Evidence map›Paper›PMID 33073388›Full record

ReviewDiabetic medicine : a journal of the British Diabetic Association2021

Time in range: A best practice guide for UK diabetes healthcare professionals in the context of the COVID-19 global pandemic.

E G Wilmot, A Lumb, P Hammond, H R Murphy, E Scott, F W Gibb, J Platts, P Choudhary

Abstract readReview
In one paragraph

Review in Diabetic medicine : a journal of the British Diabetic Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
–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

21 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. [Diabetes technology (Update 2026)].Wiener klinische Wochenschrift · 2026
    Guideline
  2. Pooled it
  3. Pooled it
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2023
    Article
  14. Article
  15. Article
  16. [Insulin pump therapy and continuous glucose monitoring].Wiener klinische Wochenschrift · 2023
    Article
  17. Variation of glucose time in range in type 1 diabetes.Endocrinology, diabetes & metabolism · 2022
    Article
  18. Article
  19. Pediatric Endocrinology of Post-Pandemic Era.Chonnam medical journal · 2021
    Review
  20. Review
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

8 authors.

E G WilmotDiabetes Department, Royal Derby Hospital, University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK.ORCID 0000-0002-8698-6207
A LumbOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.ORCID 0000-0001-7041-9534
P HammondDiabetes Resource Centre, Harrogate District Hospital, Harrogate and District NHS Foundation Trust, Harrogate, UK.
H R MurphyNorwich Medical School, University of East Anglia, Norwich, UK.ORCID 0000-0001-6876-8727
E ScottLeeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
F W GibbEdinburgh Centre for Endocrinology & Diabetes, Royal Infirmary of Edinburgh, Little France Crescent, Edinburgh, UK.ORCID 0000-0002-5576-6463
J PlattsDiabetes Centre, University Hospital Llandough, Cardiff and Vale University Health Board, Penarth, UK.
P ChoudharyDepartment of Diabetes, School of Life Course Sciences, King's College London, London, UK.ORCID 0000-0001-7635-4735

Funding

Abbott Diabetes CareDepartment of Health 16/35/01Department of Health CDF-2013-06-035
6 · The paper itself

Abstract

The emergence of continuous glucose monitoring has driven improvements in glycaemic control and quality of life for people with diabetes. Recent changes in access to continuous glucose monitoring systems within UK health services have increased the number of people able to benefit from these technologies. The COVID-19 pandemic has created an opportunity for diabetes healthcare professionals to use continuous glucose monitoring technology to remotely deliver diabetes services to support people with diabetes. This opportunity can be maximized with improved application and interpretation of continuous glucose monitoring-generated data. Amongst the diverse measures of glycaemic control, time in range is considered to be of high value in routine clinical care because it is actionable and is visibly responsive to changes in diabetes management. Importantly, it is also been linked to the risk of developing complications associated with diabetes and can be understood by people with diabetes and healthcare professionals alike. The 2019 International Consensus on Time in Range has established a series of target glucose ranges and recommendations for time spent within these ranges that is consistent with optimal glycaemic control. The recommendations cover people with type 1 or type 2 diabetes, with separate targets indicated for elderly people or those at higher risk from hypoglycaemia, as well as for women with type 1 diabetes during pregnancy. The aim of this best practice guide was to clarify the intent and purpose of these international consensus recommendations and to provide practical insights into their implementation in UK diabetes care.

Indexed as

Health PersonnelPractice Guidelines as TopicSARS-CoV-2AgedBlood Glucose Self-MonitoringComorbidityConsensusCOVID-19Delivery of Health CareDiabetes ComplicationsDiabetes MellitusFemaleGlycated HemoglobinHealth Plan ImplementationHumansPandemicsGlycated Hemoglobin

Identifiers

PMID33073388
PMCPMC7645943

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.