Evidence mapPaperPMID 40937073Full record

ArticleTherapeutic advances in endocrinology and metabolism2025

Glucose interpretation meaning and action: enhancing type 1 diabetes decision-making with textual descriptions.

Rujiravee Kongdee, Bijan Parsia, Hood Thabit, Simon Harper

Abstract read
In one paragraph

Article in Therapeutic advances in endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Rujiravee KongdeeDepartment of Computer Science, University of Manchester, LF1 Kilburn Building, Oxford Road, Manchester M13 9PL, UK.ORCID https://orcid.org/0000-0003-0375-2833
Bijan ParsiaDepartment of Computer Science, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-3222-7571
Hood ThabitDivision of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-6076-6997
Simon HarperDepartment of Computer Science, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-9301-5049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Findings from our previous study indicate that people with type 1 diabetes mellitus (T1DM) unknowingly misinterpret data displayed on glucose monitoring systems and make inaccurate treatment decisions, which increases the risk of hospitalisation. Objectives: This study aims to assess the effectiveness of incorporating textual descriptions in glucose monitoring systems compared to existing systems. The main goal is to minimise the effort required in glucose data interpretation, facilitating better self-management and ultimately improving haemoglobin A1C levels. Methods: A two-arm and mixed-methods evaluation was conducted. Participants were randomly allocated to the control arm (existing systems) or the experimental arm (newly developed systems incorporating textual descriptions). In the first part, a task-based usability assessment was conducted to compare performance between the two arms. The second part evaluated participant preferences, agreement with textual descriptions and perceptions of the new systems. Results: A total of 86 participants were recruited. The experimental arm achieved an 85.15% total correctness score, compared to 74.38% in the control arm ( Conclusion: Incorporating textual description into glucose monitoring systems enhances treatment decision-making for people with T1DM. It suggests that we are on the right path to helping them better understand their glucose data and assist their self-management. Extensive research is required to focus more on the patient-centred approach in information presentation and prioritise it in parallel with other advancements in glucose monitoring technologies.

Indexed as

blood glucose monitoringdata visualisationinterpretationself-managementtype 1 diabetesusability

Identifiers

PMID40937073
PMCPMC12420987

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.