Evidence map›Paper›PMID 37629214›Full record

ArticleJournal of clinical medicine2023

When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles.

Johana Monthuy-Blanc, Usef Faghihi, Mahan Najafpour Ghazvini Fardshad, Giulia Corno, Sylvain Iceta, Marie-Josée St-Pierre, Stéphane Bouchard

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Johana Monthuy-BlancUnité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Usef FaghihiUnité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Mahan Najafpour Ghazvini FardshadUnité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Giulia CornoUnité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Sylvain IcetaCentre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Quebec, QC G1V 0A6, Canada.ORCID 0000-0002-0054-1865
Marie-Josée St-PierreUnité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Stéphane BouchardUnité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.ORCID 0000-0002-5995-340X

Funding

Canada research Chairs program 210762 and 231039Fonds de Recherche du Québec-Santé (FRQS) 289006Foundation of the University Institute of Mental Health of Montreal affiliated with the Re-search Center of the University Institute of Mental Health of Montreal NAFoundations of RBC Royal Bank and the Takeda Canada 0288007
6 · The paper itself

Abstract

backgroundThe aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis.

methodsA total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach.

resultsThis study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles.

conclusionsThis conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions.

Indexed as

body dissatisfactionbulimiaCausal ReasoningCOVID-19intuitive eatingrestraint

Identifiers

PMID37629214
PMCPMC10455794

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.