Evidence map›Paper›PMID 28581821›Full record

Trial reportDiabetes technology & therapeutics2017

Randomized Controlled Trial of Technology-Assisted Case Management in Low Income Adults with Type 2 Diabetes.

Leonard E Egede, Joni S Williams, Delia C Voronca, Rebecca G Knapp, Jyotika K Fernandes

2 registry-linked trialsAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Diabetes technology & therapeutics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 25 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 5 pooled it
2.3field-weighted citation impact, top 11% 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.

NCT01373489 nacompletednot on this map

Effectiveness of Technology-Assisted Case Management in Low Income Adults With Type 2 Diabetes

TypeinterventionalSponsorState University of New York at BuffaloRan2011 to 2016Enrolled113ConditionsDiabetesArmsTechnology-Assisted Case Management
NCT05541120 nawithdrawnnot on this mapstarted 2022, after this paper: background citation

Effect of Remote Patient Monitoring and Patient Education on Patient Activation and Glycemic Control in Individuals With Type 2 Diabetes

TypeinterventionalSponsorTracy Jalbuena MDRan2022 to 2026Enrolled0ConditionsDiabetes Mellitus, Type 2ArmsRemote Patient Monitoring
3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 5 syntheses or guidelines pooled it, 45 citations in OpenAlex.

  1. Pooled it
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  6. Trial
  7. Review
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  14. Burden and management of type 2 diabetes in rural United States.Diabetes/metabolism research and reviews · 2021
    Review
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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

5 authors at 2 institutions in 1 country.

Leonard E Egede1 Division of General Internal Medicine, Department of Medicine, Medical College of Wisconsin , Milwaukee, Wisconsin.
Joni S Williams1 Division of General Internal Medicine, Department of Medicine, Medical College of Wisconsin , Milwaukee, Wisconsin.
Delia C Voronca3 Emmes, Vaccine and Infectious Diseases, Rockville, Maryland.
Rebecca G Knapp4 Department of Public Health Sciences, Medical University of South Carolina , Charleston, South Carolina.
Jyotika K Fernandes5 Division of Endocrinology, Diabetes and Medical Genetics, Department of Medicine, Medical University of South Carolina , Charleston, South Carolina.
Medical College of Wisconsin · USMedical University of South Carolina · US

Funding

Technology-Intensified Diabetes Education/Skills Intervention in AAs with DM-2R01DK098529 · NIDDK · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI EGEDE, LEONARD E. · 2013 to 2016
$2.7M
NIDDK NIH HHS R01 DK098529
6 · The paper itself

Abstract

objectiveTo assess the efficacy of technology-assisted case management (TACM) with medication titration by nurses using guideline-based algorithms, under physician supervision in improving glycemic control in low-income rural adults with poorly controlled type 2 diabetes. RESEARCH DESIGN AND

methodsAdults (aged ≥18 years) from the southeastern United States with hemoglobin A1c [HbA1c] ≥8% were randomized to TACM or usual care. Evidence-based guidelines were used to develop medication titration algorithms in conjunction with clinic physicians. Participants were given a telehealth device that uploaded blood glucose and blood pressure readings daily to a central server. A nurse case manager was trained on the algorithms and authorized to titrate medications every 2 weeks based on the algorithm under the supervision of an internist and an endocrinologist. Participants were assessed at baseline, 3 months, and 6 months. The primary outcome was HbA1c at 6-months postrandomization in the intent-to-treat (ITT) population.

resultsOne hundred thirteen participants were randomized to either TACM intervention or usual care. Based on ITT population after multiple imputation, the analysis of covariance with baseline HbA1c as covariate showed that HbA1c at 6 months for TACM was significantly lower compared to the usual care group (-0. 99, P = 0.024). Moreover, longitudinal mixed effects analysis suggested that the rate of decline in HbA1c over time for TACM was significantly faster compared to the usual care group (-0.16, P = 0.038). Results based on per-protocol population were similar.

conclusionsTechnology-assisted case management by a nurse with medication titration under physician supervision is efficacious in improving glycemic control in low-income rural adults with poorly controlled type 2 diabetes.

Indexed as

Case ManagementAdultAgedAlgorithmsBlood GlucoseDiabetes Mellitus, Type 2FemaleGlycated HemoglobinHumansMaleMiddle AgedPovertyTelemedicineTreatment OutcomeYoung AdultBlood GlucoseGlycated HemoglobinCase managementDiabetes technologyTelemedicine

Identifiers

PMID28581821
PMCPMC13170820
OpenAlexW2624638128

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.