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Background:

The GOLD trial demonstrated that continuous glucose monitoring (CGM) in people with type 1 diabetes (T1D) managed with multiple daily insulin injections (MDI) improved not only glucose control but also overall well-being and treatment satisfaction. This analysis investigated which factors contributed to improved well-being and treatment satisfaction with CGM.

Methods:

The GOLD trial was a randomized crossover trial comparing CGM versus self-monitored blood glucose (SMBG) over 16 months. Endpoints included well-being measured by the World Health Organization–Five Well-Being Index (WHO-5) and treatment satisfaction by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) as well as glucose metrics. Multivariable R2-decomposition was used to understand which variables contributed most to treatment satisfaction.

Results:

A total of 139 participants were included. Multivariable analyses revealed that increased convenience and flexibility contributed to 60% (95% confidence interval [CI] = 50%-69%) of the improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire change version [DTSQc]) observed with CGM, whereas perceived effects on hypoglycemia and hyperglycemia only contributed to 6% (95% CI = 2%-11%) of improvements. Significant improvements in well-being (WHO-5) by CGM were observed for the following: feeling cheerful (P = .025), calm and relaxed (P = .024), being active (P = .046), and waking up fresh and rested (P = .044). HbA1c reductions and increased time in range (TIR) were associated with increased treatment satisfaction, whereas glycemic variability was not. HbA1c reduction showed also an association with increased well-being and increased TIR with less diabetes-related distress.

Conclusions:

While CGM improves glucose control in people with T1D on MDI, increased convenience and flexibility through CGM is of even greater importance for treatment satisfaction and patient well-being. These CGM-mediated effects should be taken into account when considering CGM initiation.

Aim

To evaluate whether both bolus insulin injection frequency and smart pen engagement were associated with changes in glycaemic control, using real-world data from adults with type 1 diabetes (T1D).

Materials and Methods

Adults using a smart pen (NovoPen 6) to administer bolus insulin (fast-acting insulin aspart or insulin aspart) alongside continuous glucose monitoring were eligible for inclusion. Smart pen engagement was characterized by number of days with pen data uploads over the previous 14 days. Glycaemic control was evaluated by analysing glucose metrics.

Results

Overall, data from 1194 individuals were analysed. The number of daily bolus injections was significantly associated with time in range (TIR; 3.9-10.0 mmol/L [70-180 mg/dL]; P < 0.0001). Individuals administering, on average, three daily bolus insulin injections had an estimated 11% chance of achieving >70% TIR. The probability of achieving >70% TIR increased with the mean number of daily bolus injections. However, the percentage of TIR was lower on days when individuals administered higher-than-average numbers of injections. The observed mean number of daily bolus injections administered across the study population was lower than the optimal number required to reach glycaemic targets (4.8 injections vs. 6-8 injections). Smart pen engagement was significantly associated with improved TIR.

Conclusions

Glycaemic control was associated with daily bolus insulin injection frequency and smart pen engagement. A treatment regimen combining an optimal bolus injection strategy, and effective smart pen engagement, may improve glycaemic control among adults with T1D.

Aims/hypothesis

Previous studies have shown that individuals with similar mean glucose levels (MG) or percentage of time in range (TIR) may have different HbA_1c values. The aim of this study was to further elucidate how MG and TIR are associated with HbA_1c.

Methods

Data from the randomised clinical GOLD trial (n=144) and the follow-up SILVER trial (n=98) of adults with type 1 diabetes followed for 2.5 years were analysed. A total of 596 paired HbA_1c/continuous glucose monitoring measurements were included. Linear mixed-effects models were used to account for intra-individual correlations in repeated-measures data.

Results

In the GOLD trial, the mean age of the participants (± SD) was 44±13 years, 63 (44%) were female, and the mean HbA_1c (± SD) was 72±9.8 mmol/mol (8.7±0.9%). When correlating MG with HbA_1c, MG explained 63% of the variation in HbA_1c (r=0.79, p<0.001). The variation in HbA_1c explained by MG increased to 88% (r=0.94, p value for improvement of fit <0.001) when accounting for person-to-person variation in the MG–HbA_1c relationship. Time below range (TBR; <3.9 mmol/l), time above range (TAR) level 2 (>13.9 mmol/l) and glycaemic variability had little or no effect on the association. For a given MG and TIR, the HbA_1c of 10% of individuals deviated by >8 mmol/mol (0.8%) from their estimated HbA_1c based on the overall association between MG and TIR with HbA_1c. TBR and TAR level 2 significantly influenced the association between TIR and HbA_1c. At a given TIR, each 1% increase in TBR was related to a 0.6 mmol/mol lower HbA_1c (95% CI 0.4, 0.9; p<0.001), and each 2% increase in TAR level 2 was related to a 0.4 mmol/mol higher HbA_1c (95% CI 0.1, 0.6; p=0.003). However, neither TIR, TBR nor TAR level 2 were significantly associated with HbA_1c when accounting for MG.

Conclusions/interpretation

Inter-individual variations exist between MG and HbA_1c, as well as between TIR and HbA_1c, with clinically important deviations in relatively large groups of individuals with type 1 diabetes. These results may provide important information to both healthcare providers and individuals with diabetes in terms of prognosis and when making diabetes management decisions.

Background:

People with type 1 diabetes generally view it easier to exercise when having continuous information of the glucose levels. We evaluated whether patients with type 1 diabetes managed with multiple daily insulin injections (MDI) exercised more after initiating continuous glucose monitoring (CGM) and whether the improved glycemic control and well-being associated with CGM translates into improved blood lipids and markers of inflammation.

Method:

The GOLD trial was a randomized cross-over trial over 16 months where patients used either CGM or capillary self-monitoring of blood glucose (SMBG) over six months, with a four-month wash-out period between the two treatment periods. We compared grade of physical activity, blood lipids, apolipoproteins, and high-sensitivity C-reactive protein (hsCRP) levels during CGM and SMBG.

Results:

There were 116 patients with information of physical activity estimated by the International Physical Activity Questionnaire (IPAQ) during both CGM and SMBG. No changes were found during CGM or SMBG, IPAQ scores 3305 versus 3878 (P = .16). In 136 participants with information of blood lipid levels with no change in lipid-lowering medication during the two treatment periods, HbA1c differed by 4.2 mmol/mol (NGSP 0.39%) between SMBG and CGM treatment (P < .001). No significant changes existed in low-density lipoprotein, high-density lipoprotein, triglycerides, total cholesterol, apolipoprotein A1, apolipoprotein B1, or hsCRP, during CGM and SMBG.

Conclusion:

Although many patients experience it easier to perform physical activity when monitoring glucose levels with CGM, it does not influence the amount of physical activity in persons with type 1 diabetes. Blood lipids, apolipoprotein, and hsCRP levels were similar during CGM and SMBG.

Introduction

To improve the utilization of continuous- and flash glucose monitoring (CGM/FGM) data we have tested the hypothesis that a machine learning (ML) model can be trained to identify the most likely root causes for hypoglycemic events.

Methods

CGM/FGM data were collected from 449 patients with type 1 diabetes. Of the 42,120 identified hypoglycemic events, 5041 were randomly selected for classification by two clinicians. Three causes of hypoglycemia were deemed possible to interpret and later validate by insulin and carbohydrate recordings: (1) overestimated bolus (27%), (2) overcorrection of hyperglycemia (29%) and (3) excessive basal insulin presure (44%). The dataset was split into a training (n = 4026 events, 304 patients) and an internal validation dataset (n = 1015 events, 145 patients). A number of ML model architectures were applied and evaluated. A separate dataset was generated from 22 patients (13 ‘known’ and 9 ‘unknown’) with insulin and carbohydrate recordings. Hypoglycemic events from this dataset were also interpreted by five clinicians independently.

Results

Of the evaluated ML models, a purpose-built convolutional neural network (HypoCNN) performed best. Masking the time series, adding time features and using class weights improved the performance of this model, resulting in an average area under the curve (AUC) of 0.921 in the original train/test split. In the dataset validated by insulin and carbohydrate recordings (n = 435 events), i.e. ‘ground truth,’ our HypoCNN model achieved an AUC of 0.917.

Conclusions

The findings support the notion that ML models can be trained to interpret CGM/FGM data. Our HypoCNN model provides a robust and accurate method to identify root causes of hypoglycemic events.

Background

The development of entrustable professional activities (EPAs) as a framework for work-based training and assessment in undergraduate medical education has become popular. EPAs are defined as units of a professional activity requiring adequate knowledge, skills, and attitudes, with a recognized output of professional labor, independently executable within a time frame, observable and measurable in its process and outcome, and reflecting one or more competencies. Before a new framework is implemented in a specific context, it is valuable to explore social validity, that is, the acceptability by relevant stakeholders.

Aim

The aim of our work was to define Core EPAs for undergraduate medical education and further explore the social validity of the constructs.

Method and material

In a nationwide collaboration, EPAs were developed using a modified Delphi procedure and validated according to EQual by a group consisting of teachers nominated from each of the seven Swedish medical schools, two student representatives, and an educational developer (n = 16). In the next step, social validity was explored in a nationwide survey. The survey introduced the suggested EPAs. For each EPA, the importance of the EPA was rated, as was the rater’s perception of the present graduates’ required level of supervision when performing the activity. Free-text comments were also included and analyzed.

Results

Ten Core EPAs were defined and validated. The validation scores for EQual ranged from 4.1 to 4.9. The nationwide survey had 473 responders. All activities were rated as “important” by most responders, ranging from 54 to 96%. When asked how independent current graduates were in performing the ten activities, 6 to 35% reported “independent”. The three themes of the free text comments were: ‘relevant target areas and content’; ‘definition of the activities’; and ‘clinical practice and learning’.

Conclusion

Ten Core EPAs were defined and assessed as relevant for Swedish undergraduate medical education. There was a consistent gap between the perceived importance and the certainty that the students could perform these professional activities independently at the time of graduation. These results indicate that the ten EPAs may have a role in undergraduate education by creating clarity for all stakeholders.

Introduction

This study utilized continuous glucose monitoring data to analyze the effects of switching to treatment with fast-acting insulin aspart (faster aspart) in adults with type 1 diabetes (T1D) in clinical practice.

Methods

A noninterventional database review was conducted in Sweden among adults with T1D using multiple daily injection (MDI) regimens who had switched to treatment with faster aspart as part of basal-bolus treatment. Glycemic data were retrospectively collected during the 26 weeks before switching (baseline) and up to 32 weeks after switching (follow-up) to assess changes in time in glycemic range (TIR; 70–180 mg/dL), mean sensor glucose, glycated hemoglobin (HbA1c) levels, coefficient of variation, time in hyperglycemia (level 1, > 180 to ≤ 250 mg/dL; level 2, > 250 mg/dL), and time in hypoglycemia (level 1, ≥ 54 to < 70 mg/dL; level 2, < 54 mg/dL) (ClinicalTrials.gov Identifier NCT03895515).

Results

Overall, 178 participants were included in the study cohort. The analysis population included 82 individuals (mean age 48.5 years) with adequate glucose sensor data. From baseline to follow-up, statistically significant improvements were reported for TIR (mean increase 3.3%-points [approximately 48 min/day]; p = 0.006) with clinically relevant improvement (≥ 5%) in 43% of participants. Statistically significant improvements from baseline were observed for mean sensor glucose levels, HbA1c levels, and time in hyperglycemia (levels 1 and 2), with no statistically significant changes in time spent in hypoglycemia.

Conclusions

Switching to faster aspart was associated with improvements in glycemic control without increasing hypoglycemia in adults with T1D using MDI in this real-world setting.

OBJECTIVE: Continuous glucose monitoring (CGM) reduces HbA(1c) and time spent in hypoglycemia in people with type 1 diabetes (T1D) treated with multiple daily insulin injections (MDI) when evaluated over shorter time periods. It is unclear to what extent CGM improves and helps to maintain glucose control, treatment satisfaction, diabetes distress, hypoglycemic concerns, and overall well-being over longer periods of time.

RESEARCH DESIGN AND METHODS: The GOLD trial was a randomized crossover trial performed over 16 months of CGM treatment in people with T1D treated with MDI. People completing the trial (n = 141) were invited to participate in the current SILVER extension study in which 107 patients continued CGM treatment over 1 year along with the support of a diabetes nurse every 3 months.

RESULTS: The primary end point of the change in HbA(1c) over 1.0-1.5 years of CGM use compared with previous self-monitoring of blood glucose during GOLD showed a decrease in HbA(1c) of 0.35% (95% CI 0.19-0.50, P < 0.001). Time spent in hypoglycemia <3.0 mmol/L (54 mg/dL) and <4.0 mmol/L (72 mg/dL) decreased from 2.1% to 0.6% (P < 0.001) and from 5.4% to 2.9% (P < 0.001), respectively. Overall well-being (World Health Organization 5-item well-being index, P = 0.009), treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire, P < 0.001), and hypoglycemic confidence (P < 0.001) increased, while hypoglycemic fear (Hypoglycemia Fear Survey-Worry, P = 0.016) decreased and diabetes distress tended to decrease (Problem Areas in Diabetes Scale, P = 0.06). From randomization and screening in GOLD, HbA(1c) was lowered by 0.45% (P < 0.001) and 0.68% (P < 0.001) after 2.3 and 2.5 years, respectively.

CONCLUSIONS: The SILVER study supports beneficial long-term effects from CGM on HbA(1c), hypoglycemia, treatment satisfaction, well-being, and hypoglycemic confidence in people with T1D managed with MDI.

Aim To identify responders to continuous glucose monitoring (CGM) in relation to reductions in HbA1c and percentage of time spent in hypoglycaemia after initiation of CGM for individuals with type 1 diabetes treated with multiple daily insulin injections.

Materials and Methods We analysed data from 142 participants in the GOLD randomized clinical trial. We evaluated how many lowered their HbA1c by more than 0.4% (>4.7 mmol/mol) or decreased the time spent in hypoglycaemia over 24 hours by more than 20 or 30 minutes, and which baseline variables were associated with those improvements.

Results Lower reduction of HbA1c was associated with greater reduction of hypoglycaemia (r = -0.52; P < .0001). During CGM, 47% of participants lowered their HbA1c values by more than 0.4% (>4.7 mmol/mol) than with self-measurement of blood glucose, and 47% decreased the time spent in hypoglycaemia by more than 20 minutes over 24 hours. Overall, 78% either reduced their HbA1c by more than 0.4% (>4.7 mmol/mol) or the time spent in hypoglycaemia by more than 20 minutes over 24 hours, but only 14% improved both. Higher HbA1c, a lower percentage of time at less than 3.0 or 3.9 mmol/L, a lower coefficient of variation (CV) and a higher percentage of time above 13.9 mmol/L (P = .016) were associated with greater HbA1c reduction during CGM. The variables associated with a greater reduction of time in hypoglycaemia were female sex, greater time with glucose levels at less than 3.0 mmol/L, higher CV, and higher hypoglycaemia confidence as evaluated by a hypoglycaemic confidence questionnaire.

Conclusion The majority of people with type 1 diabetes managed by multiple daily insulin injections benefit from CGM; some experienced reduced HbA1c while others reduced the time spent in hypoglycaemia. These factors need to be considered by healthcare professionals and decision-makers for reimbursement and diabetes guidelines.