Evidence map›Paper›PMID 28846611›Full record

Trial reportNutrients2017

High Intensity Exercise: Can It Protect You from A Fast Food Diet?

Christian Duval, Marc-Antoine Rouillier, Rémi Rabasa-Lhoret, Antony D Karelis

Abstract readClinical Trial
In one paragraph

Trial report in Nutrients, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. 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.

Christian DuvalDepartment of Exercise Science, Université du Québec à Montréal, Montreal, QC H3C 3P8, Canada. duval.christian@uqam.ca.
Marc-Antoine RouillierDepartment of Exercise Science, Université du Québec à Montréal, Montreal, QC H3C 3P8, Canada. ma.rouillier@gmail.com.
Rémi Rabasa-LhoretInstitut de Recherches Cliniques de Montréal (IRCM), Montreal, QC H2W 1R7, Canada. Remi.Rabasa-Lhoret@ircm.qc.ca.
Antony D KarelisDepartment of Exercise Science, Université du Québec à Montréal, Montreal, QC H3C 3P8, Canada. karelis.antony@uqam.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this study was to assess the ability of high intensity exercise to counteract the deleterious effects of a fast food diet on the cardiometabolic profile of young healthy men. Fifteen men were subjected to an exclusive fast food diet from a popular fast food restaurant chain (three extra value meals/day + optional snack) for 14 consecutive days. Simultaneously, participants were asked to perform each day high intensity interval training (HIIT) (15 × 60 sec sprint intervals (~90% of maximal heart rate)) on a treadmill. Fast food diet and energy expenditure profiles of the participants during the intervention were assessed as well as body composition (DXA), cardiometabolic profile (lipid, hepatic enzymes, glycated hemoglobin, glucose, insulin, hsC-reactive protein (hsCRP) and blood pressure) and estimated maximal oxygen consumption (VO2 max) pre- and post-experiment. We found significant improvements for fat mass, lean body mass, estimated VO₂ max, fasting glucose, serum lipoprotein(a) and hsCRP after the intervention (

Indexed as

High-Intensity Interval TrainingInsulin ResistanceAdiposityAdolescentAdultBody Mass IndexCardiovascular DiseasesDiet, WesternEnergy IntakeEnergy MetabolismFast FoodsGlucose IntoleranceHumansMaleMuscle DevelopmentOverweightbody compositionenergy expenditureenergy intakelipid and inflammation profile

Identifiers

PMID28846611
PMCPMC5622703

What OpenQuestion holds

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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.