| Title |
Use of Bioimpedianciometer as Predictor of Mountain Marathon Performance
|
|---|---|
| Published in |
Journal of Medical Systems, March 2017
|
| DOI | 10.1007/s10916-017-0722-7 |
| Pubmed ID | |
| Authors |
Vicente Javier Clemente-Suarez, Pantelis Theodoros Nikolaidis |
| Abstract |
This study aimed to examine the relation among body composition, training experience and race time during a mountain marathon. Body composition and training pre-race experience analyses were conducted previous to a mountain marathon in 52 male athletes. A significant correlation between race time and mountain marathon with chronological age, body fat mass, percentage of body fat (BF), level of abdominal obesity, sport experience and daily training volume was revealed. In addition, BF and athlete's chronological age were negatively associated with race performance. In contrast, the daily training volume was positively associated with mountain marathon time. A regression analysis showed that race time could be predicted (R(2) = .948) by the daily training load, sports experience, age, body fat mass, BF and level of abdominal obesity. The comparison between performance groups regarding to body composition and training characteristics showed that the higher performance group was lighter with lower BF, fat mass and level of abdominal obesity, and with more days of training per week compared with the lower performance group (p < .05). Therefore, coaches and fitness trainers working with mountain marathon runners should develop exercise and nutritional strategies to reduce BF and consider increasing mean daily training volume to improve performance. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 48 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 12% |
| Professor | 5 | 10% |
| Student > Ph. D. Student | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Unspecified | 4 | 8% |
| Other | 8 | 16% |
| Unknown | 17 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Sports and Recreations | 14 | 29% |
| Medicine and Dentistry | 5 | 10% |
| Unspecified | 4 | 8% |
| Nursing and Health Professions | 2 | 4% |
| Social Sciences | 2 | 4% |
| Other | 4 | 8% |
| Unknown | 18 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 April 2017.
All research outputs
#18,541,268
of 22,963,381 outputs
Outputs from Journal of Medical Systems
#816
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Outputs of similar age
#235,692
of 309,701 outputs
Outputs of similar age from Journal of Medical Systems
#18
of 22 outputs
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