Evidence map›Paper›PMID 39058085›Full record

ArticleSports (Basel, Switzerland)2024

Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach.

José Eduardo Teixeira, Luís Branquinho, Ricardo Ferraz, Ryland Morgans, Samuel Encarnação, Joana Ribeiro, Pedro Afonso, Nemat Ruzmetov, Tiago M Barbosa, António M Monteiro and 1 more

Abstract read
In one paragraph

Article in Sports (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

José Eduardo TeixeiraDepartment of Sports Sciences, Polytechnic of Guarda, 6300-559 Guarda, Portugal.ORCID 0000-0003-4612-3623
Luís BranquinhoResearch Center in Sports, Health and Human Development, 6201-001 Covilhã, Portugal.ORCID 0000-0001-9000-5419
Ricardo FerrazResearch Center in Sports, Health and Human Development, 6201-001 Covilhã, Portugal.ORCID 0000-0002-7530-512X
Ryland MorgansSchool of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff CF5 2YB, UK.ORCID 0000-0003-2007-4827
Samuel EncarnaçãoDepartment of Sports Sciences, Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal.
Joana RibeiroCI-ISCE, ISCE Douro, 4560-547 Penafiel, Portugal.
Pedro AfonsoResearch Center in Sports, Health and Human Development, 6201-001 Covilhã, Portugal.ORCID 0009-0000-1077-7233
Nemat RuzmetovDepartment of Physical Culture and Sports, Urgench State University, Urgench 220100, Uzbekistan.
Tiago M BarbosaDepartment of Sports Sciences, Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal.ORCID 0000-0001-7071-2116
António M MonteiroDepartment of Sports Sciences, Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal.ORCID 0000-0003-4467-1722
Pedro ForteDepartment of Sports Sciences, Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal.ORCID 0000-0003-0184-6780

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019-2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (

Indexed as

intensitymonitoringtracking technologieswearable devicesworkloadyouth

Identifiers

PMID39058085
PMCPMC11280859

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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.