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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Blood-Borne Markers of Fatigue in Competitive Athletes – Results from Simulated Training Camps
|
|---|---|
| Published in |
PLOS ONE, February 2016
|
| DOI | 10.1371/journal.pone.0148810 |
| Pubmed ID | |
| Authors |
Anne Hecksteden, Sabrina Skorski, Sascha Schwindling, Daniel Hammes, Mark Pfeiffer, Michael Kellmann, Alexander Ferrauti, Tim Meyer |
| Abstract |
Assessing current fatigue of athletes to fine-tune training prescriptions is a critical task in competitive sports. Blood-borne surrogate markers are widely used despite the scarcity of validation trials with representative subjects and interventions. Moreover, differences between training modes and disciplines (e.g. due to differences in eccentric force production or calorie turnover) have rarely been studied within a consistent design. Therefore, we investigated blood-borne fatigue markers during and after discipline-specific simulated training camps. A comprehensive panel of blood-born indicators was measured in 73 competitive athletes (28 cyclists, 22 team sports, 23 strength) at 3 time-points: after a run-in resting phase (d 1), after a 6-day induction of fatigue (d 8) and following a subsequent 2-day recovery period (d 11). Venous blood samples were collected between 8 and 10 a.m. Courses of blood-borne indicators are considered as fatigue dependent if a significant deviation from baseline is present at day 8 (Δfatigue) which significantly regresses towards baseline until day 11 (Δrecovery). With cycling, a fatigue dependent course was observed for creatine kinase (CK; Δfatigue 54±84 U/l; Δrecovery -60±83 U/l), urea (Δfatigue 11±9 mg/dl; Δrecovery -10±10 mg/dl), free testosterone (Δfatigue -1.3±2.1 pg/ml; Δrecovery 0.8±1.5 pg/ml) and insulin linke growth factor 1 (IGF-1; Δfatigue -56±28 ng/ml; Δrecovery 53±29 ng/ml). For urea and IGF-1 95% confidence intervals for days 1 and 11 did not overlap with day 8. With strength and high-intensity interval training, respectively, fatigue-dependent courses and separated 95% confidence intervals were present for CK (strength: Δfatigue 582±649 U/l; Δrecovery -618±419 U/l; HIIT: Δfatigue 863±952 U/l; Δrecovery -741±842 U/l) only. These results indicate that, within a comprehensive panel of blood-borne markers, changes in fatigue are most accurately reflected by urea and IGF-1 for cycling and by CK for strength training and team sport players. |
X Demographics
The data shown below were collected from the profiles of 26 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 | 4 | 15% |
| Germany | 4 | 15% |
| Netherlands | 2 | 8% |
| United Kingdom | 2 | 8% |
| Switzerland | 2 | 8% |
| Finland | 1 | 4% |
| Austria | 1 | 4% |
| Unknown | 10 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 46% |
| Practitioners (doctors, other healthcare professionals) | 7 | 27% |
| Scientists | 6 | 23% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | <1% |
| Sweden | 1 | <1% |
| France | 1 | <1% |
| Unknown | 170 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 17% |
| Student > Master | 30 | 17% |
| Researcher | 19 | 11% |
| Student > Bachelor | 12 | 7% |
| Student > Doctoral Student | 11 | 6% |
| Other | 27 | 16% |
| Unknown | 44 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Sports and Recreations | 66 | 38% |
| Medicine and Dentistry | 17 | 10% |
| Biochemistry, Genetics and Molecular Biology | 10 | 6% |
| Agricultural and Biological Sciences | 9 | 5% |
| Nursing and Health Professions | 6 | 3% |
| Other | 16 | 9% |
| Unknown | 49 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 11 March 2021.
All research outputs
#1,538,690
of 22,849,304 outputs
Outputs from PLOS ONE
#19,908
of 194,892 outputs
Outputs of similar age
#27,017
of 298,010 outputs
Outputs of similar age from PLOS ONE
#561
of 5,397 outputs
Altmetric has tracked 22,849,304 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 194,892 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done well, scoring higher than 89% 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 298,010 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 90% of its contemporaries.
We're also able to compare this research output to 5,397 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.