Dose-Response Relationships between Training Load Measures and Physical Fitness in Professional Soccer Players.
Saeid YounesiAlireza RabbaniFilipe Manuel ClementeRui SilvaHugo SarmentoAntónio José Barata FigueiredoPublished in: International journal of environmental research and public health (2021)
The aim of this cohort study was two-fold: (i) to analyze within-group changes of final velocity in a 30-15 intermittent fitness test (VIFT), final velocity in a Vameval test (Vvameval), 20-m sprint and countermovement jump (CMJ); (ii) to explore the relationships between VIFT and Vvameval outcomes and their changes with internal and external loads. Twenty-two professional soccer players (mean ± SD; age 27.2 ± 3.4 years, height 174.2 ± 3.6 cm, body mass 69.1 ± 6.4 kg, and body fat 10.4 ± 4.1%, 3.1 ± 1.5 years in the club) participated in this study. External and internal loads were obtained using global positioning system, heart rate and rate of perceived effort (sRPE) after each training session. Players were assessed in CMJ, 20-m sprint, Vameval and 30-15 intermittent fitness test, before and after the observed period. Very large relationships were observed between VIFT and Vameval for pre- (r = 0.76), post (r = 0.80) and pooled-data (r = 0.81). Vvameval showed less sensitivity (-22.4%, [-45.0 to 9.4]), ES -0.45 [-1.05 to 0.16]) than VIFT. ∆VIFT had unclear associations with all sRPE, but had moderate correlations with objective internal and external measures, while, ∆Vvameval varied between large and very large relationships with all sRPE, but had unclear associations with all other selected training loads. Objective internal and external loads may be used to track aerobic power related changes from VIFT.
Keyphrases
- high intensity
- heart rate
- physical activity
- resistance training
- heart rate variability
- blood pressure
- virtual reality
- body composition
- mental health
- body mass index
- depressive symptoms
- blood flow
- social support
- electronic health record
- clinical trial
- metabolic syndrome
- adipose tissue
- weight loss
- data analysis
- open label
- deep learning
- double blind