Evidence map›Paper›PMID 37252243›Full record

ArticleFrontiers in nutrition2023

The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device.

Matthew Breit, Jonathan Padia, Tyson Marden, Dan Forjan, Pan Zhaoxing, Wenru Zhou, Tonmoy Ghosh, Graham Thomas, Megan A McCrory, Edward Sazonov and 1 more

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Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Matthew BreitDepartment of Medicine, Division of Endocrinology, Metabolism, and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Jonathan PadiaColorado Clinical and Translational Sciences Institute, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Tyson MardenColorado Clinical and Translational Sciences Institute, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Dan ForjanColorado Clinical and Translational Sciences Institute, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Pan ZhaoxingResearch Institute Biostatistics Core, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Wenru ZhouResearch Institute Biostatistics Core, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Tonmoy GhoshDepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, United States.
Graham ThomasDepartment of Psychiatry and Human Behavior, Weight Control and Diabetes Research Center, Alpert Medical School of Brown University and The Miriam Hospital, Providence, RI, United States.
Megan A McCroryDepartment of Health Sciences, Boston University, Boston, MA, United States.
Edward SazonovDepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, United States.
Janine HigginsDepartment of Medicine, Division of Endocrinology, Metabolism, and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options. Methods: Data from 25 participants and 116 total days (7 men, 18 women, M Results: The most prevalent eating environment among all eating occasions was at home and with one or more screens in use (breakfast: 48.1%, lunch: 42.2%, dinner: 50%, and snacks: 55%), eating alone (breakfast: 75.9%, lunch: 89.2%, dinner: 74.3%, snacks: 74.3%), in the dining room (breakfast: 36.7%, lunch: 30.1%, dinner: 45.8%) or living room (snacks: 28.0%), and in multiple locations (breakfast: 44.3%, lunch: 28.8%, dinner: 44.8%, snacks: 41.3%). Discussion: Results suggest a passive capture device can provide accurate detection of food intake in multiple eating environments. To our knowledge, this is the first study to classify eating occasions in multiple eating environments and may be a useful tool for future behavioral research studies to accurately codify eating environments.

Indexed as

dietary intakeeating environmentobesitypassive capturewearable device

Identifiers

PMID37252243
PMCPMC10212618

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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.