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Pacing in a 94-year-old runner during a 6-hour run

Overview of attention for article published in Open Access Journal of Sports Medicine, February 2018
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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4 X users
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1 Facebook page
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1 Google+ user

Citations

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22 Mendeley
Title
Pacing in a 94-year-old runner during a 6-hour run
Published in
Open Access Journal of Sports Medicine, February 2018
DOI 10.2147/oajsm.s155526
Pubmed ID
Authors

Beat Knechtle, Pantelis T Nikolaidis

Abstract

It is well known that elderly people up to 90 years of age are able to finish a marathon. We have no knowledge, however, how runners at the age of 90 years or older pace during a long run. In this case report, we describe the pacing of a 94-year-old man competing in a 6-hour run in order to prepare for a marathon at the age of 95 years in category M95. In the "6-Stunden-Lauf " held in Brugg, Switzerland, participants have to run as many laps of 0.934 km as possible on a completely flat circuit within 6 hours to achieve as many kilometers as possible. Before and after the competition we measured body weight, percentage of body fat, fat-free mass and percentage of body water using a bioelectrical impedance scale. On the day before the start, 24 hours after the finish and then every 24 hours for the following 4 days, capillary blood samples at a fingertip were drawn to determine hemoglobin, hematocrit, leukocytes, platelets, C-reactive protein, creatine kinase, creatinine and potassium and sodium. The runner achieved 26 laps during the 6 hours, equal to 24.304 km. Lap times increased continuously and running speed decreased nearly linearly. A large main effect of time point (hours) of the race on running speed was observed (p=0.015,η2=0.48) with running speed being slower in the last hour than that in the first hour (3.5±1.4 km/h versus 5.3±0.4 km/h). Body mass decreased by 0.6%, percent body fat by 1.4% and fat-free mass by 0.7%. During recovery, hemoglobin, hematocrit and the number of thrombocytes increased, whereas the number of leukocytes remained unchanged. C-reactive protein was highest on day 1 after the race and decreased by day 4 nearly to zero. Creatine kinase was slightly elevated pre-race, highest the day after the race and remained slightly elevated until day 4. Creatinine and potassium were increased pre-race but returned to normal values during recovery. Sodium remained within normal values on all days. Based on the linear decrease in running speed, we extrapolated for the marathon distance to run a marathon in age group M95 (i.e., male marathoners aged 95-99 years). In the worst-case scenario (i.e., the athlete develops maximal fatigue), he would stop the race before 40 km, in the best scenario (i.e., the athlete develops minimal fatigue), he would achieve an overall race time of ~8.3 hours and in the most probable scenario (i.e., the athlete can continue in the same manner), the final race time will be longer than 11 hours.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 3 14%
Student > Ph. D. Student 2 9%
Student > Master 2 9%
Lecturer 1 5%
Librarian 1 5%
Other 5 23%
Unknown 8 36%
Readers by discipline Count As %
Sports and Recreations 7 32%
Medicine and Dentistry 3 14%
Nursing and Health Professions 1 5%
Unspecified 1 5%
Psychology 1 5%
Other 1 5%
Unknown 8 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 February 2018.
All research outputs
#7,856,238
of 25,584,565 outputs
Outputs from Open Access Journal of Sports Medicine
#121
of 251 outputs
Outputs of similar age
#147,062
of 450,135 outputs
Outputs of similar age from Open Access Journal of Sports Medicine
#3
of 6 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 251 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one has gotten more attention than average, scoring higher than 50% 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 450,135 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.