Salient Characteristics of Youth with Type 1 Diabetes Initiating Continuous Glucose Monitoring

Teló, Gabriela Heiden; Volkening, Lisa K.; Butler, Deborah A.; Laffel, Lori M.

doi:10.1089/dia.2014.0290

Cited by 27 publications

(23 citation statements)

References 31 publications

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“…Patients with good diabetes management, evaluated by the Self‐Care Inventory, the Diabetes Behavior Rating Scale, the Diabetes Self‐Management Profile, the Diabetes Interview, or a composite score, have lower HbA1c levels . In contrast, missing injections or poor self‐monitoring of blood glucose was correlated with increased HbA1c level.…”

Section: Resultsmentioning

confidence: 99%

“…The sample size range is 70 to 30 708 patients with a median sample size of 210 T1D patients. Thirty‐two studies were performed in several European countries, 31 in USA, one in Canada, three in Israel, and the rest in other countries (one in Japan, one in New‐Zealand, three in Turkey, one in Australia, and three are multinational). Most studies (54 studies) were performed on T1D children (median of mean age reported in articles 12.5 [range: 4.1‐17.7]), 22 included both children and young adults (median of mean age of 14.8 [range: 10.1‐21.6]), and one included only young adults (mean age of 19.4 ± 0.9 [range: 18‐21]).…”

Section: Resultsmentioning

confidence: 99%

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A systematic review of non‐genetic predictors and genetic factors of glycated haemoglobin in type 1 diabetes one year after diagnosis

Gloaguen

Bendelac

Nicolino

et al. 2018

Diabetes Metabolism Res

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Type 1 diabetes (T1D) results from autoimmune destruction of the pancreatic βcells. Although all T1D patients require daily administration of exogenous insulin, their insulin requirement to achieve good glycaemic control may vary significantly. Glycated haemoglobin (HbA1c) level represents a stable indicator of glycaemic control and is a reliable predictor of long-term complications of T1D. The purpose of this article is to systematically review the role of non-genetic predictors and genetic factors of HbA1c level in T1D patients after the first year of T1D, to exclude the honeymoon period. A total of 1974 articles published since January 2011 were identified and 78 were finally included in the analysis of non-genetic predictors. For genetic factors, a total of 277 articles were identified and 14 were included. The most significantly associated factors with HbA1c level are demographic (age, ethnicity, and socioeconomic status), personal (family characteristics, parental care, psychological traits...) and features related to T1D (duration of T1D, adherence to treatment …). Only a few studies have searched for genetic factors influencing HbA1c level, most of which focused on candidate genes using classical genetic statistical methods, with generally limited power and incomplete adjustment for confounding factors and multiple testing. Our review shows the complexity of explaining HbA1c level variations, which involves numerous correlated predictors. Overall, our review underlines the lack of studies investigating jointly genetic and non-genetic factors and their interactions to better understand factors influencing glycaemic control for T1D patients.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A systematic review of non‐genetic predictors and genetic factors of glycated haemoglobin in type 1 diabetes one year after diagnosis

Gloaguen

Bendelac

Nicolino

et al. 2018

Diabetes Metabolism Res

View full text Add to dashboard Cite

show abstract

“…12 In youth, the following characteristics predict interest in starting CGM: lower A1C, more frequent BG monitoring, greater likelihood of being on an insulin pump, and having a two-parent family, greater diabetes adherence, lower diabetes-specific family conflict, and higher quality of life. 40 Finally, being non-Hispanic white is also associated with more frequent CGM use in youth younger than 13 years old. 12 For SAP, a low A1C, and frequency of use of the sensor predicts more frequent long-term use.…”

Section: Demographic Characteristics Of Technology Usersmentioning

confidence: 98%

Diabetes Technology

Naranjo

Tanenbaum

Iturralde

et al. 2016

J Diabetes Sci Technol

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Patients managing type 1 diabetes have access to new technologies to assist in management. This manuscript has two aims: 1) to briefly review the literature on diabetes technology use and how this relates to psychological factors and 2) to present an example of human factors research using our data to examine psychological factors associated with technology use. Device/technology uptake and use has increased over the years and at present day is a common clinical practice. There are mixed results in terms of health and psychosocial outcomes, with specific subgroups doing better than others with technology. Our data demonstrated that patients have moderately elevated diabetes distress across differing types of technology used, from low-tech to high-tech options, possibly meaning that technology does not add or take away distress. In addition, users on multiple daily injections compared to all other technology groups have less positive attitudes about technology. Finally, we discuss implications for clinical practice and future research.

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“…I n a very interesting article in the current issue of Diabetes Technology & Therapeutics, Telo et al 1 found that only 28% of young patients with type 1 diabetes (T1D) who were offered continuous glucose monitoring (CGM) accepted the invitation. Compared with a control group from the same diabetes center, the subjects who declined were less likely to use an insulin pump, had a higher hemoglobin A1c level, carried out blood glucose (BG) monitoring less often, were generally less adherent to their diabetes program, had a higher rate of family conflict related to diabetes, and returned lower quality of life values by questionnaire.…”

mentioning

confidence: 99%