findings highlight the potential of GV metrics as indicators for the development of DPN, advocating for their inclusion in diabetes management strategies to potentially mitigate neuropathy risk. Longitudinal studies with longer observation periods and larger sample sizes are necessary to validate these associations across diverse populations.

This study aims to explore blood glucose variations before and after short-term intensive exercise in the morning or afternoon of a day and the trend of blood glucose fluctuations during exercise in patients with T2DM (type 2 diabetes, T2DM).
Blood glucose variations of Fouty during morning exercise 8:00-12:00 hours and twenty during afternoon exercise 14:30-18:30 hours). Patients with T2DM discharged from the hospital were analyzed retrospectively, with the baseline data checked through the medical record system before intervention. We were asked to perform seven times of treadmill aerobic exercise, which lasted for 30 minutes with incremental intensity for each time, for two weeks under the supervision of the Continuous Glucose Monitor (CGM) and the heart rate armband. The exercise intensity has been adjusted by the clinicians and specialist nurses from the Department of Diabetes Mellitus according to the blood glucose levels and heart rate curves during exercise; data including the height, weight, body mass index (BMI), waist-to-hip ratio, fasting blood glucose, glycosylated hemoglobin, in-exercise CGM-measured blood glucose value/min, and after-exercise fingertip blood glucose value of patients with T2DM were collected after the intensive exercise (2 weeks). SPSS 22.0 and GraphPad Prism 7 were adopted for statistical analysis using the T-test and ANOVA.
No difference was observed in the baseline data between the morning and afternoon exercise groups before intervention; compared to the morning exercise group, the fasting C-peptide value (2.15±0.97 vs. 1.53±0.46) in the afternoon exercise group was higher than that in the morning exercise group, with a superior (p=0.029) effect after two weeks of intervention, exhibiting a significant difference in the results. According to the results of repeated variance ANOVA analysis, the time for the appearance of significant improvement in blood glucose in the afternoon exercise group was 5 minutes earlier (11th minute vs 1 minute)than that in the morning exercise group (15th minute vs 1 min); significant differences were observed in both time (p=0.048 vs p<0.01) between the two groups on exercise days, as revealed by the results of bivariate ANOVA; in comparison to the morning exercise group (7.42±1.68), there was a significant difference (p=0.049)in the mean blood glucose between the two groups 25 min after patients with T2DM in the afternoon exercise group (6.25±1.53) started to exercise; in addition, a significant statistical difference (p=0.021) was revealed in the CGM-measured hourly the mean blood glucose on exercise days between the morning(8.18±1.88) and afternoon exercise (6.75±1.40)groups at 4:00 pm in week one and two w.
Glycaemic improvement in the short-term intensive afternoon exercise group may be superior to that of the morning exercise group, which may be related to greater fasting C-peptide secretion and longer effective exercise duration. The time to exercise is a factor affecting blood glucose variations during exercise. However, significant variations in the level of blood glucose during exercise must be further observed through exercise intervention over a more extended period.

OBJECTIVE: To evaluate the real-world effectiveness of automated insulin delivery (AID) systems in patients with type 1 diabetes (T1D).
METHODS: PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov were searched for studies published up until 2 March 2024. We included pragmatic randomized controlled trials (RCTs), cohort studies, and before-after studies that compared AID systems with conventional insulin therapy in real-world settings and reported continuous glucose monitoring outcomes. Percent time in range (TIR; 3.9-10 mmol/L), time below range (TBR; <3.9 mmol/L), time above range (TAR; >10 mmol/L), and glycated haemoglobin (HbA1c) level were extracted. Data were summarized as mean differences (MDs) with 95% confidence interval.
RESULTS: A total of 23 before-after studies (101 704 participants) were included in the meta-analysis. AID systems were associated with an increased percentage of TIR (11.61%, 10.47 to 12.76; p < 0.001). The favourable effect of AID systems was consistently observed when used continuously for 6 (11.76%) or 12 months (11.33%), and in both children (12.16%) and adults (11.04%). AID systems also showed favourable effects on TBR (-0.53%, -0.63 to -0.42), TAR (-9.65%, -10.63 to -8.67) and HbA1c level (-0.42%, -0.47 to -0.37) when compared with previous treatments.
CONCLUSIONS: Similar improvements in glycaemic parameters were observed in real-world settings in RCTs using AID systems in T1D. AID systems benefit both children and adults by increasing TIR for both short- and long-term interventions.

UNASSIGNED: This pilot study aimed to prospectively investigate the effects of a wearable monitoring device, based on an Internet management platform, on the comprehensive management of type 2 diabetes mellitus (T2DM) patients.
UNASSIGNED: A total of 120 hospitalized patients with T2DM were enrolled and randomly divided into the control group and the intervention group. Patients in the control group only received conventional diabetes treatments, while patients in the intervention group were provided with a wearable monitoring device in addition to conventional diabetes treatments. Moreover, the wearable device could connect to an Internet platform for diabetes management and upload self-monitoring data. All patients were followed for 3 months. The changes in parameters representing glucose metabolism, blood lipids, renal function, and patient satisfaction were compared between the two groups. All results were analyzed on an intention-to-treat basis.
UNASSIGNED: One hundred twenty subjects met all criteria and agreed to participate in this study. During the follow-up period, 5 and 4 subjects were lost to follow-up in the intervention and control groups, respectively. Compared with the control group, the blood glucose of the intervention group decreased significantly after 3 months (p < 0.05). Subgroup analysis found that females, those younger than 60 years, with baseline glycated hemoglobin A1c (HbA1c) levels of 8% or greater, and patients with good adherence showed significant improvements in HbA1c (p < 0.05). However, there was no significant difference in blood lipid and renal function. The intervention group showed a better adherence rate to blood glucose, comprehensive adherence rate, and diabetes treatment satisfaction (p < 0.05). One subject in the intervention group and two subjects in the control group reported mild hypoglycemia. No other adverse events such as infections and skin allergies occurred in the two groups.
UNASSIGNED: The intervention of a wearable monitoring device based on an Internet management platform significantly improved blood glucose control in T2DM patients, as well as the overall adherence rate and patient satisfaction with treatment.
UNASSIGNED: NCT04973644.

Continuous glucose monitoring (CGM) is of great importance to the treatment and prevention of diabetes. As a proven commercial technology, electrochemical glucose sensor based on interstitial fluid (ISF) sensing has high sensitivity and wide detection range. Therefore, it has good promotion prospects in noninvasive or minimally-invasive CGM system. However, since there are concentration differences and time lag between glucose in plasma and ISF, the accuracy of this type of sensors are still limited. Typical calibration algorithms rely on simple linear regression which do not account for the variability of the sensitivity of sensors. To enhance the accuracy and stability of CGM based on ISF, optimization of calibration algorithm for sensors is indispensable. While there have been considerable researches on improving calibration algorithms for CGM, they have still received less attention. This article reviews the problem of typical calibration and presents the outstanding calibration algorithms in recent years. Finally, combined with existing research and emerging sensing technologies, this paper makes an outlook on the future calibration algorithms for CGM sensors.