Improved Glycemic Outcomes With Diabetes Technology Use Independent of Socioeconomic Status in Youth With Type 1 Diabetes.

OBJECTIVE: Technology use in type 1 diabetes (T1D) is impacted by socioeconomic status (SES). This analysis explored relationships between SES, glycemic outcomes, and technology use. RESEARCH DESIGN AND METHODS: A cross-sectional analysis of HbA1c data from 2,822 Australian youth with T1D was undertaken. Residential postcodes were used to assign SES based on the Index of Relative Socio-Economic Disadvantage (IRSD). Linear regression models were used to evaluate associations among IRSD quintile, HbA1c, and management regimen. RESULTS: Insulin pump therapy, continuous glucose monitoring, and their concurrent use were associated with lower mean HbA1c across all IRSD quintiles (P < 0.001). There was no interaction between technology use and IRSD quintile on HbA1c (P = 0.624), reflecting a similar association of lower HbA1c with technology use across all IRSD quintiles. CONCLUSIONS: Technology use was associated with lower HbA1c across all socioeconomic backgrounds. Socioeconomic disadvantage does not preclude glycemic benefits of diabetes technologies, highlighting the need to remove barriers to technology access.

Clinical Outcomes with MiniMedTM 780G Advanced Hybrid Closed-Loop Therapy in 2- to 6-Year-Old Children with Type 1 Diabetes.

Advanced hybrid closed-loop (AHCL) therapy with the Medtronic MiniMed™ 780G system improves glycemia; however, the clinical outcomes in younger children remain less established. This pilot study aimed to explore the continuous glucose monitoring (CGM) metrics in very young children on AHCL. Children between 2 and 7 years of age and on insulin pump therapy were recruited. A 2-week phase in manual mode was followed by a 6-week AHCL phase. CGM metrics were analyzed to review glycemic outcomes. Out of 11 participants enrolled [mean (standard deviation [SD]) age 5.3 (0.8) years], 10 completed the study. Time in closed loop was 96.7 (3.9)%. In AHCL, participants had a mean (SD) time in range of 72.6 (7.4)% and spent 3.0 (1.74)% and 0.63 (0.46)% in time <70 and <54 mg/dL, respectively. AHCL is a feasible option for management of young children with type 1 diabetes.

Socioeconomic status and diabetes technology use in youth with type 1 diabetes: a comparison of two funding models.

Background: Technology use, including continuous glucose monitoring (CGM) and insulin pump therapy, is associated with improved outcomes in youth with type 1 diabetes (T1D). In 2017 CGM was universally funded for youth with T1D in Australia. In contrast, pump access is primarily accessed through private health insurance, self-funding or philanthropy. The study aim was to investigate the use of diabetes technology across different socioeconomic groups in Australian youth with T1D, in the setting of two contrasting funding models. Methods: A cross-sectional evaluation of 4957 youth with T1D aged <18 years in the national registry was performed to determine technology use. The Index of Relative Socio-Economic Disadvantage (IRSD) derived from Australian census data is an area-based measure of socioeconomic status (SES). Lower quintiles represent greater disadvantage. IRSD based on most recent postcode of residence was used as a marker of SES. A multivariable generalised linear model adjusting for age, diabetes duration, sex, remoteness classification, and location within Australia was used to determine the association between SES and device use. Results: CGM use was lower in IRSD quintile 1 in comparison to quintiles 2 to 5 (p<0.001) where uptake across the quintiles was similar. A higher percentage of pump use was observed in the least disadvantaged IRSD quintiles. Compared to the most disadvantaged quintile 1, pump use progressively increased by 16% (95% CI: 4% to 31%) in quintile 2, 19% (6% to 33%) in quintile 3, 35% (21% to 50%) in quintile 4 and 51% (36% to 67%) in the least disadvantaged quintile 5. Conclusion: In this large national dataset, use of diabetes technologies was found to differ across socioeconomic groups. For nationally subsidised CGM, use was similar across socioeconomic groups with the exception of the most disadvantaged quintile, an important finding requiring further investigation into barriers to CGM use within a nationally subsidised model. User pays funding models for pump therapy result in lower use with socioeconomic disadvantage, highlighting inequities in this funding approach. For the full benefits of diabetes technology to be realised, equitable access to pump therapy needs to be a health policy priority.

Impact of Body Composition on the Accuracy of a Medtronic Guardian Continuous Glucose Monitoring System.

Continuous glucose monitoring (CGM) systems are used in therapeutic decisions for diabetes management, however, the impact of body composition on CGM accuracy is not known. Body composition variables (body mass index [BMI], midarm circumference, percentage fat, and impedance) were collected in an observational study designed to determine the accuracy of an investigational Medtronic Guardian™ sensor 3. Seven days of sensor glucose data were analyzed from 112 participants >7 years of age with mean BMI Z score 0.48 (<18 years) and BMI 26.7 kg/m2 (≥18 years). The outcome was the absolute relative difference between the sensor and blood glucose readings. Data were analyzed using generalized estimating equations to account for correlation between repeated measures. No statistically significant associations between measures of body composition and device accuracy were found. Body composition does not have a meaningful impact on the accuracy of CGM systems.

Impact of Missing Data on the Accuracy of Glucose Metrics from Continuous Glucose Monitoring Assessed Over a 2-Week Period.

Objective: To explore the impact of missing data on the accuracy of continuous glucose monitoring (CGM) metrics collected for a 2-week period in a clinical trial. Research Design and Methods: Simulations were conducted to examine the effect of various patterns of missingness on the accuracy of CGM metrics as compared with a "complete" data set. The proportion of missing data, the "block size" in which the data were missing, and the missing mechanism were modified for each "scenario." The degree of agreement between simulated and "true" glycemic measures under each scenario was presented as R2. Results: Under all missing patterns, R2 declined as the proportion of missing data increased, however, as the "block size" of missing data increased, the percentage of missing data had a more pronounced effect on the agreement between measures. For a 14-day CGM data set to be considered representative for percentage time in range (%TIR), at least 70% of CGM data should be available over at least 10 days (R2 > 0.9). Skewed outcome measures, such as percentage time below range and coefficient of variation, were more affected by missing data than the less skewed measures (%TIR, percentage time above range, mean glucose). Conclusions: Both the degree and pattern of missing data impact upon the accuracy of recommended CGM-derived glycemic measures. In planning research, an understanding of patterns of missing data in the study population is required to gauge the likely effects of missing data on outcome accuracy. Trial registration number: Australian New Zealand Clinic Trials Registry ACTRN12616000753459.

Reproducibility of plasma glucose responses to moderate-intensity exercise in individuals with type 1 diabetes.

AIM: To examine the within-person variability in plasma glucose responses to moderate-intensity morning exercise in young individuals with type 1 diabetes after overnight fasting and under basal insulin conditions. METHODS: In this pilot study, eight participants completed 40 min of moderate-intensity exercise at 60% V̇O2 peak on three separate days. The within-person standard deviation (SDw) in plasma glucose response was analysed both during and 1 h after exercise using the two visits per participant most closely matched by pre-exercise plasma glucose level. RESULTS: When the two closest matched visits per individual were included for analysis, mean (±SD) change in plasma glucose level was -1.8 ± 1.1 mmoL/L during exercise and -0.6 ± 1.0 mmoL/L during recovery, with the SDw of these changes being 0.5 mmol (95% CI 0.2, 0.8) during exercise and 0.8 mmoL/L (95% CI 0.4, 1.3) during recovery. The median intra-individual difference in plasma glucose level change was 0.3 mmoL/L [IQR 0.1, 0.7] during exercise and 0.8 mmoL/L [IQR 0.4, 1.0] during recovery. CONCLUSION: Within-person plasma glucose responses to moderate-intensity exercise may be reproducible under fasting and basal insulin conditions and similar pre-exercise plasma glucose levels. This finding may assist the design of future studies investigating both the reproducibility of glycaemic responses to exercise and blood glucose management for individuals with type 1 diabetes.

The relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio required to maintain glycaemia is non-linear in young people with type 1 diabetes: A randomized crossover trial.

OBJECTIVE: To determine if the relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio (ICR) required to maintain glycaemia is linear in people with type 1 diabetes. METHODS: We used an open labelled randomized four-arm cross-over study design. Participants (N = 31) aged 12-27 years, HbA1c ≤ 64 mmol/mol (8.0%) received insulin doses based on the individual's ICR and the study breakfast carbohydrate quantity and then consumed four breakfasts containing 20, 50, 100 and 150 g of carbohydrate over four consecutive days in randomized order. The breakfast fat and protein percentages were standardized. Postprandial glycaemia was assessed by 5 h continuous glucose monitoring. The primary outcome was percent time in range (TIR) and secondary outcomes included hypoglycaemia, glucose excursion and incremental area under the curve. Statistical analysis included linear mixed modelling and Wilcoxon signed rank tests. RESULTS: The 20 g carbohydrate breakfast had the largest proportion of TIR (0.74 ± 0.29 p < 0.04). Hypoglycaemia was more frequent in the 50 g (n = 13, 42%) and 100 g (n = 15, 50%) breakfasts compared to the 20 g (n = 6, 20%) and 150 g (n = 7, 26%) breakfasts (p < 0.029). The 150 g breakfast glucose excursion pattern was different from the smaller breakfasts with the lowest glucose excursion 0-2 h and the highest excursion from 3.5 to 5 h. CONCLUSIONS: A non-linear relationship between insulin requirement and breakfast carbohydrate content was observed, suggesting that strengthened ICRs are needed for meals with ≤20 and ≥150 g of carbohydrate. Meals with ≥150 g of carbohydrate may benefit from dual wave bolusing.

A Novel Mobile Health App to Educate and Empower Young People With Type 1 Diabetes to Exercise Safely: Prospective Single-Arm Mixed Methods Pilot Study.

BACKGROUND: Empowering young people with type 1 diabetes (T1D) to manage their blood glucose levels during exercise is a complex challenge faced by health care professionals due to the unpredictable nature of exercise and its effect on blood glucose levels. Mobile health (mHealth) apps would be useful as a decision-support aid to effectively contextualize a blood glucose result and take appropriate action to optimize glucose levels during and after exercise. A novel mHealth app acT1ve was recently developed, based on expert consensus exercise guidelines, to provide real-time support for young people with T1D during exercise. OBJECTIVE: Our aim was to pilot acT1ve in a free-living setting to assess its acceptability and functionality, and gather feedback on the user experience before testing it in a larger clinical trial. METHODS: A prospective single-arm mixed method design was used. Ten participants with T1D (mean age 17.7 years, SD 4.2 years; mean HbA1c, 54 mmol/mol, SD 5.5 mmol/mol [7.1%, SD 0.5%]) had acT1ve installed on their phones, and were asked to use the app to guide their exercise management for 6 weeks. At the end of 6 weeks, participants completed both a semistructured interview and the user Mobile Application Rating Scale (uMARS). All semistructured interviews were transcribed. Thematic analysis was conducted whereby interview transcripts were independently analyzed by 2 researchers to uncover important and relevant themes. The uMARS was scored for 4 quality subscales (engagement, functionality, esthetics, and information), and a total quality score was obtained from the weighted average of the 4 subscales. Scores for the 4 objective subscales were determined by the mean score of each of its individual questions. The perceived impact and subjective quality of acT1ve for each participant were calculated by averaging the scores of their related questions, but were not considered in the total quality score. All scores have a maximal possible value of 5, and they are presented as medians, IQRs, and ranges. RESULTS: The main themes arising from the interview analysis were "increased knowledge," "increased confidence to exercise," and "suitability" for people who were less engaged in exercise. The uMARS scores for acT1ve were high (out of 5) for its total quality (median 4.3, IQR 4.2-4.6), engagement (median 3.9, IQR 3.6-4.2), functionality (median 4.8, IQR 4.5-4.8), information (median 4.6, IQR 4.5-4.8), esthetics (median 4.3, IQR 4.0-4.7), subjective quality (median 4.0, IQR 3.8-4.2), and perceived impact (median 4.3, IQR 3.6-4.5). CONCLUSIONS: The acT1ve app is functional and acceptable, with a high user satisfaction. The efficacy and safety of this app will be tested in a randomized controlled trial in the next phase of this study. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619001414101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378373.

Effect of a Hybrid Closed-Loop System on Glycemic and Psychosocial Outcomes in Children and Adolescents With Type 1 Diabetes: A Randomized Clinical Trial.

Importance: Hybrid closed-loop (HCL) therapy has improved glycemic control in children and adolescents with type 1 diabetes; however, the efficacy of HCL on glycemic and psychosocial outcomes has not yet been established in a long-term randomized clinical trial. Objective: To determine the percentage of time spent in the target glucose range using HCL vs current conventional therapies of continuous subcutaneous insulin infusion or multiple daily insulin injections with or without continuous glucose monitoring (CGM). Design, Setting, and Participants: This 6-month, multicenter, randomized clinical trial included 172 children and adolescents with type 1 diabetes; patients were recruited between April 18, 2017, and October 4, 2019, in Australia. Data were analyzed from July 25, 2020, to February 26, 2021. Interventions: Eligible participants were randomly assigned to either the control group for conventional therapy (continuous subcutaneous insulin infusion or multiple daily insulin injections with or without CGM) or the intervention group for HCL therapy. Main Outcomes and Measures: The primary outcome was the percentage of time in range (TIR) within a glucose range of 70 to 180 mg/dL, measured by 3-week masked CGM collected at the end of the study in both groups. Secondary outcomes included CGM metrics for hypoglycemia, hyperglycemia, and glycemic variability and psychosocial measures collected by validated questionnaires. Results: A total of 135 patients (mean [SD] age, 15.3 [3.1] years; 76 girls [56%]) were included, with 68 randomized to the control group and 67 to the HCL group. Patients had a mean (SD) diabetes duration of 7.7 (4.3) years and mean hemoglobin A1c of 64 (11) mmol/mol, with 110 participants (81%) receiving continuous subcutaneous insulin infusion and 72 (53%) receiving CGM. In the intention-to-treat analyses, TIR increased from a mean (SD) of 53.1% (13.0%) at baseline to 62.5% (12.0%) at the end of the study in the HCL group and from 54.6% (12.5%) to 56.1% (12.2%) in the control group, with a mean adjusted difference between the 2 groups of 6.7% (95% CI, 2.7%-10.8%; P = .002). Hybrid closed-loop therapy also reduced the time that patients spent in a hypoglycemic (<70 mg/dL) range (difference, -1.9%; 95% CI, -2.5% to -1.3%) and improved glycemic variability (coefficient of variation difference, -5.7%; 95% CI, -10.2% to -0.9%). Hybrid closed-loop therapy was associated with improved diabetes-specific quality of life (difference, 4.4 points; 95% CI, 0.4-8.4 points), with no change in diabetes distress. There were no episodes of severe hypoglycemia or diabetic ketoacidosis in either group. Conclusions and Relevance: In this randomized clinical trial, 6 months of HCL therapy significantly improved glycemic control and quality of life compared with conventional therapy in children and adolescents with type 1 diabetes. Trial Registration: ANZCTR identifier: ACTRN12616000753459.

Demographic and clinical characteristics of a population-based pediatric cohort of type 1 and type 2 diabetes in Western Australia (1999-2019).

OBJECTIVES: To determine demographic and clinical characteristics of youth diagnosed with type 1 (T1D) or type 2 (T2D) diabetes aged ≤15 years from 1999 to 2019 in Western Australia, and examine time to first diagnosis of diabetes complications. METHODS: A retrospective cohort study was conducted of patients identified from the population-based, prospective Western Australian Children's Diabetes Database and longitudinal data extracted for available demographic and clinical variables. Patients were followed from diagnosis to transition to adult services, death, or December 31, 2019. Cox proportional hazards regression models were used to analyse time to first diagnosis of hypertension, high cholesterol or microalbuminuria, after adjusting for sex, age at diagnosis, time period of diagnosis, hemoglobin A1c , and body max index Z-score. RESULTS: 2438 eligible patients were identified (2209 [91%] T1D: 229 [9%] T2D). The mean age at diagnosis was lower in patients with T1D (8.5 [±4.0] vs. 12.7 [±2.0] years). A higher proportion of patients with T2D were female (58% vs. 47%) and of Aboriginal ethnicity (59% vs. 2%). The median HbA1c (interquartile range) at diagnosis was lower 8.9% [6.7, 11.5] (74 mmol/mol [50, 102]) versus 11.6% [10.1, 13.3] (103 mmol/mol [87, 122]) and mean body max index Z-score higher (2.05 [±0.66] vs. 0.37 [±0.95]), in patients with T2D compared to T1D. Patients with T2D had a higher risk of hypertension, high cholesterol, and microalbuminuria (aHR 3.39 [95%CI:2.04, 5.63], 2.69 [95%CI:1.21, 5.98], and 19.79 [95%CI:10.99, 35.64] respectively).

Acute hyperglycaemia does not have a consistent adverse effect on exercise performance in recreationally active young people with type 1 diabetes: a randomised crossover in-clinic study.

AIMS/HYPOTHESIS: In individuals with type 1 diabetes, chronic hyperglycaemia impairs aerobic fitness. However, the effect of acute marked hyperglycaemia on aerobic fitness is unclear, and the impact of insulin level has not been examined. In this study, we explored if acute hyperglycaemia with higher or low insulin levels affects [Formula: see text] and other exercise performance indicators in individuals with type 1 diabetes. METHODS: Eligible participants were aged 14 to 30 years, with complication-free, type 1 diabetes and HbA1c ≤ 75 mmol/mol (≤9%). Participants exercised in a clinical laboratory under three clamp (constant insulin, variable glucose infusion) conditions: euglycaemia (5 mmol/l) with 20 mU [m2 BSA]-1 min-1 insulin (where BSA is body surface area) (Eu20); hyperglycaemia (17 mmol/l) with 20 mU [m2 BSA]-1 min-1 insulin (Hyper20); and hyperglycaemia (17 mmol/l) with 5 mU [m2 BSA]-1 min-1 insulin (Hyper5) on separate days. Participants and the single testing assessor were blinded to condition, with participants allocated to randomised testing condition sequences as they were consecutively recruited. Standardised testing (in order) conducted on each of the three study days included: triplicate 6 second sprint cycling, grip strength, single leg static balance, vertical jump and modified Star Excursion Balance Test, ten simple and choice reaction times and one cycle ergometer [Formula: see text] test. The difference between conditions in the aforementioned testing measures was analysed, with the primary outcome being the difference in [Formula: see text]. RESULTS: Twelve recreationally active individuals with type 1 diabetes (8 male, mean ± SD 17.9 ± 3.9 years, HbA1c 61 ± 11 mmol/mol [7.7 ± 1.0%], 7 ± 3 h exercise/week) were analysed. Compared with Eu20, [Formula: see text] was lower in Hyper20 (difference 0.17 l/min [95% CI 0.31, 0.04; p = 0.02] 6.6% of mean Eu20 level), but Hyper5 was not different (p = 0.39). Compared with Eu20, sprint cycling peak power was not different in Hyper20 (p = 0.20), but was higher in Hyper5 (64 W [95% CI 13, 115; p = 0.02] 13.1%). Hyper20 reaction times were not different (simple: p = 0.12) but Hyper5 reaction times were slower (simple: 11 milliseconds [95% CI 1, 22; p = 0.04] 4.7%) than Eu20. No differences between Eu20 and either hyperglycaemic condition were observed for the other testing measures (p > 0.05). CONCLUSIONS/INTERPRETATION: Acute marked hyperglycaemia in the higher but not low insulin state impaired [Formula: see text] but to a small extent. Acute hyperglycaemia had an insulin-dependent effect on sprint cycling absolute power output and reaction time but with differing directionality (positive for sprint cycling and negative for reaction time) and no effect on the other indicators of exercise performance examined. We find that acute hyperglycaemia is not consistently adverse and does not impair overall exercise performance to an extent clinically relevant for recreationally active individuals with type 1 diabetes. FUNDING: This research was funded by Diabetes Research Western Australia and Australasian Paediatric Endocrine Group grants.

Impact of Hybrid Closed Loop Therapy on Hypoglycemia Awareness in Individuals with Type 1 Diabetes and Impaired Hypoglycemia Awareness.

Objective: This study evaluated the efficacy of using a hybrid closed loop (HCL) system in restoring hypoglycemia awareness in individuals with impaired awareness of hypoglycemia (IAH). Research Design and Methods: Participants with IAH (Gold score ≥4) were recruited into a randomized crossover pilot study. They participated in two 8-week periods using a HCL system (Medtronic 670G™) (intervention) and standard insulin pump therapy (control). Hyperinsulinemic hypoglycemic clamp studies were undertaken at baseline and at the end of each study period for the evaluation of the counter-regulatory hormonal and symptomatic responses to hypoglycemia. Results: Seventeen participants (mean age [standard deviation] 35.8 years [11.2 years]) were included in the study. Peak epinephrine levels (median, interquartile range [IQR]) in response to hypoglycemia were similar postintervention and control periods; 234.7 pmol/L (109.2; 938.9) versus 188.3 pmol/L (133.7; 402.9), P = 0.233. However, both peak adrenergic and neuroglycopenic symptom scores were higher after intervention; 5.0 (4.5; 9.0) versus 4.0 (4.0; 5.5), P = 0.009, and 8.5 (6.0; 15.0) versus 6.5 (6.0; 7.0) P = 0.014, respectively. Self-reported hypoglycemia awareness improved: median (IQR) Gold score was 4.0 (3.0; 5.5) versus 5.5 (4.5; 6.0); intervention versus control, P = 0.033. Time spent <3.9 and <3.0 mmol/L was lower in the intervention group than in control, P = 0.002. Other patient-reported outcomes (hypoglycemia fear and diabetes treatment satisfaction) did not change. Conclusions: A short-term use of a HCL system failed to demonstrate an improvement in counter-regulatory hormonal responses. However, higher hypoglycemia symptom scores during controlled hypoglycemia, better self-reported hypoglycemia awareness, and less time spent in hypoglycemia suggest the potential benefits of a HCL system in people with IAH. Trial Registration: anzctr.org.au Identifier: ACTRN12616000909426.

Effect of frequency of sensor use on glycaemic control in individuals on sensor-augmented pump therapy with and without Predictive Low Glucose Management System.

Improved frequency of sensor use improves glycaemic control. Furthermore, there is no deterioration of glycaemic control with increased sensor use in individuals on Predictive Low Glucose Management (PLGM) system. Younger children are more likely to have better sensor uptake than older children.

Use of Continuous Glucose Monitoring Trends to Facilitate Exercise in Children with Type 1 Diabetes.

Diabetes care during exercise frequently requires interruptions to activity and adds extra challenges particularly for young individuals with type 1 diabetes (T1D). This study investigated the use of a carbohydrate (CHO) intake algorithm based on continuous glucose monitoring (CGM) trends during physical activity. Children with T1D diagnosed for >1 year, ages 8-12 years, with a glycated hemoglobin of <10% were recruited into a randomized crossover study. They attended two similar mornings of fun-based physical activity and adhered to either a CHO intake algorithm based on CGM trends (intervention) or to standard exercise guidelines (consumption of 0.5 g CHO/kg/h when glucose <8 mmol/L) (control). Outcome measures included events such as exercise interruptions, CHO intake, and hypoglycemia events and percentage time spent in different sensor glucose ranges. Fourteen children completed the study. No episodes of significant hypoglycemia (sensor glucose level <3.0 mmol/L) occurred in either arm. Mean CHO intake was the same in both arms, 0.3 ± 0.2 g/kg/h. However, the intervention algorithm resulted in fewer CHO intake events per day: rate [95% confidence interval] 2.4 [1.6-2.3] versus 0.9 [0.4-1.5], P < 0.001, and exercise interruptions: 7.2 [5.9-8.8] versus 1.4 [0.8-2.1], P < 0.001, compared with control. There was no evidence of a difference in percentage time in range (3.9-10 mmol/L) and percentage time spent high between study arms. Both control and intervention protocols prevented significant hypoglycemia. Using a CHO intake algorithm based on CGM trends resulted in fewer CHO intake events and fewer interruptions to exercise. Use of this algorithm may reduce the burden of diabetes management with potential to facilitate activity in young people with T1D.

Characteristics of Automated Insulin Suspension and Glucose Responses with the Predictive Low-Glucose Management System.

BACKGROUND: The Predictive Low-Glucose Management (PLGM) system suspends basal insulin when hypoglycemia is predicted and reduces hypoglycemia. The aim of this analysis was to explore the characteristics of automated insulin suspension and sensor glucose (SG) responses following PLGM-initiated pump suspension. RESEARCH DESIGN AND METHODS: Children and adolescents with type 1 diabetes used the Medtronic MiniMed™ 640G pump as part of a randomized controlled trial. Data collected on a subgroup of participants on PLGM (suspend before low enabled) from CareLink® Therapy Management Software were analyzed to explore the time and duration of PLGM-initiated pump suspension. Day and nighttime were defined as 06:00 am to 10:00 pm and 10:00 pm to 6:00 am, respectively. RESULTS: There were 20,183 suspend before low events in 8523 days (2.37 events/day). The mean suspend duration was 55.0 ± 32.7 min (day 50.0 ± 30.1, night 71.7 ± 35.1; P < 0.001). Although a 2-h pump suspension was more frequent at night (day 5%, night 18%), a patient-initiated resumption occurred more during day (day 34%, night 12%). SG values did not reach <3.5 and <3 mmol/L in 79% and 91% of the events, respectively. The 2-h SG following pump resumption was higher following autoresumption during the day (day vs. night 9.3 mmol/L vs. 8.4 mmol/L; P < 0.001). CONCLUSIONS: Longer suspends and fewer glycemic excursions occur at night compared with day. The higher glycemic daytime excursions could be due to carbohydrate consumption to increase glucose levels and highlights the need for health care professionals to educate patients about carbohydrate intake around pump suspension.

The Use of Continuous Glucose Monitoring With Remote Monitoring Improves Psychosocial Measures in Parents of Children With Type 1 Diabetes: A Randomized Crossover Trial.

OBJECTIVE: This study aimed to explore the effect of continuous glucose monitors with remote monitoring on psychosocial outcomes in parents of children with type 1 diabetes. RESEARCH DESIGN AND METHODS: Children with type 1 diabetes, aged 2-12 years, along with their parents, were studied in a randomized crossover study. They participated in two 3-month periods using conventional blood glucose monitoring (control) or the Dexcom G5 Mobile continuous glucose monitoring (CGM) system with remote monitoring (intervention). The primary outcome was parental fear of hypoglycemia score assessed by the Hypoglycemia Fear Survey. RESULTS: Parental Hypoglycemia Fear Survey scores were lower while the child was using CGM with remote monitoring (P < 0.001). Furthermore, parental health-related quality of life and family functioning, stress, anxiety, and sleep measures also improved significantly after intervention. CONCLUSIONS: CGM with remote monitoring was found to improve multiple measures of quality of life, reduce family stress, and improve parental sleep.

Real-world outcomes of insulin pump compared to injection therapy in a population-based sample of children with type 1 diabetes.

BACKGROUND: Long-term glycemic outcomes in people with type 1 diabetes (T1D) on insulin pump therapy (continuous subcutaneous insulin infusion [CSII]) with appropriate control data are limited. Randomized controlled studies of technology in diabetes care are generally limited in duration and likely to have a selection bias. Hence, evaluation of population-based data provides a robust alternative evaluation of the benefits of insulin pump therapy. AIM: To investigate the outcomes of insulin pump therapy, as compared to injection therapy, in children with T1D attending a state-wide diabetes service in Western Australia. METHODS: Patients using insulin pump therapy between January 1999 and July 2016 were matched to patients on injection therapy on the basis of age, date of diagnosis, and hemoglobin A1C (HbA1c) at the start of pump therapy. RESULTS: A total of 513 pump-injection matches were identified. The pump cohort had a significantly lower mean HbA1c for the first 6 years of follow-up. The difference in HbA1c between the cohorts was observed by 6 months (3 mmol/mol [0.3%], standard error of the mean (SEM) 0.05, N = 463 matched pairs, P < 0.001) and was sustained with the greatest difference in HbA1c at 6 years (4 mmol/mol [0.4%], SEM 0.21, N = 112 matched pairs, P = 0.04). Beyond 6 years of follow-up, the HbA1c was not significantly lower in the pump cohort (N < 70 matched pairs). CONCLUSIONS: Patients using insulin pump therapy had a better long-term glycemic control relative to the matched injection therapy cohort. Large population-based cohort studies using real-world data provide a valuable perspective on evaluation of new technologies in children with T1D.