Research on elite female athletes with disabilities is extremely rare. Therefore, using the Sixteen Personality Factor Questionnaire (Cattell, Cattell, & Cattell, 1993) and Profile of Mood States (Droppleman, Lorr, & McNair, 1992), we examined differences between the top 12 athletes comprising the gold medal winning 2004 USA women’s Paralympic basketball team and 13 athletes attending the selection camp who did not make the team. Multivariate analysis of variance with follow-up tests revealed that athletes who made the Paralympic team scored higher on tough-mindedness (M = 5.7 vs. 4.3) and lower in anxiety (M = 5.6 vs. 7.8). For mood state, the Paralympians scored higher in vigor (M = 19.5 vs. 14.8) and lower in depressed mood (M = 3.9 vs. 6.7) and confusion (M = 5.5 vs. 7.5). The effect sizes were large (e.g., Cohen’s d = 0.91 - 1.69) for all five results.
Jeffrey J. Martin, Laurie A. Malone and James C. Hilyer
James J. Malone, Rocco Di Michele, Ryland Morgans, Darren Burgess, James P. Morton and Barry Drust
To quantify the seasonal training load completed by professional soccer players of the English Premier League.
Thirty players were sampled (using GPS, heart rate, and rating of perceived exertion [RPE]) during the daily training sessions of the 2011–12 preseason and in-season period. Preseason data were analyzed across 6 × 1-wk microcycles. In-season data were analyzed across 6 × 6-wk mesocycle blocks and 3 × 1-wk microcycles at start, midpoint, and end-time points. Data were also analyzed with respect to number of days before a match.
Typical daily training load (ie, total distance, high-speed distance, percent maximal heart rate [%HRmax], RPE load) did not differ during each week of the preseason phase. However, daily total distance covered was 1304 (95% CI 434–2174) m greater in the 1st mesocycle than in the 6th. %HRmax values were also greater (3.3%, 1.3−5.4%) in the 3rd mesocycle than in the first. Furthermore, training load was lower on the day before match (MD-1) than 2 (MD-2) to 5 (MD-5) d before a match, although no difference was apparent between these latter time points.
The authors provide the 1st report of seasonal training load in elite soccer players and observed that periodization of training load was typically confined to MD-1 (regardless of mesocycle), whereas no differences were apparent during MD-2 to MD-5. Future studies should evaluate whether this loading and periodization are facilitative of optimal training adaptations and match-day performance.
James J. Malone, Ric Lovell, Matthew C. Varley and Aaron J. Coutts
Athlete-tracking devices that include global positioning system (GPS) and microelectrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete-tracking devices and to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal, and data-filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision, and software/firmware versions in any published research. In addition, details of inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.
Mohamed S. Fessi, Fayçal Farhat, Alexandre Dellal, James J. Malone and Wassim Moalla
Purpose: To investigate the difference between straight-line (STL) and change-of-direction (COD) intermittent-running exercises in soccer players. Methods: Seventeen male professional soccer players performed the agility T test and 6 intermittent-running exercises: 10 s at 130% of maximal aerobic speed (MAS) alternated with 10 s of rest (10-10), 15 s at 120% of MAS alternated with 15 s of rest (15-15), and 30 s at 110% of MAS alternated with 30 s of rest (30-30) both in STL and with COD. All exercises were monitored using a global positioning system. Heart rate was measured during exercises, and rating of perceived exertion (RPE) was collected postexercise. The difference (Δ) between covered distance in STL and COD exercises at a similar load was calculated, and relationships between T test and Δ distance were analyzed. Results: COD intermittent exercises showed a significantly decreased distance covered and an increase in the number of accelerations, peak heart rate, and RPE compared with STL intermittent exercises at a similar load. High relationships were observed between T-test performance and Δ distance in 10-10 (r = .72, P < .01) and 15-15 (r = .77, P < .01), whereas no significant relationships were observed between T-test performance and Δ distance in 30-30 (r = −.37, P = .2). Conclusion: Intermittent COD exercises were associated with higher acceleration, peak heart rate, and RPE than STL during 10-10 and 15-15 exercises. The ability to rapidly change direction is crucial to perform intense sport-specific running in professional soccer players.
Matthew C. Varley, Arne Jaspers, Werner F. Helsen and James J. Malone
Sprints and accelerations are popular performance indicators in applied sport. The methods used to define these efforts using athlete-tracking technology could affect the number of efforts reported. This study aimed to determine the influence of different techniques and settings for detecting high-intensity efforts using global positioning system (GPS) data.
Velocity and acceleration data from a professional soccer match were recorded via 10-Hz GPS. Velocity data were filtered using either a median or an exponential filter. Acceleration data were derived from velocity data over a 0.2-s time interval (with and without an exponential filter applied) and a 0.3-second time interval. High-speed-running (≥4.17 m/s2), sprint (≥7.00 m/s2), and acceleration (≥2.78 m/s2) efforts were then identified using minimum-effort durations (0.1–0.9 s) to assess differences in the total number of efforts reported.
Different velocity-filtering methods resulted in small to moderate differences (effect size [ES] 0.28–1.09) in the number of high-speed-running and sprint efforts detected when minimum duration was <0.5 s and small to very large differences (ES –5.69 to 0.26) in the number of accelerations when minimum duration was <0.7 s. There was an exponential decline in the number of all efforts as minimum duration increased, regardless of filtering method, with the largest declines in acceleration efforts.
Filtering techniques and minimum durations substantially affect the number of high-speed-running, sprint, and acceleration efforts detected with GPS. Changes to how high-intensity efforts are defined affect reported data. Therefore, consistency in data processing is advised.
James J. Malone, Arne Jaspers, Werner Helsen, Brenda Merks, Wouter G.P. Frencken and Michel S. Brink
The purpose of this investigation was to (1) quantify the training load practices of a professional soccer goalkeeper and (2) investigate the relationship between the training load observed and the subsequent self-reported wellness response. One male goalkeeper playing for a team in the top league of the Netherlands participated in this case study. Training load data were collected across a full season using a global positioning system device and session-RPE (rating of perceived exertion). Data were assessed in relation to the number of days to a match (MD− and MD+). In addition, self-reported wellness response was assessed using a questionnaire. Duration, total distance, average speed, PlayerLoad™, and load (derived from session-RPE) were highest on MD. The lowest values for duration, total distance, and PlayerLoad™ were observed on MD−1 and MD+1. Total wellness scores were highest on MD and MD−3 and were lowest on MD+1 and MD−4. Small to moderate correlations between training load measures (duration, total distance covered, high deceleration efforts, and load) and the self-reported wellness response scores were found. This exploratory case study provides novel data about the physical load undertaken by a goalkeeper during 1 competitive season. The data suggest that there are small to moderate relationships between training load indicators and self-reported wellness response. This weak relation indicates that the association is not meaningful. This may be due to the lack of position-specific training load parameters that practitioners can currently measure in the applied context.
James M. Rhodes, Barry S. Mason, Bertrand Perrat, Martin J. Smith, Laurie A. Malone and Victoria L. Goosey-Tolfrey
To quantify the activity profiles of elite wheelchair rugby (WCR) players and establish classification-specific arbitrary speed zones. In addition, indicators of fatigue during full matches were explored.
Seventy-five elite WCR players from 11 national teams were monitored using a radio-frequency-based, indoor tracking system across 2 international tournaments. Players who participated in complete quarters (n = 75) and full matches (n = 25) were included and grouped by their International Wheelchair Rugby Federation functional classification: groups I (0.5), II (1.0–1.5), III (2.0–2.5), and IV (3.0–3.5).
During a typical quarter, significant increases in total distance (m), relative distance (m/min), and mean speed (m/s) were associated with an increase in classification group (P < .001), with the exception of groups III and IV. However, group IV players achieved significantly higher peak speeds (3.82 ± 0.31 m/s) than groups I (2.99 ± 0.28 m/s), II (3.44 ± 0.26 m/s), and III (3.67 ± 0.32 m/s). Groups I and II differed significantly in match intensity during very-low/low-speed zones and the number of high-intensity activities in comparison with groups III and IV (P < .001). Full-match analysis revealed that activity profiles did not differ significantly between quarters.
Notable differences in the volume of activity were displayed across the functional classification groups. However, the specific on-court requirements of defensive (I and II) and offensive (III and IV) match roles appeared to influence the intensity of match activities, and consequently training prescription should be structured accordingly.