| Title |
A technique to determine the fastest age-adjusted masters marathon world records
|
|---|---|
| Published in |
SpringerPlus, September 2016
|
| DOI | 10.1186/s40064-016-3190-5 |
| Pubmed ID | |
| Authors |
Paul M. Vanderburgh |
| Abstract |
This study's purpose was to develop and employ a technique to determine the fastest masters marathon world records (WR), ages 35-79 years, adjusted for age (WRadj). From single-age WR data, a best-fit polynomial curve (WRpred1) was developed for the larger age range of 29-80 years for women and 30-80 years for men to improve curve stability in the 35-79 years range. Due to the relatively large degree of data scatter about the curve and the resultant age bias in favor of older runners, a subsample was constituted consisting of those with the lowest WR/WRpred1 ratio within each five-year age group (N = 11). A new polynomial best-fit curve (WRpred2) was developed from this subsample to become the standard against which WR would be compared across age. WRadj was computed from WR/WRpred2 for all runners, 35-79 years, from which the top ten fastest were then determined. The WRpred2 model reduced data scatter and eliminated the age bias. Tatyana Pozdniakova, 50 years, WR = 2:31:05, WRadj = 2:12:40; and Ed Whitlock, 73 years, WR = 2:54:48, WRadj = 1:59:57, had the fastest WRadj for women and men, respectively. This technique of iterative curve-fitting may be an optimal way of determining the fastest masters WRadj and may also be useful in better understanding the upper limits of human performance by age. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 47% |
| Canada | 2 | 11% |
| Uganda | 1 | 5% |
| Netherlands | 1 | 5% |
| Unknown | 6 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 68% |
| Scientists | 5 | 26% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 6 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 1 | 17% |
| Librarian | 1 | 17% |
| Student > Ph. D. Student | 1 | 17% |
| Researcher | 1 | 17% |
| Unknown | 2 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 1 | 17% |
| Medicine and Dentistry | 1 | 17% |
| Engineering | 1 | 17% |
| Unknown | 3 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 23 March 2021.
All research outputs
#1,972,256
of 25,416,581 outputs
Outputs from SpringerPlus
#100
of 1,877 outputs
Outputs of similar age
#33,952
of 342,783 outputs
Outputs of similar age from SpringerPlus
#15
of 221 outputs
Altmetric has tracked 25,416,581 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,877 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done particularly well, scoring higher than 94% of its peers.
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We're also able to compare this research output to 221 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.