Evidence map›Paper›PMID 39251670›Full record

ArticleScientific reports2024

Mealtime prediction using wearable insulin pump data to support diabetes management.

Baiying Lu, Yanjun Cui, Prajakta Belsare, Catherine Stanger, Xia Zhou, Temiloluwa Prioleau

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

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

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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. 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

6 authors.

Baiying LuDepartment of Computer Science, Dartmouth College, Hanover, 03755, USA.
Yanjun CuiDepartment of Computer Science, Dartmouth College, Hanover, 03755, USA.
Prajakta BelsareIntegrated Science and Technology, James Madison University, Harrisonburg, 22807, USA.
Catherine StangerCenter for Technology and Behavioral Health, Dartmouth College, Lebanon, 03766, USA.
Xia ZhouDepartment of Computer Science, Columbia University, New York, 10027, USA.
Temiloluwa PrioleauDepartment of Computer Science, Dartmouth College, Hanover, 03755, USA. tprioleau@dartmouth.edu.

Funding

Thinking Outside the Clinic: A Digital Health Approach for Young Adults with Type 1 DiabetesR01DK124428 · NIDDK · DARTMOUTH COLLEGE · PI CATHERINE STANGER, CATHERINE STANGER CATHERINE STANGER · 2020 to 2024
$3.1M
National Science Foundation 2127309National Science Foundation 2322879NIDDK NIH HHS R01 DK124428NIDDK NIH HHS R01DK124428
6 · The paper itself

Abstract

Many patients with diabetes struggle with post-meal high blood glucose due to missed or untimely meal-related insulin doses. To address this challenge, our research aims to: (1) study mealtime patterns in patients with type 1 diabetes using wearable insulin pump data, and (2) develop personalized models for predicting future mealtimes to support timely insulin dose administration. Using two independent datasets with over 45,000 meal logs from 82 patients with diabetes, we find that the majority of people (

Indexed as

Blood GlucoseDiabetes Mellitus, Type 1InsulinInsulin Infusion SystemsMealsWearable Electronic DevicesAdultFemaleHumansHypoglycemic AgentsMaleMiddle AgedBlood GlucoseHypoglycemic AgentsInsulinDiabetesDietary monitoringInsulin pumpPersonalized modelingWearable medical device

Identifiers

PMID39251670
PMCPMC11385183

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.