The DOI matches its Crossref record; 27 of the 29 references it cites were found in a registry or an index.
Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials
Open accessAbstract
OBJECTIVE: This study evaluated the association of time in range (TIR) of 70–180 mg/dL (3.9–10 mmol/L) with the development or progression of retinopathy and development of microalbuminuria using the Diabetes Control and Complications Trial (DCCT) data set in order to validate the use of TIR as an outcome measure for clinical trials. RESEARCH DESIGN AND METHODS: In the DCCT, blood glucose concentrations were measured at a central laboratory from seven fingerstick samples (seven-point testing: pre- and 90-min postmeals and at bedtime) collected during 1 day every 3 months. Retinopathy progression was assessed every 6 months and urinary microalbuminuria development every 12 months. Proportional hazards models were used to assess the association of TIR and other glycemic metrics, computed from the seven-point fingerstick data, with the rate of development of microvascular complications. RESULTS: Mean TIR of seven-point profiles for the 1,440 participants was 41 ± 16%. The hazard rate of development of retinopathy progression was increased by 64% (95% CI 51–78), and development of the microalbuminuria outcome was increased by 40% (95% CI 25–56), for each 10 percentage points lower TIR (P < 0.001 for each). Results were similar for mean glucose and hyperglycemia metrics. CONCLUSIONS: Based on these results, a compelling case can be made that TIR is strongly associated with the risk of microvascular complications and should be an acceptable end point for clinical trials. Although hemoglobin A1c remains a valuable outcome metric in clinical trials, TIR and other glycemic metrics—especially when measured with continuous glucose monitoring—add value as outcome measures in many studies.
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 particular, glucose time in range (70 -180 mg/dl [3.9 -10.0 mmol/l]) and time in hypoglycemia ( < 70 mg/dl [3.9 mmol/l] and < 54 mg/dl [3.0 mmol/l]) have been used as outcomes for clinical trials 259,260 and have been endorsed as appropriate metrics for clinical care. 229”
From KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (2022) · cites this paper as [259]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although the evidence of HbA 1c reduction remains the most robust measure associated with chronic diabetes complications and is the only measure that is prospectively validated, more recent studies have begun to examine the relationship between time that glucose is within the target range and long-term complications and have provided the basis for glycemic targets with newer glucose monitoring”
From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [45]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“GMI is calculated based on the average sensor glucose over the last 14 days and provides an approximation of a laboratory-measured HbA 1c in some individuals, but it may be higher or lower than actual HbA 1c in others (45).”
From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [45]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“TIR is associated with microvascular complications (45,46), and a TIR of 70% roughly corresponds to an HbA 1c of 53 mmol/mol (7.0%).”
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Most recently, the beneficial effects of new medications, such as sodium glucose cotransporter-2 (SGLT-2) agents have helped individuals with type 1 diabetes increase TIR (49-51).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [50]
Experimental — accuracy not yet measured
In the citing paper
“TIR has been found to be strongly associated with the development or progression of diabetic retinopathy and microalbuminuria in type 1 and type 2 DM.16 17”
From Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease (2020) · cites this paper as [16]
In the citing paper
“In a post hoc analysis from the Diabetes Control and Complications Trial (DCCT), Beck et al. (8) calculated TIR with seven-point fingerstick glucose values and demonstrated significant associations of TIR with the development of diabetic retinopathy and microalbuminuria.”
From Time in Range in Relation to All-Cause and Cardiovascular Mortality in Patients With Type 2 Diabetes: A Prospective Cohort Study (2021) · cites this paper as [8]
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
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069
Self-citationSeaquist, E. R., Anderson, J., Childs, B., Cryer, P., Dagogo-Jack, S., Fish, L., Heller, S. R., Rodriguez, H., Rosenzweig, J., & Vigersky, R. (2013) · Diabetes Care · doi:10.2337/dc12-2480 · Cited by 1,389
International Hypoglycaemia Study Group (2017) · Diabetes Care · doi:10.2337/dc16-2215 · Cited by 608
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
Self-citationThe DCCT Research Group (1986) · Diabetes · doi:10.2337/diabetes.35.5.530 · Cited by 466
Agiostratidou, G., Anhalt, H., Ball, D., Blonde, L., Gourgari, E., Harriman, K. N., Kowalski, A. J., Madden, P., McAuliffe-Fogarty, A. H., McElwee-Malloy, M., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1624 · Cited by 397
The Effect of Glucose Variability on the Risk of Microvascular Complications in Type 1 Diabetes
Kilpatrick, E. S., Rigby, A. S., & Atkin, S. L. (2006) · Diabetes Care · doi:10.2337/dc06-0293 · Cited by 376
The International Hypoglycaemia Study Group (2016) · Diabetologia · doi:10.1007/s00125-016-4146-6 · Cited by 367
The impact of non-severe hypoglycemic events on work productivity and diabetes management
Brod, M., Christensen, T., Thomsen, T. L., & Bushnell, D. M. (2011) · Value in Health · doi:10.1016/j.jval.2011.02.001 · Cited by 284
Flagged first, then by citations
2 of the 29 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 701–792 of 792
Rodrigues, J. V. S., Deroide, M. B., Santana, A. P., de Molon, R. S., & Theodoro, L. H. (2024) · Revista de Odontologia da UNESP · doi:10.1590/1807-2577.03024
Chen, P., & Xie, Y. (2024) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-024-01432-3
李, 康. (2024) · Journal of Clinical Personalized Medicine · doi:10.12677/jcpm.2024.34367
Hussain, I., MacLeod, J., Dunn, F., Bickel, P. E., Abreu, M., McNulty, N., Lingvay, I., Kermani, A., & Abreu, M. (2020) · American Diabetes Association eBooks · doi:10.2337/9781580406314.ch03
Alarcon, G., Lyons, S. K., BUCKINGHAM, D. A., Erraguntla, M., Sonabend, R., & DeSalvo, D. J. (2025) · Clinical Diabetes · doi:10.2337/cd24-0065
Sobczak, M., Pyle, L. L., Sawyer, A., Forlenza, G. P., & Alonso, G. T. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0500
Driscoll, K. A., Trojanowski, P. J., Williford, D. N., O’Donnell, H. K., Flynn, E., Mara, C. A., Wetter, S. E., Himelhoch, A. C., Manis, H., Pardon, A., et al. (2025) · Contemporary Clinical Trials · doi:10.1016/j.cct.2025.107849
Kotyk, M. (2024) · Reporter of the Priazovskyi State Technical University Section Technical sciences · doi:10.31498/2225-6733.49.2.2024.321357
Alqutub, S. T., & Aljehani, F. (2025) · Digital Health · doi:10.1177/20552076251325987 · Cited by 1
Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logic and Reinforcement Learning
Liu, X., Hu, F., Su, Z., & Yang, F. (2024) · International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM) · doi:10.1109/aiim64537.2024.10934697
Moderne Technik der Therapieüberwachung bei Diabetes mellitus
Allendorf, I. (2025) · Gefässchirurgie · doi:10.1007/s00772-025-01200-5
Glucose meters and sensors for type 1 diabetes (part 2)
Slee, P. H. T. J., Helmerhorst, F. M., & Nieuwhof, M. (2023) · Geneesmiddelenbulletin · doi:10.35351/gebu.2023.6.10
石, 来. (2025) · Advances in Clinical Medicine · doi:10.12677/acm.2025.1551562
Andreassen, J., Nikontovic, A., Iversen, E., Borðoy, R. Á. F., Rasmussen, P., Petersen, M. S., & Johannesen, H. (2025) · AACE endocrinology and diabetes · doi:10.1016/j.aed.2025.05.005
Prado, F. (2025) · Revista Mexicana de Endocrinología Metabolismo y Nutrición · doi:10.24875/rme.24000043
Épidémiologie du diabète et de la rétinopathie diabétique
Massin, P., Dupas, B., Erginay, A., Feldman, S., & Lecleire-Collet, A. (2025) · Elsevier eBooks · doi:10.1016/b978-2-294-78350-0.00001-5
Herranz-Antolín, S., Ramos-Garrido, S., Esteban-Monge, V., Cotón-Batres, C., López-Virgos, M. C., Lallena-Pérez, S., & Torralba, M. (2025) · Acta Diabetologica · doi:10.1007/s00592-025-02563-7
Herranz-Antolín, S., Esteban-Monge, V., López-Virgos, M. C., Ramos-Garrido, S., Cotón-Batres, C., Lallena-Pérez, S., & Torralba, M. (2025) · Endocrine · doi:10.1007/s12020-025-04379-5
Targets for glycemic control and its monitoring
Moriconi, D., & Nannipieri, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13825-6.00283-1
Ferm, M. L., DeSalvo, D. J., Zhou, C., Bradford, M. C., Malik, F. S., O’Donnell, M. B., Pihoker, C., Rosenberg, A. R., Yi‐Frazier, J. P., & Hilliard, M. E. (2025) · Diabetes Technology and Obesity Medicine · doi:10.1177/29986702251388027
He, L., Chen, J., Chen, W., & Shen, X. (2025) · Journal of Laboratory Medicine · doi:10.1515/labmed-2025-0017
Continuous glucose monitoring in patients with type 2 diabetes mellitus on oral hypoglycemic therapy
Кузина, И. А., Акрамовна, Э. Э., & Petunina, N. A. (2025) · Meditsinskiy sovet = Medical Council · doi:10.21518/ms2025-414
Garabédian, C., Sénat, M., Sananès, N., Berveiller, P., Brillac, T., Lous, M. L., Lemaître, M., Mitanchez, D., Morel, O., Paquin, S., et al. (2025) · Gynécologie Obstétrique Fertilité & Sénologie · doi:10.1016/j.gofs.2025.12.001 · Cited by 1
Ahmad, K., Rule, P., Bañez, B., Hale, D., & Antwerp, B. V. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251389630
Liu, Y., Chen, D., He, K., Wei, J., Liang, M., Li, H., Li, J., Li, X., & Wang, X. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1608458
Frizziero, L., Barratt, J., Cheung, C. M. G., Korol, A. R., Midena, E., Talks, J. S., Tsilimbaris, M. K., Pearce, I., & Kozak, I. (2025) · Eye · doi:10.1038/s41433-025-04112-6
Tokutsu, A., Okada, Y., Torimoto, K., & Tanaka, Y. (2020) · Figshare · doi:10.6084/m9.figshare.12799284
Huang, F., Wu, X., Xie, Y., Liu, F., Li, J., Li, X., & Zhou, Z. (2020) · Figshare · doi:10.6084/m9.figshare.13279202
Huang, B., Jiang, Q., Wu, T., Shen, Q., Wang, W., Wang, S., Huang, Y., Wang, S., Huang, P., Lin, M., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21638856
Al-Harbi, M. Y., Albunyan, A., Alnahari, A., Kao, K., Brandner, L., Jammal, M. E., & Dunn, T. C. (2022) · Figshare · doi:10.6084/m9.figshare.19695806
Godoi, A., Reis Marques, I., Padrão, E. M. H., Mahesh, A., Hespanhol, L. C., Riceto Loyola Júnior, J. E., de Souza, I. A. F., Moreira, V. C. S., Silva, C. H., Miyawaki, I. A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26620396
Bezerra, M. F., Neves, C., Neves, J. S., & Carvalho, D. (2023) · Figshare · doi:10.6084/m9.figshare.24636469
Insulin pumps and artificial pancreas
Markakis, K. P., & Vazeou, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13825-6.00296-x
Morar-Stan, A., Dobrotă, L., Radutu, A., & Domnariu, C. D. (2025) · medRxiv · doi:10.64898/2025.12.19.25342708
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
Chagas, H. V., Laurindo, L. F., Rodrigues, V. D., Haber, J. F. D. S., Chagas, E. F. B., & Barbalho, S. M. (2026) · Diseases · doi:10.3390/diseases14010028
Makeeva, T. I., Koltakova, M. P., Федоринин, А. А., Bezlepkina, O. B., & Лаптев, Д. Н. (2026) · Diabetes Mellitus · doi:10.14341/dm13395
Quintal-Medina, I. A., López-Prieto, R. S., Romero-Torres, K. A., García-Ulloa, A. C., Gomez-Perez, F. J., & Almeda‐Valdés, P. (2026) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2025.501758
Continuous Blood Glucose Monitoring: Potential Applications from Diabetes Management
Manaf, F. A., Amin, M. Q. A. M., Ahmad, A., Farizan, N. H., Sukri, N. M., & Maliki, A. B. H. M. (2025) · Malaysian Journal of Medicine and Health Sciences · doi:10.47836/mjmhs.21.4.41
Zhou, L., Wu, C., Wang, C., Yu, X., Qu, D., Yang, Y., Xu, X., Liu, J., Zhu, Q., Liao, X., et al. (2026) · Endocrine · doi:10.1007/s12020-025-04509-z
Yang, Y., Liu, Z., HUANG, Y. ‐., Deng, H., Yang, X., Yan, J., Zeng, L., Lin, B., & Xu, W. (2026) · BMC Endocrine Disorders · doi:10.1186/s12902-026-02204-2 · Cited by 1
Arethiya, N., Krammer, L., David, J. A., Bakshi, V., Basuchoudhary, A., Bhuiyan, U., Sen, S., Mazumder, R., & McNeely, P. M. (2026) · medRxiv · doi:10.64898/2026.03.04.26347604
Lu, X., You, J., Xia, T., & Xu, R. (2026) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-026-02138-8
Zeng, W., Manikam, A. D. N., & Khoo, J. J. C. (2026) · JMIR Formative Research · doi:10.2196/preprints.94885
Munsch, K., Sun, J., Canceill, T., Slisse, P., Pegouret, C., Diemer, S., Loubières, P., Laurencin‐Dalicieux, S., Minty, M., Gourdy, P., et al. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1794873
Matabuena, M., & Crainiceanu, C. M. (2026) · The Annals of Applied Statistics · doi:10.1214/26-aoas2139
Heng, T. A. N. G., Zhifeng, C. H. E. N., Wang, J., Meng, H., & ZHANG, W. (2026) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2026.109315
González-Maroto, I., Moreno-Fernández, J., Beato-Víbora, P. I., & Quirós, C. (2026) · Diabetes & Metabolism · doi:10.1016/j.diabet.2026.101757
Hayek, A. A., Zahrani, W. M. A., Alotaibi, H. F., Saleh, J. Y., & Dawish, M. A. A. (2026) · Primary care diabetes · doi:10.1016/j.pcd.2026.04.002
Casadei, G., Baldari, M., Brognara, L., Saudelli, E., Moscatiello, S., Flamigni, S., Traina, F., Paccapelo, A., Pagotto, U., & Dalmazi, G. D. (2026) · Journal of Diabetes Investigation · doi:10.1111/jdi.70310
Gusmano, C., Cannarella, R., Finocchiaro, C., Gruttadauria, G., Randazzo, R., Condorelli, R. A., Vignera, S. L., Calogero, A. E., & Papa, G. (2026) · Biomedicines · doi:10.3390/biomedicines14051136
李, 晓. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1652017
Gai, B., Chen, W., & Qiu, L. (2026) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2026.10.009
Yoo, J., Rachim, V. P., Lee, Y., Lee, J., & Park, S.-J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.25.727637
Dario, P. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261455365 · Cited by 1
Mikrovaskuläre Komplikationen bei Typ-1-Diabetes
Schmidt, A.-S., & Ebert, T. (2026) · CardioVasc · doi:10.1007/s15027-026-4013-4
Ashraf, T., & Lessan, N. G. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113369
Cuevas‐Budhart, M. Á., Becerra-Barrera, J. S., Silva-Rueda, R., Vallejo-Avalos, D., Ponce-Villavicencio, M., Ilabaca-Avendaño, M. B., Ávila, M., & Paniagua, R. (2026) · Medical Sciences · doi:10.3390/medsci14020324
Quintal-Medina, I. A., López-Prieto, R. S., Romero-Torres, K. A., García-Ulloa, A. C., Gomez-Perez, F. J., & Almeda‐Valdés, P. (2026) · Endocrinología Diabetes y Nutrición (English ed ) · doi:10.1016/j.endien.2026.501758
Association Between Longitudinal Time in Range and Albuminuria in Insulin-Treated Diabetes
Yoshimura, K., Miyazaki, Y., Kobayashi, M., Nakatsuji, M., Saito, S., Nishikage, S., Yamamoto, A., Takayoshi, T., Asahara, S., Ogawa, W., et al. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261462229
Rákóczi, G., Csányi, D., Molontay, R., Agócs, G., Szentes, B. L., Lee, J., Rancz, A., Hegyi, P., & Sipter, E. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113394
Qian, T.-M., Guo, D., Zhang, M., Liu, Y.-H., & Jin, J. (2026) · Diabetic Medicine · doi:10.1111/dme.70404
Terbosh, M., Dubaj, M., Ignarska, J., Judzińska, A., `Mzabi, S. K., Kwolek, M., Osipowska, J., Piech, B., Sobczyńska, K., & Trojanowska, E. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.2(50).2026.5614
Andreadi, A., Andreadi, S., Todaro, F., Cerilli, M., Lodeserto, P., Pinto, G., Meloni, M., Bellia, A., Coppeta, L., Magrini, A., et al. (2026) · Healthcare · doi:10.3390/healthcare14131979 · Cited by 1
Is time in range an independent predictor of early diabetic kidney disease beyond HbA1c?
Hong, W., Shen, X., & Wu, X. (2026) · International Urology and Nephrology · doi:10.1007/s11255-026-05259-1
Sun, Y., Li, X., & Xu, L. (2026) · Endocrine · doi:10.1007/s12020-026-04699-0
Masri, S., Albela, M., Ciglenečki, I., Boulle, P., Hachem, T., Bahous, S., Prieto‐Merino, D., & Perel, P. (2026) · Journal of Comparative Effectiveness Research · doi:10.57264/cer-2026-0036
The role of CGM sensor wear-time on the relationship between time-in-range and HbA1c
Johnson, A., Straton, E., Maya, G., WHITE, S., Barber, J., Majidi, S., Perkins, A., & Streisand, R. M. (2026) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2026.109378
Lin, Y., Jm, L., Ozaki, R., Yuen, S. K., Szeto, C. C., Ng, J. K., & Chow, E. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113442
Time in Range and Adverse Outcomes in Type 2 Diabetes: A Quantitative Synthesis
Qu, F., Yang, Q., Zeng, Q., Li, Z., & Li, J. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15145713
Self-citationJiang, Y., Kang, J., & Chen, Y. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1884596
Su, H., Ni, J., Cai, J., Zhu, W., Wang, Y., Zhou, J., Lu, J., & Ma, X. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71143
Su, Y., Han, G., & Su, Q. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1925994
Time in tight range (TITR): emerging evidence and clinical perspectives
Ohno, T. (2026) · Diabetology International · doi:10.1007/s13340-026-00923-4
Interpretation of CGM and Insulin Pump Downloads
McDonnell, D. J., Smith, D., & Forde, H. E. (2026) · Springer eBooks · doi:10.1007/978-3-032-24854-1_7
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
Ameur, A. (2026) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2026.1941126
Time in Target Range: The Optimal Marker for Kidney Protection
Pasricha, S. V., Roushani, J., & Tobe, S. W. (2026) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2026.07.005
Knowledge and Attitudes Regarding Continuous Glucose Monitors as Predictors of Current Use
Gallegos, S. P., Zohn, J. H., Hutchins, A. M., Derksen, L. A., & Benton, M. J. (2026) · The Science of Diabetes Self-Management and Care · doi:10.1177/26350106261483745
Le, J., Li, X.-X., & Qin, S.-L. (2026) · Digital Health · doi:10.1177/20552076261494727
Lee, M. J., Seo, B.-J., & Cho, J.-H. (2026) · Life · doi:10.3390/life16101667
Empirical transition probability modeling of continuous glucose monitoring data
Yu, Y., Banker, M. M., Sherr, J., & Scholtens, D. M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0358361
Arisanty, I. P., Mukai, K., Nakajima, Y., Yamada, A., Iryanidar, I., Abu, M., & Oe, M. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113604
Delrue, C., & Speeckaert, M. M. (2026) · Metabolism and Target Organ Damage · doi:10.20517/mtod.2026.169
Tail-Risk-Aware Model Predictive Control for Hypoglycemia Mitigation in Automated Insulin Delivery
Zhang, K., Han, C., Guo, H., Kang, S., Jing, S., Wang, C., Zang, T., & Zhao, Y. (2026) · Metabolites · doi:10.3390/metabo16100759
Aktürk, H. K., Karakus, K. E., Mason, E., Yilmaz, Z. Y., Garg, S. K., & Cengiz, D. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261496613
Hayek, A. A., Zahrani, W. M. A., Alotaibi, H. F., Jammal, M. E., & Dawish, M. A. A. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261489265
Tang, M., Nathan, D. M., Wexler, D. J., Putman, M. S., Higgins, J. M., Zheng, H., Rhee, E. P., de Boer, I. H., & Kalim, S. (2026) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2026.109439
Six-month real-world glycemic improvement after Smart MDI system in type 1 diabetes
Rossi, G., Pastore, I., Montefusco, L. B., CANOVA, L. P., D’Addio, F., & Fiorina, P. (2026) · Journal of the Endocrine Society · doi:10.1210/jendso/bvag244
Metadata
- Authors
- Beck, R. W., Bergenstal, R. M., Riddlesworth, T. D., Kollman, C., Li, Z., Brown, A. S., & Close, K. L.
- Affiliations
- Jaeb Center for Health Research (US)
- Park Nicollet Health Services (US)
- Close Concerns (United States) (US)
- The diaTribe Foundation, San Francisco, CA
- Corresponding author
- Beck, Roy W.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2018-10-23
- Volume
- 42
- Issue
- 3
- Pages
- 400-405
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC6905478
- OpenAlex
- W2897457722
- Cited by
- 793
- References
- 29
- Citations by year
- 2026: 73 · 2025: 93 · 2024: 87 · 2023: 106 · 2022: 138 · 2021: 134 · 2020: 103 · 2019: 54 · 2018: 2
- FWCI
- 57.92
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Jaeb Center for Health Research Foundation
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- time in range, glycemic control, retinopathy progression, microalbuminuria, microvascular complications, outcome measure validation, Diabetes Control and Complications Trial, continuous glucose monitoring, HbA1c, glycemic metrics
- MeSH terms
- Adult, Blood Glucose, Clinical Trials as Topic, Diabetes Mellitus, Female, Glycated Hemoglobin, Humans, Longitudinal Studies, Male, Middle Aged, Patient Care Planning, Prognosis, Research Design, Time Factors, Blood Glucose Self-Monitoring, Treatment Outcome, Outcome Assessment, Health Care, Disease Progression, Diabetes Complications
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
