Evidence map›Paper›PMID 42156180›Full record

ArticleDiabetes, obesity & metabolism2026

Randomised Controlled Multicentre Trial to Investigate the Effectiveness of a Mobile Artificial Intelligence Solution for Diabetes Adapted Care: The MELISSA Trial Protocol.

Elisabeth J den Brok, Cecilie H Svensson, Mikkel T Olsen, Nefeli M Dimitropoulou, Sander M J van Kuijk, Maria Panagiotou, Lubnaa Abdur Rahman, Stephan Proennecke, Peter R Mertens, Stavros Athanasopoulos and 6 more

Abstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes, obesity & metabolism, 2026. 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

16 authors.

Elisabeth J den BrokCARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands.ORCID https://orcid.org/0009-0002-5289-8070
Cecilie H SvenssonDepartment of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hillerod, Denmark.
Mikkel T OlsenDepartment of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hillerod, Denmark.
Nefeli M DimitropoulouCARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands.
Sander M J van KuijkDepartment of Clinical Epidemiology & Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, the Netherlands.
Maria PanagiotouARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Lubnaa Abdur RahmanARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Stephan ProenneckeDebiotech SA, Lausanne, Switzerland.
Peter R MertensDepartment of Kidney and Hypertension Diseases, Diabetology and Endocrinology, Otto-Von-Guericke-Univeristat Magdeburg, Magdeburg, Germany.
Stavros AthanasopoulosDiabetes Center, National and Kapodistrian University of Athens, Athens, Greece.ORCID https://orcid.org/0000-0003-0421-0227
Cassy F DingenaCARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands.
Konstantinos MakrilakisDiabetes Center, National and Kapodistrian University of Athens, Athens, Greece.
Stavroula MougiakakouARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Ulrik Pedersen-BjergaardDepartment of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0003-0588-4880
Bastiaan E de GalanCARIM School for Cardiovascular Disease, Maastricht University, Maastricht, the Netherlands.
MELISSA consortium

Funding

European Union 101057730
6 · The paper itself

Abstract

aimsAchieving optimal glycaemic control remains a burden for many people with diabetes on intensive insulin treatment. The MELISSA trial aims to clinically validate the artificial intelligence (AI)-based MELISSA system to support people with Type 1 diabetes on multiple daily insulin injections (MDI) with personalised insulin dose recommendations and an innovative approach for automatic carbohydrate estimation. In addition, feasibility will be explored in people with Type 2 diabetes. MATERIALS AND

methodsThe MELISSA trial is a 22-week European multicentre, prospective randomised open-label blinded endpoint trial including people with Type 1 (n = 278) and an exploratory cohort of people with Type 2 diabetes (n = 50) on MDI. The MELISSA system consists of two AI-driven features: an adaptive basal-bolus advisor (ABBA), which can be supported by an automated dietary assessment system, goFOOD

resultsThe primary endpoint is the between-group change in percentage of time spent in the target range (3.9-10.0 mmol/L [70-180 mg/dL]) from baseline to study end. Secondary outcomes include additional glycaemic metrics, patient-reported-outcomes, and safety information. The trial was approved by the Sponsor's Medical Ethics Research Committee (NL-009099).

conclusionThe MELISSA system will potentially improve glycaemic control and quality of life in people with Type 1 diabetes on MDI. The trial outcomes will provide necessary input for obtaining Conformité Européenne certification (class IIb) for the MELISSA system.

Indexed as

Artificial IntelligenceDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Hypoglycemic AgentsInsulinMobile ApplicationsBlood GlucoseContinuous Glucose MonitoringFemaleGlycated HemoglobinGlycemic ControlHumansIntelligent SystemsMaleMulticenter Studies as TopicProspective StudiesBlood GlucoseGlycated HemoglobinHypoglycemic AgentsInsulinartificial intelligenceautomatic carbohydrate countingbasal‐bolus calculatorcontinuous glucose monitoringdiabetes managementdiabetes technologyinsulin therapy

Identifiers

PMID42156180
PMCPMC13341326

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.