Evidence map›Paper›PMID 31030289›Full record

ReviewCurrent diabetes reports2019

Effect of Health Information Technologies on Cardiovascular Risk Factors among Patients with Diabetes.

Yilin Yoshida, Suzanne A Boren, Jesus Soares, Mihail Popescu, Stephen D Nielson, Richelle J Koopman, Diana R Kennedy, Eduardo J Simoes

Open access · hybridAbstract readReview
In one paragraph

Review in Current diabetes reports, 2019. 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
1.5field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  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

8 authors at 3 institutions in 1 country.

Yilin YoshidaDepartment of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, CE707 CS&E Bldg., One Hospital Drive, Columbia, MO, 65212, USA.
Suzanne A BorenDepartment of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, CE707 CS&E Bldg., One Hospital Drive, Columbia, MO, 65212, USA.
Jesus SoaresCenters for Disease Control and Prevention, Division of High-Consequence Pathogens and Pathology, Prion and Public Health Office, Atlanta, GA, USA.
Mihail PopescuDepartment of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, CE707 CS&E Bldg., One Hospital Drive, Columbia, MO, 65212, USA.
Stephen D NielsonMercy Medical Center, Sioux City, IA, USA.
Richelle J KoopmanDepartment of Family and Community Medicine, School of Medicine, University of Missouri-Columbia, Columbia, MO, USA.
Diana R KennedyDepartment of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, CE707 CS&E Bldg., One Hospital Drive, Columbia, MO, 65212, USA.
Eduardo J SimoesDepartment of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, CE707 CS&E Bldg., One Hospital Drive, Columbia, MO, 65212, USA. simoese@health.missouri.edu.
University of Missouri · USCenters for Disease Control and Prevention · USMercy Medical Center Sioux City · US

Funding

Washington University Center for Diabetes Translation Research P30DK092950 · NIDDK · WASHINGTON UNIVERSITY · PI Ross C Brownson, Debra Haire-Joshu · 2011 to 2026
$11.7M
NIDDK NIH HHS P30 DK092950
6 · The paper itself

Abstract

purpose of reviewTo identify a common effect of health information technologies (HIT) on the management of cardiovascular disease (CVD) risk factors among people with type 2 diabetes (T2D) across randomized control trials (RCT). RECENT

findingsCVD is the most frequent cause of morbidity and mortality among patients with diabetes. HIT are effective in reducing HbA1c; however, their effect on cardiovascular risk factor management for patients with T2D has not been evaluated. We identified 21 eligible studies (23 estimates) with measurement of SBP, 20 (22 estimates) of DBP, 14 (17 estimates) of HDL, 14 (17 estimates) of LDL, 15 (18 estimates) of triglycerides, and 10 (12 estimates) of weight across databases. We found significant reductions in SBP, DBP, LDL, and TG, and a significant improvement in HDL associated with HIT. As adjuvants to standard diabetic treatment, HIT can be effective tools for improving CVD risk factors among patients with T2D, especially in those whose CVD risk factors are not at goal.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Medical InformaticsHumansRisk FactorsTriglyceridesTriglyceridesCardiovascular risk factorHealth information technologiesType 2 diabetes

Identifiers

PMID31030289
PMCPMC6486904
OpenAlexW2941680418

What Socratic holds

Textmetadata
LicenceCC BY
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.