Evidence map›Paper›PMID 34123398›Full record

ArticleObesity science & practice2021

Two-year weight trajectories following completion of a behavioral weight loss maintenance intervention.

Kara L Gavin, Corrine I Voils, William S Yancy, Maren K Olsen

Open access · goldAbstract read
In one paragraph

Article in Obesity science & practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  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.

Kara L GavinDepartment of Surgery University of Wisconsin School of Medicine and Public Health Madison Wisconsin USA.ORCID https://orcid.org/0000-0002-5293-7781
Corrine I VoilsDepartment of Surgery University of Wisconsin School of Medicine and Public Health Madison Wisconsin USA.
William S YancyDepartment of Medicine Duke University School of Medicine Durham North Carolina USA.
Maren K OlsenCenter for Health Services Research in Primary Care Durham VA Health Care System Durham North Carolina USA.
Duke University · USUniversity of Wisconsin–Madison · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLong-term effects of behavioral weight loss maintenance interventions need to be assessed in order to understand their durability of effects. This can be evaluated with the use of weights recorded in the electronic medical record. The goal of this study was to use electronic health record (EHR)-recorded weight to examine outcomes 2 years beyond the completion of a trial in which participants were randomized to receive a weight maintenance intervention or usual care after required initial weight loss.

methodsWeights collected in the Veteran's Affairs national EHR were obtained for 2 years following trial completion. Outliers and implausible weights were identified and removed prior to analysis. Mixed-effects models with quadratic time were fit to estimate between-arm differences in weight change.

resultsModel-estimated weight at trial completion was 109.7 kg for usual care and 106.8 kg for intervention, estimated difference of -2.9 kg (95% confidence interval [CI]: -8.8, 3.0;

conclusionsWhile not statistically significant, weights from the EHR suggest the possibility of a clinically meaningful difference that should be confirmed by future adequately powered studies.

Indexed as

electronic health recordinterventionweight loss maintenance

Identifiers

PMID34123398
PMCPMC8170567
OpenAlexW3128592367

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.