Evidence map›Paper›PMID 39567546›Full record

ArticleScientific reports2024

The fastest 24-hour ultramarathoners are from Eastern Europe.

Beat Knechtle, David Valero, Elias Villiger, Volker Scheer, Katja Weiss, Pedro Forte, Mabliny Thuany, Rodrigo Luiz Vancini, Claudio Andre Barbosa de Lira, Pantelis T Nikolaidis and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

12 authors.

Beat KnechtleMedbase St. Gallen Am Vadianplatz, Vadianstrasse 26, St. Gallen, 9001, Switzerland. beat.knechtle@hispeed.ch.ORCID 0000-0002-2412-9103
David ValeroUltra Sports Science Foundation, 109 Boulevard de l'Europe, Pierre-Benite, 69310, France.ORCID 0000-0003-4133-4843
Elias VilligerInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID 0000-0001-8371-1390
Volker ScheerUltra Sports Science Foundation, 109 Boulevard de l'Europe, Pierre-Benite, 69310, France.ORCID 0000-0003-0074-3624
Katja WeissInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID 0000-0003-1247-6754
Pedro ForteCI-ISCE, Higher Institute of Educational Sciences of the Douro, Penafiel, Portugal.ORCID 0000-0003-0184-6780
Mabliny ThuanyFaculty of Sports, University of Porto, Porto, Portugal.ORCID 0000-0002-6858-1871
Rodrigo Luiz VanciniPhysical Education Sport Center of Federal, MoveAgeLab, University of Espirito Santo, Vitoria, ES, Brazil.ORCID 0000-0003-1981-1092
Claudio Andre Barbosa de LiraFaculdade de Educação Física e Dança, Universidade Federal de Goiás, Goiás, Brazil.ORCID 0000-0001-5749-6877
Pantelis T NikolaidisSchool of Health and Caring Sciences, University of West Attica, Athens, Greece.ORCID 0000-0001-8030-7122
Nejmeddine OuerghiHigh Institute of Sport and Physical Education of Kef, University of Jendouba , Kef, UR22JS01, 7100, Tunisia.ORCID 0000-0002-8840-1735
Thomas RosemannInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-6436-6306

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultramarathon running is of increasing popularity, where the time-limited 24-hour run is one of the most popular events. Although we have a high scientific knowledge about different topics for this specific race format, we do not know where the best 24-hour runners originate from and where the fastest races are held. The purpose of the present study was to investigate the origin of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed based on the athlete´s age, gender, country of origin and the country where the race takes place. Model explainability tools were used to investigate how each independent variable would influence the predicted running speed. A sample of 171,358 race records from 63,514 unique runners from 73 countries participating in 24-hour races held in 57 countries between 1807 and 2022 was analyzed. Most of the athletes originated from the USA, France, Germany, Great Britain, Italy, Japan, Russia, Australia, Austria, and Canada. Tunisian athletes achieved the fastest average running speed, followed by runners from Russia, Latvia, Lithuania, Island, Croatia, Slovenia, and Israel. Regarding the country of the event, the ranking looks quite similar to the participation by the athlete, suggesting a high correlation between the country of origin and the country of the event. The fastest 24-hour races are recorded in Israel, Romania, Korea, the Netherlands, Russia, and Taiwan. On average, men were 0.4 km/h faster than women, and the fastest runners belonged to age groups 35-39, 40-44, and 45-49 years. In summary, the 24-hour race format is spread over the world, and the fastest athletes mainly originate from Eastern Europe, while the fastest races were organized in European and Asian countries.

Indexed as

AthletesAdultAthletic PerformanceEurope, EasternFemaleHumansMachine LearningMaleMarathon RunningMiddle AgedRunningYoung AdultMachine learningNationalityOriginPerformanceUltra-endurance

Identifiers

PMID39567546
PMCPMC11579506

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.