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Evaluating the effects of oral contraceptive use on biomarkers and body composition during a competitive season in collegiate female soccer players. [Dataset]

Swinton, Paul

Authors



Contributors

Brittany N. Bozzini
Data Collector

Bridget A. McFadden
Data Collector

Kirsty J. Elliott-Sale
Data Collector

Shawn M. Arent
Data Collector

Abstract

High training demands throughout the competitive season in female collegiate soccer players have been shown to induce changes in biomarkers indicative of stress, inflammation, and reproduction, which may be exacerbated in athletes using oral contraceptives (OCs). The purpose of this study was to compare biomarkers and body composition between OC-using and non-using (CON) female soccer players throught a competative season. The file accompaning this record presents graphical output of the Bayesian hierarchical generalized linear models fitted to the biomarker data.

Citation

BOZZINI, B.N., MCFADDEN, B.A., ELLIOTT-SALE, K.J., SWINTON, P.A. and ARENT, S.M. [2021]. Evaluating the effects of oral contraceptive use on biomarkers and body composition during a competitive season in collegiate female soccer players. [Dataset]. Hosted on Figshare [online]. Available from: https://doi.org/10.6084/m9.figshare.12996794

Acceptance Date Apr 26, 2021
Online Publication Date Apr 26, 2021
Publication Date Apr 26, 2021
Deposit Date Apr 29, 2021
Publicly Available Date Apr 29, 2021
Publisher American Physiological Society
DOI https://doi.org/10.6084/m9.figshare.12996794
Keywords Female athletes; Hormonal contraceptives; Training loads; Performance
Public URL https://rgu-repository.worktribe.com/output/1323824
Related Public URLs https://rgu-repository.worktribe.com/output/1323802
Type of Data Supplementary figures.
Collection Date Apr 26, 2021
Collection Method Female collegiate soccer players were monitored throughout a competitive fall season to determine the effects of OC use on body composition and biomarkers indicative of stress, inflammation, reproduction, anabolism, metabolism, and hematological status. Prior to the start of pre-season, players underwent maximal performance testing that was used to determine their endurance and power characteristics as well as to individualize each athlete’s Polar TeamPro monitor. The Polar TeamPro system utilized GPS, accelerometry, and HR monitoring technology to determine training load (TL) and exercise energy expenditure (EEE) for all team training sessions, practices, and games. Additionally, body composition and biomarkers assessments were performed prior to pre-season as well as on weeks 2, 4, 8, 12, and immediately post-season. Female collegiate soccer players (N=30) were monitored throughout the course of the competitive season. Players were stratified into two groups: oral contraceptive (OC: n=6; Mean ±SD: age=19±1yr; weight= 67.6±3.0 kg; height= 168.4±4.4 cm) and control (CON: n=17; age=19±1yr; weight= 66.0±8.0 kg; height= 168.2±6.5 cm) based on their reported OC use. OC usage was determined by a Menstrual Status Questionnaire completed prior to the start of pre-season, and was also repeated post-season for confirmation of OC status. Prior to the start of pre-season and upon completion of the competitive season, players underwent a battery of performance tests and body composition assessments. All pre- and post-season testing sessions, as well as blood draws, occurred within a one-week period. Prior to the start of season, players reported to the lab ≥2 hours fasted and having refrained from exercise in the preceding 12-hours. Blood draws were performed prior to pre-season, on weeks 2 (end of pre-season), 4, 8, & 12 of the season, and post-season. Athletes reported to the lab between 0700 and 0900h and were instructed to arrive in an euhydrated state following an overnight fast. The purpose of the statistical analysis was to model the time series nature of biomarker and body composition data and assess the extent to which values changed across the season for both OC and CON groups. To conduct the analyses, hierarchical generalized linear models (HGLMs) were fitted within a Bayesian framework. HGLMs accounted for structure in the data and were fitted to smooth the time series data, identifying the underlying shape of the physiological signal. A more detailed description of methods used in this study can be found in section 2 of the published article (https://doi.org/10.1152/japplphysiol.00818.2020).

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