Evidence map›Paper›PMID 25751133›Full record

Trial reportDiabetes technology & therapeutics2015

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

Stein Olav Skrøvseth, Eirik Årsand, Fred Godtliebsen, Ragnar M Joakimsen

Open access · hybridAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Diabetes technology & therapeutics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 5 pooled it
11.0field-weighted citation impact, top 2% of its field
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

24 citing papers in PubMed, 5 syntheses or guidelines pooled it, 53 citations in OpenAlex.

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  16. Smartphone-Based Technology in Diabetes Management.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2020
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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

4 authors at 2 institutions in 1 country.

Stein Olav Skrøvseth1 Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway , Tromsø, Norway .
Eirik Årsand1 Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway , Tromsø, Norway .
Fred Godtliebsen2 Department of Mathematics and Statistics, University of Tromsø , Tromsø, Norway .
Ragnar M Joakimsen4 Division of Internal Medicine, University Hospital of North Norway , Tromsø, Norway .
University Hospital of North Norway · NOUiT The Arctic University of Norway · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA mobile phone-based application can be useful for patients with type 1 diabetes in managing their disease. This results in large datasets accumulated on the patient's devices, which can be used for individualized feedback. The effect of such feedback is investigated in this article. MATERIALS AND

methodsWe developed an application that included a data-driven feedback module known as Diastat for patients on self-measured blood glucose regimens. Using a stepped-wedge design, both groups initially received an application without Diastat. Group 1 activated Diastat after 4 weeks, whereas Group 2 activated Diastat 12 weeks after startup (T1). End points were glycated hemoglobin (HbA1c) level and number of out-of-range (OOR) measurements (i.e., outside the range 72-270 mg/dL).

resultsThirty patients were recruited to the study, and 15 were assigned to each group after the initial meeting. There were no significant differences between groups at T1 in HbA1c or OOR events. Overall, all patients had a decrease of 0.6 percentage points in mean HbA1c (P < 0.001) and 14.5 in median OOR events over 2 weeks (P < 0.001).

conclusionsThe study does not provide evidence that data-driven feedback improves glycemic control. The decrease in HbA1c was sizeable and significant, even though the study was not powered to detect this. The overall improvement in glycemic control suggests that, in general, mobile phone-based interventions can be useful in diabetes self-management.

Indexed as

Cell PhoneFeedbackMobile ApplicationsAdultBlood GlucoseDiabetes Mellitus, Type 1Glycated HemoglobinHumansHypoglycemic AgentsMaleMiddle AgedSelf CareTelemedicineBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanHypoglycemic Agents

Identifiers

PMID25751133
PMCPMC4504254
OpenAlexW2326212167

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

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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.