Performance Prediction Equation for 2000 m Youth Indoor Rowing Using a 100 m Maximal Test.

athletic performance mathematical model rowing sport young athlete

Journal

Biology
ISSN: 2079-7737
Titre abrégé: Biology (Basel)
Pays: Switzerland
ID NLM: 101587988

Informations de publication

Date de publication:
22 Oct 2021
Historique:
received: 04 10 2021
revised: 12 10 2021
accepted: 14 10 2021
entrez: 27 11 2021
pubmed: 28 11 2021
medline: 28 11 2021
Statut: epublish

Résumé

The exhaustive series of tests undergone by young athletes of Olympic rowing prior to important competitions imply loads of physical stress that can ultimately impact on mood and motivation, with negative consequences for their training and performance. Thus, it is necessary to develop a tool that uses only the performance of short distances but is highly predictive, offering a time expectancy with high reliability. Such a test must use variables that are easy to collect with high practical applicability in the daily routine of coaches. The objective of the present study was to develop a mathematical model capable of predicting 2000 m rowing performance from a maximum effort 100 m indoor rowing ergometer (IRE) test in young rowers. The sample consisted of 12 male rowing athletes in the junior category (15.9 ± 1.0 years). A 100 m time trial was performed on the IRE, followed by a 2000 m time trial 24-h later. The 2000 m mathematical model to predict performance in minutes based on the maximum 100 m test demonstrated a high correlation (r = 0.734; The mathematical model developed to predict 2000 m performance is effective and has a statistically significant reliability index while being easy to implement with low cost.

Sections du résumé

BACKGROUND BACKGROUND
The exhaustive series of tests undergone by young athletes of Olympic rowing prior to important competitions imply loads of physical stress that can ultimately impact on mood and motivation, with negative consequences for their training and performance. Thus, it is necessary to develop a tool that uses only the performance of short distances but is highly predictive, offering a time expectancy with high reliability. Such a test must use variables that are easy to collect with high practical applicability in the daily routine of coaches.
OBJECTIVE OBJECTIVE
The objective of the present study was to develop a mathematical model capable of predicting 2000 m rowing performance from a maximum effort 100 m indoor rowing ergometer (IRE) test in young rowers.
METHODS METHODS
The sample consisted of 12 male rowing athletes in the junior category (15.9 ± 1.0 years). A 100 m time trial was performed on the IRE, followed by a 2000 m time trial 24-h later.
RESULTS RESULTS
The 2000 m mathematical model to predict performance in minutes based on the maximum 100 m test demonstrated a high correlation (r = 0.734;
CONCLUSION CONCLUSIONS
The mathematical model developed to predict 2000 m performance is effective and has a statistically significant reliability index while being easy to implement with low cost.

Identifiants

pubmed: 34827075
pii: biology10111082
doi: 10.3390/biology10111082
pmc: PMC8615280
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Luiz Felipe da Silva (LFD)

Health Sciences Center, Department of Physical Education, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

Paulo Francisco de Almeida-Neto (PF)

Health Sciences Center, Department of Physical Education, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

Dihogo Gama de Matos (DG)

Cardiorespiratory & Physiology of Exercise Research Laboratory, Faculty of Kinesiology and Recreation Management, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.

Steven E Riechman (SE)

Department of Health and Kinesiology, Texas A&M University, College Station, TX 77843, USA.

Victor de Queiros (V)

Health Sciences Center, Department of Physical Education, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

Joseane Barbosa de Jesus (JB)

Group of Studies and Research of Performance, Sport, Health and Paralympic Sports GEPEPS, The Federal University of Sergipe, UFS, São Cristovão 49100-000, Brazil.
Graduate Program in of Physical Education, Federal University of Sergipe-UFS, São Cristovão 49100-000, Brazil.

Victor Machado Reis (VM)

Research Center in Sports Sciences, Health Sciences, and Human Development (CIDESD), Trás os Montes and Alto Douro University, 5001-801 Vila Real, Portugal.

Filipe Manuel Clemente (FM)

Sports and Leisure, Polytechnic Institute of Viana do Castelo, Rua Industrial and Commercial School of Nun'Álvares, 4900-347 Viana do Castelo, Portugal.
Telecommunications Institute, Delegation of Covilhã, 1049-001 Lisbon, Portugal.

Bianca Miarka (B)

Laboratory of Psychophysiology and Performance in Sports & Combats, Postgraduate Program in Physical Education, School of Physical Education and Sport, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil.

Felipe J Aidar (FJ)

Group of Studies and Research of Performance, Sport, Health and Paralympic Sports GEPEPS, The Federal University of Sergipe, UFS, São Cristovão 49100-000, Brazil.
Graduate Program in of Physical Education, Federal University of Sergipe-UFS, São Cristovão 49100-000, Brazil.
Department of Physical Education, Federal University of Sergipe-UFS, São Cristovão 49100-000, Brazil.
Program of Physiological Science, Federal University of Sergipe-UFS, São Cristovão 49100-000, Brazil.

Paulo Moreira Silva Dantas (PMS)

Health Sciences Center, Department of Physical Education, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

Breno Guilherme de Araújo Tinoco Cabral (BGAT)

Health Sciences Center, Department of Physical Education, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

Classifications MeSH