Publications by authors named "Linda Raimond"
Article Synopsis
- The study looks into problems that come up when people using open-source insulin delivery systems talk to their doctors and nurses.
- It found three main issues: people don't share their experiences with their healthcare providers, there is confusion about who is responsible for problems, and each side has different expectations about technology and support.
- To improve these interactions, both users and healthcare professionals need to communicate better, understand the technology more, and focus on the emotional needs of the users.
Open-source automated insulin delivery systems, commonly referred to as do-it-yourself automated insulin delivery systems, are examples of user-driven innovations that were co-created and supported by an online community who were directly affected by diabetes. Their uptake continues to increase globally, with current estimates suggesting several thousand active users worldwide. Real-world user-driven evidence is growing and provides insights into safety and effectiveness of these systems.
View Article and Find Full Text PDFCorrection to: Flash glucose monitoring and automated bolus calculation in type 1 diabetes treated with multiple daily insulin injections: a 26 week randomised, controlled, multicentre trial.
Diabetologia
January 2022
Flash glucose monitoring and automated bolus calculation in type 1 diabetes treated with multiple daily insulin injections: a 26 week randomised, controlled, multicentre trial.
Diabetologia
December 2021
Aims/hypothesis: We aimed to compare the effects of intermittently scanned continuous glucose monitoring (isCGM) and carbohydrate counting with automated bolus calculation (ABC) with usual care.
Methods: In a randomised, controlled, open-label trial carried out at five diabetes clinics in the Capital Region of Denmark, 170 adults with type 1 diabetes for ≥1 year, multiple daily insulin injections and HbA > 53 mmol/mol (7.0%) were randomly assigned 1:1:1:1 with centrally prepared envelopes to usual care (n = 42), ABC (n = 41), isCGM (n = 48) or ABC+isCGM (n = 39).
Carbohydrate counting and use of automated bolus calculators (ABCs) can help reduce HbA in type 1 diabetes but only limited evidence exists in type 2 diabetes. We evaluated the efficacy of advanced carbohydrate counting (ACC) and use of an ABC compared with manual insulin bolus calculation (MC) in persons with type 2 diabetes. A 24-week open-label, randomized clinical study was conducted in 79 persons with type 2 diabetes treated with basal-bolus insulin (mean age 62.
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