Evidence map›Paper›PMID 41084119›Full record

ArticleDiabetic medicine : a journal of the British Diabetic Association2025

Insights into the effective use of the Smart MDI system: Data from the first 1852 type 1 diabetes users.

Andrea Laurenzi, Shannon N Edd, Peter Adolfsson, Fabio Di Piazza, Benedikt Voelker, Glen Im, Tim van den Heuvel, Ohad Cohen

Abstract readMulticenter Study
In one paragraph

Article in Diabetic medicine : a journal of the British Diabetic Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Diabetes Technology in the School Setting for Preschool-Aged Children.Journal of diabetes science and technology · 2026
    Article
  2. Review
  3. Article
  4. Insights into the effective use of the Smart MDI system: Data from the first 1852 type 1 diabetes users.Diabetic medicine : a journal of the British Diabetic Association · 2025
    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

8 authors.

Andrea LaurenziMedicina Interna Ad Indirizzo Diabetologico Ed Endocrino-Metabolico, IRCCS Ospedale San Raffaele, Milan, Italy.
Shannon N EddMedtronic Diabetes, Medtronic International Trading Sàrl, Tolochenaz, Switzerland.ORCID https://orcid.org/0000-0002-9361-6597
Peter AdolfssonDepartment of Diabetes at Högsbo Hospital, Gothenburg, Sweden.
Fabio Di PiazzaMedtronic Diabetes, Medtronic International Trading Sàrl, Tolochenaz, Switzerland.
Benedikt VoelkerMedtronic Diabetes, Medtronic International Trading Sàrl, Tolochenaz, Switzerland.
Glen ImMedtronic Diabetes, Northridge, California, USA.
Tim van den HeuvelMedtronic Diabetes, Medtronic International Trading Sàrl, Tolochenaz, Switzerland.ORCID https://orcid.org/0000-0002-3907-6876
Ohad CohenMedtronic Diabetes, Medtronic International Trading Sàrl, Tolochenaz, Switzerland.

Funding

Medtronic
6 · The paper itself

Abstract

aimsThe study analysed real-world data of people with type 1 diabetes (PWT1D) using the Smart Multiple Daily Insulin (MDI) system with the aim of assessing associations between user interaction behaviours and parameters of glycaemic control to provide educational insights for optimal system use.

methodsA retrospective cohort analysis was conducted using data from 1852 PWT1D users of the Smart MDI system across 21 countries. The system comprises the InPen™ insulin injector, Simplera™ CGM, and the InPen™ phone application. Users were included if they had at least 10 days of InPen and CGM data. Glycaemic outcomes were correlated with user interaction, focusing on responses to missed dose alerts (MDA) and correct high glucose alerts (CHGA).

resultsThe mean time in range (TIR) was 55.7%, with users responding with a bolus dose to 49.3% of MDA and 46.6% of CHGA. However, those users who responded within 1 h to over 75% of alerts with a bolus dose achieved higher TIRs of 67.2% (MDA) and 71.5% (CHGA). Prompt responses (within 10 min) showed greater TIR. The study highlighted significant associations between alert responsiveness and better glycaemic outcomes without increased hypoglycaemia risk.

conclusionsUser engagement with the Smart MDI system is crucial for optimal glycaemic outcomes. The study underscores the need for structured education on system use and alert responses. While causality cannot be confirmed, the findings suggest that proactive user interactions contribute to meeting glycaemic targets. Further research is needed to explore these relationships and enhance educational strategies.

Indexed as

Diabetes Mellitus, Type 1Hypoglycemic AgentsInsulinInsulin Infusion SystemsAdolescentAdultBlood GlucoseBlood Glucose Self-MonitoringChildFemaleGlycated HemoglobinGlycemic ControlHumansMaleMiddle AgedRetrospective StudiesBlood GlucoseGlycated HemoglobinHypoglycemic AgentsInsulincontinuous glucose monitoringglycaemic controlretrospective studiessmart insulin pensubcutaneous injectionstime in rangetype 1 diabetes

Identifiers

PMID41084119
PMCPMC12628721

What OpenQuestion holds

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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.