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Applying the cost of generating force hypothesis to uphill running

Overview of attention for article published in PeerJ, July 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

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9 X users
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1 patent
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1 peer review site
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4 Facebook pages
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1 Google+ user
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1 Redditor
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1 podcast

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102 Mendeley
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Article details
Title
Applying the cost of generating force hypothesis to uphill running
Published in
PeerJ, July 2014
DOI 10.7717/peerj.482
Pubmed ID
Authors
Abstract

Historically, several different approaches have been applied to explain the metabolic cost of uphill human running. Most of these approaches result in unrealistically high values for the efficiency of performing vertical work during running uphill, or are only valid for running up steep inclines. The purpose of this study was to reexamine the metabolic cost of uphill running, based upon our understanding of level running energetics and ground reaction forces during uphill running. In contrast to the vertical efficiency approach, we propose that during incline running at a certain velocity, the forces (and hence metabolic energy) required for braking and propelling the body mass parallel to the running surface are less than during level running. Based on this idea, we propose that the metabolic rate during uphill running can be predicted by a model, which posits that (1) the metabolic cost of perpendicular bouncing remains the same as during level running, (2) the metabolic cost of running parallel to the running surface decreases with incline, (3) the delta efficiency of producing mechanical power to lift the COM vertically is constant, independent of incline and running velocity, and (4) the costs of leg and arm swing do not change with incline. To test this approach, we collected ground reaction force (GRF) data for eight runners who ran thirty 30-second trials (velocity: 2.0-3.0 m/s; incline: 0-9°). We also measured the metabolic rates of eight different runners for 17, 7-minute trials (velocity: 2.0-3.0 m/s; incline: 0-8°). During uphill running, parallel braking GRF approached zero for the 9° incline trials. Thus, we modeled the metabolic cost of parallel running as exponentially decreasing with incline. With that assumption, best-fit parameters for the metabolic rate data indicate that the efficiency of producing mechanical power to lift the center of mass vertically was independent of incline and running velocity, with a value of ∼29%. The metabolic cost of uphill running is not simply equal to the sum of the cost of level running and the cost of performing work to lift the body mass against gravity. Rather, it reflects a constant cost of perpendicular bouncing, decreased costs of parallel braking and propulsion and of course the cost of lifting body mass against gravity.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
New Zealand 1 <1%
Australia 1 <1%
Unknown 100 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 20 20%
Student > Ph. D. Student 19 19%
Researcher 14 14%
Student > Bachelor 7 7%
Student > Doctoral Student 6 6%
Other 17 17%
Unknown 19 19%
Readers by discipline
Readers by discipline Count As %
Sports and Recreations 46 45%
Engineering 9 9%
Agricultural and Biological Sciences 7 7%
Medicine and Dentistry 6 6%
Social Sciences 3 3%
Other 9 9%
Unknown 22 22%
Attention Score in Context

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 17 February 2026.
All research outputs
#2,344,758
of 32,261,701 outputs
Outputs from PeerJ
#2,077
of 16,411 outputs
Outputs of similar age
#19,961
of 262,941 outputs
Outputs of similar age from PeerJ
#24
of 133 outputs
Altmetric has tracked 32,261,701 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 16,411 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 done well, scoring higher than 87% 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 262,941 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 92% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.