The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
Improving the Clinical Value and Utility of CGM Systems: Issues and Recommendations
Open accessAbstract
The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionize the use of intensive insulin therapy in diabetes; however, progress toward that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardized format for displaying results, and uncertainty on how best to use CGM data to make therapeutic decisions. This Scientific Statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In many countries, the benefits and utility of CGM are now recognized by national and international medical organizations for individuals with insulin-requiring diabetes and/or those at risk for hypoglycemia (16-21).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [19]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
McCambridge, J., Witton, J., & Elbourne, D. R. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.08.015 · Cited by 2,748
Continuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559
Beck, R. W., Riddlesworth, T., Ruedy, K., Ahmann, A., Bergenstal, R., Haller, S., Kollman, C., Kruger, D., McGill, J. B., Polonsky, W., et al. (2017) · JAMA · doi:10.1001/jama.2016.19975 · Cited by 1,185
Self-citationBolinder, J., Antuna, R., Geelhoed-Duijvestijn, P., Kröger, J., & Weitgasser, R. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31535-5 · Cited by 893
Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities
Rodbard, D. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0417 · Cited by 768
Threshold-based insulin-pump interruption for reduction of hypoglycemia
Bergenstal, R. M., Klonoff, D. C., Garg, S. K., Bode, B. W., Meredith, M., Slover, R. H., Ahmann, A. J., Welsh, J. B., Lee, S. W., & Kaufman, F. R. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1303576 · Cited by 652
Self-citationSafety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes
Bergenstal, R. M., Garg, S., Weinzimer, S. A., Buckingham, B. A., Bode, B. W., Tamborlane, W. V., & Kaufman, F. R. (2016) · JAMA · doi:10.1001/jama.2016.11708 · Cited by 644
Self-citationHaak, T., Hanaire, H., Ajjan, R., Hermanns, N., Riveline, J.-P., & Rayman, G. (2017) · Diabetes Therapy · doi:10.1007/s13300-016-0223-6 · Cited by 623
International Hypoglycaemia Study Group (2017) · Diabetes Care · doi:10.2337/dc16-2215 · Cited by 608
Pickup, J. C., Freeman, S. C., & Sutton, A. J. (2011) · BMJ · doi:10.1136/bmj.d3805 · Cited by 541
Flagged first, then by citations
1 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–137 of 137
Shi, J., Weng, J., Ding, Y., Xia, Y. Y., Zhou, Y., Wang, X., Zhang, F., Zhang, P., Luo, S., Zheng, X., et al. (2024) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70001 · Cited by 2
Bruttomesso, D., Petrie, J. R., Evans, M., Fleming, G. A., Hanaire, H. P., Holl, R. W., Sherr, J. L., Bergenstal, R. M., Heinemann, L., & Peters, A. L. (2026) · Diabetologia · doi:10.1007/s00125-025-06651-9 · Cited by 2
Self-citationTecce, N., Menafra, D., Proganò, M., Tecce, M. F., Pivonello, R., & Colao, A. (2024) · Healthcare · doi:10.3390/healthcare12181823 · Cited by 2
Chawla, M. S., Chawla, P., Saboo, B., Chawla, R., Gangopadhyay, K. K., Kalra, S., Aravind, S. R., Sinha, B., Shah, T. U., Kesavadev, J., et al. (2022) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2022.102587 · Cited by 2
Gulati, C., Kanimozhi, N., Prajna, K. B., Sujatha, C. K., Sinduja, K., & Kathiravan, M. N. (2023) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss58487.2023.10250757 · Cited by 2
Jenkins, A. J., Kilov, G., SCOTT, E. S., Fulcher, G., O'Neal, D. N., & Januszewski, A. S. (2018) · Madridge Journal of Diabetes · doi:10.18689/mjd-1000111 · Cited by 2
Lalau, J., Bennis, Y., Al‐Salameh, A., Hurtel‐Lemaire, A., & Fendri, S. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14554 · Cited by 2
Time-in-range: a promising glycemic control metric for bariatric surgery
Kang, M., & Li, W. (2022) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2022.08.002 · Cited by 2
Diabetes Care in 2020: Following and Leading the Stories of Diabetes
Riddle, M. C. (2019) · Diabetes Care · doi:10.2337/dci19-0065 · Cited by 3
Monnier, L., & Colette, C. (2022) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2021.12.006 · Cited by 2
Individualized, self-supervised deep learning for blood glucose predictionPreprint
Fuest, J., Tacke, M., Ullman, L., & Washington, P. Y. (2023) · medRxiv · doi:10.1101/2023.08.19.23294318 · Cited by 2
Goswami, S., Gaikwad, P. M., Mondal, S., Chowdhury, A. S., Das, N., Palui, R., Mandal, S., Baidya, A., & Sengupta, N. (2025) · Diabetes Technology and Obesity Medicine · doi:10.1177/29986702251406273 · Cited by 1
AL-Khafaji, R. D. M., Pusppanathan, J., Moolla, A., & Phang, F. A. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-97578-3_8 · Cited by 1
Advances in Electrocardiogram‐Based Non‐Invasive Blood Glucose Monitoring Technology
Zeng, Q., Ni, J., Zhang, Z., Cai, J., Lu, J., & Zhou, J. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70638 · Cited by 2
Yuan, J., Wang, Y., Chen, P., Wang, J., Mu, Y., Fritschi, C., Hu, Y., Chen, Y., Reutrakul, S., & Zhu, B. (2026) · Journal of Sleep Research · doi:10.1111/jsr.70370 · Cited by 1
Paglialunga, S., Morimoto, B. H., de la Peña, A., & Fortier, C. (2018) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.437 · Cited by 2
Should all patients on insulin be using continuous glucose monitoring?
Distiller, L. A. (2018) · Journal of Endocrinology Metabolism and Diabetes of South Africa · doi:10.1080/16089677.2018.1515164 · Cited by 1
Towards blood glucose and insulin estimation using off-line data and missing information
Hernandez, C. M., Isaza, J. A., & Rivadeneria, S. (2021) · IEEE Colombian Conference on Automatic Control (CCAC) · doi:10.1109/ccac51819.2021.9633302 · Cited by 1
Taofiq Olanrewaju, M. U. S. A., ADJEVI, A., JACCOJWANG, D. O., ADELEYE, N., OPEYEMI, D. A., Uzun, S., Yıldız, M. Z., Lazım, A., Peter, R., & Yaylacı, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7410777/v1 · Cited by 1
Expanding The Use Of Glucose Monitoring Technology To Enhance Diabetes Care
Petrie, J. R., & Heinemann, L. (2018) · Science Trends · doi:10.31988/scitrends.9381
Self-citationFall 5: Therapie – 25 Jahre, ♂, DM Typ 1, stark schwankende Blutzuckerwerte
Wetekam, B. (2018) · Springer eBooks · doi:10.1007/978-3-662-55972-7_5
11th Annual Symposium on Self-Monitoring of Blood Glucose: April 12–14, 2018, Oslo, Norway
Parkin, C. G., Homberg, A., & Hinzmann, R. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0284
Fleming, G. A., & Harvey, B. E. (2019) · Springer eBooks · doi:10.1007/978-3-030-11748-1_19
Self-citationAutosurveillance glycémique : objectifs de glycémie, objectifs d’HbA1c
Bismuth, É., & Coutant, R. (2018) · Elsevier eBooks · doi:10.1016/b978-2-294-74942-1.00013-6
«TIME IN RANGE» INDICATOR DEPENDING ON THE DEGREE OF VIOLATIONS OF THE GLUCOSE CONTROL SYSTEM
Mustafaeva, S. A. (2020) · Bulletin of Problems Biology and Medicine · doi:10.29254/2077-4214-2020-1-155-167-170
Thérapeutique des désordres glycémiques
(2019) · Elsevier eBooks · doi:10.1016/b978-2-294-75889-8.00007-5
(2019) · Elsevier eBooks · doi:10.1016/b978-2-294-75889-8.00004-x · Cited by 1
Monnier, L., & Halimi, S. (2019) · Elsevier eBooks · doi:10.1016/b978-2-294-75889-8.00029-4
Time in range: new parameter to evaluate blood glucose control
Flekač, M. (2022) · Vnitřní lékařství · doi:10.36290/vnl.2022.066
Анатольевна, П. О., Куликов, Д. А., & Kotova, А. А. (2024) · Sibirskij medicinskij vestnik · doi:10.31549/2541-8289-2024-8-3-70-77
Mohammadi, A., Daroudi, R., Mousavi, A., Olyaeemanesh, A., Nasli‐Esfahani, E., Shafiee, G., Heshmat, R., Takian, A., & Darvishi, A. (2025) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2025.2586670
Taofiq Olanrewaju, M. U. S. A., ADJEVI, A., JACCOJWANG, D. O., ADELEYE, N., OPEYEMI, D. A., Uzun, S., Yıldız, M. Z., Lazım, A., & Mwita, R. P. (2026) · Academic Platform Journal of Engineering and Smart Systems · doi:10.21541/apjess.1714659
Mader, J. K., & Heinemann, L. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261427022
Self-citationSchoonhoven, A. D., Bakker, J. J., Mauro, A. D., van der Vaart, A., Been, R. A., van Beek, A. P., Choudhary, P., Bouma, H. R., & van Dijk, P. R. (2026) · PLoS ONE · doi:10.1371/journal.pone.0343703
Glucose Monitoring: Advances in Technology
McDonnell, D. J., Smith, D., & Forde, H. E. (2026) · Springer eBooks · doi:10.1007/978-3-032-24854-1_4
Kumari, A., Mishra, R., & Sharma, B. (2026) · International Journal of Drug Delivery Technology · doi:10.25258/ijddt.16.86s.37
Metadata
- Authors
- Petrie, J. R., Peters, A. L., Bergenstal, R. M., Holl, R. W., Fleming, G. A., & Heinemann, L.
- Affiliations
- University of Glasgow (GB)
- University of Southern California (US)
- Park Nicollet Health Services (US)
- Universität Ulm (DE)
- Kinexum, Harpers Ferry, WV
- Science & Co, Dusseldorf, Germany
- Corresponding author
- Petrie, John R.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2017-10-25
- Volume
- 40
- Issue
- 12
- Pages
- 1614-1621
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2766548367
- Cited by
- 135
- References
- 53
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 12 · 2021: 18 · 2020: 21 · 2019: 25 · 2018: 28 · 2017: 1
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- 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 › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- continuous glucose monitoring, diabetes management, intensive insulin therapy, measurement reliability, human factors, regulatory oversight, self-monitoring of blood glucose
- MeSH terms
- Blood Glucose, Cost-Benefit Analysis, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Europe, Humans, Insulin, Insulin Infusion Systems, Quality of Life, Societies, Medical, United States, Blood Glucose Self-Monitoring, Meta-Analysis as Topic, Reproducibility of Results, Randomized Controlled Trials as Topic
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 ›
Are you an author, or do you see a problem with this page? Request a review