Search Results

You are looking at 11 - 13 of 13 items for

  • Author: Marco Cardinale x
  • Refine by Access: All Content x
Clear All Modify Search
Restricted access

Brian Cunniffe, Kevin A. Morgan, Julien S. Baker, Marco Cardinale, and Bruce Davies

This study evaluated the effect of game venue and starting status on precompetitive psychophysiological measures in elite rugby union. Saliva samples were taken from players (starting XV, n = 15, and nonstarters, n = 9) on a control day and 90 min before 4 games played consecutively at home and away venues against local rivals and league leaders. Precompetition psychological states were assessed using the Competitive State Anxiety Inventory−2. The squad recorded 2 wins (home) and 2 losses (away) over the study period. Calculated effect sizes (ESs) showed higher pregame cortisol- (C) and testosterone- (T) difference values before all games than on a baseline control day (ES 0.7−1.5). Similar findings were observed for cognitive and somatic anxiety. Small between-venues C differences were observed in starting XV players (ES 0.2−0.25). Conversely, lower home T- (ES 0.95) and higher away C- (ES 0.6) difference values were observed in nonstarters. Lower T-difference values were apparent in nonstarters (vs starting XV) before home games, providing evidence of a between-groups effect (ES 0.92). Findings show an anticipatory rise in psychophysiological variables before competition. Knowledge of starting status appears a moderating factor in the magnitude of player endocrine response between home and away games.

Restricted access

Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale, and Aki I.T. Salo

Purpose:

An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.

Methods:

Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.

Results:

PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R 2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R 2 = .77; 1.97% standard error of estimate).

Conclusions:

Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.

Open access

Pitre C. Bourdon, Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, Aaron J. Coutts, Darren J. Burgess, Warren Gregson, and N. Timothy Cable

Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on “Monitoring Athlete Training Loads—The Hows and the Whys” was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.