Evidence map›Paper›PMID 33007591›Full record

ArticleComputer methods and programs in biomedicine2020

Automatically accounting for physical activity in insulin dosing for type 1 diabetes.

Basak Ozaslan, Stephen D Patek, Chiara Fabris, Marc D Breton

Registry-linked trialOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05736263 (Setting a New Algorithm For the Management of Exercise-induced Glycemic Variations in Patients With Type 1 Diabetes in Intensive Therapy With Hybrid Closed-loop Systems), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 30% 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.

NCT05736263 naunknown statusnot on this mapstarted 2023, after this paper: background citation

Setting a New Algorithm For the Management of Exercise-induced Glycemic Variations in Patients With Type 1 Diabetes in Intensive Therapy With Hybrid Closed-loop Systems

TypeinterventionalSponsorUniversity of Roma La SapienzaRan2023 to 2024Enrolled50ConditionsType 1 Diabetes, ExerciseArmsModerate intensity exercise, High intensity interval exercise, Combined exercise, Resistance exercise
3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 14 citations in OpenAlex.

  1. Trial
  2. 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 at 2 institutions in 1 country.

Basak OzaslanUniversity of Virginia, Charlottesville, VA, United States.
Stephen D PatekDexcom, Inc., Charlottesville, VA, United States.
Chiara FabrisUniversity of Virginia, Charlottesville, VA, United States.
Marc D BretonUniversity of Virginia, Charlottesville, VA, United States. Electronic address: mb6nt@virginia.edu.
University of Virginia · USDexcom (United States) · US

Funding

Models, signals, and distributed bio-behavioral control of exercise in diabetesDP3DK106826 · NIDDK · UNIVERSITY OF VIRGINIA · PI BRETON, MARC D · 2015 to 2015
$3.0M
NIDDK NIH HHS DP3 DK106826
6 · The paper itself

Abstract

BACKGROUND AND

objectiveType 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes.

methodsWe leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to "re-simulate" the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold.

resultsSimulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5).

conclusionsIntegrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.

Indexed as

Diabetes Mellitus, Type 1InsulinBlood GlucoseExerciseHumansHypoglycemic AgentsInsulin Infusion SystemsBlood GlucoseHypoglycemic AgentsInsulinActivity on boardDiabetesInsulin dosingPhysical activity

Identifiers

PMID33007591
PMCPMC7704570
OpenAlexW3087996731

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

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 Socratic graph.