Mitigating Severe Hypoglycemia in Users of Advanced Diabetes Technologies: Impaired Awareness of Hypoglycemia and Unhelpful Hypoglycemia Beliefs as Targets for Interventions

Abstract

Objective:

A subgroup analysis of the Hypoglycemia Awareness Restoration Programme for people with type 1 diabetes and problematic hypoglycemia persisting despite optimized care (HARPdoc) trial was conducted to explore the impact of Blood Glucose Awareness Training (BGAT, a hypoglycemia awareness training program) and the HARPdoc (a psychoeducation addressing unhelpful hypoglycemia beliefs) in reducing severe hypoglycemia (SH) in individuals using advanced diabetes technologies (ADTs).

Methods:

Data from trial participants who utilized ADTs, including continuous glucose monitors or automated insulin delivery systems, were extracted. Generalized linear mixed-effects models with Poisson distribution or linear mixed-effects models were used to evaluate SH incidence, and Gold questionnaire, Attitudes to Awareness of Hypoglycemia (A2A), Problem Areas in Diabetes (PAID), Hospital Anxiety and Depress Scale (HADS)-anxiety, and HADS-depression scores as measures of hypoglycemia awareness, unhelpful hypoglycemia beliefs, diabetes distress, and anxiety and depression symptoms, respectively.

Results:

In the 45 participants using ADTs, the BGAT and HARPdoc interventions both reduced SH incidence by more than 50% (P < 0.0001) and yielded improvements in hypoglycemia awareness (P < 0.05). HARPdoc outperformed BGAT in reducing SH at month 24 (P = 0.01). HARPdoc also mitigated unhelpful hypoglycemia beliefs (P < 0.0001), diabetes distress (P < 0.05), and anxiety symptoms (P < 0.05); BGAT demonstrated no significant impacts in these respects. Neither HARPdoc nor BGAT had significant effects on depression symptoms.

Conclusion:

Psychoeducation (BGAT and HARPdoc) was effective in reducing SH in people using ADTs. HARPdoc may also provide greater long-term SH reduction and improves psychological well-being in this patient group.

Introduction

Severe hypoglycemia (SH), characterized by neuroglycopenia-induced impaired cognitive and/or physical function and requiring assistance for treatment and recovery,¹ is a devastating and potentially life-threatening complication of insulin therapy for type 1 diabetes.² Structured diabetes education that focuses on providing patients with information about diabetes self-management, including hypoglycemia prevention and management, has exemplified the efficacy of knowledge-based interventions in reducing SH.^3
–5 Advanced diabetes technologies (ADTs), such as intermittently scanned and real-time continuous glucose monitoring systems (CGMs) offer continuous glucose information without or with hypoglycemia alarms.⁶ Automated insulin delivery systems (AIDs), including CGM plus insulin pumps capable of automatically suspending insulin infusion or more recently hybrid closed-loop insulin pumps (HCLs) with algorithm-based insulin dose reduction, have the additional ability to mitigate hypoglycemia.^7,8 These ADTs, with their ability to improve glycemic outcomes, including reducing hypoglycemia, have become the standard of care for all people living with type 1 diabetes⁹ in countries where they can be afforded. However, data from clinical trials,^10,11 health care institutions,^12,13 and national patient registries^8,14 reveal that 13–35% of adults with type 1 diabetes either experience SH or spend substantial time with dangerously low glucose levels despite using these technologies. More remained to be understood about the factors contributing to patients’ development of SH despite access to the standard of care, which includes ADT use.¹⁵

Recurrent hypoglycemia can lead to impaired awareness of hypoglycemia (IAH), which is often accompanied with loss of physiological glucose counterregulatory defenses¹⁶ and is associated with a sixfold greater risk for developing SH.¹⁷ Blood Glucose Awareness Training (BGAT), aiming to enhance one’s sensitivity to symptoms at extreme blood glucose levels, has since been shown to improve hypoglycemia awareness and reduce SH.¹⁸ Furthermore, unhelpful hypoglycemia health beliefs have been described by people with IAH and subsequently associated with SH.^19,20 A remaining question was whether IAH and unhelpful hypoglycemia beliefs promote the development of SH in people who use ADTs, who have access to continuous glucose information, and possibly also equipped with AIDs.

Observational studies have indicated that adults with type 1 diabetes who use ADTs, both IAH and unhelpful hypoglycemia beliefs, were associated with SH development.^12,13 Consistent to these findings, hypoglycemia clamp studies demonstrated that physiological glucose counterregulatory responses might not fully recover despite long-term use of CGM²¹ and AIDs.²² Qualitative interviews with people who have type 1 diabetes and use ADTs suggest that individuals may mistrust CGM hypoglycemia data and thus omit hypoglycemia self-management during asymptomatic hypoglycemic episodes.²³ Undertreatment of hypoglycemia due to minimal concerns about hypoglycemia or overdosing insulin to avoid hyperglycemia has been observed and may explain the development of SH events in users of ADT.²³ Despite supportive observational data, evidence from clinical trial data is nonetheless needed to confirm these possible mechanisms.²⁴

The Hypoglycemia Awareness Restoration Programme for people with type 1 diabetes and problematic hypoglycemia persisting despite optimized care (HARPdoc) was developed to target unhelpful hypoglycemia beliefs. HARPdoc is intended to improve hypoglycemia beliefs and awareness to reduce SH in adults living with type 1 diabetes.²⁵ The program was pilot tested²⁶ and then evaluated via a cross-Atlantic multicenter trial to assess its impact, in comparison with BGAT, in reducing SH.²⁷ All participants in the HARPdoc trial were adults with type 1 diabetes who were experiencing IAH and at least one SH event in the past 2 years; all had experienced structured education in insulin self-management and close to half of the participants were using ADTs at baseline. This cohort thus provides a unique opportunity to explore the effects of distinctive interventions that enhance hypoglycemia awareness versus addressing unhelpful hypoglycemia beliefs.²⁷ Findings can shed light on the mechanisms promoting SH development in ADT users.

To explore the impact of interventions focusing on improving hypoglycemia awareness and addressing unhelpful hypoglycemia beliefs in reducing SH in adults using ADTs, we performed a post hoc analysis with HARPdoc trial participants who were using ADTs to assess the effectiveness of BGAT and HARPdoc in reducing SH in this subgroup. We also examined the impacts of these two programs on improving hypoglycemia awareness; addressing unhelpful hypoglycemia beliefs; and reducing diabetes distress along with anxiety and depression symptoms.

Methods

HARPdoc trial

The HARPdoc trial study protocol²⁵ and results from all participants²⁷ have been published. In brief, the HARPdoc trial (NCT02940873) was a two-arm comparative randomized clinical trial designed to test the hypothesis that an intervention (HARPdoc) addressing unhelpful hypoglycemia health beliefs could reduce SH in people with otherwise treatment-resistant problematic hypoglycemia.²⁵ The recruitment began in March 2017 and was completed in March 2019, with the entire study completed in April 2021. Adults with type 1 diabetes who continued to develop at least one SH event in the past 2 years and were also identified as having IAH despite the present standard of care (e.g., receiving structured diabetes education and having access to ADTs) were recruited at four study sites as follows: three in the United Kingdom and one in the United States. After participants were deemed eligible and provided informed consent, 99 were randomized to either HARPdoc or BGAT (as a comparator) and were followed for 2 years to assess the number of SH events (primary outcome) along with secondary outcomes (e.g., hypoglycemia awareness, hypoglycemia beliefs, diabetes distress, and anxiety and depression symptoms). The trial demonstrated that participants assigned to either the HARPdoc or BGAT group showed roughly 80% reductions in the number of SH events as well as improvements in hypoglycemia awareness over the 2-year period. Only HARPdoc participants also exhibited improvements in hypoglycemia beliefs and mental health (i.e., anxiety, depression, and diabetes distress).²⁷

Inclusion criteria of the subgroup

This post hoc subgroup analysis examined the impact of HARPdoc and BGAT on adults using ADTs, including SH events, hypoglycemia awareness, and unhelpful hypoglycemia beliefs. We included participants from the HARPdoc trial who were using ADTs at baseline. “ADTs” in this case referred to either intermittently scanned CGMs or real-time CGMs, including people who used AIDs available at the time of recruitment. Devices used included automated insulin suspension at predicted and/or actual hypoglycemia, while fully hybrid closed-loop systems were not generally available. This research involved post hoc analysis of deidentified data and approval of the institutional review board was not required.

Outcome measures

The primary outcome was the number of SH (events over the preceding year) measured using the 12- and 24-month anonymized SH hypoglycemia recall form. Where anonymized data were missing, participants were asked for permission to use data reported in the equivalent open form, and to confirm that they agreed the data were a true reflection of their present experience.²⁷ Hypoglycemia awareness status was evaluated with the Gold questionnaire.²⁸ Unhelpful hypoglycemia beliefs were assessed using the 12-item Attitudes to Awareness of Hypoglycemia (A2A) questionnaire.²⁰ Diabetes distress was measured via the Problem Areas in Diabetes (PAID) questionnaire.²⁹ Anxiety and depression symptoms were evaluated based on the Hospital Anxiety and Depression Scale (HADS).³⁰

Statistical analysis

Descriptive analysis was carried out to summarize participant demographics, study sites, and outcome data. Student t-tests and chi-square tests were performed to compare continuous and categorical participant characteristics, respectively, between participants using ADTs who were assigned to the HARPdoc and BGAT groups; between participants who were and were not using ADTs; and between participants who were using ADTs with and without missing primary outcome data. Generalized linear mixed-effects models with a Poisson distribution were used to evaluate changes in the incidence of SH events over time. Linear mixed-effects models were used to identify changes in participants’ scores on the Gold, A2A, PAID, and HADS questionnaires.

Results

Of all randomized participants in the HARPdoc trial, 45 were using ADTs at baseline (20 in the HARPdoc group and 25 in the BGAT group). Slightly more than half (58%) of these participants were females, with a mean (SD) age of 55 (14) years and an HbA1c of 7.3% (1.4). No differences in baseline characteristics were observed between the BGAT and HARPdoc groups (Table 1). Twelve participants were using intermittently scanned CGMs; 34 participants were using real-time CGMs, including 15 using AIDs. One participant in the BGAT group used both intermittently scanned and real-time CGMs. Participants’ ADT use did not change during the study period, including in terms of the CGM type used or discontinuation of CGMs and AIDs. With the exception of study sites, no differences were identified in baseline characteristics (i.e., demographics, the number of SH events, and Gold and A2A scores) between participants who were and were not using ADTs (Supplementary Table S1).

Table 1.

Baseline Characteristics of Participants Who Were Using Advanced Diabetes Technologies Categorized by Treatment Group

	All (n = 45)	BGAT group (n = 25)	HARPdoc group (n = 20)	P value^a
Age, mean (SD) year	55 (14)	53 (14)	58 (14)	0.22
Sex, n (%)				0.79
Male	19 (42%)	11 (44%)	8 (40%)
Female	26 (58%)	14 (56%)	12 (60%)
Ethnicity, n (%)				0.27
Caucasian	43 (96%)	25 (100%)	18 (90%)
Hispanic	1 (2%)	0 (0%)	1 (5%)
Mixed	1 (2%)	0 (0%)	1 (5%)
Study site, n (%)				0.88
Bournemouth	5 (11%)	3 (12%)	2 (10%)
Joslin	13 (29%)	7 (28%)	6 (30%)
London	20 (44%)	12 (48%)	8 (40%)
Sheffield	7 (16%)	3 (12%)	4 (20%)
Duration of diabetes, mean (SD) year	36 (18)	34 (16)	39 (20)	0.35
HbA1c, mean (SD)				0.64
%	7.3 (1.4)	7.2 (1.4)	7.4 (1.4)
mmol/mol	56 (16)	55 (16)	57 (16)
Advanced diabetes technology used, n (%)				0.39
Intermittently scanned CGM	12 (27)^b	9 (36)^b	3 (15)
Real-time CGM only	19 (42)^b	10 (40)^b	9 (45)
Real-time CGM with AIDs	15 (33)	7 (28)	8 (40)
Number of SH events over preceding year, mean (SD)	15 (43)	22 (56)	7 (12)	0.27
Gold score, mean (SD)	5.5 (1.1)	5.3 (1.0)	5.8 (1.2)	0.14
Missing value, n (%)	1 (2)	0 (0)	1 (5)
Total A2A score, mean (SD)	11 (6)	10 (5)	13 (7)	0.13

To compare the HARPdoc and BGAT groups, Student t-test was conducted for continuous variables and chi-square test was conducted for categorical variables.

One participant was using both intermittently scanned and real-time CGM.

A2A, Attitudes to Awareness of Hypoglycemia questionnaire; AID, automated insulin delivery system; BGAT, Blood Glucose Awareness Training; CGM, continuous glucose monitoring system; HARPdoc, Hypoglycemia Awareness Restoration Programme for people with type 1 diabetes and problematic hypoglycemia persisting despite optimized self-care; HbA1c, hemoglobin A1C; SH, severe hypoglycemia.

Severe hypoglycemic events, hypoglycemia awareness, and hypoglycemia health beliefs

Results indicated significant reductions in the number of SH events in participants who were using ADTs and receiving either BGAT or HARPdoc. In the BGAT group, SH event incidence decreased by 65% (P < 0.0001) at 12 months and by 54% (P < 0.0001) at 24 months (Fig. 1). Similarly, in the HARPdoc group, the SH event incidence decreased by 56% (P < 0.0001) at 12 months and by 73% (P < 0.0001) at 24 months (Fig. 2). When comparing the HARPdoc and BGAT groups, no differences in the reduction of SH incidence were identified at 12 months (P = 0.18), but the HARPdoc group had a significantly greater reduction in the SH incidence than the BGAT group at 24 months (P = 0.01).

FIG. 1.

(A) Number of SHEs over the preceding year, (B) Gold score, (C) total A2A scores at baseline, 12 months, and 24 months in advanced diabetes technology users who received BGAT. Data presented as mean (standard deviation). A2A, Attitudes to Awareness of Hypoglycemia questionnaire; BGAT, Blood Glucose Awareness Training; SHE, severe hypoglycemia events. *P < .05; ***P < .001; ****P < .0001 with generalized linear mixed-effects model with Poisson distribution for (A) and linear mixed-effects models for (B) and (C).

FIG. 2.

(A) Number of SHEs over the preceding year, (B) Gold score, (C) total A2A scores at baseline, 12 months, and 24 months in advanced diabetes technology users who received HARPdoc. Data presented as mean (standard deviation). A2A, Attitudes to Awareness of Hypoglycemia questionnaire; HARPdoc, Hypoglycemia Awareness Restoration Program for people with type 1 diabetes and problematic hypoglycemia persisting despite optimized self-care; SHE, severe hypoglycemia events. ***P < .001; ****P < .0001 with generalized linear mixed-effects model with Poisson distribution for (A) and linear mixed-effects models for (B) and (C).

Regarding hypoglycemia awareness, the BGAT group’s Gold score decreased by 0.78 (SE 0.29; P = 0.010) at 12 months and by 1.16 (SE 0.32; P = 0.0008) at 24 months. In the HARPdoc group, the Gold score declined by 1.34 (SE 0.32; P = 0.0001) at 12 months and by 1.81 (SE 0.36; P < 0.0001) at 24 months. No significant differences accompanied these reductions between the two treatment groups (P = 0.36). As for hypoglycemia beliefs, no significant change in the A2A score emerged in the BGAT group at 12 months (P = 0.36) and 24 months (P = 0.24). The A2A score decreased significantly by 5.5 (SE 1.2; P < 0.0001) at 12 months and by 5.9 (SE 0.9; P < 0.0001) at 24 months in the HARPdoc group. This group also displayed a greater reduction in A2A scores compared with the BGAT group (P = 0.0009).

Diabetes distress and anxiety and depression symptoms

No significant reductions were identified in the BGAT group’s PAID scores or in their HADS anxiety and depression scores (Fig. 3). The HARPdoc group’s PAID scores fell by 8.6 (SE 3.8; P = 0.028) at 12 months and by 12.1 (SE 3.7; P = 0.002) at 24 months; the group’s HADS anxiety scores fell by 1.9 (SE 0.8; P = 0.019) at 12 months and by 2.5 (SE 0.9; P = 0.009) at 24 months (Fig. 4). No significant reductions in HADS depression scores appeared at 12 months (P = 0.14) or 24 months (P = 0.49). We also did not observe significant differences in the reduction of PAID (P = 0.19), HADS anxiety (P = 0.18), and HADS depression (P = 0.44) scores between treatment groups.

FIG. 3.

(A) PAID, (B) HADS-Anxiety, (C) HADS-Depression scores at baseline, 12 months, and 24 months in advanced diabetes technology users who received BGAT. Data presented as mean (standard deviation). BGAT, Blood Glucose Awareness Training; HADS, Hospital Anxiety and Depression Scale; PAID, Problem Areas in Diabetes. Analyzed with linear mixed-effects models.

FIG. 4.

(A) PAID, (B) HADS-Anxiety, (C) HADS-Depression scores at baseline, 12 months, and 24 months in advanced diabetes technology users who received HARPdoc. Data presented as mean (standard deviation). HADS, Hospital Anxiety and Depression Scale; HARPdoc, Hypoglycemia Awareness Restoration Program for people with type 1 diabetes and problematic hypoglycemia persisting despite optimized self-care; PAID, Problem Areas in Diabetes. *P < 0.05; **P < 0.01 with linear mixed-effects models.

Missing data

Five participants (two in the HARPdoc group and three in the BGAT group) did not have follow-up primary outcome data. No differences in baseline characteristics were identified between these five individuals and the remaining participants who were using ADTs (Supplementary Table S2).

Evaluating the impacts of the intervention programs on participants who were not using ADTs

Given the aforementioned highly significant findings for ADT users, and to determine whether the results of the original trial were driven exclusively by the ADT-using participants, the same analyses were conducted with individuals who were not using ADTs at baseline.

Significant reductions in the number of SH events were again observed in participants not using ADTs who received either BGAT or HARPdoc. The SH event incidence decreased by 88% (P < 0.0001) at 12 months and by 94% (P < 0.0001) at 24 months in the BGAT group. Similarly, in the HARPdoc group, the SH event incidence decreased by 88% (P < 0.0001) at 12 months and by 95% (P < 0.0001) at 24 months. Compared with the HARPdoc group, the BGAT group had a greater reduction in SH events at 12 months (P = 0.02) but not at 24 months. Hypoglycemia awareness improved in both BGAT and HARPdoc groups, while the data also signal improvements in hypoglycemia beliefs in the HARPdoc group (Supplementary Table S3). Reductions in diabetes distress and depression symptoms also appeared in the HARPdoc group at 24 months (Supplementary Table S4).

Discussion

This post hoc analysis of the HARPdoc trial demonstrated that both BGAT and HARPdoc lead to statistically and clinically significant reductions in the incidence of SH events, as well as improvements in hypoglycemia awareness, in participants who continued to experience problematic hypoglycemia despite receiving the standard of care, including active ADT use. In this analysis, HARPdoc also outperformed BGAT in terms of decreasing SH at the 24-month mark. Moreover, HARPdoc effectively addressed unhelpful hypoglycemia beliefs and led to less diabetes distress and lower anxiety symptoms in this cohort, suggesting that unhelpful hypoglycemia beliefs may also contribute to raise diabetes distress and anxiety in ADT users.

Our subgroup analysis underscores that the BGAT and HARPdoc intervention programs had impacts on the subset of ADT users, mirroring outcomes observed in the entire cohort of the original trial: BGAT exclusively targeted hypoglycemia awareness, whereas the effects of HARPdoc extended to mitigating unhelpful hypoglycemia beliefs and enhancing psychological well-being.²⁷ Furthermore, the impacts of both intervention programs endured for at least 2 years as shown in the full trial cohort. The supplementary analysis of participants who were not using ADTs further confirmed that results observed in the entire cohort were not solely attributable to changes among ADT users.

The original trial findings and this subgroup analysis differed in that HARPdoc was deemed superior in reducing SH at 24 months. This discrepancy may have arisen because the impact from improving hypoglycemia awareness could be less significant in individuals who already possess “artificial awareness” due to the availability of continuous information and possibly CGM hypoglycemia alarms. By comparison, addressing the unhelpful beliefs in which ADT users are entrenched might better reduce SH over time. The reduction in A2A scores implies that there may be a relationship between the strength of these beliefs and HARPdoc’s mechanism to reduce SH, in the short and long term, and improve psychological well-being. In BGAT, we speculate that the SH reduction may be mediated by other (possibly behavioral and knowledge) factors, which lead to improvement in SH but do not mediate psychological well-being.

Although the HARPdoc trial was not designed to compare its effectiveness with BGAT among people using and not using ADTs, both interventions’ impacts may be somewhat smaller for ADT users (e.g., at Year 2, HARPdoc reduced SH occurrence by 95% in those not using ADTs but only by 73% in those who were using them). This trend suggests that minimizing SH in ADT users may be especially challenging. A plausible explanation is that although ADTs can reduce hypoglycemia overall, the technologies may interfere with self-management of hypoglycemia. Studies,³¹ including recent qualitative data, have pointed to social barriers to hypoglycemia self-management in this group, including unwanted attention from hypoglycemia alarms or potential social stigma associated with wearing CGM devices on the body.^23,32 Further research is needed to determine if ADT users are indeed more resistant than non-ADT users to interventions. Subsequent studies in this area may also identify factors contributing to this resistance.

While the depression symptom improvements were not observed with HARPdoc in the ADT users, this may be attributed to the low depression symptom level at baseline and thus the possible floor effect.

This study has several notable strengths. Including both BGAT and HARPdoc interventions, without cross-contamination, enabled an investigation into the mechanistic impacts of IAH and unhelpful hypoglycemia beliefs on SH development. Stringent recruitment criteria (e.g., documented prior receipt of structured diabetes education) allowed for clear control of participant characteristics, including diabetes knowledge levels. The quality of this randomization process was evidenced by balanced participant characteristics among subgroups (i.e., participants who were and were not using ADTs) as well as between the BGAT and HARPdoc groups within the ADT user subgroup. The original study was not intended to evaluate these interventions’ impacts on ADT users, yet the existing sample size, together with acceptably balanced ADT and non-ADT users, facilitated this analysis. Minimal missing data and the lack of differences in participant characteristics (i.e., between those with and without missing data) also enhanced findings’ reliability.

This study has several limitations. Its exploratory nature as a post hoc analysis and the relatively small sample size highlight the need for larger scale studies to confirm HARPdoc’s effectiveness and superiority over BGAT in ADT users. These additional inspections can shed greater light on the roles of IAH and unhelpful hypoglycemia beliefs in SH development in this growing population. Furthermore, neither supplemental education on ADT use nor monitoring of participants’ technology usage was implemented, as the original trial was not designed to assess the intervention programs’ efficacy plus strict ADT use. Finally, access to more advanced AIDs, including HCLs, was limited at the time of the study, and thus, a separate study is needed to confirm the effectiveness of the tested interventions in users of the most advanced AIDs.

We believe our findings are highly clinically relevant. Recent research has shown that nearly 10% of people with type 1 diabetes continue to grapple with problematic hypoglycemia even when equipped with ADTs.⁸ This tendency highlights the need to consider interventions that go beyond conventional knowledge-based diabetes education and ADTs, particularly as the access to islet transplantation, the ultimate hypoglycemia management intervention, remains extremely limited.³³ BGAT and HARPdoc represent prospective intervention programs to reduce SH in this population. In addition, individuals experiencing elevated levels of diabetes distress or anxiety may derive additional benefits from HARPdoc in particular.

Conclusions

This subgroup analysis of the HARPdoc trial provided compelling evidence that interventions focusing on improving hypoglycemia awareness and addressing unhelpful hypoglycemia beliefs might reduce SH development in adults with type 1 diabetes who use ADTs. Addressing unhelpful beliefs also added benefits of a greater long-term reduction in SH and of improving diabetes distress and alleviating anxiety symptoms in this population.

Footnotes

Authors’ Contributions

Y.K.L., N.d.Z., and S.A.A. were involved in the conception, design, and interpretation of the results. H.R. was involved in the conception of the study. S.A.A., A.B., E.T., D.K., and S.H. were involved in the data collection. Y.K.L. and W.Y. were involved in the analysis. Y.K.L. wrote the first draft of the article, and all authors edited, reviewed, and approved the final version of the article.

Data Availability Statement

Data are available on request to Stephanie A. Amiel (stephanie.amiel@kcl.ac.uk).

Author Disclosure Statement

S.A.A. has spoken at educational meetings sponsored by Novo Nordisk and Sanofi, and has served on advisory boards for Vertex Pharmaceuticals. Other authors have no conflicts of interest relevant to this study to disclose.

Funding Information

This work was supported by the Juvenile Diabetes Research Foundation (4-SRA-2017-266-M-N), the National Institute of Diabetes and Digestive and Kidney Diseases (K23DK129724, 2021), and the Michigan Center for Clinical and Translational Research (P30DK092926, 2021).

Supplementary Material

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

References

Glucose concentrations of less than 3.0 mmol/L (54 mg/dL) should be reported in clinical trials: A joint position statement of the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care, 2017; 40(1):155–157; doi: 10.2337/dc16-2215

Amiel

. The consequences of hypoglycaemia. Diabetologia, 2021; 64(5):963–970; doi: 10.1007/s00125-020-05366-3

Hopkins

, Lawrence

, Mansell

, et al. Improved biomedical and psychological outcomes 1 year after structured education in flexible insulin therapy for people with type 1 diabetes. The UK DAFNE experience. Diabetes Care, 2012; 35(8):1638–1642; doi: 10.2337/dc11-1579

Sämann

, Mühlhauser

, Bender

, et al. Glycaemic control and severe hypoglycaemia following training in flexible, intensive insulin therapy to enable dietary freedom in people with type 1 diabetes: A prospective implementation study. Diabetologia, 2005; 48(10):1965–1970; doi: 10.1007/s00125-005-1905-1

Sämann

, Mühlhauser

, Müller

. Flexible intensive insulin therapy in adults with type 1 diabetes and high risk for severe hypoglycemia and diabetic ketoacidosis: Response to Pennant et al. Diabetes Care, 2007; 30(3):e5–e6; doi: 10.2337/dc06-2408

Battelino

, Danne

, Bergenstal

, et al. Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care, 2019; 42(8):1593–1603; doi: 10.2337/dci19-0028

Sherr

, Heinemann

, Fleming

, et al. Automated insulin delivery: Benefits, challenges, and recommendations. A consensus report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association. Diabetologia, 2023; 66(1):3–22; doi: 10.1007/s00125-022-05744-z

Sherr

, Laffel

, Liu

, et al. Severe hypoglycemia and impaired awareness of hypoglycemia persist in people with type 1 diabetes despite use of diabetes technology: Results from a cross-sectional survey. Diabetes Care, 2024; doi: 10.2337/dc23-1765

Peters

, Ahmann

, Hirsch

, et al. Advances in glucose monitoring and automated insulin delivery: Supplement to endocrine society clinical practice guidelines. J Endocr Soc, 2018; 2(11):1214–1225; doi: 10.1210/js.2018-00262

10.

Seyed Ahmadi

, Westman

, Pivodic

, et al. The association between hba1c and time in hypoglycemia during CGM and self-monitoring of blood glucose in people with type 1 diabetes and multiple daily insulin injections: A randomized clinical trial (GOLD-4). Diabetes Care, 2020; 43(9):2017–2024; doi: 10.2337/dc19-2606

11.

Ólafsdóttir

, Bolinder

, Heise

, et al. The majority of people with type 1 diabetes and multiple daily insulin injections benefit from using continuous glucose monitoring: An analysis based on the GOLD randomized trial (GOLD-5). Diabetes Obes Metab, 2021; 23(2):619–630; doi: 10.1111/dom.14257

12.

Lin

, Hung

, Sharma

, et al. Impaired awareness of hypoglycemia continues to be a risk factor for severe hypoglycemia despite the use of continuous glucose monitoring systems in type 1 diabetes. Endocr Pract, 2019; 25(6):517–525; doi: 10.4158/ep-2018-0527

13.

Lin

, Richardson

, Dobrin

, et al. Beliefs around hypoglycemia and their impacts on hypoglycemia outcomes in individuals with type 1 diabetes and high risks for hypoglycemia despite using advanced diabetes technologies. Diabetes Care, 2022; 45(3):520–528; doi: 10.2337/dc21-1285

14.

Akturk

, Dowd

, Shankar

, et al. Real-world evidence and glycemic improvement using dexcom g6 features. Diabetes Technol Ther, 2021; 23(S1):S21–S26; doi: 10.1089/dia.2020.0654

15.

Lin

, Fisher

, Pop-Busui

. Hypoglycemia unawareness and autonomic dysfunction in diabetes: Lessons learned and roles of diabetes technologies. J Diabetes Investig, 2020; 11(6):1388–1402; doi: 10.1111/jdi.13290

16.

Holt

RIG

, DeVries

, Hess-Fischl

, et al. The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care, 2021; 44(11):2589–2625; doi: 10.2337/dci21-0043

17.

Cryer

. The barrier of hypoglycemia in diabetes. Diabetes, 2008; 57(12):3169–3176; doi: 10.2337/db08-1084

18.

Cox

, Gonder-Frederick

, Polonsky

, et al. Blood glucose awareness training (BGAT-2): long-term benefits. Diabetes Care, 2001; 24(4):637–642; doi: 10.2337/diacare.24.4.637

19.

Rogers

, de Zoysa

, Amiel

. Patient experience of hypoglycaemia unawareness in Type 1 diabetes: Are patients appropriately concerned? Diabet Med, 2012; 29(3):321–327; doi: 10.1111/j.1464-5491.2011.03444.x

20.

Cook

, DuBose

, Foster

, et al. Cognitions associated with hypoglycemia awareness status and severe hypoglycemia experience in adults with type 1 diabetes. Diabetes Care, 2019; 42(10):1854–1864; doi: 10.2337/dc19-0002

21.

Rickels

, Peleckis

, Dalton-Bakes

, et al. Continuous glucose monitoring for hypoglycemia avoidance and glucose counterregulation in long-standing type 1 diabetes. J Clin Endocrinol Metab, 2018; 103(1):105–114; doi: 10.1210/jc.2017-01516

22.

Flatt

, Peleckis

, Dalton-Bakes

, et al. Automated insulin delivery for hypoglycemia avoidance and glucose counterregulation in long-standing type 1 diabetes with hypoglycemia unawareness. Diabetes Technol Ther, 2023; 25(5):302–314; doi: 10.1089/dia.2022.0506

23.

Lin

, Agni

, Chuisano

, et al. ‘You have to use everything and come to some equilibrium’: a qualitative study on hypoglycemia self-management in users of continuous glucose monitor with diverse hypoglycemia experiences. BMJ Open Diabetes Res Care, 2023; 11(3):e003415; doi: 10.1136/bmjdrc-2023-003415

24.

Lin

, Aikens

, de Zoysa

, et al. An mHealth text messaging program providing symptom detection training and psychoeducation to improve hypoglycemia self-management: Intervention development study. JMIR Form Res, 2023; 7(:e50374; doi: 10.2196/50374

25.

Amiel

, Choudhary

, Jacob

, et al. Hypoglycaemia awareness restoration programme for people with type 1 diabetes and problematic hypoglycaemia persisting despite optimized self-care (HARPdoc): protocol for a group randomized controlled trial of a novel intervention addressing cognitions. BMJ Open, 2019; 9(6):e030356; doi: 10.1136/bmjopen-2019-030356

26.

de Zoysa

, Rogers

, Stadler

, et al. A psychoeducational program to restore hypoglycemia awareness: The DAFNE-HART pilot study. Diabetes Care, 2014; 37(3):863–866; doi: 10.2337/dc13-1245

27.

Amiel

, Potts

, Goldsmith

, et al. A parallel randomized controlled trial of the Hypoglycaemia Awareness Restoration Programme for adults with type 1 diabetes and problematic hypoglycemia despite optimized self-care (HARPdoc). Nat Commun, 2022; 13(1):2229; doi: 10.1038/s41467-022-29488-x

28.

Gold

, Macleod

, Frier

. Frequency of severe hypoglycemia in patients with type i diabetes with impaired awareness of hypoglycemia. Diabetes Care, 1994; 17(7):697–703; doi: 10.2337/diacare.17.7.697

29.

Polonsky

, Anderson

, Lohrer

, et al. Assessment of diabetes-related distress. Diabetes Care, 1995; 18(6):754–760; doi: 10.2337/diacare.18.6.754

30.

Zigmond

, Snaith

. The hospital anxiety and depression scale. Acta Psychiatr Scand, 1983; 67(6):361–370; doi: 10.1111/j.1600-0447.1983.tb09716.x

31.

Patton

, Clements

. Psychological reactions associated with continuous glucose monitoring in youth. J Diabetes Sci Technol, 2016; 10(3):656–661; doi: 10.1177/1932296816638109

32.

Chatwin

, Broadley

, Hendrieckx

, et al. Unmet support needs relating to hypoglycemia among adults with type 1 diabetes: Results of a multi-country web-based qualitative study. Diabet Med, 2022; 39(1):e14727; doi: 10.1111/dme.14727

33.

Choudhary

, Amiel

. Hypoglycemia in type 1 diabetes: Technological treatments, their limitations and the place of psychology. Diabetologia, 2018; 61(4):761–769; doi: 10.1007/s00125-018-4566-6

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