Evidence map›Paper›PMID 42746697›Full record

ArticleJournal of diabetes research2026

Characterizing Continuous Glucose Monitoring Prescription Patterns Between 2020 and 2024: A Retrospective Study at a Single Primary Care Facility in the United States.

Rahma Mkuu, Protiva Rahman, Daniela Rivero-Mendoza, William Troy Donahoo, Elizabeth Shenkman, Diandra Ojo, Marcos R Lamas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Rahma MkuuDepartment of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA, ufl.edu.ORCID https://orcid.org/0000-0001-7921-1430
Protiva RahmanDepartment of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA, ufl.edu.ORCID https://orcid.org/0000-0003-0000-8558
Daniela Rivero-MendozaDepartment of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA, ufl.edu.ORCID https://orcid.org/0000-0002-7649-8728
William Troy DonahooDepartment of Medicine, University of Florida, Gainesville, Florida, USA, ufl.edu.ORCID https://orcid.org/0000-0002-2126-860X
Elizabeth ShenkmanDepartment of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA, ufl.edu.ORCID https://orcid.org/0000-0003-4903-1804
Diandra OjoDepartment of Electrical & Computer Engineering, University of Florida, Gainesville, Florida, USA, ufl.edu.
Marcos R LamasDepartment of Medicine, University of Florida, Gainesville, Florida, USA, ufl.edu.ORCID https://orcid.org/0009-0005-0708-7246

Funding

Preventing Cervical Cancer through HPV Self-collection and Patient Navigation among Women Living with Chronic Conditions and Social VulnerabilityK01CA292583 · NCI · UNIVERSITY OF FLORIDA · PI RAHMA S Mkuu · 2024 to 2026
$516k
NCI NIH HHS K01 CA292583NIH HHS K01CA292583University of Florida Gatorade Royalties
6 · The paper itself

Abstract

objectiveExamine the factors associated with continuous glucose monitoring (CGM) device prescriptions in a primary care facility. MATERIALS AND

methodsWe conducted a retrospective study of 1476 patients with Type 1 and Type 2 diabetes receiving care at a single primary care facility between 2020 and 2024. We examined the associations between receiving a CGM prescription and demographic characteristics, health factors, de-identified physician information, and insurance status. We conducted univariate analysis using chi-square tests. Multivariate logistic regression models accounted for multiple factors predicting receiving a CGM prescription.

resultsThe average CGM prescription rate per physician was less than 4%. Various factors were significantly associated with receiving a CGM prescription. Notably, younger patients on insulin, with Type 1 diabetes, and higher glycated hemoglobin (HbA1C) values were more likely to be prescribed a CGM device. Patients with nutritional and metabolic diseases or abnormal lab values were less likely to receive a CGM prescription.

conclusionsOur findings are similar to those of other CGM studies, which show that CGM prescriptions are significantly higher among patients who are younger and who have Type 1 diabetes. There is a need for future studies that aim to better understand CGM prescribing decision-making among PCPs to inform the development of targeted interventions to improve CGM prescribing rates for all eligible patients.

Indexed as

Blood GlucoseContinuous Glucose MonitoringDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Practice Patterns, Physicians'AdultAgedBlood Glucose Self-MonitoringFemaleGlycated HemoglobinHumansHypoglycemic AgentsInsulinMaleMiddle AgedPrimary Health CareBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanHypoglycemic AgentsInsulin

Identifiers

PMID42746697
PMCPMC13579378

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.