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In a 13-week, randomized trial involving persons 6 to 79 years of age with type 1 diabetes, use of a bionic pancreas was associated with a greater reduction in the glycated hemoglobin level than standard care.

Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals 1 . This could be related to a lack of expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10–21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n  = 54) or by physicians (physician arm, n  = 54). The results for the primary efficacy measure—the percentage of time spent within the target glucose range (70–180 mg dl −1 (3.9–10.0 mmol l −1 ))—in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P  < 1 × 10 −7 ). The percentage of readings below 54 mg dl −1 (<3.0 mmol l −1 ) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P  < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers. The randomized-controlled trial ADVICE4U demonstrates non-inferiority of an automated AI-based decision support system compared with advice from expert physicians for optimal insulin dosing in youths with type 1 diabetes.

OBJECTIVE: The benefits of real-time continuous glucose monitoring (CGM) have been demonstrated in patients with type 1 diabetes. Our aim was to compare the effect of two modes of use of CGM, patient led or physician driven, for 1 year in subjects with poorly controlled type 1 diabetes. RESEARCH DESIGN AND METHODS: Patients with type 1 diabetes aged 8–60 years with HbA1c ≥8% were randomly assigned to three groups (1:1:1). Outcomes for glucose control were assessed at 1 year for two modes of CGM (group 1: patient led; group 2: physician driven) versus conventional self-monitoring of blood glucose (group 3: control). RESULTS: A total of 257 subjects with type 1 diabetes underwent screening. Of these, 197 were randomized, with 178 patients completing the study (age: 36 ± 14 years; HbA1c: 8.9 ± 0.9%). HbA1c improved similarly in both CGM groups and was reduced compared with the control group (group 1 vs. group 3: –0.52%, P = 0.0006; group 2 vs. group 3: –0.47%, P = 0.0008; groups 1 + 2 vs. group 3: –0.50%, P < 0.0001). The incidence of hypoglycemia was similar in the three groups. Patient SF-36 questionnaire physical health score improved in both experimental CGM groups (P = 0.004). Sensor consumption was 34% lower in group 2 than in group 1 (median [Q1–Q3] consumption: group 1: 3.42/month [2.20–3.91] vs. group 2: 2.25/month [1.27–2.99], P = 0.001). CONCLUSIONS: Both patient-led and physician-driven CGM provide similar long-term improvement in glucose control in patients with poorly controlled type 1 diabetes, but the physician-driven CGM mode used fewer sensors.

The effectiveness of real-time continuous glucose monitoring (rtCGM) in avoidance of hypoglycaemia among high-risk individuals with type 1 diabetes treated with multiple daily insulin injections (MDI) is unknown. We aimed to ascertain whether the incidence and severity of hypoglycaemia can be reduced through use of rtCGM in these individuals. The HypoDE study was a 6-month, multicentre, open-label, parallel, randomised controlled trial done at 12 diabetes practices in Germany. Eligible participants had type 1 diabetes and a history of impaired hypoglycaemia awareness or severe hypoglycaemia during the previous year. All participants wore a masked rtCGM system for 28 days and were then randomly assigned to 26 weeks of unmasked rtCGM (Dexcom G5 Mobile system) or to the control group (continuing with self-monitoring of blood glucose). Block randomisation with 1:1 allocation was done centrally, with the study site as the stratifying variable. Masking of participants and study sites was not possible. Control participants wore a masked rtCGM system during the follow-up phase (weeks 22–26). The primary outcome was the baseline-adjusted number of hypoglycaemic events (defined as glucose ≤3·0 mmol/L for ≥20 min) during the follow-up phase. The full dataset analysis comprised participants who wore the rtCGM system during the baseline and follow-up phases. The intention-to-treat analysis comprised all randomised participants. This trial is registered with ClinicalTrials.gov, number NCT02671968. Between March 4, 2016, and Jan 12, 2017, 149 participants were randomly assigned (n=74 to the control group; n=75 to the rtCGM group) and 141 completed the follow-up phase (n=66 in the control group, n=75 in the rtCGM group). The mean number of hypoglycaemic events per 28 days among participants in the rtCGM group was reduced from 10·8 (SD 10·0) to 3·5 (4·7); reductions among control participants were negligible (from 14·4 [12·4] to 13·7 [11·6]). Incidence of hypoglycaemic events decreased by 72% for participants in the rtCGM group (incidence rate ratio 0·28 [95% CI 0·20–0·39], p<0·0001). 18 serious adverse events were reported: seven in the control group, ten in the rtCGM group, and one before randomisation. No event was considered to be related to the investigational device. Usage of rtCGM reduced the number of hypoglycaemic events in individuals with type 1 diabetes treated by MDI and with impaired hypoglycaemia awareness or severe hypoglycaemia. Dexcom Inc.

With randomised trial data confirming that continuous glucose monitoring (CGM) is associated with improvements in maternal glucose control and neonatal health outcomes, CGM is increasingly used in antenatal care. Across pregnancy, the ambition is to increase the CGM time in range (TIR), while reducing time above range (TAR), time below range (TBR) and glycaemic variability measures. Pregnant women with type 1 diabetes currently spend, on average, 50% (12 h), 55% (13 h) and 60% (14 h) in the target range of 3.5–7.8 mmol/l (63–140 mg/dl) during the first, second and third trimesters, respectively. Hyperglycaemia, as measured by TAR, reduces from 40% (10 h) to 33% (8 h) during the first to third trimester. A TIR of >70% (16 h, 48 min) and a TAR of <25% (6 h) is achieved only in the final weeks of pregnancy. CGM TBR data are particularly sensor dependent, but regardless of the threshold used for individual patients, spending ≥4% of time (1 h) below 3.5 mmol/l or ≥1% of time (15 min) below 3.0 mmol/l is not recommended. While maternal hyperglycaemia is a well-established risk factor for obstetric and neonatal complications, CGM-based risk factors are emerging. A 5% lower TIR and 5% higher TAR during the second and third trimesters is associated with increased risk of large for gestational age infants, neonatal hypoglycaemia and neonatal intensive care unit admissions. For optimal neonatal outcomes, women and clinicians should aim for a TIR of >70% (16 h, 48 min) and a TAR of <25% (6 h), from as early as possible during pregnancy.

As the increasing prevalence of type 2 diabetes mellitus has put pressure on health systems to appropriately manage these patients, there have been a growing number of mobile apps designed to improve the self-management of diabetes. One such app, BlueStar, has been shown to significantly reduce hemoglobin A (HbA ) levels in small studies and is the first app in the United States to receive Food and Drug Administration approval as a mobile prescription therapy. However, the impact of the app across real-world population among different clinical sites and health systems remains unclear. The primary objective of this study was to conduct a pragmatic randomized controlled trial of the BlueStar mobile app to determine if app usage leads to improved HbA levels among diverse participants in real-life clinical contexts. We hypothesized that this mobile app would improve self-management and HbA levels compared with controls. The study consisted of a multicenter pragmatic randomized controlled trial. Overall, 110 participants randomized to the immediate treatment group (ITG) received the intervention for 6 months, and 113 participants randomized to the wait-list control (WLC) group received usual care for the first 3 months and then received the intervention for 3 months. The primary outcome was glucose control measured by HbA levels at 3 months. Secondary outcomes assessed intervention impact on patient self-management, experience of care, and self-reported health utilization using validated scales, including the Problem Areas in Diabetes, the Summary of Diabetes Self-Care Activities, and the EuroQol-5D. Intervention usage data were collected directly from the app. The results of an analysis of covariance controlling for baseline HbA levels did not show evidence of intervention impact on HbA levels at 3 months (mean difference [ITG-WLC] -0.42, 95% CI -1.05 to 0.21; P=.19). Similarly, there was no intervention effect on secondary outcomes measuring diabetes self-efficacy, quality of life, and health care utilization behaviors. An exploratory analysis of 57 ITG participants investigating the impact of app usage on HbA levels showed that each additional day of app use corresponded with a 0.016-point decrease in participants' 3-month HbA levels (95% CI -0.03 to -0.003). App usage varied significantly by site, as participants from 1 site logged in to the app a median of 36 days over 14 weeks (interquartile range [IQR] 10.5-124); those at another site used the app significantly less (median 9; IQR 6-51). The results showed no difference between intervention and control arms for the primary clinical outcome of glycemic control measured by HbA levels. Although there was low usage of the app among participants, results indicate contextual factors, particularly site, had a significant impact on overall usage. Future research into the patient and site-specific factors that increase app utilization are needed. Clinicaltrials.gov NCT02813343; https://clinicaltrials.gov/ct2/show/NCT02813343 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02813343).

Previous studies on telemedicine interventions have shown that older diabetic patients experience difficulty in using computers, which is a barrier to remote communication between medical teams and older diabetic patients. However, older people in China tend to find it easy to use mobile phones and personal messaging apps that have a user-friendly interface. Therefore, we designed a mobile health (mHealth) system for older people with diabetes that is based on mobile phones, has a streamlined operation interface, and incorporates maximum automation. The goal of the research was to investigate the use of mobile phone-based telemedicine apps for management of older Chinese patients with type 2 diabetes mellitus (T2DM). Variables of interest included efficacy and safety. A total of 91 older (aged over 65 years) patients with T2DM who presented to our department were randomly assigned to one of two groups. Patients in the intervention group (n=44) were provided glucometers capable of data transmission and received advice pertaining to medication, diet, and exercise via the mHealth telemedicine system. Patients assigned to the control group (n=47) received routine outpatient care with no additional intervention. Patients in both groups were followed up at regular 3-month intervals. After 3 months, patients in the intervention group showed significant (P<.05) improvement in postprandial plasma glucose level. After 6 months, patients in the intervention group exhibited a decreasing trend in postprandial plasma glucose and glycated hemoglobin levels compared with the baseline and those in the control group (P<.05). Mobile phone-based telemedicine apps help improve glycemic control in older Chinese patients with T2DM. China Clinical Trial Registration Center ChiCTR 1800015214; http://www.chictr.org.cn/showprojen.aspx?proj=25949 (Archived by WebCite at http://www.webcitation.org/73wKj1GMq).

In this randomized study, patients undergoing intensive therapy for type 1 diabetes mellitus who had glycated hemoglobin levels of 7.0 to 10.0% were stratified into three prespecified age groups and were assigned to receive continuous glucose monitoring or usual monitoring. The primary outcome was the change in glycated hemoglobin levels after 26 weeks. Continuous glucose monitoring was associated with improved glycemic control in adults but not in children and adolescents with type 1 diabetes. Continuous glucose monitoring was associated with improved glycemic control in adults but not in children and adolescents with type 1 diabetes. Despite the increased use of insulin pumps and multiple-injection regimens and the introduction of insulin analogues, intensive treatment of type 1 diabetes mellitus often does not achieve the target glycated hemoglobin levels recommended by the Diabetes Control and Complications Trial (DCCT) more than 15 years ago. 1 Although self-monitoring of blood glucose plays an important role in achieving target glycated hemoglobin levels, few patients with type 1 diabetes measure glucose levels after meals or overnight. Consequently, postprandial hyperglycemia and asymptomatic nocturnal hypoglycemia are commonly seen, even in patients with well-controlled type 1 diabetes who measure blood glucose several times daily with . . .

Objective Artificial pancreas (AP) systems have been shown to improve glycemic control throughout the day and night in adults, adolescents, and children. However, AP testing remains limited during intense and prolonged exercise in adolescents and children. We present the performance of the Tandem Control‐IQ AP system in adolescents and children during a winter ski camp study, where high altitude, low temperature, prolonged intense activity, and stress challenged glycemic control. Methods In a randomized controlled trial, 24 adolescents (ages 13‐18 years) and 24 school‐aged children (6‐12 years) with Type 1 diabetes (T1D) participated in a 48 hours ski camp (∼5 hours skiing/day) at three sites: Wintergreen, VA; Kirkwood, and Breckenridge, CO. Study participants were randomized 1:1 at each site. The control group used remote monitored sensor‐augmented pump (RM‐SAP), and the experimental group used the t: slim X2 with Control‐IQ Technology AP system. All subjects were remotely monitored 24 hours per day by study staff. Results The Control‐IQ system improved percent time within range (70‐180 mg/dL) over the entire camp duration: 66.4 ± 16.4 vs 53.9 ± 24.8%; P = .01 in both children and adolescents. The AP system was associated with a significantly lower average glucose based on continuous glucose monitor data: 161 ± 29.9 vs 176.8 ± 36.5 mg/dL; P = .023. There were no differences between groups for hypoglycemia exposure or carbohydrate interventions. There were no adverse events. Conclusions The use of the Control‐IQ AP improved glycemic control and safely reduced exposure to hyperglycemia relative to RM‐SAP in pediatric patients with T1D during prolonged intensive winter sport activities.

Aims/hypothesis The aim of this study was to compare the effectiveness of stand-alone intermittently scanned continuous glucose monitoring (isCGM) with or without a structured education programme and blood glucose monitoring (BGM) in adults with type 2 diabetes on multiple daily insulin injections (MDI). Methods In this 24 week randomised open-label multicentre trial, adults with type 2 diabetes on intensive insulin therapy with HbA 1c levels of 58–108 mmol/mol (7.5–12.0%) were randomly assigned in a 1:1:1 ratio to isCGM with a structured education programme on adjusting insulin dose and timing according to graphical patterns in CGM (intervention group), isCGM with conventional education (control group 1) or BGM with conventional education (control group 2). Block randomisation was conducted by an independent statistician. Due to the nature of the intervention, blinding of participants and investigators was not possible. The primary outcome was change in HbA 1c from baseline at 24 weeks, assessed using ANCOVA with the baseline value as a covariate. Results A total of 159 individuals were randomised ( n =53 for each group); 148 were included in the full analysis set, with 52 in the intervention group, 49 in control group 1 and 47 in control group 2. The mean (± SD) HbA 1c level at baseline was 68.19±10.94 mmol/mol (8.39±1.00%). The least squares mean change (± SEM) from baseline HbA 1c at 24 weeks was −10.96±1.35 mmol/mol (−1.00±0.12%) in the intervention group, −6.87±1.39 mmol/mol (−0.63±0.13%) in control group 1 ( p =0.0367 vs intervention group) and −6.32±1.42 mmol/mol (−0.58±0.13%) in control group 2 ( p =0.0193 vs intervention group). Adverse events occurred in 28.85% (15/52) of individuals in the intervention group, 26.42% (14/53) in control group 1 and 48.08% (25/52) in control group 2. Conclusions/interpretation Stand-alone isCGM offers a greater reduction in HbA 1c in adults with type 2 diabetes on MDI when education on the interpretation of graphical patterns in CGM is provided. Trial registration ClinicalTrials.gov NCT04926623. Funding This study was supported by Daewoong Pharmaceutical Co., Ltd. Graphical Abstract