Evidence map›Paper›PMID 27570626›Full record

ArticleBMC sports science, medicine & rehabilitation2016

An empirical study of race times in recreational endurance runners.

Andrew J Vickers, Emily A Vertosick

Abstract read
In one paragraph

Article in BMC sports science, medicine & rehabilitation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Trial
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  8. Modelling human endurance: power laws vs critical power.European journal of applied physiology · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels.International journal of environmental research and public health · 2021
    Article
  15. Running Performance Variability among Runners from Different Brazilian States: A Multilevel Approach.International journal of environmental research and public health · 2021
    Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Observational
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

2 authors.

Andrew J VickersMemorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017 USA.ORCID 0000-0003-1525-6503
Emily A VertosickMemorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017 USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Michael Jason de la Cruz · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

backgroundStudies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment.

methodsWe examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time.

resultsSex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races).

conclusionsOur findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

Indexed as

PerformancePrediction modelingRunningSports training

Identifiers

PMID27570626
PMCPMC5000509

What Socratic 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 Socratic graph.