Evidence map›Paper›PMID 36192533›Full record

ArticleInternational journal of obesity (2005)2022

Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.

Abul Doulah, Tonmoy Ghosh, Delwar Hossain, Tyson Marden, Jason M Parton, Janine A Higgins, Megan A McCrory, Edward Sazonov

Abstract read
In one paragraph

Article in International journal of obesity (2005), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Observational
  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.

Abul DoulahDepartment of Electrical and Computer Engineering (ECE), The University of Alabama, Tuscaloosa, USA.ORCID 0000-0002-8161-6602
Tonmoy GhoshDepartment of Electrical and Computer Engineering (ECE), The University of Alabama, Tuscaloosa, USA.
Delwar HossainDepartment of Electrical and Computer Engineering (ECE), The University of Alabama, Tuscaloosa, USA.
Tyson MardenColorado Clinical and Translational Sciences Institute, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Jason M PartonDepartment of Information Systems, Statistics, and Management Science, Culverhouse College of Commerce and Business Administration, University of Alabama, Tuscaloosa, AL, USA.
Janine A HigginsDepartment of Pediatrics, University of Colorado, Anschutz Medical Campus, Denver, CO, USA.
Megan A McCrory *Department of Health Sciences, Boston University, Boston, MA, USA. mamccr@bu.edu.ORCID 0000-0002-5024-1465
Edward Sazonov *Department of Electrical and Computer Engineering (ECE), The University of Alabama, Tuscaloosa, USA. esazonov@eng.ua.edu.

Funding

Assessing Food Intake with the Automatic Ingestion MonitorR01DK100796 · NIDDK · UNIVERSITY OF ALABAMA IN TUSCALOOSA · PI MCCRORY, MEGAN A, SAZONOV, EDWARD S · 2014 to 2018
$1.8M
Objective Monitoring of Energy Intake and Ingestive Behavior in a Free Living PopR21DK085462 · NIDDK · UNIVERSITY OF ALABAMA IN TUSCALOOSA · PI SAZONOV, EDWARD S · 2010 to 2011
$401k
NIDDK NIH HHS R01 DK100796NIDDK NIH HHS R21 DK085462
6 · The paper itself

Abstract

objectivesDietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard. SUBJECTS/

methodsThirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences.

resultsSensor-derived EI from regression modeling (R

conclusionsEstimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

Indexed as

Energy IntakeWearable Electronic DevicesAdultDietDiet RecordsHumansMeals

Identifiers

PMID36192533
PMCPMC12938214

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.