Evidence map›Paper›PMID 24956325›Full record

Trial reportContemporary clinical trials2014

Design and methods of "diaBEAT-it!": a hybrid preference/randomized control trial design using the RE-AIM framework.

Fabio A Almeida, Kimberlee A Pardo, Richard W Seidel, Brenda M Davy, Wen You, Sarah S Wall, Erin Smith, Mark H Greenawald, Paul A Estabrooks

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Contemporary clinical trials, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

9 authors.

Fabio A AlmeidaFralin Translational Obesity Research Center, Department of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: falmeida@vt.edu.
Kimberlee A PardoFralin Translational Obesity Research Center, Department of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: skim13@vt.edu.
Richard W SeidelDepartment of Psychiatry, Carilion Clinic, Roanoke, VA 24014, United States. Electronic address: rwseidel@carilionclinic.org.
Brenda M DavyFralin Translational Obesity Research Center, Department of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: bdavy@vt.edu.
Wen YouDepartment of Agriculture and Applied Economics, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: wenyou@vt.edu.
Sarah S WallFralin Translational Obesity Research Center, Department of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: sarahsw@vt.edu.
Erin SmithFralin Translational Obesity Research Center, Department of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: erinsmith@vt.edu.
Mark H GreenawaldDepartment of Family and Community Medicine, Carilion Clinic, Roanoke, VA 24013, United States. Electronic address: mhgreenawald@carilionclinic.org.
Paul A EstabrooksFralin Translational Obesity Research Center, Department of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24061, United States; Department of Family and Community Medicine, Carilion Clinic, Roanoke, VA 24013, United States. Electronic address: estabrkp@vt.edu.

Funding

The Reach and Effectiveness of Technology-Enhanced Diabetes Prevention ProgramsR18DK091811 · NIDDK · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI ALMEIDA, FABIO ARAUJO · 2012 to 2016
$3.2M
NIDDK NIH HHS 1R18DK091811-01A1NIDDK NIH HHS R18 DK091811
6 · The paper itself

Abstract

backgroundDiabetes prevention is a public health priority that is dependent upon the reach, effectiveness, and cost of intervention strategies. However, understanding each of these outcomes within the context of randomized controlled trials is problematic. PURPOSE: To describe the methods and design of a hybrid preference/randomized control trial using the RE-AIM framework.

methodsThe trial, which was developed using the RE-AIM framework, will contrast the effects of 3 interventions: (1) a standard care, small group, diabetes prevention education class (SG), (2) the small group intervention plus 12 months of interactive voice response telephone follow-up (SG-IVR), and (3) a DVD version of the small group intervention with the same IVR follow-up (DVD-IVR). Each intervention includes personal action planning with a focus on key elements of the lifestyle intervention from the Diabetes Prevention Program (DPP). Adult patients at risk for diabetes will be randomly assigned to either choice or RCT. Those assigned to choice (n=240) will have the opportunity to choose between SG-IVR and DVD-IVR. Those assigned to RCT group (n=360) will be randomly assigned to SG, SG-IVR, or DVD-IRV. Assessment of primary (weight loss, reach, & cost) and secondary (physical activity, & dietary intake) outcomes will occur at baseline, 6, 12, and 18 months.

conclusionThis will be the first diabetes prevention trial that will allow the research team to determine the relationships between reach, effectiveness, and cost of different interventions.

Indexed as

Research DesignBody Mass IndexBody Weights and MeasuresChoice BehaviorCost-Benefit AnalysisDiabetes Mellitus, Type 2DietExerciseHealth BehaviorHealth EducationHealth PromotionHumansLife StyleRisk FactorsSocioeconomic FactorsWeight LossDiabetes preventionDVDHybrid designIVRRE-AIMWeight loss

Identifiers

PMID24956325
PMCPMC4150391

What OpenQuestion holds

Textmetadata
LicenceTDM
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.