↓ Skip to main content
Altmetric Badge

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 (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
13 X users
facebook
1 Facebook page

Readers on

mendeley
169 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
A Model for the Training Effects in Swimming Demonstrates a Strong Relationship between Parasympathetic Activity, Performance and Index of Fatigue
Published in
PLOS ONE, December 2012
DOI 10.1371/journal.pone.0052636
Pubmed ID
Authors
Abstract

Competitive swimming as a physical activity results in changes to the activity level of the autonomic nervous system (ANS). However, the precise relationship between ANS activity, fatigue and sports performance remains contentious. To address this problem and build a model to support a consistent relationship, data were gathered from national and regional swimmers during two 30 consecutive-week training periods. Nocturnal ANS activity was measured weekly and quantified through wavelet transform analysis of the recorded heart rate variability. Performance was then measured through a subsequent morning 400 meters freestyle time-trial. A model was proposed where indices of fatigue were computed using Banister's two antagonistic component model of fatigue and adaptation applied to both the ANS activity and the performance. This demonstrated that a logarithmic relationship existed between performance and ANS activity for each subject. There was a high degree of model fit between the measured and calculated performance (R(2)=0.84±0.14,p<0.01) and the measured and calculated High Frequency (HF) power of the ANS activity (R(2)=0.79±0.07, p<0.01). During the taper periods, improvements in measured performance and measured HF were strongly related. In the model, variations in performance were related to significant reductions in the level of 'Negative Influences' rather than increases in 'Positive Influences'. Furthermore, the delay needed to return to the initial performance level was highly correlated to the delay required to return to the initial HF power level (p<0.01). The delay required to reach peak performance was highly correlated to the delay required to reach the maximal level of HF power (p=0.02). Building the ANS/performance identity of a subject, including the time to peak HF, may help predict the maximal performance that could be obtained at a given time.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 169 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Italy 2 1%
South Africa 1 <1%
United States 1 <1%
Portugal 1 <1%
United Kingdom 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 161 95%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 25 15%
Student > Bachelor 24 14%
Student > Master 23 14%
Researcher 18 11%
Other 12 7%
Other 35 21%
Unknown 32 19%
Readers by discipline
Readers by discipline Count As %
Sports and Recreations 78 46%
Medicine and Dentistry 15 9%
Agricultural and Biological Sciences 10 6%
Psychology 7 4%
Nursing and Health Professions 6 4%
Other 18 11%
Unknown 35 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 10 February 2021.
All research outputs
#4,996,594
of 34,360,889 outputs
Outputs from PLOS ONE
#51,396
of 224,520 outputs
Outputs of similar age
#44,162
of 345,808 outputs
Outputs of similar age from PLOS ONE
#1,090
of 5,203 outputs
Altmetric has tracked 34,360,889 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 224,520 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 77% 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 345,808 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 5,203 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.