Successful training not only must involve overload but also must avoid the combination of excessive overload plus inadequate recovery. Athletes can experience short-term performance decrement without severe psychological or lasting other negative symptoms. This functional overreaching will eventually lead to an improvement in performance after recovery. When athletes do not sufficiently respect the balance between training and recovery, nonfunctional overreaching (NFOR) can occur. The distinction between NFOR and overtraining syndrome (OTS) is very difficult and will depend on the clinical outcome and exclusion diagnosis. The athlete will often show the same clinical, hormonal, and other signs and symptoms. A keyword in the recognition of OTS might be "prolonged maladaptation" not only of the athlete but also of several biological, neurochemical, and hormonal regulation mechanisms. It is generally thought that symptoms of OTS, such as fatigue, performance decline, and mood disturbances, are more severe than those of NFOR. However, there is no scientific evidence to either confirm or refute this suggestion. One approach to understanding the etiology of OTS involves the exclusion of organic diseases or infections and factors such as dietary caloric restriction (negative energy balance) and insufficient carbohydrate and/or protein intake, iron deficiency, magnesium deficiency, allergies, and others together with identification of initiating events or triggers. In this article, we provide the recent status of possible markers for the detection of OTS. Currently, several markers (hormones, performance tests, psychological tests, and biochemical and immune markers) are used, but none of them meet all the criteria to make their use generally accepted.

Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine.

High-intensity interval training (HIT), in a variety of forms, is today one of the most effective means of improving cardiorespiratory and metabolic function and, in turn, the physical performance of athletes. HIT involves repeated short-to-long bouts of rather high-intensity exercise interspersed with recovery periods. For team and racquet sport players, the inclusion of sprints and all-out efforts into HIT programmes has also been shown to be an effective practice. It is believed that an optimal stimulus to elicit both maximal cardiovascular and peripheral adaptations is one where athletes spend at least several minutes per session in their 'red zone,' which generally means reaching at least 90% of their maximal oxygen uptake (VO2max). While use of HIT is not the only approach to improve physiological parameters and performance, there has been a growth in interest by the sport science community for characterizing training protocols that allow athletes to maintain long periods of time above 90% of VO2max (T@VO2max). In addition to T@VO2max, other physiological variables should also be considered to fully characterize the training stimulus when programming HIT, including cardiovascular work, anaerobic glycolytic energy contribution and acute neuromuscular load and musculoskeletal strain. Prescription for HIT consists of the manipulation of up to nine variables, which include the work interval intensity and duration, relief interval intensity and duration, exercise modality, number of repetitions, number of series, as well as the between-series recovery duration and intensity. The manipulation of any of these variables can affect the acute physiological responses to HIT. This article is Part I of a subsequent II-part review and will discuss the different aspects of HIT programming, from work/relief interval manipulation to the selection of exercise mode, using different examples of training cycles from different sports, with continued reference to T@VO2max and cardiovascular responses. Additional programming and periodization considerations will also be discussed with respect to other variables such as anaerobic glycolytic system contribution (as inferred from blood lactate accumulation), neuromuscular load and musculoskeletal strain (Part II).

High-intensity interval training, solutions to the programming puzzle: Part I: cardiopulmonary emphasis.

There are 3 distinct yet closely integrated processes that operate together to satisfy the energy requirements of muscle. The anaerobic energy system is divided into alactic and lactic components, referring to the processes involved in the splitting of the stored phosphagens, ATP and phosphocreatine (PCr), and the nonaerobic breakdown of carbohydrate to lactic acid through glycolysis. The aerobic energy system refers to the combustion of carbohydrates and fats in the presence of oxygen. The anaerobic pathways are capable of regenerating ATP at high rates yet are limited by the amount of energy that can be released in a single bout of intense exercise. In contrast, the aerobic system has an enormous capacity yet is somewhat hampered in its ability to delivery energy quickly. The focus of this review is on the interaction and relative contribution of the energy systems during single bouts of maximal exercise. A particular emphasis has been placed on the role of the aerobic energy system during high intensity exercise. Attempts to depict the interaction and relative contribution of the energy systems during maximal exercise first appeared in the 1960s and 1970s. While insightful at the time, these representations were based on calculations of anaerobic energy release that now appear questionable. Given repeated reproduction over the years, these early attempts have lead to 2 common misconceptions in the exercise science and coaching professions. First, that the energy systems respond to the demands of intense exercise in an almost sequential manner, and secondly, that the aerobic system responds slowly to these energy demands, thereby playing little role in determining performance over short durations. More recent research suggests that energy is derived from each of the energy-producing pathways during almost all exercise activities. The duration of maximal exercise at which equal contributions are derived from the anaerobic and aerobic energy systems appears to occur between 1 to 2 minutes and most probably around 75 seconds, a time that is considerably earlier than has traditionally been suggested.

Energy System Interaction and Relative Contribution During Maximal Exercise

Exercise-induced arterial hypoxemia (EIAH) at or near sea level is now recognized to occur in a significant number of fit, healthy subjects of both genders and of varying ages. Our review aims to d...

Exercise-induced arterial hypoxemia

The purpose of this study was to evaluate the physical demands of English Football Association (FA) Premier League soccer of three different positional classifications (defender, midfielder and striker). Computerised time-motion video-analysis using the Bloomfield Movement Classification was undertaken on the purposeful movement (PM) performed by 55 players. Recognition of PM had a good inter-tester reliability strength of agreement (κ = 0.7277). Players spent 40.6 ± 10.0% of the match performing PM. Position had a significant influence on %PM time spent sprinting, running, shuffling, skipping and standing still (p 0.05). Players spent 48.7 ± 9.2% of PM time moving in a directly forward direction, 20.6 ± 6.8% not moving in any direction and the remainder of PM time moving backward, lateral, diagonal and arced directions. The players performed the equivalent of 726 ± 203 turns during the match; 609 ± 193 of these being of 0° to 90° to the left or right. Players were involved in the equivalent of 111 ± 77 on the ball movement activities per match with no significant differences between the positions for total involvement in on the ball activity (p > 0.05). This study has provided an indication of the different physical demands of different playing positions in FA Premier League match-play through assessment of movements performed by players.

Physical demands of different positions in FA Premier League soccer

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.

/pdf/monitoring-athlete-training-loads-consensus-statement-3fhlq6hr7y.pdf

Monitoring Athlete Training Loads: Consensus Statement.

The activity profi les of highly trained young soc- cer players were examined in relation to age, playing position and physical capacity. Time- motion analyses (global positioning system) were performed on 77 players (U13 - U18; fullbacks (FB), centre-backs (CB), midfi elders (MD), wide midfi elders (W), second strikers (2 nd S) and strik- ers (S)) during 42 international club games. Total distance covered (TD) and very high-intensity activities (VHIA; > 16.1 km · h − 1 ) were computed during 186 entire player-matches. Physical capac- ity was assessed via fi eld test measures (e. g., peak running speed during an incremental fi eld test, V Vam-eval ). Match running performance showed an increasing trend with age ( P < 0.001, partial

Match running performance and fitness in youth soccer.

Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players.

Ninety-one percent (n=182) of the female members of South Australian representative squads in 14 sports volunteered to act as subjects. Twenty-seven percent of them had represented Australia. The underwater weighing method together with the measurement of residual volume (RV) by helium dilution were used to determine body density (BD); the percent body fat (% BF) was then computed according to Siri. A stepwise multiple regression analysis yielded a correlation coefficient (R) of 0.863 between the criterion (BD) and the best weighted sum of predictors (anthropometric variables): BD (g·cm−3)=1.14075−0.04959 (log10 ∑ triceps, subscapular, supraspinale and calf skinfolds in mm)+0.00044 (age in decimal years)−0.000612 (waist girth in cm)+0.000284 (height in cm)−0.000505 (gluteal girth in cm)+0.000331 (breast girth in cm). Only those predictors which resulted in a statistically significant increase inR (p⩽0.05) were included. The standard error of estimate of 0.00597 g · cm−3 was equivalent to 2.7% BF at the mean. This equation was shown to be largely population specific. There was a range of 7.6–35.8% of BF and the overall mean of 18.5% was significantly lower (p<0.001) than that of 23.4% obtained on a moderately active reference sample of similar age (n=135). If group sizes of only one or two are regarded as too small for meaningful comparison, then the lowest mean of 13.5% was achieved by the long-distance runners (n=14). The highest averages were registered by the heavyweight rowers (24.2%;n=7) and soccer players (22.0%;n=11). The overall average for games players (n=107) was 19.4%.

/pdf/relative-body-fat-and-anthropometric-prediction-of-body-4qquapkbd1.pdf

Relative body fat and anthropometric prediction of body density of female athletes.

The aims of the current study were to examine the magnitude of between-GPS-models differences in commonly reported running-based measures in football, examine between-units variability, and assess the effect of software updates on these measures. Fifty identical-brand GPS units (15 SPI-proX and 35 SPIproX2, 15 Hz, GPSports, Canberra, Australia) were attached to a custom-made plastic sled towed by a player performing simulated match running activities. GPS data collected during training sessions over 4 wk from 4 professional football players (N = 53 files) were also analyzed before and after 2 manufacturer-supplied software updates. There were substantial differences between the different models (eg, standardized difference for the number of acceleration >4 m/s2 = 2.1; 90% confidence limits [1.4, 2.7], with 100% chance of a true difference). Between-units variations ranged from 1% (maximal speed) to 56% (number of deceleration >4 m/s2). Some GPS units measured 2-6 times more acceleration/deceleration occurrences than others. Software updates did not substantially affect the distance covered at different speeds or peak speed reached, but 1 of the updates led to large and small decreases in the occurrence of accelerations (-1.24; -1.32, -1.15) and decelerations (-0.45; -0.48, -0.41), respectively. Practitioners are advised to apply care when comparing data collected with different models or units or when updating their software. The metrics of accelerations and decelerations show the most variability in GPS monitoring and must be interpreted cautiously.

Pitre C. Bourdon

Papers

Monitoring Athlete Training Loads: Consensus Statement.

Match running performance and fitness in youth soccer.

Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players.

Relative body fat and anthropometric prediction of body density of female athletes.

Monitoring accelerations with GPS in football: time to slow down?