Evidence map›Paper›PMID 28321588›Full record

ArticleJournal of medical systems2017

Use of Bioimpedianciometer as Predictor of Mountain Marathon Performance.

Vicente Javier Clemente-Suarez, Pantelis Theodoros Nikolaidis

Abstract read
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In one paragraph

Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. A Systematic Review of the Factors Associated with Performance in Non-Elite Runners.Journal of functional morphology and kinesiology · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Predictors of Athlete's Performance in Ultra-Endurance Mountain Races.International journal of environmental research and public health · 2021
    Article
  7. Article
  8. Skinfold Thickness Distribution in Recreational Marathon Runners.International journal of environmental research and public health · 2020
    Article
  9. Article
  10. Effectiveness of Reverse vs. Traditional Linear Training Periodization in Triathlon.International journal of environmental research and public health · 2019
    Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Vicente Javier Clemente-SuarezApplied Psychophysiological Research Group, European University of Madrid, Calle Tajo, s/n, 28670 Villaviciosa de Odón, Madrid, Spain. vctxente@yahoo.es.
Pantelis Theodoros NikolaidisDepartment of Physical and Cultural Education, Hellenic Army Academy, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

AltitudeBody Fat DistributionAdultAnthropometryAthletic PerformanceForecastingHumansMalePhysical Education and TrainingPhysical EnduranceRegression AnalysisRunningBody fatCorrelation analysisUltra-endurance mountain performance

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.