Authors:
György SzigetiDepartment of Sport Medicine and Sport Science, Hungarian Football Federation, Budapest, Hungary
Department of Health Sciences and Sport Medicine, University of Physical Education, Budapest, Hungary

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Gábor SchuthDepartment of Sport Medicine and Sport Science, Hungarian Football Federation, Budapest, Hungary
Department of Health Sciences and Sport Medicine, University of Physical Education, Budapest, Hungary

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Tamás KovácsDepartment of Sport Medicine and Sport Science, Hungarian Football Federation, Budapest, Hungary

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Péter RevisnyeiMTA-BME Information Systems Research Group, Budapest University of Technology and Economics (BME), Budapest, Hungary

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Alija PasicMTA-BME Information Systems Research Group, Budapest University of Technology and Economics (BME), Budapest, Hungary

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Ádám SzilasDepartment of Sport Medicine and Sport Science, Hungarian Football Federation, Budapest, Hungary

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Tim GabbettGabbett Performance Solutions, Brisbane, QLD, Australia
Centre for Health Research, University of Southern Queensland, Ipswich, QLD, Australia
Health Innovation and Transformation Centre, Federation University, Ballarat, VIC, Australia

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Gábor PavlikDepartment of Health Sciences and Sport Medicine, University of Physical Education, Budapest, Hungary

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Abstract

Objective

Creatine kinase (CK) is widely used as a monitoring tool to make inferences on fatigue and readiness in elite soccer. Previous studies have examined the relationship between CK and GPS parameters, however these metrics may not accurately describe the players' load during soccer-specific movements. Football Movement Profile (FMP) monitoring is a viable option for such purposes, providing solely inertial sensor-based data and categorizing movements according to intensity (very low, low, medium, high) and movement type (running-linear locomotive, dynamic – change of direction or speed).

Methods

We investigated the relationship between the FMP distribution of youth (U16–U21) national team soccer players and the absolute day-to-day change in CK. We applied Spearman's correlations, principal component analysis and K-means clustering to classify players' CK responses according to their specific FMP.

Results

Moderate to large negative associations were found between very low intensity FMP parameters and CK change (r = −0.43 ± 0.12) while large positive associations were identified between CK change and other FMP metrics (r = 0.62 ± 0.12). Best fitting clustering methods were used to group players depending on their CK sensitivity to FMP values. Principal component analysis explained 83.0% of the variation with a Silhouette score of 0.61 for the 4 clusters.

Conclusions

Our results suggest that soccer players can be clustered based on the relationship between FMP measures and the CK change. These findings can help to plan soccer training or recovery sessions according to the desired load on skeletal muscle, as FMP monitoring might bridge the limitations of GPS telemetry.

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Editor-in-Chief

László ROSIVALL (Semmelweis University, Budapest, Hungary)

Managing Editor

Anna BERHIDI (Semmelweis University, Budapest, Hungary)

Co-Editors

  • Gábor SZÉNÁSI (Semmelweis University, Budapest, Hungary)
  • Ákos KOLLER (Semmelweis University, Budapest, Hungary)
  • Zsolt RADÁK (University of Physical Education, Budapest, Hungary)
  • László LÉNÁRD (University of Pécs, Hungary)
  • Zoltán UNGVÁRI (Semmelweis University, Budapest, Hungary)

Assistant Editors

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  • Zsuzsanna MIKLÓS (Semmelweis University, Budapest, Hungary)
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Hungarian Editorial Board

  • György BENEDEK (University of Szeged, Hungary)
  • Zoltán BENYÓ (Semmelweis University, Budapest, Hungary)
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  • László CSERNOCH (University of Debrecen, Hungary)
  • Magdolna DANK (Semmelweis University, Budapest, Hungary)
  • László DÉTÁRI (Eötvös Loránd University, Budapest, Hungary)
  • Zoltán GIRICZ (Semmelweis University, Budapest, Hungary and Pharmahungary Group, Szeged, Hungary)
  • Zoltán HANTOS (Semmelweis University, Budapest and University of Szeged, Hungary)
  • László HUNYADI (Semmelweis University, Budapest, Hungary)
  • Gábor JANCSÓ (University of Pécs, Hungary)
  • Zoltán KARÁDI (University of Pecs, Hungary)
  • Miklós PALKOVITS (Semmelweis University, Budapest, Hungary)
  • Gyula PAPP (University of Szeged, Hungary)
  • Gábor PAVLIK (University of Physical Education, Budapest, Hungary)
  • András SPÄT (Semmelweis University, Budapest, Hungary)
  • Gyula SZABÓ (University of Szeged, Hungary)
  • Zoltán SZELÉNYI (University of Pécs, Hungary)
  • Lajos SZOLLÁR (Semmelweis University, Budapest, Hungary)
  • Gyula TELEGDY (MTA-SZTE, Neuroscience Research Group and University of Szeged, Hungary)
  • József TOLDI (MTA-SZTE Neuroscience Research Group and University of Szeged, Hungary)
  • Árpád TÓSAKI (University of Debrecen, Hungary)

International Editorial Board

  • Dragan DJURIC (University of Belgrade, Serbia)
  • Christopher H.  FRY (University of Bristol, UK)
  • Stephen E. GREENWALD (Blizard Institute, Barts and Queen Mary University of London, UK)
  • Osmo Otto Päiviö HÄNNINEN (Finnish Institute for Health and Welfare, Kuopio, Finland)
  • Helmut G. HINGHOFER-SZALKAY (Medical University of Graz, Austria)
  • Tibor HORTOBÁGYI (University of Groningen, Netherlands)
  • George KUNOS (National Institutes of Health, Bethesda, USA)
  • Massoud MAHMOUDIAN (Iran University of Medical Sciences, Tehran, Iran)
  • Tadaaki MANO (Gifu University of Medical Science, Japan)
  • Luis Gabriel NAVAR (Tulane University School of Medicine, New Orleans, USA)
  • Hitoo NISHINO (Nagoya City University, Japan)
  • Ole H. PETERSEN (Cardiff University, UK)
  • Ulrich POHL (German Centre for Cardiovascular Research and Ludwig-Maximilians-University, Planegg, Germany)
  • Andrej A. ROMANOVSKY (University of Arizona, USA)
  • Anwar Ali SIDDIQUI (Aga Khan University, Karachi, Pakistan)
  • Csaba SZABÓ (University of Fribourg, Switzerland)
  • Eric VICAUT (Université de Paris, UMRS 942 INSERM, France)
  • Nico WESTERHOF (Vrije Universiteit Amsterdam, The Netherlands)

 

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2021  
Web of Science  
Total Cites
WoS
330
Journal Impact Factor 1,697
Rank by Impact Factor

Physiology 73/81

Impact Factor
without
Journal Self Cites
1,697
5 Year
Impact Factor
1,806
Journal Citation Indicator 0,47
Rank by Journal Citation Indicator

Physiology 69/86

Scimago  
Scimago
H-index
31
Scimago
Journal Rank
0,32
Scimago Quartile Score Medicine (miscellaneous) (Q3)
Physiology (medical) (Q3)
Scopus  
Scopus
Cite Score
2,7
Scopus
CIte Score Rank
Physiology (medical) 69/101 (Q3)
Scopus
SNIP
0,591

 

2020  
Total Cites 245
WoS
Journal
Impact Factor
2,090
Rank by Physiology 62/81 (Q4)
Impact Factor  
Impact Factor 1,866
without
Journal Self Cites
5 Year 1,703
Impact Factor
Journal  0,51
Citation Indicator  
Rank by Journal  Physiology 67/84 (Q4)
Citation Indicator   
Citable 42
Items
Total 42
Articles
Total 0
Reviews
Scimago 29
H-index
Scimago 0,417
Journal Rank
Scimago Physiology (medical) Q3
Quartile Score  
Scopus 270/1140=1,9
Scite Score  
Scopus Physiology (medical) 71/98 (Q3)
Scite Score Rank  
Scopus 0,528
SNIP  
Days from  172
submission  
to acceptance  
Days from  106
acceptance  
to publication  

2019  
Total Cites
WoS
137
Impact Factor 1,410
Impact Factor
without
Journal Self Cites
1,361
5 Year
Impact Factor
1,221
Immediacy
Index
0,294
Citable
Items
34
Total
Articles
33
Total
Reviews
1
Cited
Half-Life
2,1
Citing
Half-Life
9,3
Eigenfactor
Score
0,00028
Article Influence
Score
0,215
% Articles
in
Citable Items
97,06
Normalized
Eigenfactor
0,03445
Average
IF
Percentile
12,963
Scimago
H-index
27
Scimago
Journal Rank
0,267
Scopus
Scite Score
235/157=1,5
Scopus
Scite Score Rank
Physiology (medical) 73/99 (Q3)
Scopus
SNIP
0,38

 

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