Literature DB >> 33419133

Usefulness of Linear Mixed-Effects Models to Assess the Relationship between Objective and Subjective Internal Load in Team Sports.

Alice Iannaccone1,2, Daniele Conte2, Cristina Cortis1, Andrea Fusco1.   

Abstract

Internal load can be objectively measured by heart rate-based models, such as Edwards' summated heart rate zones, or subjectively by session rating of perceived exertion. The relationship between internal loads assessed via heart rate-based models and session rating of perceived exertion is usually studied through simple correlations, although the Linear Mixed Model could represent a more appropriate statistical procedure to deal with intrasubject variability. This study aimed to compare conventional correlations and the Linear Mixed Model to assess the relationships between objective and subjective measures of internal load in team sports. Thirteen male youth beach handball players (15.9 ± 0.3 years) were monitored (14 training sessions; 7 official matches). Correlation coefficients were used to correlate the objective and subjective internal load. The Linear Mixed Model was used to model the relationship between objective and subjective measures of internal load data by considering each player individual response as random effect. Random intercepts were used and then random slopes were added. The likelihood-ratio test was used to compare statistical models. The correlation coefficient for the overall relationship between the objective and subjective internal data was very large (r = 0.74; ρ = 0.78). The Linear Mixed Model using both random slopes and random intercepts better explained (p < 0.001) the relationship between internal load measures. Researchers are encouraged to apply the Linear Mixed Models rather than correlation to analyze internal load relationships in team sports since it allows for the consideration of the individuality of players.

Entities:  

Keywords:  RPE; beach handball; correlation; heart rate; monitoring; statistical analysis; team sports; training load; youth athletes

Mesh:

Year:  2021        PMID: 33419133      PMCID: PMC7825485          DOI: 10.3390/ijerph18020392

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  34 in total

1.  Mixed-model regression analysis and dealing with interindividual differences.

Authors:  Hans P A Van Dongen; Erik Olofsen; David F Dinges; Greg Maislin
Journal:  Methods Enzymol       Date:  2004       Impact factor: 1.600

Review 2.  On the joys of missing data.

Authors:  Todd D Little; Terrence D Jorgensen; Kyle M Lang; E Whitney G Moore
Journal:  J Pediatr Psychol       Date:  2013-07-08

3.  The validity of the session-RPE method for quantifying training load in water polo.

Authors:  Corrado Lupo; Laura Capranica; Antonio Tessitore
Journal:  Int J Sports Physiol Perform       Date:  2013-11-13       Impact factor: 4.010

4.  Specific physiological and biomechanical performance in elite, sub-elite and in non-elite male team handball players.

Authors:  Herbert Wagner; Philip X Fuchs; Serge P von Duvillard
Journal:  J Sports Med Phys Fitness       Date:  2017-05-30       Impact factor: 1.637

5.  Workload and well-being across games played on consecutive days during in-season phase in basketball players.

Authors:  Daniele Conte; Paulius Kamarauskas; Davide Ferioli; Aaron T Scanlan; Sigitas Kamandulis; Henrikas Paulauskas; Inga Lukonaitienė
Journal:  J Sports Med Phys Fitness       Date:  2020-10-22       Impact factor: 1.637

6.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

7.  Random-effects models for longitudinal data.

Authors:  N M Laird; J H Ware
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

8.  Effects of specific versus cross-training on running performance.

Authors:  C Foster; L L Hector; R Welsh; M Schrager; M A Green; A C Snyder
Journal:  Eur J Appl Physiol Occup Physiol       Date:  1995

9.  Effect of Progressive Fatigue on Session RPE.

Authors:  Andrea Fusco; William Sustercich; Keegan Edgerton; Cristina Cortis; Salvador J Jaime; Richard P Mikat; John P Porcari; Carl Foster
Journal:  J Funct Morphol Kinesiol       Date:  2020-02-17

Review 10.  Monitoring training load to understand fatigue in athletes.

Authors:  Shona L Halson
Journal:  Sports Med       Date:  2014-11       Impact factor: 11.136

View more
  1 in total

1.  Bioelectrical Impedance Vector Analysis of Young Elite Team Handball Players.

Authors:  Andrea Di Credico; Giulia Gaggi; Anastasios Vamvakis; Sofia Serafini; Barbara Ghinassi; Angela Di Baldassarre; Pascal Izzicupo
Journal:  Int J Environ Res Public Health       Date:  2021-12-08       Impact factor: 3.390

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.