Customizing the Types of Technologies Used by Patients With Type 1 Diabetes Mellitus for Diabetes Treatment: Case Series on Patient Experience

Background: To measure the relationship between variability in HbA1c and microalbuminuria (MA) and diabetic retinopathy (DR) in the long term. Methods: A prospective case-series study, was conducted on 366 Type 1 Diabetes Mellitus patients with normoalbuminuria and without diabetic retinopathy at inclusion. The cohort was followed for a period of 12 years. The Cox survival analysis was used for the multivariate statistical study. The effect of variability in microangiopathy (retinopathy and nephropathy) was evaluated by calculating the standard deviation of HbA1c (SD-HbA1c), the coefficient of variation of HbA1c (CV-HbA1c), average real variability (ARV-HbA1c) and variability irrespective of the mean (VIM-HbA1c) adjusted for the other known variables. Results: A total of 106 patients developed diabetic retinopathy (29%) and 73 microalbuminuria (19.9%). Overt diabetic nephropathy, by our definition, affected only five patients (1.36%). Statistical results show that the current age, mean HbA1c, SD-HbA1c and ARV-HbA1c are significant in the development of diabetic retinopathy. Microalbuminuria was significant for current age, mean HbA1c, CV-HbA1c and ARV-HbA1c. Conclusions: By measuring the variability in HbA1c, we can use SD-HbA1c and ARV-HbA1c as possible targets for judging which patients are at risk of developing DR and MA, and CV-HbA1c as the target for severe DR.

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Management of type 1 diabetes mellitus using open-source automated insulin delivery during pregnancy: a case series

Diabetes Technology & Therapeutics ◽

10.1089/dia.2021.0260 ◽

2021 ◽

Author(s):

Anna Schütz ◽

Ingrid Schütz-Fuhrmann ◽

Marietta Stadler ◽

Hannah Staudinger ◽

Julia K Mader

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Type 1 Diabetes Mellitus ◽

Open Source ◽

Case Series ◽

Insulin Delivery

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Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes Mellitus

Endocrinology and Metabolism Clinics of North America ◽

10.1016/j.ecl.2017.10.012 ◽

2018 ◽

Vol 47 (1) ◽

pp. 65-79 ◽

Cited By ~ 15

Author(s):

Savitha Subramanian ◽

Irl B. Hirsch

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Type 1 Diabetes Mellitus ◽

Cardiovascular Outcomes ◽

Diabetes Treatment

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Sleep disorder and behavior problems in children with type 1 diabetes mellitus

Journal of Pediatric Endocrinology and Metabolism ◽

10.1515/jpem-2021-0523 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Evin Ilter Bahadur ◽

Şervan Özalkak ◽

Asena Ayça Özdemir ◽

Semra Çetinkaya ◽

Elif Nursel Özmert

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Behavior Problems ◽

Type 1 Diabetes Mellitus ◽

Sleep Problems ◽

Treatment Modalities ◽

Diabetes Treatment ◽

Sleep Habits ◽

And Behavior

Abstract Objectives To examine sleep and behavior problems in children with type 1 Diabetes Mellitus (T1DM) compared to nondiabetic controls in a bridging country between east and west and to evaluate the interaction of sleep on behavior problems, maternal sleep, and maternal depressive symptoms. Methods The study included children with T1DM (4–12 years old) and age/sex-matched healthy controls. Parents completed the Children Sleep Habits Questionnaire (CSHQ), Children Behavior Checklist/4–18 (CBCL/4–18), Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory (BDI), and a study-specific sociodemographic questionnaire. Clinical parameters regarding T1DM were collated from medical records. Results Participants included 75 children with T1DM and 49 controls. Based on CSHQ results 65.3% of all participants in both groups had sleep problems. Children with T1DM slept less and had higher daytime sleepiness problems than controls (p=0.024, p=0.008, respectively). No association was found between CSHQ or sleep duration and mode of diabetes treatment (pump, multiple daily injections) or glycemic control. CSHQ correlated with maternal PSQI (r=0.336 p=0.004) and BDI (r=0.341 p=0.004) in children with T1DM, but there was no association amongst controls. Children with T1DM had higher internalizing problems compared to controls. CSHQ and BDI correlated with internalizing, externalizing, and the total scores of the CBCL/4–18 in children with T1DM (R2=0.260 p<0.001; R2=0.207 p<0.001, R2=0.381 p<0.001 respectively). In controls, only BDI was associated with internalizing, externalizing, and the total scores of the CBCL/4–18. Conclusions Children with T1DM should be evaluated for sleep pattern and quality at follow-up, to identify those at risk for behavior problems and improve maternal life quality. Large longitudinal studies are necessary to assess the effect of new diabetes treatment modalities on sleep.

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Customizing the Types of Technologies Used by Patients With Type 1 Diabetes Mellitus for Diabetes Treatment: Case Series on Patient Experience (Preprint)

10.2196/preprints.11527 ◽

2018 ◽

Author(s):

Anna Holubová ◽

Martina Vlasáková ◽

Jan Mužík ◽

Jan Brož

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Type 1 Diabetes Mellitus ◽

Diabetes Management ◽

Case Series ◽

Diabetes Control ◽

Self Management ◽

Telemedicine System ◽

Specific Patient

BACKGROUND Despite the fact there are many wearable and mobile medical devices that enable patients to better self-manage their diabetes, not many patients are aware of all the options they have. In addition, there are those who are not fully satisfied with the devices they use, and those who often do not use them effectively. OBJECTIVE The study aimed to propose possible changes to the combination of devices used by 6 specific patients for diabetes self-management. We assessed the suitability of selected technical devices for diabetes control. METHODS Data of 6 patients (3 men and 3 women) with type 1 diabetes mellitus, who had been using the Diani telemedicine system for at least 3 months, were analyzed. The suitability of selected technical devices for diabetes control was ascertained using the data obtained via the Diani telemedicine system, as well as the patients’ subjective feelings and statements, their everyday life habits, and self-management of diabetes. Informed consent was signed and obtained from each of the patients included. RESULTS Each of the presented case studies describes how a given patient handled the system and its specific components based on his or her lifestyle, level of education, habits related to diabetes management, personality type, and other factors. At the conclusion of each case study, the best composition of devices for patients with similar personal descriptions was suggested. CONCLUSIONS We believe this study can provide relevant guidance on how to help particular patients choose the technology that is best suited for their needs, based on the specific patient information we are able to obtain from them. Furthermore, clinicians or educators should be aware of available technologies a given patient can choose from. In addition, there is a substantial need for proper patient education in order for them to effectively use devices for diabetes self-management.

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