Flash Glucose Monitoring Accepted in Daily Life of Children and Adolescents with Type 1 Diabetes and Reduction of Severe Hypoglycemia in Real-Life Use

Abstract

Background:

Flash glucose monitoring (FGM) is covered by the Belgian public health insurance for type 1 diabetes since 2016. The objective of this study was to describe the use of FGM and diabetes outcomes in type 1 diabetic children and adolescents 1 year after reimbursement.

Methods:

All patients had the choice to convert to FGM or to continue with self-monitoring of blood glucose (SMBG). Clinical data were collected at baseline, at the next visit, and after 12 months; glucose profiles at next visit and after 12 months. Regression analyses were performed to identify predictors of FGM acceptance and changes in metabolic control.

Results:

A total of 334 subjects were included, of whom 278 (83.2%) switched to FGM. They were younger (13.6 vs. 15.2 years; P = 0.012) and performed more SMBG testing at baseline than patients who did not switch (4.3 vs. 4.1 tests daily; P = 0.008). At the end of follow-up, the rate of severe hypoglycemia decreased by 53% in the group of FGM users (P = 0.012) while it remained stable in SMBG users. Median glycated hemoglobin did not change significantly in both groups. Among subjects who switched to FGM, 15.8% reverted to SMBG after a median use of 5.3 months. Adverse events, diabetes duration, and FGM utilization were independent predictors of the risk for reverting. FGM-related adverse events were associated with a fivefold increased risk to revert to SMBG (hazard ratio = 5.12; P < 0.0001).

Conclusions:

FGM is relatively well accepted and decreases the risk of severe hypoglycemic events in our pediatric population. FGM is more often discontinued in patients experiencing adverse events and with longer diabetes duration.

Introduction

Maintaining blood glucose concentrations close to normal range is one of the main goals in the management of type 1 diabetes to avoid long-term complications.¹ Frequent self-monitoring of blood glucose (SMBG) is associated with better diabetes control² and was until recently the only widely available method for blood glucose control. However, SMBG provides only a snapshot of the whole glycemic profile and may be painful, especially in children.³

Continuous glucose monitoring (CGM) overcomes some of the disadvantages of SMBG but is an expensive technology that only a few patients may afford. For some subjects, CGM can also be a source of discomfort, for instance because of the need for capillary calibrations.⁴

The factory-calibrated FreeStyle Libre flash glucose monitoring (FGM) system (Abbott Diabetes Care, Alameda, CA) represents a good compromise between SMBG and CGM. By continuously measuring glucose in the interstitial fluid, it provides glucose values at any time, just by scanning the sensor. In a similar way to the CGM, the readings are displayed together with the latest 8 h glucose profile and with a trend arrow but without need of capillary calibrations. On the other side, as the FGM device is not able to transmit data in real time, there is no generation of glucose alarms. This could be a disadvantage for patients experiencing severe hypoglycemic events (SH).

The accuracy and safety of the FGM for type 1 diabetes management have been demonstrated recently in both adult⁵ and pediatric populations.⁶ However, only few articles provide a follow-up (FU) of more than 3 months,^6

–9 and there is no such publication in a pediatric population at this time.

In Belgium, the cost of FGM, unlike CGM, is fully covered by the public health insurance for type 1 diabetic children and adolescents since August 2016, and this popularized the use of the FGM system in Belgian pediatric clinics very rapidly.

The aim of this observational prospective study was to describe the use of FGM during the first year of its reimbursement in our population of children and adolescents with type 1 diabetes.

Methods

Study design

All eligible subjects were invited to participate in this monocentric, prospective, observational cohort study.

The primary aim was to describe the use of FGM during the first year of its reimbursement in our population of children and adolescents with type 1 diabetes. Secondary goals were to characterize diabetes control, the occurrence of SH, and FGM-related adverse events during the period of FU.

Study population

Inclusion criteria were patients with onset of diabetes before 16 years of age, diabetes duration of more than 1 year, and aged from 4 to 20 years, followed in the Diabetology Clinic at the University Children's Hospital Queen Fabiola (Brussels, Belgium). In the context of its reimbursement, each patient had the choice to convert to FGM or to continue with the usual SMBG. In case, they accepted to switch to FGM, the patients and their family were trained by dedicated diabetes educators for inserting the sensor and interpreting the FGM data. All the patients wore the sensor on the back of the arm, as recommended by Abbott. They were instructed to keep their usual insulin treatment schema (two daily insulin injections of an individualized mixture of rapid- and intermediate-acting insulins also called “Freemix regimen,” multiple daily injections [MDI], or pump), as previously described, that is, flexible insulin therapy, based on predetermined actions in response to glucose monitoring.¹⁰ Dietary recommendations were those reported before.¹¹

Data collection

At each visit, SH, adverse events, and insulin doses were assessed by reviewing the logbook of the patient and were adjudicated by an endocrinologist. This logbook is a part of our standardized clinical FU.

Baseline visit was defined as either the first day of FGM utilization or the first visit after August 1, 2016, for patients continuing with SMBG. Medical history, clinical data, and glucose profile (either SMBG or FGM when appropriate) were collected at baseline, at the nearest next visit (referred further in the text as “first visit”), and at FU. Body mass index (BMI) was converted to standard deviation scores (SDS) based on the 1990 English references.¹² Total insulin daily dose (TDD) was collected at baseline and at the end of FU. SH and adverse events were assessed at baseline and at the end of FU by reviewing clinical files of the last 12 months. FGM satisfaction score was determined by the mean of a Likert-type scale at the end of FU.¹³ Blood samples were taken nonfasting. Glycated hemoglobin (HbA_1c) was measured by ion exchange high-performance liquid chromatography (normal value <6.2% or 44 mmol/mol). FGM data were uploaded by using the proprietary software (Abbott Diabetes Care). FGM utilization was defined as the percentage of data collected during the period between two visits. This study was approved by the medical ethics committee of the hospital, and all participants signed an informed consent.

Statistical analyses

Results were reported as mean ± standard deviation for parametric data and as median (interquartile range) if skewed. Analyses were performed according to an intention-to-treat basis, unless otherwise stated. We calculated that a minimum sample size of 131 subjects would be enough to show a difference of 0.3% in HbA_1c with a significance level of 0.05.

Comparisons between groups were performed using either independent samples t test or Wilcoxon–Mann–Whitney test, respectively, for parametric and nonparametric variables. Comparisons within groups were performed using paired samples t test or Wilcoxon signed rank sum test as appropriate for continuous data. χ ² test, with Yates' correction, or Fisher's exact test was used for discrete data comparisons. Categorical variables were recoded using the dummy coding system. For predicting the time to stop FGM use, a Cox proportional-hazards regression model was used.

Prespecified clinical variables entered in the univariate analysis were age; sex; age at diagnosis; diabetes duration; TDD; BMI-SDS at baseline; change in BMI-SDS during FU; type of insulin therapy; change of insulin therapy during FU; HbA_1c at baseline; frequency of SMBG testing and FGM scans (when available) at baseline, at first visit, and at the end of FU; FGM utilization at first visit; SH at baseline and at the end of FU; and reported adverse events. Then, those correlated at P-value <0.05 were entered in the multivariate analysis. Kaplan–Meier method was used to estimate the switch to SMBG. Variables entered were those already described. Linear regression was used to detect predictor for HbA_1c outcome.

Variables tested in the univariate analysis were age; sex; age at diagnosis; diabetes duration; TDD; BMI-SDS at baseline; change in BMI-SDS during FU; type of insulin therapy; change of insulin therapy during FU; HbA_1c at baseline; frequency of SMBG testing and FGM scans (when available) at baseline, at first visit, and at the end of FU; FGM utilization at first visit; SH at baseline and at the end of FU; and reported adverse events. Each variable correlated at P-value ≤0.1 was included in a stepwise multivariate regression model. Statistical tests were performed by SPSS for Windows version 24.0 (IBM SPSS Statistics, Chicago, IL). A P-value <0.05 was considered statistically significant.

Results

Of the 400 patients registered in our clinic, 334 patients with type 1 diabetes were included in the study (see Supplementary Fig. S1 for the study flow diagram). Clinical characteristics of our study population are listed in Table 1. The median time of FU was 12.7 (11.9–13.5) months. The switch from SMBG to FGM was accepted by 83.2% of the patients (n = 278). At baseline, FGM users were younger, with a median age of 13.6 (10.9–16.3) versus 15.2 (12.2–17.5) years (P = 0.012) and performed more SMBG than SMBG users: 4.3 (3.8–5.0) versus 4.1 (3.5–4.4) tests per day (P = 0.008). Other baseline clinical characteristics were similar in both groups.

Table 1.

Demographic Characteristics of the Study Population

Parameters	Baseline	End of FU	P
FGM users (n = 278)
Age, years	13.6 (10.9–16.3)^†	—	—
Age at diagnosis, years	6.6 (3.6–9.7)	—	—
Diabetes duration, years	5.5 (3.4–7.8)	—	—
Male, n (%)	137 (49.3)	—	—
BMI-SDS, mean ± SD	0.85 ± 0.99	0.91 ± 0.96	0.004
Total daily dose, UI/(kg·d)	0.97 (0.77–1.23)	0.98 (0.79–1.20)	0.710
Insulin schema, n (%)
Freemix	233 (83.8)	211 (75.9)	0.015
MDI	24 (8.6)	37 (6.7)	0.056
CSII	18 (6.5)	27 (4.9)	0.162
Premix	3 (1.1)	3 (0.5)	1.000
SMBG users (n = 56)
Age, years	15.2 (12.2–17.5)^†	—	—
Age at diagnosis, years	7.3 (4.8–10.8)	—	0.460
Diabetes duration, years	5.3 (3.1–9.4)	—	—
Male, n (%)	35 (62.5)	—	—
BMI-SDS, mean ± SD	1.01 ± 1.02	1.08 ± 0.99	0.209
Total daily dose, UI/(kg·d)	0.99 (0.82–1.20)	1.01 (0.85–1.26)	0.611
Insulin schema, n (%)
Freemix	46 (82.1)	43 (76.8)	0.483
MDI	9 (16.1)	10 (17.9)	0.801
CSII	1 (1.8)	3 (5.4)	0.309
Premix	0	0	—

All values are shown as median (interquartile range) excluding gender and insulin schema as n (%) and BMI-SDS as mean ± SD. Comparisons between baseline versus the end of FU were performed using paired samples t test (BMI-SDS) or Wilcoxon signed rank sum test (total daily dose) or χ ² test, with Yates' correction (insulin schema).

Values in boldface are statistically significant at P < 0.05.

†

FGM users versus SMBG users (P = 0.012) performed using Wilcoxon–Mann–Whitney test.

BMI, body mass index; CSII, continuous subcutaneous insulin infusion; FGM, flash glucose monitoring; FU, follow-up; MDI, multiple daily injections; SD, standard deviation; SDS, standard deviation scores; SMBG, self-monitoring of blood glucose.

FGM users

Among the patients who switched to FGM, 84.2% (n = 234) were still using the FGM at the end of the FU period. Cox regression analysis identified adverse events, duration of diabetes, and utilization of the FGM at the first visit [2.8 (2.0–3.4) months after baseline] as independent predictors of reverting to SMBG (Table 2). Kaplan–Meier analysis showed that adverse events were associated with a fivefold increased risk to revert to SMBG (hazard ratio = 5.12; P < 0.001).

Table 2.

Cox Proportional Hazards Regression Analysis for Prediction of Stopping Flash Glucose Monitoring (FGM) Using Characteristics of the FGM Users

Covariates	Univariate analysis		Multivariate analysis
Covariates	HR (95% CI)	P	HR (95% CI)	P
Age	1.05 (0.96–1.15)	0.262	—	NE
Age at diagnosis	0.94 (0.87–1.03)	0.170	—	NE
Duration of diabetes	1.11 (1.03–1.19)	0.009	1.11 (1.02–1.20)	0.019
Gender, male vs. female	1.07 (0.59–1.93)	0.831	—	NE
Total insulin dose	1.76 (0.81–3.85)	0.159	—	NE
BMI-SDS at baseline	1.18 (0.87–1.61)	0.281	—	NE
Change in BMI-SDS	0.84 (0.44–1.61)	0.604	—	NE
Insulin schema	1.01 (0.63–1.61)	0.981	—	NE
Change of schema during FU	0.48 (0.12–1.99)	0.258	—	NE
HbA_1c at baseline	1.26 (1.06–1.51)	0.018	NS	0.077
SMBG at baseline	0.85 (0.67–1.07)	0.149	—	NE
Frequency of scans at the first visit	0.89 (0.81–0.99)	0.011	NS	0.100
Utilization at the first visit	0.97 (0.96–0.99)	0.001	0.98 (0.97–1.00)	0.024
Severe hypoglycemic events at baseline	1.09 (0.70–1.69)	0.733	—	NE
Severe hypoglycemic events during FU	1.11 (0.57–2.15)	0.772	—	NE
Adverse events	6.10 (3.35–11.09)	<0.001	5.12 (2.69–9.74)	<0.001

Values in boldface are statistically significant at P < 0.05.

CI, confidence interval; HbA_1c, glycated hemoglobin; HR, hazard ratio; NE, not selected for entry in the multivariate model; NS, not significant.

Time above range (>160 mg/dL), time in range (70–160 mg/dL), and time below 70 mg/dL were not different at first visit in comparison with the end of FU: 51% (44%–60%) versus 50% (42%–63%) (P = 0.753), 34% (29%–41%) versus 34% (24%–38%) (P = 0.527), and 14% (9%–19%) versus 17% (10%–21%) (P = 0.893), respectively.

Median HbA_1c did not change significantly during FU in both FGM and SMBG users (Table 3). However, when analyzing HbA_1c changes on an individual basis, we found that the reduction of HbA_1c was positively correlated with HbA_1c at baseline (Supplementary Fig. S2), and FGM scanning rate and BMI increase at the end of FU (multivariate R ² = 0.274; P < 0.001), as shown in Table 4. Results were similar when considering only FGM users who did not revert to SMBG (Supplementary Table S1).

Table 3.

Secondary Diabetes Outcomes

Parameters	Baseline	End of FU	P
FGM users (n = 278)
FU duration, months	—	12.7 (12.0–13.5)
HbA_1c, %	7.5 (6.9–8.1)	7.5 (6.9–8.2)	0.460
SMBG, n/day	4.3 (3.8–5.0)^†	—	0.011 ^*
Scan, n/day	—	7.0 (5.0–10.0)	0.011 ^*
At least one SH last year, n (%)	19 (6.8)	9 (3.2)	0.012
Satisfaction score, 1–15	—	13 (12–15)	—
SMBG users (n = 56)
FU duration, months	—	13.1 (11.9–13.3)
HbA_1c, %	7.6 (6.7–8.6)	7.8 (6.9–8.9)	0.229
SMBG, n/day	4.1 (3.5–4.4)^†	4.0 (3.5–4.9)	0.355
Scan, n/day	—	—	—
At least one SH last year, n (%)	4 (7.1)	5 (8.9)	0.317

All values are shown as median (interquartile range) excluding SH as n (%). Comparisons between baseline versus the end of FU were performed using Wilcoxon signed rank sum test (HbA_1c, SMBG) or χ ² test, with Yates' correction (SH).

Values in boldface are statistically significant at P < 0.05.

SMBG versus scan performed using Wilcoxon signed rank sum test.

†

FGM users versus SMBG users (P = 0.008) performed using Wilcoxon–Mann–Whitney test.

SH, severe hypoglycemic event.

Table 4.

Adjusted Linear Regression Analyses of Change in Glycated Hemoglobin in All Flash Glucose Monitoring Users (Intention-to-Treat Analysis)

	Univariate analysis		Multivariate analysis^*
	b (95% CI)	P	b (95% CI)	P
Age	−0.005 (−0.040 to 0.029)	0.756	—	NE
Age at diagnosis	−0.011 (−0.042 to 0.021)	0.504	—	NE
Duration of diabetes	0.007 (−0.025 to 0.040)	0.659	—	NE
Gender, male vs. female	−0.191 (−0.423 to 0.041)	0.107	—	NE
Total insulin dose	0.088 (−0.235 to 0.411)	0.593	—	NE
BMI-SDS at baseline	0.176 (0.060 to 0.292)	0.003	NS	0.101
Change in BMI-SDS	−0.390 (−0.640 to −0.140)	0.002	−0.242 (−0.476 to −0.009)	0.042
Insulin schema	−0.028 (−0.217 to 0.161)	0.772	—	NE
Change of schema during FU	0.169 (−0.239 to 0.576)	0.416	—	NE
HbA_1c at baseline	−0.353 (−0.438 to −0.269)	<0.001	−0.363 (−0.450 to −0.276)	<0.001
SMBG at baseline	−0.030 (−0.109 to 0.050)	0.465	—	NE
Frequency of scans at the first visit	−0.037 (−0.061 to −0.014)	0.002	NS	0.231
Frequency of scans at the end	−0.036 (−0.066 to −0.007)	0.014	−0.048 (−0.079 to −0.016)	0.003
Utilization at the first visit	−0.003 (−0.010 to 0.004)	0.407	—	NE
Severe hypoglycemic events at baseline	0.049 (−0.413 to 0.512)	0.834	—	NE
Severe hypoglycemic events during FU	0.161 (−0.499 to 0.820)	0.479	—	NE
Adverse events	−0.020 (−0.304 to 0.264)	0.889	—	NE

Forward stepwise multiple regression model R = 0.524; R ² = 0.274.

Values in boldface are statistically significant at P < 0.05.

FGM users who reverted to SMBG

On those 278 subjects who switched to FGM, 15.8% (n = 44) reverted to SMBG during the FU, after a median use of 5.3 (3.1–8.7) months. Median age at inclusion of those who reverted to SMBG was 14.0 (11.7–15.8) years versus those who did not switch 13.5 (10.6–16.4) years (P = 0.304). Among these patients, adverse events were more often reported, including premature losses of the sensor (31.8% vs. 12.4%; P = 0.001), skin reactions (18.2% vs. 2.6%; P < 0.001), and local pain (6.8% vs. 0%; P < 0.001). Subjects who reverted to SMBG had also a diabetes of longer duration, 7.3 (4.5–10.5) versus 5.2 (3.1–7.7) years (P = 0.002), and a higher baseline HbA_1c, 7.9% (7.5%–8.4%) versus 7.5% (6.8%–8.0%) or 63 (58–68) versus 58 (51–64) mmol/mol (P = 0.002). At the first visit after baseline, they scanned slightly less: 6.0 (4.0–7.8) versus 7.0 (5.0–10.0) scans per day (P = 0.022), and FGM utilization was lower: 78% (56%–88%) versus 85% (75%–92%), P = 0.003. They were also less satisfied by the FGM compared with continuing users (P < 0.001).

SMBG users

On the 334 subjects included in the study, 16.8% (n = 56) refused to switch to FGM. They were older and performed less SMBG testing at baseline in comparison with FGM users. Other clinical characteristics were similar (Tables 1 and 3). Main reasons for not using FGM were the apprehension to wear the sensor (93%), the fear of injury during fighting sports (5%), or because they already used CGM (2%).

Severe hypoglycemia

At baseline, the proportion of patients with at least one SH episode per year was similar in both groups (6.8% vs. 7.1% in FGM vs. SMBG users, respectively). At the end of FU, this proportion decreased by 53% in FGM users (P = 0.012), whereas it remained stable in SMBG users (Table 3). Among the nine FGM users who experienced SH during the FU, six of them had already experienced one or more SH episodes in the year preceding the switch to FGM while it was the first SH episode(s) for the last three patients.

When considering only these FGM users who did not revert to SMBG, the proportion of patients experiencing at least one SH episode per year decreased ever more, from 5.6% at baseline to 0.8% at the end of FU, meaning an 86% reduction (P = 0.037).

Discussion

The organization of diabetes care in Belgium allowed us to study FGM use in our pediatric population without any financial constraints. Indeed, FGM is fully supported by the public health insurance since its introduction in Belgian pediatric clinics, as it was already the case for SMBG. In this context, according to a recent study,¹⁴ we show that FGM was initially well accepted even if, unexpectedly, only 70% of our patients still use it after 1-year of FU. There are several reasons for this. First, we show that the main cause of reverting to SMBG was FGM-related adverse events, mostly premature losses of the sensor, and skin reactions. Stopping FGM was also more frequent in patients with diabetes of longer duration and with higher HbA_1c, the two conditions often associated with higher glucose variability.¹⁵ We know that the accuracy of the FGM is low in hypoglycemia and during rapid change of glucose values.¹⁶ Thus, a feeling of poor reliability could have played a role in the decision to stop the FGM, especially in case of diabetes with higher variability. Anyway, only one of our patients pointed this as the main cause of reverting to SMBG.

Acceptance rate to FGM among adults with type 1 diabetes is unknown in a context of full reimbursement. In our pediatric population, >15% of the subjects refused to switch to FGM during the year of FU. Those are mainly older children and adolescents, typically more difficult to manage.¹⁷ In adolescents, body image concerns increase,¹⁸ and this could be a barrier to the use of FGM. On the other side, FGM is best accepted in younger children, for whom parents are typically more involved. Parents of young children appreciate FGM because they can test glucose levels more often, even if the child is asleep.¹⁹

We did not find any change in time spent below 70 mg/dL between the first visit after the switch to FGM and at the end of FU. A randomized controlled trial in an adult population with type 1diabetes showed that the reduction of time in hypoglycemic range occurs mainly in the first 2 weeks of the FGM usage.⁵ Because of the design of this study, we were not able to record changes in hypoglycemic events that could have happened between baseline and the first visit.

A recent study comparing FGM and CGM showed that FGM is relatively inefficient to decrease hypoglycemia in type 1 diabetic adults with impaired awareness of hypoglycemia (IAH).²⁰ As IAH is also prevalent in pediatric populations,²¹ we cannot exclude that a high prevalence of IAH in our cohort biased our findings toward less efficacy on reducing hypoglycemia.

However, even though we did not document systematically IAH in our patients, we show that SH events decreased during the first year of FGM use, a finding already demonstrated in adults with type 1 diabetes⁵ and type 2 diabetes⁸ but not yet in a pediatric population. Noteworthily, these good results were obtained while our patients scan less the sensor than in previous studies^5,20, even though FGM, unlike CGM, does not provide real-time data with glucose alarms. It has been recently shown that CGM could reduce more effectively hypoglycemia in people with type 1 diabetes compared with FGM,²⁰ most probably by providing alarms for hypoglycemia and rapid glucose change. We hypothesize that the decreased occurrence of SH in our study is mainly related to the increased frequency of glucose controls allowed by the FGM as we know that the hypoglycemia risk may be affected by glucose monitoring frequency.²²

Unexpectedly, we did not observe an HbA_1c reduction in the group of the FGM users, in contrast to a recent retrospective study,²³ but this finding should be mitigated by the good glycemic control of our population at baseline (median HbA_1c 7.5%) and is in line with a prospective randomized trial that showed no HbA_1c reduction in well-controlled adults with type 1 diabetes after 6 months of FGM usage.⁵ Moreover, most of our patients are using a Freemix regimen, which may be less flexible than classical multiple daily injections (MDI), especially in case of post-lunch hyperglycemia. So, changes in outcomes may be lighter in our population than in MDI users. In FGM users, the reduction of HbA_1c was correlated with BMI increase, as we know that improved metabolic control is accompanied by weight gain in type 1 diabetes.²⁴

FGM was rather new at the onset of the study. The use of the trend arrows for insulin adjustments was implemented step-by-step by our patients and was not systematized, as there were no published recommendations at that time. For our patients on Freemix regimen, the routine downloads of FGM profiles revealed more than expected post-breakfast glucose peaks that led us to add a fast-acting insulin in the first injection of the day and to adapt our education program accordingly.

Thus, we hypothesize that using a decision algorithm implementing the trend arrows in a structured way,²⁵ together with the clinical experience gained by our team during the study, could further improve glucose control and acceptance of FGM in our population. Another option could be to switch from Freemix regimen to MDI for some patients with postprandial glucose peaks.

We conclude that FGM is relatively well accepted in our pediatric population and decreases the risk of severe hypoglycemic events after 1 year of FU in youths with good baseline glycemic control. FGM was more often discontinued in patients experiencing adverse events and with diabetes of longer duration.

Footnotes

Acknowledgment

This study was supported by Grant of the Belgian Kids' Fund for Pediatric Research.

Authors' Contributions

A.M. designed the study, collected the data, undertook analysis, interpreted results, wrote the article, reviewed and edited the article, and takes full responsibility for the integrity of data and the accuracy of data analysis. S.T. reviewed the article. L.C. designed the study, undertook analysis, interpreted results, wrote the article, and reviewed and edited the article.

Author Disclosure Statement

The authors declare that no competing financial interests exist.

Supplementary Material

Supplementary Figure S1

Supplementary Figure S2

Supplementary Table S1

References

Diabetes Control and Complication Trial Research Group: Effect of intensive diabetes treatment on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med, 1993; 329:977–986.

Karter

, Ackerson

, Darbinian

, et al.: Self-monitoring of blood glucose levels and glycemic control: the Northern California Kaiser Permanent Diabetes Registry. Am J Med, 2011; 111:1–9.

Wagner

, Malchoff

, Abbott

: Invasiveness as a barrier to self-monitoring of blood glucose in diabetes. Diabetes Technol Ther, 2005; 7:612–619.

T1D Exchange Clinic Network: Real-time continuous glucose monitoring among participants in the T1D exchange clinic registry. Diabetes Care, 2014; 37:2702–2709.

Bolinder

, Antuna

, Geelhoed-Duijvestijn

, et al.: Novel glucose-sensing technology and hypoglycaemia in type 1 diabetes: a multicentre, non-masked, randomised controlled trial. Lancet, 2016; 388:2254–2263.

Edge

, Acerini

, Campbell

, et al.: An alternative sensor-based method for glucose monitoring in children and young people with diabetes. Arch Dis Child, 2017; 102:543–549.

Ish-Shalom

, Wainstein

, Raz

, Mosenzon

: Improvement in glucose control in difficult-to-control patients with diabetes using a novel flash glucose monitoring device. J Diabetes Sci Technol, 2016; 10:1412–1413.

Haak

, Hanaire

, Ajjan

, et al.: Use of flash glucose-sensing technology for 12 months as a replacement for blood glucose monitoring in insulin-treated type 2 diabetes. Diabetes Ther, 2017; 8:573–586.

Oskarsson

, Antuna

, Geelhoed-Duijvestijn

, et al.: Impact of flash glucose monitoring on hypoglycaemia in adults with type 1 diabetes managed with multiple daily injection therapy: a pre-specified subgroup analysis of the IMPACT randomised controlled trial. Diabetologia, 2018; 61:539–550.

10.

Dorchy

: Insulin regimens and injection adjustments in diabetic children, adolescents and young adults: personal experience. Diabetes Metab, 2000; 26:500–507.

11.

Dorchy

: Dietary management for children and adolescents with diabetes mellitus: personal experience and recommendations. J Pediatr Endocrinol Metab, 2003; 16:131–148.

12.

Cole

, Freeman

, Preece

: Body mass index reference curves for the UK, 1990. Arch Dis Child, 1990; 73:17–24.

13.

Risser

: Development of an instrument to measure patient satisfaction with nurses and nursing care in primary care settings. Nurs Res, 1975; 24:45–52.

14.

Vergier

, Samper

, Dalla-Vale

, et al.: Evaluation of flash glucose monitoring after long-term use: a pediatric survey. Prim Care Diabetes, 2019; 13:63–70.

15.

Sartore

, Chilelli

, Burlina

, et al.: The importance of HbA_1c and glucose variability in patients with type 1 and type 2 diabetes: outcome of continuous glucose monitoring (CGM). Acta Diabetol, 2012; 49:S153–S160.

16.

Cengiz

, Tamborlane

: A tale of two compartments: interstitial versus blood glucose monitoring. Diabetes Technol Ther, 2009; 11:S11–S16.

17.

Bryden

, Peveler

, Stein

, et al.: Clinical and psychological course of diabetes from adolescence to young adulthood: a longitudinal cohort study. Diabetes Care, 2001; 24:1536–1540.

18.

Chiang

, Kirkman

, Laffel

, Peters

; on behalf of the type 1 diabetes Sourcebook Authors: Type 1 diabetes through the life span: a position statement of the American Diabetes Association. Diabetes Care, 2014; 37:2034–2054.

19.

Rai

, Hulse

, Kumar

: Feasibility and acceptability of ambulatory glucose profile in children with Type 1 diabetes mellitus: a pilot study. Indian J Endocrinol Metab, 2016; 20:790–794.

20.

Reddy

, Jugnee

, El Laboudi

, et al.: A randomized controlled pilot study of continuous glucose monitoring and flash glucose monitoring in people with type 1 diabetes and impaired awareness of hypoglycaemia. Diabet Med, 2018; 35:483–490.

21.

Abraham

, Gallego

, Brownlee

, et al.: Reduced prevalence of impaired awareness of hypoglycemia in a population-based clinic sample of youth with type 1 diabetes. Pediatr Diabetes, 2017; 18:729–733.

22.

Ziegler

, Heidtmann

, Hilgard

, et al.: Frequency of SMBG correlates with HbA_1c and acute complications in children and adolescents with type 1 diabetes. Pediatr Diabetes, 2011; 12:11–17.

23.

McKnight

, Gibb

: Flash glucose monitoring is associated with improved glycaemic control but use is largely limited to more affluent people in a UK diabetes centre. Diabet Med, 2017; 34:732.

24.

The DCCT Research Group: Weight gain associated with intensive therapy in the Diabetes Control and Complications Trial. Diabetes Care, 1988; 11:567–573.

25.

Pettus

, Edelman

: Recommendations for using real-time continuous glucose monitoring (rtCGM) data for insulin adjustments in type 1 diabetes. J Diabetes Sci Technol, 2017; 11:138–147.