ABSTRACT
AIMS: Our study examined changes in average blood glucose levels (ABG), measurement frequency (MF), and data uploading (DU) before and during the COVID-19 pandemic in 882-day spans, which were divided into further 20-week intervals to highlight the pandemic's impact. METHODS: T-Tests assessed the statistical significance of blood glucose data from 26,655/20,936 patients and 19.5/16.6 million records during pre-COVID/COVID. RESULTS: During COVID, patients had significantly lower ABG levels (9.1/8.9 mmol/L, p < 0.001). Weekly DU decreased (155,945/128,445, p < 0.05), while daily MF increased (0.83/0.87, p < 0.001). Comparing the last 20 weeks pre-COVID to the first 20 weeks during COVID, ABG levels were lower (9.0 /8.9, p < 0.01), MF increased (0.83 /0.99, p < 0.001), and DU decreased (153,133/145,381, p < 0.05). In the initial 20 weeks of COVID compared to the second 20 weeks of COVID, ABG increased (8.9/9.1, p < 0.01), MF decreased (0.99/0.95, p < 0.001), and DU decreased (145,381/140,166, p < 0.05). Our most striking observation was the temporary dramatic fall in glucose uploads during the first few weeks of COVID. The changes of ABG and MF values were statistically significant, but were not deemed clinically relevant. CONCLUSIONS: Despite COVID's prolonged impact, diabetic patients showed improved attitudes. A significant drop in data uploads occurred during the first 20 weeks of COVID; home office and lockdowns apparently disrupted patient routines.
ABSTRACT
AIMS: The aim of the article is to describe the method for creating a close to ideal diabetes database. The MÉRY Diabetes Database (MDD) consists of a large quantity of reliable, well-maintained, precise and up-to-date data suited for clinical research with the intention to improve diabetes care in terms of maintaining targeted blood glucose levels, avoiding hypoglycemic episodes and complications and improving patient compliance and quality of life. METHODS: Based on the analysis of the databases found in the literature and the experience of our research team, nine important characteristics were identified as critical to an ideal diabetes database. The data for our database is collected using MÉRYkék glucometers, a device that meets all requirements of international regulations and measures blood glucose levels within the normal range with appropriate precision (10 %). RESULTS: Using the key characteristics defined, we were able to create a database suitable for the analysis of a large amount of data regarding diabetes care and outcomes. CONCLUSIONS: The MDD is a reliable and ever growing database which provides stable and expansive foundation for extensive clinical investigations that hold the potential to significantly influence the trajectory of diabetes care and enhance patient outcomes.
