Evidence map›Paper›PMID 36145075›Full record

ArticleNutrients2022

Dietary Intake of Recreational Endurance Runners Associated with Race Distance-Results from the NURMI Study (Step 2).

Derrick Tanous, Karl-Heinz Wagner, Claus Leitzmann, Mohamad Motevalli, Gerold Wirnitzer, Thomas Rosemann, Beat Knechtle, Katharina Wirnitzer

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2022. 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
0.5field-weighted citation impact, top 43% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Self-reported carbohydrate supercompensation and supplementation strategies adopted by Olympic triathlon athletes.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2025
    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

8 authors at 4 institutions in 3 countries.

Derrick TanousDepartment of Sport Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0002-6294-8513
Karl-Heinz WagnerDepartment of Nutritional Sciences, University of Vienna, 1090 Vienna, Austria.ORCID 0000-0002-1683-7265
Claus LeitzmannInstitute of Nutrition, University of Gießen, 35390 Gießen, Germany.
Mohamad MotevalliDepartment of Sport Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0001-6453-9695
Gerold WirnitzerAdventureV & Change2V, 6135 Stans, Austria.
Thomas RosemannInstitute of Primary Care, University of Zurich, 8091 Zurich, Switzerland.ORCID 0000-0002-6436-6306
Beat KnechtleInstitute of Primary Care, University of Zurich, 8091 Zurich, Switzerland.ORCID 0000-0002-2412-9103
Katharina WirnitzerDepartment of Sport Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0002-6208-4655
Universität Innsbruck · ATUniversity of Zurich · CHJustus-Liebig-Universität Gießen · DEUniversity of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the popularity of distance running is growing worldwide, endurance runners’ dietary challenges associated with their prolonged training and racing activities have not yet been fully understood. The present investigation was conducted with the aim of examining the association between race distance and dietary intake of distance runners. A total of 317 runners initially participated, and after data clearance, 211 endurance runners (57% females) were finally considered the study sample. Runners were assigned to three race distance groups: 10-km (n = 74), half-marathon (n = 83), and marathon/ultra-marathon (n = 54). An online survey was used to collect data; dietary intake was monitored using a comprehensive food frequency questionnaire, including 53 food groups categorized in 14 basic and three umbrella clusters. There was no significant difference (p > 0.05) between race distance groups in consumption of most food clusters except for “fruits and vegetables” and “total of protein”, with a predominance of 10-km runners compared to half-marathoners and (ultra-)marathoners (p ≤ 0.05). Age was a significant predictor for the consumption of only five (out of 17) food clusters (p ≤ 0.05), including “fruit and vegetables”, “unprocessed meat”, “processed meat”, “eggs”, and “plant protein”. Future investigations with a larger sample size and more differentiated (sub)groups may help provide comparable data to develop a better understanding of the dietary behaviors among shorter versus longer distance runners.

Indexed as

Physical EnduranceRunningEatingNutritional StatusVegetablesdietendurancefood frequencyhalf-marathonhealthmarathonnutritionphysical activityrunningultra-marathon

Identifiers

PMID36145075
PMCPMC9503531
OpenAlexW4296112985

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.