OBJECTIVE: To understand healthcare providers\' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians\' use of patientgenerated data from Type 1 diabetes devices.
METHODS: This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview.
RESULTS: 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1.
CONCLUSIONS: The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians\' willingness to leverage algorithmic support.
CONCLUSIONS: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians\' experience in reviewing device data.

To investigate the potential relationship between HLA alleles and haplotypes and the age at diagnosis of type 1 diabetes (T1DAgeD) in an admixed Brazilian population. This nationwide study was conducted in public clinics across 12 Brazilian cities. We collected demographic and genetic data from 1,600 patients with T1D. DNA samples were utilised to determine genomic ancestry (GA) and perform HLA typings for DRB1, DQA1 and DQB1. We explored allele and haplotype frequencies and GA in patients grouped by T1DAgeD categories (<6 years, ≥6-<11 years, ≥11-<19 years and ≥19 years) through univariate and multivariate analyses and primary component analyses. Additionally, we considered self-reported colour-race and identified a familiar history of T1D in first-degree relatives. The homozygosity index for DRB1~DQA1~DQB1 haplotypes exhibited the highest variation among T1DAgeD groups, and the percentages of Sub-Saharan African and European ancestries showed opposite trends in principal component analysis (PCA) analyses. Regarding the association of alleles and haplotypes with T1DAgeD, risk alleles such as HLA-DQB1*03:02g, -DQA1*03:01g, -02:01g, DRB1*04:05g and -04:02g were more frequently observed in heterozygosity or homozygosity in T1D patients with an early disease onset. Conversely, alleles such as DRB1*07:01g, -13:03g, DQB1*06:02g and DQA1*02:01 were more prevalent in older T1D patients. The combination DR3/DR4.5 was significantly associated with early disease onset. However, gender, GA, familiar history of T1D and self-reported colour-race identity did not exhibit significant associations with the onset of T1D. It is worth noting that the very common risk haplotype DRB1*03:01g~DQA1*05:01g~DQB1*02:01g did not differentiate between T1DAgeD groups. In the admixed Brazilian population, the high-risk haplotype DRB1*04:05~DQA1*03:01~DQB1*03:02 was more prevalent in individuals diagnosed before 6 years of age. In contrast, the protective alleles DQA1*01:02g, DQB1*06:02g, DRB1*07:01g and DRB1*13:03g and haplotypes DRB1*13:03g~DQA1*05:01g~DQB1*03:01g and DRB1*16:02g~DQA1*01:02g~DQB1*05:02g were more frequently observed in patients diagnosed in adulthood. Notably, these associations were independent of factors such as sex, economic status, GA, familiar history of T1D and region of birth in Brazil. These alleles and haplotypes contribute to our understanding of the disease onset heterogeneity and may have implications for early interventions when detected in association with well-known genomic risk or protection factors for T1D.

BACKGROUND: Evidence suggests that glucose levels in menstruating females with type 1 diabetes change throughout the menstrual cycle, reaching a peak during the luteal phase. The Type 1 Diabetes Exercise Initiative (T1DEXI) study provided the opportunity to assess glycemic metrics between early and late phases of the menstrual cycle, and whether differences could be explained by exercise, insulin, and carbohydrate intake.
METHODS: One hundred and sixty two adult females were included in the analysis. Glycemic metrics, carbohydrate intake, insulin requirements, and exercise habits during the early vs. late phases of the menstrual cycles (i.e. 2-4 days after vs. 2-4 days before reported menstruation start date) were compared.
RESULTS: Mean glucose increased from 8.2±1.5 mmol/L (148±27 mg/dL) during the early follicular phase to 8.6±1.6 mmol/L (155±29 mg/dL) during the late luteal phase (p<0.001). Mean percent time-in-range (3.9-10.0 mmol/L [70-180 mg/dL] ) decreased from 73±17% to 70±18% (p=0.002), and median percent time >10.0 mmol/L (>180 mg/dL) increased from 21% to 23% (p<0.001). Median total daily insulin requirements increased from 37.4 units during the early follicular to 38.5 units during the late luteal phase (p=0.02) and mean daily carbohydrate consumption increased slightly from 127±47 g to 133±47 g (p=0.05), but the difference in mean glucose during early follicular vs. late luteal phase was not explained by differences in exercise duration, total daily insulin units, or reported carbohydrate intake.
CONCLUSIONS: Glucose levels during the late luteal phase were higher than the early follicular phase of the menstrual cycle. These glycemic changes suggest that glucose management for females with type 1 diabetes may need to be fine-tuned within the context of their menstrual cycles.

OBJECTIVE: Exercise is a recommended part of type 1 diabetes (T1D) treatment, as high physical activity levels improve health outcomes. However, many people with T1D do not meet physical activity recommendations. The aim of this study was to identify factors influencing physical activity levels in people with T1D.
METHODS: This questionnaire-based study included adults with T1D from 1 outpatient clinic in the UK and 2 in Denmark. Exercise characteristics, motivators and barriers was assessed. Physical activity level was measured using Saltin-Grimby Physical Activity Level Scale. Respondents were categorized into three activity groups: inactive, light active and moderate-to-vigourous active.
RESULTS: Out of 332 respondents, 8.4% rated themselves as inactive, 48% light active and 43% moderate-to-vigorous active. 78% of inactive and light active repondents expressed a desire to become more physically active. 53% of respondents had received guidance concerning exercise/physical activity from their diabetes team. Being male and having received guidance, was associated with higher physical activity level. Important motivators for exercising/being physically active were improved mental and physical health and glycaemic control, while most frequent barriers were busyness with work/private life and lack of motivation. Worries about glucose excursions, costs, lack of knowledge, and health related reasons were more prevalent barriers in the least active groups.
CONCLUSIONS: This study found that 78% of inactive and light active respondents reported wishing to become more physically active. Receiving guidance about exercise/physical activity was associated with higher physical activity level, but only 53% of respondents had received support from their diabetes team.

BACKGROUND: Individuals with chronic diseases often search for health information online. The Diabetes Online Community (DOC) is an active community with members who exchange health information; however, few studies have examined health information brokering in the DOC.
OBJECTIVE: The aim of this study was to develop and validate the Attitudes Toward Seeking Health Information Online (ATSHIO) scale in a sample of adults with type 1 diabetes (T1D).
METHODS: People with T1D were recruited through the DOC, specifically Facebook and Twitter. They were provided with a Qualtrics link to complete the survey. This was a mixed methods study that used thematic analysis along with existing theory and formative research to design the quantitative ATSHIO scale.
RESULTS: A total of 166 people with T1D participated in this study. Confirmatory factor analyses determined a 2-factor scale (Trusting and Evaluating Online Health Information in the DOC and Engaging With Online Health Information in the DOC) with good convergent validity and discriminant validity. Correlations were found between social support, online health information-seeking, diabetes distress, and disease management.
CONCLUSIONS: The ATSHIO scale can be used to investigate how people with diabetes are using the internet for obtaining health information, which is especially relevant in the age of telehealth and Health 2.0.

OBJECTIVE: Heterogeneity in the rate of β-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis.
METHODS: We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in β-cell mass measured as fasting C-peptide.
RESULTS: Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in β-cell function. The second signature was related to translation and viral infection was inversely associated with change in β-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid β-cell decline.
CONCLUSIONS: Features that differ between individuals with slow and rapid decline in β-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.

UNASSIGNED: To determine inequalities in access to diabetes technologies and the effect of socioeconomic factors on families with children with type 1 diabetes.
UNASSIGNED: In this multicenter cross-sectional study, parents of children with type 1 diabetes completed a questionnaire about household sociodemographic characteristics, latest HbA1c values, continuous glucose monitoring (CGM) and insulin pump use of children, the education and working status of parents. These characteristics were compared between technology use (only-CGM, only-pump, CGM+pump, no technology use).
UNASSIGNED: Among 882 families, only-CGM users, only-pump users, and CGM+pump users compared with no technology users, adjusting for age, sex, region, education levels, number of working parents, and household income. Children living in the least developed region had lower odds of having only-CGM (OR=0.20, 95%CI 0.12-0.34) and having CGM+pump (OR=0.07, 95%CI 0.03-0.22) compared with those living in the most developed region. Children with parents who had not finished high school had lower odds of having only-CGM (Mothers: OR=0.36, 95%CI 0.19-0.66; fathers: OR=0.32, 95%CI 0.18-0.60) or both CGM+pump (OR=0.27, 95%CI 0.11-0.64; fathers: OR=0.34, 95%CI 0.15-0.79) rather than no-technology compared to children whose parents has a university degree. Every $840 increase in the household income increased the odds by 5% for having only-CGM (OR=1.05, 95%CI 1.02-1.09) and CGM+pump (OR=1.05, 95%CI 1.01-1.08).
UNASSIGNED: Socioeconomic factors such as education, regions, and income were associated with inequality in access to technologies. The inequalities are more prominent in access to CGM while CGM had a bigger contribution to glycemic control.

BACKGROUND: The Medtronic MiniMed™ 780G (MM780G) system uses an algorithm that includes autocorrection bolus (AB) delivery. This study evaluates the impact of omitted meal boluses and the system settings, glucose target and active insulin time (AIT), on the AB.
METHODS: Retrospective observational study on data uploaded by all MiniMed 780G users in our healthcare area, obtained through the remote monitoring platform Care Connect, from April to August 2023. Downloads with a sensor usage time <95% were excluded.
RESULTS: 235 downloads belonging to 235 users were analysed. AB delivery was significantly higher at 2 h AIT (36.08 ± 13.17%) compared to the rest of settings (2.25-4 h) (26.43 ± 13.2%) (p < 0.001). AB differences based on the glucose target were not found. Patients with <3 meal boluses per day had higher AB delivery (46.91 ± 19.00% vs 27.53 ± 11.54%) (p < 0.001) and had more unfavourable glucometric parameters (GMI 7.12 ± 0.45%, TIR 67.46 ± 12.89% vs GMI 6.78 ± 0.3%, TIR 76.51 ± 8.37%) (p < 0.001). However, the 2-h AIT group presented similar TAR, TIR and GMI regardless of the number of meal boluses.
CONCLUSIONS: The fewer user-initiated boluses, the greater the autocorrection received. The active insulin time of 2 h entails a more active autocorrection pattern that makes it possible to more effectively compensate for the omission of meal boluses without increasing hypoglycaemias.

UNASSIGNED: To compare the changes in body weight and glycemic control before and during the COVID-19 pandemic in people with type 1 diabetes (T1D).
UNASSIGNED: In 47,065 individuals with T1D from the German Diabetes Prospective Follow-up Registry (DPV), we compared the adjusted mean changes in BMI-Z-scores and HbA1c as well as the distribution of individual changes between four periods from March 2018 to February 2022, by sex and age group (4- < 11, 11- < 16, 16-50 years).
UNASSIGNED: At population level, the only significant pandemic effects were a slight increase in BMI Z-score in prepubertal children (girls: + 0.03 in the first COVID year vs. before, P < 0.01; boys: + 0.04, P < 0.01) as well as a stabilization of HbA1c in all subgroups or even improvement in women (- 0.08%, P < 0.01). At individual level, however, heterogeneity increased significantly (p < 0.01), especially in children. More prepubertal children gained weight (girls: 45% vs. 35% before COVID; boys: 39% vs. 33%). More pubertal girls lost weight (30% vs. 21%) and fewer gained weight (43% vs. 54%). More children had a decreasing HbA1c (prepubertal group: 29% vs. 22%; pubertal girls: 33% vs. 28%; pubertal boys: 32% vs. 25%) and fewer had increasing values. More women had stable HbA1c and fewer had increasing values (30% vs. 37%). In men, no significant changes were observed.
UNASSIGNED: This real-world analysis shows no detrimental consequences of the two first COVID years on weight and HbA1c in T1D on average, but reveals, beyond the mean trends, a greater variability at the individual level.

UNASSIGNED: Advanced hybrid closed loop (AHCL) systems have the potential to improve glycemia and reduce burden for people with type 1 diabetes (T1D). Children and youth, who are at particular risk for out-of-target glycemia, may have the most to gain from AHCL. However, no randomized controlled trial (RCT) specifically targeting this age group with very high HbA1c has previously been attempted. Therefore, the CO-PILOT trial (Closed lOoP In chiLdren and yOuth with Type 1 diabetes and high-risk glycemic control) aims to evaluate the efficacy and safety of AHCL in this group.
UNASSIGNED: A prospective, multicenter, parallel-group, open-label RCT, comparing MiniMed™ 780G AHCL to standard care (multiple daily injections or continuous subcutaneous insulin infusion). Eighty participants aged 7-25 years with T1D, a current HbA1c ≥ 8.5% (69 mmol/mol), and naïve to automated insulin delivery will be randomly allocated to AHCL or control (standard care) for 13 weeks. The primary outcome is change in HbA1c between baseline and 13 weeks. Secondary outcomes include standard continuous glucose monitor glycemic metrics, psychosocial factors, sleep, platform performance, safety, and user experience. This RCT will be followed by a continuation phase where the control arm crosses over to AHCL and all participants use AHCL for a further 39 weeks to assess longer term outcomes.
UNASSIGNED: This study will evaluate the efficacy and safety of AHCL in this population and has the potential to demonstrate that AHCL is the gold standard for children and youth with T1D experiencing out-of-target glucose control and considerable diabetes burden.
UNASSIGNED: This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry on 14 November 2022 (ACTRN12622001454763) and the World Health Organization International Clinical Trials Registry Platform (Universal Trial Number U1111-1284-8452).
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40200-024-01397-4.