The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
REPLACE-BG: A Randomized Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Well-Controlled Type 1 Diabetes
Open accessAbstract
OBJECTIVE: To determine whether the use of continuous glucose monitoring (CGM) without confirmatory blood glucose monitoring (BGM) measurements is as safe and effective as using CGM adjunctive to BGM in adults with well-controlled type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: A randomized noninferiority clinical trial was conducted at 14 sites in the T1D Exchange Clinic Network. Participants were ≥18 years of age (mean 44 ± 14 years), had T1D for ≥1 year (mean duration 24 ± 12 years), used an insulin pump, and had an HbA1c ≤9.0% (≤75 mmol/mL) (mean 7.0 ± 0.7% [53 ± 7.7 mmol/mol]); prestudy, 47% were CGM users. Participants were randomly assigned 2:1 to the CGM-only (n = 149) or CGM+BGM (n = 77) group. The primary outcome was time in range (70–180 mg/dL) over the 26-week trial, with a prespecified noninferiority limit of 7.5%. RESULTS: CGM use averaged 6.7 ± 0.5 and 6.8 ± 0.4 days/week in the CGM-only and CGM+BGM groups, respectively, over the 26-week trial. BGM tests per day (including the two required daily for CGM calibration) averaged 2.8 ± 0.9 and 5.4 ± 1.4 in the two groups, respectively (P < 0.001). Mean time in 70–180 mg/dL was 63 ± 13% at both baseline and 26 weeks in the CGM-only group and 65 ± 13% and 65 ± 11% in the CGM+BGM group (adjusted difference 0%; one-sided 95% CI −2%). No severe hypoglycemic events occurred in the CGM-only group, and one occurred in the CGM+BGM group. CONCLUSIONS: Use of CGM without regular use of confirmatory BGM is as safe and effective as using CGM with BGM in adults with well-controlled T1D at low risk for severe hypoglycemia.
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In the citing paper
“Numerous studies have demonstrated significant clinical benefits of CGM use in people with diabetes regardless of insulin delivery method (1-15).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [2]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“However, the increasing accuracy of the devices and at least one RCT comparing nonadjunctive to adjunctive use (191) has led the FDA to approve most current CGM devices for nonadjunctive use in the U.S.”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [191]
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In the citing paper
“Based on the results of multiple studies reporting a strong linear relationship between percent time in range (TIR) (70–180 mg/dL) and A1C in persons with T1D and T2D”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [154]
Experimental — accuracy not yet measuredIn the citing paper
“CGM systems are generally nonadjunctive, meaning they do not require BGM confirmation for treatment decisions like insulin dosing or treating hypoglycemia, except in certain clinical situations (see BLOOD GLUCOSE MONITORING, above) (44-46).”
From 7. Diabetes Technology: Standards of Care in Diabetes—2025 (2025) · cites this paper as [44]
In the citing paper
“Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive.”
From Importance of FDA-integrated continuous glucose monitors to ensure accuracy of continuous glucose monitoring (2025) · cites this paper as [12]
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Citations
Cites
Continuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559
Miller, K. M., Foster, N. C., Beck, R. W., Bergenstal, R. M., DuBose, S. N., DiMeglio, L. A., Maahs, D. M., & Tamborlane, W. V. (2015) · Diabetes Care · doi:10.2337/dc15-0078 · Cited by 1,254
Self-citationEffectiveness of Sensor-Augmented Insulin-Pump Therapy in Type 1 Diabetes
Bergenstal, R. M., Tamborlane, W. V., Ahmann, A., Buse, J. B., Dailey, G., Davis, S. N., Joyce, C., Peoples, T., Perkins, B. A., Welsh, J. B., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1002853 · Cited by 894
Clarke, W. L., Cox, D. J., Gonder-Frederick, L. A., Julian, D., Schlundt, D., & Polonsky, W. (1995) · Diabetes Care · doi:10.2337/diacare.18.4.517 · Cited by 651
Fear of hypoglycemia: quantification, validation, and utilization
Cox, D. J., Irvine, A., Gonder-Frederick, L., Nowacek, G., & Butterfield, J. (1987) · Diabetes Care · doi:10.2337/diacare.10.5.617 · Cited by 579
Pickup, J. C., Freeman, S. C., & Sutton, A. J. (2011) · BMJ · doi:10.1136/bmj.d3805 · Cited by 541
Effect of Continuous Glucose Monitoring on Hypoglycemia in Type 1 Diabetes
Battelino, T., Phillip, M., Bratina, N., Nimri, R., Oskarsson, P., & Bolinder, J. (2011) · Diabetes Care · doi:10.2337/dc10-1989 · Cited by 496
Yeh, H.-C., Brown, T. T., Maruthur, N., Ranasinghe, P., Berger, Z., Suh, Y. D., Wilson, L. M., Haberl, E. B., Brick, J., Bass, E. B., et al. (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-157-5-201209040-00508 · Cited by 493
The effect of continuous glucose monitoring in well-controlled type 1 diabetes
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2009) · Diabetes Care · doi:10.2337/dc09-0108 · Cited by 399
Peters, A. L., Ahmann, A. J., Battelino, T., Evert, A., Hirsch, I. B., Murad, M. H., Winter, W. E., & Wolpert, H. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2534 · Cited by 250
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Items 301–327 of 327
Aslan, A., Díez, J.-L., Sanz, A. J. L., & Bondía, J. (2023) · Applied Sciences · doi:10.3390/app13095348
Trends in Time in Range–Related Publications and Clinical Trials: A Bibliometric Review
Patel, P. M., Abaniel, R. M., Dogra, N., Lo, C. B., Frazzitta, M. A., & Virdi, N. S. (2023) · doi:10.2337/figshare.22714573.v1
Trends in Time in Range–Related Publications and Clinical Trials: A Bibliometric Review
Patel, P. M., Abaniel, R. M., Dogra, N., Lo, C. B., Frazzitta, M. A., & Virdi, N. S. (2023) · doi:10.2337/figshare.22714573
Moon, S. J., Hong, J. H., Kim, S. S., Kim, K., Lim, S. K., Jin, H. Y., Hwang, Y., Lee, S., Lee, K., Kang, J. G., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4608008
Literature Review of Deep Learning for Physiological signal AnalysisPreprint
Ortiz, N., Beleño, R. D. H., Pérez, M. A., & Suárez, P. A. N. (2023) · doi:10.22541/au.170121283.33310565/v1
Identifying Type 1 Diabetes from Daily Continuous Glucose Monitoring profiles Using Machine Learning
Belkacem, I. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698385
Piersanti, A., Callegari, A., Giudice, L. L. D., Göbl, C. S., Burattini, L., Tura, A., & Morettini, M. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252628
User evaluation of the Hypo‐METRICS app: A tool for real‐time symptom reporting of hypoglycaemia
Cigler, M., Søholm, U., Broadley, M. M., Pouwer, F., Abbink, E. J., Ali, N., de Galan, B. E., Renard, É., Evans, M. L., Brøsen, J. M. B., et al. (2025) · Diabetic Medicine · doi:10.1111/dme.70185
Dwiastuti, A., Latarissa, I. R., Hidayat, A. M., Halimah, E., & Lestari, K. (2026) · Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy · doi:10.2147/dmso.s561759
Development of Continuous Glucose Monitoring Systems for Better Diabetes Management
Sharma, S., Mohite, R., Keyravan, P. J., Jha, A., Deshmukh, K., Ansari, D., Valikarimwala, H., Khatavkar, A., Purohit, B., & Srivastava, R. (2026) · Springer eBooks · doi:10.1007/978-981-95-5519-2_9
A Federated Ensemble Framework for Hypoglycemia Prediction (Preprint)Preprint
Jayagopal, J. K., Dave, D., Lawley, M., Erraguntla, M., & Mahapatra, R. (2026) · doi:10.2196/preprints.94299
Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning
Fang, S., Zhang, H., Hu, D., Yu, X., Liu, Z., Ma, D., Xu, W., Lin, F., Xie, Q., Liu, F., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115294
Lim, W., Zhong, Y., Bruno, J., Alemán, J. O., & Barua, S. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261427499
Lei, C., Lobo, B. J., & Xie, J. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111673
Recent Trends in the Early Diagnosis and Treatment of Type 1 Diabetes
Padmavathi, S., Padmaja, V., Latha, A. H. D. P., & Naidu, P. S. (2026) · Springer eBooks · doi:10.1007/978-981-95-7490-2_9
Song, J., Alvarez, J., Gent, A., Gillespie, S., Harris, R. A., McNeany, J., Daley, T. C., Kamaleswaran, R., & Stecenko, A. (2026) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2026.04.001
Mulley, J. F. (2026) · medRxiv · doi:10.64898/2026.05.13.26353094
Kinetics of Hypoglycemia in Diabetes Patients Informs Development of New Modes of Glucagon TherapyPreprint
Li, J., Byrne, C., Liang, J., Lee, S., Lyhne, M. K., Meng, A., Ling, S. R., Li, S., Lopes, A., Khosravi, P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.05.729994
Calhoun, P. M., Liggins, R., Li, Z., Riddell, M. C., Bergenstal, R. M., Simonson, G. D., Shah, V. N., Peers, S. H., Sherr, J. L., & Beck, R. W. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261461810
Self-citationINSIGHT: Ingest and Standardize Glucose Harmonization Tool
Ehlert, B., -, K. S., Katarya, D., Wu, Y., Wang, T., & Snyder, M. P. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261467778
Lu, B., Wagle, B., Zhaohui, L., Cui, Y., & Prioleau, T. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001633 · Cited by 1
Kaczmarski, M., Kamiński, P., Surdacka, L., Jagiełło, J. K., Jakubas, A., Iwanicka, K. A., Pawelec, K., & Jóźwiak, S. M. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.2(50).2026.5663
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Pellizzari, E., Prendin, F., Cappon, G., Sparacino, G., & Facchinetti, A. (2026) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-37739-5_8
Modeling continuous glucose monitoring curves by Beta generalized nonparametric models
Acar‐Denizli, N., & Delicado, P. (2026) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2026.105710
Metadata
- Authors
- Aleppo, G., Ruedy, K. J., Riddlesworth, T. D., Kruger, D. F., Peters, A. L., Hirsch, I., Bergenstal, R. M., Toschi, E., Ahmann, A. J., Shah, V. N., Rickels, M. R., Bode, B. W., Philis-Tsimikas, A., Pop-Busui, R., Rodriguez, H., Eyth, E., Bhargava, A., Kollman, C., Beck, R. W., & for the REPLACE-BG Study Group
- Affiliations
- Northwestern University (US)
- Jaeb Center for Health Research (US)
- Henry Ford Health (US)
- University of Southern California (US)
- University of Washington (US)
- Park Nicollet Health Services (US)
- Joslin Diabetes Center (US)
- Oregon Health & Science University (US)
- Barbara Davis Center for Childhood Diabetes, Aurora, CO
- University of Pennsylvania (US)
- Atlanta Diabetes Associates (US)
- Scripps Whittier Diabetes Institute, La Jolla, CA
- University of Michigan, Ann Arbor, MI
- University of South Florida (US)
- Iowa Diabetes and Endocrinology Research Center (US)
- Corresponding author
- Riddlesworth, Tonya D.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2017-02-16
- Volume
- 40
- Issue
- 4
- Pages
- 538-545
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC5864100
- OpenAlex
- W2589083807
- Semantic Scholar
- 90d778291a6be773ec0116f890b79ecf1b9a36bb
- Cited by
- 329
- References
- 25
- Citations by year
- 2026: 30 · 2025: 27 · 2024: 38 · 2023: 43 · 2022: 37 · 2021: 36 · 2020: 39 · 2019: 27 · 2018: 31 · 2017: 19
- FWCI
- 25.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Leona M. and Harry B. Helmsley Charitable Trust
- 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, type 1 diabetes, blood glucose monitoring, time in range, insulin pump therapy, hypoglycemia, glycemic control, non-inferiority, adults
- MeSH terms
- Adult, Aged, Blood Glucose, Diabetes Mellitus, Type 1, Female, Glycated Hemoglobin, Humans, Insulin Infusion Systems, Male, Middle Aged, Socioeconomic Factors, Blood Glucose Self-Monitoring, Young Adult
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