Evidence map›Paper›PMID 31983028›Full record

ReviewDiabetes therapy : research, treatment and education of diabetes and related disorders2020

Smartphone-Based Technology in Diabetes Management.

John Doupis, Georgios Festas, Christos Tsilivigos, Vasiliki Efthymiou, Alexander Kokkinos

Registry-linked trialAbstract readReview
In one paragraph

Review in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06957093 (Evaluation of the Therapeutic Efficacy and Safety of Artificial Intelligence-based Decision-making Technology in the Integrated Management of Diabetes Mellitus), which is not on this map. Cited by 52 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 1 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.

NCT06957093 narecruitingnot on this mapstarted 2025, after this paper: background citation

Evaluation of the Therapeutic Efficacy and Safety of Artificial Intelligence-based Decision-making Technology in the Integrated Management of Diabetes Mellitus: a Longitudinal, Open-labeled, Randomized Controlled Trial

TypeinterventionalSponsorThe First Hospital of Jilin UniversityRan2025 to 2026Enrolled400ConditionsDiabetes Mellitus Type 2 (T2DM), Artificial Intelligence (AI)Armsartificial intelligence, Routine diagnosis and treatment group for diabetes
3 · Its place in the literature

Who cites it

52 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. Digital Health Technology in Diabetes Management in the Asia-Pacific Region: A Narrative Review of the Current Scenario and Future Outlook.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025
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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

5 authors.

John DoupisDepartment of Internal Medicine and Diabetes, Salamis Naval and Veterans Hospital, Salamis Naval Base, 18900, Salamis Island, Attiki, Greece. john.doupis@joslin.harvard.edu.
Georgios FestasDepartment of Internal Medicine and Diabetes, Salamis Naval and Veterans Hospital, Salamis Naval Base, 18900, Salamis Island, Attiki, Greece.
Christos TsilivigosDepartment of Internal Medicine and Diabetes, Salamis Naval and Veterans Hospital, Salamis Naval Base, 18900, Salamis Island, Attiki, Greece.
Vasiliki EfthymiouFirst Department of Pediatrics, Center for Adolescent Medicine and UNESCO Chair on Adolescent Health Care, School of Medicine, National and Kapodistrian University of Athens, Aghia Sophia Children's Hospital, Athens, Greece.
Alexander KokkinosFirst Department of Propaedeutic Internal Medicine, Diabetes Centre, Laiko General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes is a group of metabolic disorders characterized by elevated levels of blood glucose which leads over time to serious complications and significant morbidity and mortality worldwide. Self-management tasks in diabetes may be quite challenging because of lack of training, difficulties in sustaining lifestyle modifications, and limited access to specialized healthcare. Nowadays, the evolution of mobile technology provides a large number of health-related smartphone applications (apps), aiming to increase the self-management skills of the patient in chronic diseases, to facilitate the communication between the patient and healthcare providers, and to increase also the patient's compliance with the treatment. In the field of diabetes there are also many diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. The aim of this paper is to review the most important diabetes-related mobile smartphone applications, including only those supported by prospective randomized controlled trials.

Indexed as

Diabetes managementMobile appsTechnology

Identifiers

PMID31983028
PMCPMC7048878

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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.