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Open access

Pitre C. Bourdon, Marco Cardinale, Warren Gregson and N. Timothy Cable

Open access

Maximilian Pelka, Alexander Ferrauti, Tim Meyer, Mark Pfeiffer and Michael Kellmann

A recovery process with optimal prerequisites that is interrupted is termed disrupted recovery. Whether this process has an influence on performance-related factors needs to be investigated. Therefore, the aim of this study was to examine how a short disturbance of a recovery phase is assessed and whether subsequent repeated-sprint performance is affected by it. A quasi-experimental 2 × 2-factor crossover design with 34 sport-science undergraduate students (age 20.3 ± 2.1 y) was applied. Factors were the type of intervention (power nap vs systematic breathing; between-subjects) and the experimental condition (disturbed vs nondisturbed break; within-subject). Repeated-sprint performance was measured through 6 × 4-s sprint protocols (with 20-s breaks) before and after a 25-min recovery break on 2 test days. Subjective evaluation of the interventions was measured through the Short Recovery and Stress Scale and a manipulation check assessing whether participants experienced the recovery phase as efficacious and pleasant. Regarding the objective data, no significant difference between sprint performances in terms of average peak velocity (m/s) on the treadmill was found. The manipulation check revealed that disturbed conditions were rated significantly lower than regular conditions in terms of appreciation, t 31 = 3.09, P = .01. Short disturbances of recovery do not seem to affect subsequent performance; nevertheless, participants assessed disturbed conditions more negatively than regular conditions. In essence, the findings indicate a negligible role of short interruptions on an objective level. Subjectively, they affected the performance-related assessment of the participants and should be treated with caution.

Open access

Stephen Seiler and Øystein Sylta

The purpose of this study was to compare physiological responses and perceived exertion among well-trained cyclists (n = 63) performing 3 different high-intensity interval-training (HIIT) prescriptions differing in work-bout duration and accumulated duration but all prescribed with maximal session effort. Subjects (male, mean ± SD 38 ± 8 y, VO2peak 62 ± 6 mL · kg–1 · min–1) completed up to 24 HIIT sessions over 12 wk as part of a training-intervention study. Sessions were prescribed as 4 × 16, 4 × 8, or 4 × 4 min with 2-min recovery periods (8 sessions of each prescription, balanced over time). Power output, HR, and RPE were collected during and after each work bout. Session RPE was reported after each session. Blood lactate samples were collected throughout the 12 wk. Physiological and perceptual responses during >1400 training sessions were analyzed. HIIT sessions were performed at 95% ± 5%, 106% ± 5%, and 117% ± 6% of 40-min time-trial power during 4 × 16-, 4 × 8-, and 4 × 4-min sessions, respectively, with peak HR in each work bout averaging 89% ± 2%, 91% ± 2%, and 94% ± 2% HRpeak. Blood lactate concentrations were 4.7 ± 1.6, 9.2 ± 2.4, and 12.7 ± 2.7 mmol/L. Despite the common prescription of maximal session effort, RPE and sRPE increased with decreasing accumulated work duration (AWD), tracking relative HR. Only 8% of 4 × 16-min sessions reached RPE 19–20, vs 61% of 4 × 4-min sessions. The authors conclude that within the HIIT duration range, performing at “maximal session effort” over a reduced AWD is associated with higher perceived exertion both acutely and postexercise. This may have important implications for HIIT prescription choices.

Open access

Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust and Warren Gregson

Purpose:

To determine the sensitivity of a range of potential fatigue measures to daily training load accumulated over the previous 2, 3, and 4 d during a short in-season competitive period in elite senior soccer players (N = 10).

Methods:

Total highspeed-running distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), countermovement-jump height (CMJ), submaximal heart rate (HRex), postexercise heart-rate recovery (HRR), and heart-rate variability (HRV: Ln rMSSD) were analyzed during an in-season competitive period (17 d). General linear models were used to evaluate the influence of 2-, 3-, and 4-d total high-speed-running-distance accumulation on fatigue measures.

Results:

Fluctuations in perceived ratings of fatigue were correlated with fluctuations in total high-speed-running-distance accumulation covered on the previous 2 d (r = –.31; small), 3 d (r = –.42; moderate), and 4 d (r = –.28; small) (P < .05). Changes in HRex (r = .28; small; P = .02) were correlated with changes in 4-d total high-speed-running-distance accumulation only. Correlations between variability in muscle soreness, sleep quality, CMJ, HRR%, and HRV and total high-speed-running distance were negligible and not statistically significant for all accumulation training loads.

Conclusions:

Perceived ratings of fatigue and HRex were sensitive to fluctuations in acute total high-speed-running-distance accumulation, although sensitivity was not systematically influenced by the number of previous days over which the training load was accumulated. The present findings indicate that the sensitivity of morning-measured fatigue variables to changes in training load is generally not improved when compared with training loads beyond the previous day’s training.

Open access

Andrew Murray

While historically adolescents were removed from their parents to prepare to become warriors, this process repeats itself in modern times but with the outcome being athletic performance. This review considers the process of developing athletes and managing load against the backdrop of differing approaches of conserving and maximizing the talent available. It acknowledges the typical training “dose” that adolescent athletes receive across a number of sports and the typical “response” when it is excessive or not managed appropriately. It also examines the best approaches to quantifying load and injury risk, acknowledging the relative strengths and weaknesses of subjective and objective approaches. Making evidence-based decisions is emphasized, while the appropriate monitoring techniques are determined by both the sporting context and individual situation. Ultimately a systematic approach to training-load monitoring is recommended for adolescent athletes to both maximize their athletic development and allow an opportunity for learning, reflection, and enhancement of performance knowledge of coaches and practitioners.

Open access

William A. Sands, Ashley A. Kavanaugh, Steven R. Murray, Jeni R. McNeal and Monèm Jemni

Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in “big data” analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.

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.

Open access

Robin T. Thorpe, Greg Atkinson, Barry Drust and Warren Gregson

The increase in competition demands in elite team sports over recent years has prompted much attention from researchers and practitioners to the monitoring of adaptation and fatigue in athletes. Monitoring fatigue and gaining an understanding of athlete status may also provide insights and beneficial information pertaining to player availability, injury, and illness risk. Traditional methods used to quantify recovery and fatigue in team sports, such as maximal physical-performance assessments, may not be feasible to detect variations in fatigue status throughout competitive periods. Faster, simpler, and nonexhaustive tests such as athlete self-report measures, autonomic nervous system response via heart-rate-derived indices, and to a lesser extent, jump protocols may serve as promising tools to quantify and establish fatigue status in elite team-sport athletes. The robust rationalization and precise detection of a meaningful fluctuation in these measures are of paramount importance for practitioners working alongside athletes and coaches on a daily basis. There are various methods for arriving at a minimal clinically important difference, but these have been rarely adopted by sport scientists and practitioners. The implementation of appropriate, reliable, and sensitive measures of fatigue can provide important information to key stakeholders in team-sport environments. Future research is required to investigate the sensitivity of these tools to fundamental indicators such as performance, injury, and illness.

Open access

Carl Foster, Jose A. Rodriguez-Marroyo and Jos J. de Koning

Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.

Open access

Sean Williams, Grant Trewartha, Matthew J. Cross, Simon P.T. Kemp and Keith A. Stokes

Purpose:

Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.

Methods:

Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.

Results:

Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.

Conclusions:

The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.