Bioimpedance Vector Patterns Changes in Response to Swimming Training: An Ecological Approach

Joana F. Reis, Catarina N. Matias, Francesco Campa, José P. Morgado, Paulo Franco, Pedro Quaresma, Nuno Almeida, Dalia Curto, Stefania Toselli and Cristina P. Monteiro
Additional contact information
Joana F. Reis: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal
Catarina N. Matias: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal
Francesco Campa: Department for Life Quality Studies, University of Bologna, 47921 Rimini, Italy
José P. Morgado: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal
Paulo Franco: Federação Portuguesa de Natação, 1500-210 Lisbon, Portugal
Pedro Quaresma: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal
Nuno Almeida: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal
Dalia Curto: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal
Stefania Toselli: Department of Biomedical and Neuromotor Science, University of Bologna, 40125 Bologna, Italy
Cristina P. Monteiro: Faculdade de Motricidade Humana, Laboratory of Physiology and Biochemistry of Exercise, Universidade de Lisboa, 1499-002 Cruz Quebrada-Dafundo, Portugal

IJERPH, 2020, vol. 17, issue 13, 1-10

Abstract: Background and aim: Monitoring bioelectric phase angle (PhA) provides important information on the health and the condition of the athlete. Together with the vector length, PhA constitutes the bioimpedance vector analysis (BIVA) patterns, and their joint interpretation exceeds the limits of the evaluation of the PhA alone. The present investigation aimed to monitor changes in the BIVA patterns during a training macrocycle in swimmers, trying to ascertain if these parameters are sensitive to training load changes across a 13-week training period. Methods: Twelve national and international level swimmers (four females; eight males; 20.9 ± 1.9 years; with a competitive swimming background of 11.3 ± 1.8 years; undertaking 16–20 h of pool training and 4–5 h of dry-land training per week and 822.0 ± 59.0 International Swimming Federation (FINA) points) were evaluated for resistance (R) and reactance (Xc) using a single frequency phase sensitive bioimpedance device at the beginning of the macrocycle (M1), just before the beginning of the taper period (M2), and just before the main competition of the macrocycle (M3). At the three-time assessment points, swimmers also performed a 50 m all-out first stroke sprint with track start (T50 m) while time was recorded. Results: The results of the Hotelling T 2 test showed a significant vector displacement due to simultaneous R and Xc changes ( p < 0.001), where shifting from top to bottom along the major axis of the R-Xc graph from M1 to M2 was observed. From M2 to M3, a vector displacement up and left along the minor axis of the tolerance ellipses resulted in an increase in PhA ( p < 0.01). The results suggest a gain in fluid with a decrease in cellular density from M1 to M2 due to decrements in R and Xc. Nevertheless, the reduced training load characterizing taper seemed to allow for an increase in PhA and, most importantly, an increase of Xc, thus demonstrating improved cellular health and physical condition, which was concomitant with a significant increase in the T50 m performance ( p < 0.01). Conclusions: PhA, obtained by bioelectrical R and Xc, can be useful in monitoring the condition of swimmers preparing for competition. Monitoring BIVA patterns allows for an ecological approach to the swimmers’ health and condition assessment without resorting to equations to predict the related body composition variables.

Keywords: BIVA; body composition; phase angle; R-Xc graph; vector length (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/13/4851/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/13/4851/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:13:p:4851-:d:380752

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().