How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions.
Andreas ThomasThomas HaakAstrid TombekBernhard KulzerDominic EhrmannOlga KordonouriJens KrögerOliver Schubert-OlesenRalf KolassaThorsten SiegmundNicola HallerLutz HeinemannPublished in: Journal of diabetes science and technology (2024)
Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.
Keyphrases
- type diabetes
- glycemic control
- blood glucose
- electronic health record
- end stage renal disease
- cardiovascular disease
- big data
- physical activity
- ejection fraction
- newly diagnosed
- randomized controlled trial
- healthcare
- chronic kidney disease
- prognostic factors
- machine learning
- primary care
- palliative care
- bone marrow
- high throughput
- deep learning
- skeletal muscle
- minimally invasive
- data analysis
- intensive care unit
- weight loss
- adipose tissue
- body mass index
- patient reported outcomes
- depressive symptoms
- liver failure
- mesenchymal stem cells
- quality improvement
- extracorporeal membrane oxygenation