Evidence map›Paper›PMID 41530274›Full record

ArticleScientific reports2026

Athletes' origin trends in participation and performance of master runners in the New York City marathon (1999-2024): a sex- and age-group analysis.

Sasa Duric, Elias Villiger, Marilia Santos Andrade, Luciano Bernardes Leite, Pedro Forte, Daniela Chlíbková, Pantelis T Nikolaidis, Katja Weiss, Thomas Rosemann, Beat Knechtle

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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. Review
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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

10 authors.

Sasa DuricLiberal Arts Department, American University of the Middle East, Egaila, Kuwait.ORCID http://orcid.org/0000-0002-3392-0087
Elias VilligerInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-8371-1390
Marilia Santos AndradePhysiology Department, Federal University of Sao Paulo, Sao Paulo, Brazil.ORCID http://orcid.org/0000-0002-7004-4565
Luciano Bernardes LeiteDepartment of Physical Education, Federal University of Viçosa, Viçosa, Brazil.ORCID http://orcid.org/0000-0002-3012-1327
Pedro ForteDepartment of Sports, Higher Institute of Educational Sciences of the Douro, Penafiel, Portugal.ORCID http://orcid.org/0000-0003-0184-6780
Daniela ChlíbkováCentre of Sports Activities, Brno University of Technology, Brno, Czech Republic.ORCID http://orcid.org/0000-0001-9592-7332
Pantelis T NikolaidisSchool of Health and Caring Sciences, University of West Attica, Athens, Greece.ORCID http://orcid.org/0000-0001-8030-7122
Katja WeissInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-1247-6754
Thomas RosemannInstitute of Primary Care, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-6436-6306
Beat KnechtleInstitute of Primary Care, University of Zurich, Zurich, Switzerland. beat.knechtle@hispeed.ch.ORCID http://orcid.org/0000-0002-2412-9103

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well known that the fastest elite marathon runners come from East African countries such as Ethiopia and Kenya. However, to date, there is no information available on the origin of the fastest age group (master) marathoners. This study aimed to determine the countries of origin of the fastest age group marathoners who have participated in the 'New York City Marathon' over the past several decades. Race data from 1,009,839 runners (626,183 male and 383,656 female finishers) who completed the 'New York City Marathon' between 1999 and 2024 were analyzed. Participants were categorized into five-year age groups: <20, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, and 75 + years. The data were stratified by sex (male and female) and country of origin. The dataset was organized into five performance-based subgroups: (i) the entire dataset, including all finishers by age group and nationality; (ii) the top 100 finishers per age group; (iii) the top 30 finishers per age group; (iv) the top 10 finishers per age group; and (v) the top individual from each country within each age group. Regression analyses were conducted to explore demographic predictors of marathon performance. Participation generally increased over the study period, with temporary declines during the COVID-19 pandemic; male participation consistently outnumbered female participation, the 40-44 years age group was the most represented for both sexes, and participation was lowest in the youngest (< 20 years) and oldest (75 + years) age groups. Crucially, analyses focusing on the fastest age-group marathoners revealed clear nationality-based performance patterns. In younger adult age groups (20-39 years), the fastest average race times were predominantly achieved by female and male runners from Kenya and Ethiopia. The < 20 years age category showed comparatively stronger performances from European runners, including those from Poland, Switzerland and Italy. In the 50 years and older age groups, the best average times were increasingly recorded by runners from the United States of America, Japan, Germany and Switzerland. This shift highlights a regional transition in peak marathon performance with increasing age, from East African to European, North American, and East Asian dominance.

Indexed as

AthletesAthletic PerformanceMarathon RunningRunningAdultAgedAge FactorsFemaleHumansMaleMiddle AgedNew York CitySex FactorsYoung AdultAge groupEnduranceNationalityPerformance

Identifiers

PMID41530274
PMCPMC12877032

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.