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An empirical study of race times in recreational endurance runners

Overview of attention for article published in BMC Sports Science, Medicine and Rehabilitation, August 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 208)
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
3 news outlets
twitter
22 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
53 Mendeley
Title
An empirical study of race times in recreational endurance runners
Published in
BMC Sports Science, Medicine and Rehabilitation, August 2016
DOI 10.1186/s13102-016-0052-y
Pubmed ID
Authors

Andrew J. Vickers, Emily A. Vertosick

Abstract

Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment. We 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. Sex, 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). Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

Twitter Demographics

The data shown below were collected from the profiles of 22 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
South Africa 1 2%
Unknown 50 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 30%
Student > Doctoral Student 7 13%
Researcher 6 11%
Student > Bachelor 5 9%
Other 4 8%
Other 8 15%
Unknown 7 13%
Readers by discipline Count As %
Sports and Recreations 16 30%
Medicine and Dentistry 8 15%
Computer Science 4 8%
Agricultural and Biological Sciences 4 8%
Psychology 3 6%
Other 7 13%
Unknown 11 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 41. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 January 2020.
All research outputs
#464,411
of 14,346,974 outputs
Outputs from BMC Sports Science, Medicine and Rehabilitation
#17
of 208 outputs
Outputs of similar age
#14,223
of 263,341 outputs
Outputs of similar age from BMC Sports Science, Medicine and Rehabilitation
#1
of 7 outputs
Altmetric has tracked 14,346,974 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 208 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,341 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them