Evidence map›Paper›PMID 41295769›Full record

ArticleSports (Basel, Switzerland)2025

Real-Time Performance Prediction in Long-Distance Trail Running: A Practical Model Based on Terrain Difficulty and Pacing Variability.

Héctor Gutiérrez, Eduardo Piedrafita, Pablo Jesús Bascuas, Irela Arbonés, César Berzosa, Ana Vanessa Bataller-Cervero

Abstract read
In one paragraph

Article in Sports (Basel, Switzerland), 2025. 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. A Systematic Review of the Factors Associated with Performance in Non-Elite Runners.Journal of functional morphology and kinesiology · 2026
    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

6 authors.

Héctor GutiérrezFacultad de Ciencias de la Salud, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca, km 299, Villanueva de Gállego, 50830 Zaragoza, Spain.ORCID 0000-0002-0411-2945
Eduardo PiedrafitaFacultad de Ciencias de la Salud, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca, km 299, Villanueva de Gállego, 50830 Zaragoza, Spain.ORCID 0000-0001-6349-2370
Pablo Jesús BascuasFacultad de Ciencias de la Salud, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca, km 299, Villanueva de Gállego, 50830 Zaragoza, Spain.ORCID 0000-0002-8770-2106
Irela ArbonésInstituto Nacional de Educación Física de Cataluña (INEFC), Universidad de Lleida, Partida la Caparrella s/n, 25192 Lérida, Spain.ORCID 0000-0002-7340-7405
César BerzosaFacultad de Ciencias de la Salud, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca, km 299, Villanueva de Gállego, 50830 Zaragoza, Spain.ORCID 0000-0001-7783-6672
Ana Vanessa Bataller-CerveroFacultad de Ciencias de la Salud, Universidad San Jorge, Autovía A-23 Zaragoza-Huesca, km 299, Villanueva de Gállego, 50830 Zaragoza, Spain.ORCID 0000-0002-6368-875X

Funding

Gobierno de Aragón S08_23R
6 · The paper itself

Abstract

Trail running is a demanding endurance sport where performance prediction models often rely on laboratory testing or pre-race data, limiting their practical application. This study presents a real-time predictive model for marathon and ultra-trail races, based on variables recorded during the race, including uphill/downhill pace-times, terrain difficulty coefficients, and partial rankings. A total of 947 runners from the 'Trail Valle de Tena' event (Spain) were analyzed to develop equations that estimate total race time using only the first third of the race. The model incorporates weighted time (WT

Indexed as

endurance sportsfatigue managementpacing strategyperformance monitoringtrail running

Identifiers

PMID41295769
PMCPMC12656631

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.