Identifying the deficiencies of currently available CGM to improve uptake and benefit
Abstract
BACKGROUND AND AIMS: The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. METHODS: Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. RESULTS: CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. CONCLUSIONS: Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.
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Reduced diabetes distress and improved quality of life may contribute to better treatment adherence and long-term engagement with self-management behaviors.
“Reduced diabetes distress and improved quality of life may contribute to better treatment adherence and long-term engagement with self-management behaviors [[48], [49], [50]].”
From Effectiveness of continuous glucose monitoring systems on glycemic control in adults with type 1 diabetes: A systematic review and meta-analysis (2025) · cites this paper as [49]
Patients discontinuing CGM as a result of alarm fatigue cannot take advantage of their beneficial effect on glycemic control.
“The patients discontinuing CGM as a result of alarm fatigue cannot take advantage of their beneficial effect on glycemic control [ 56 , 57 ].”
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- Authors
- Barnard‐Kelly, K. D., Martínez‐Brocca, M. A., Glatzer, T., & Oliver, N.
- Affiliations
- Spotlight Consultations Ltd Portsmouth UK
- Hospital Universitario Virgen Macarena (ES)
- Roche Diabetes Care GmbH (Germany) (DE)
- Imperial College London (GB)
- Venue
- Diabetic Medicine
- Publisher
- Wiley
- Published
- 2024-05-12
- Volume
- 41
- Issue
- 8
- Pages
- e15338-e15338
- ISSN
- 0742-3071 (ISSN-L)
- 1464-5491
- OpenAlex
- W4396866330
- Cited by
- 37
- References
- 46
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 8
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Roche Diabetes Care
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- continuous glucose monitoring, diabetes self-management, device usability, user experience, alarm fatigue, sleep disorders, stigma, unmet needs, personalization, diabetes technology
- MeSH terms
- Self-Management, Blood Glucose, Diabetes Mellitus, Diabetes Mellitus, Type 1, Humans, Blood Glucose Self-Monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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