= wheelchair tennis; WR = wheelchair rugby; HAND = handcycling; COMP = competition situation; TRA = training situation; HR = heart rate; TRIMP = training impulse; RPE = rating of perceived exertion; EL = external load; IL = internal load. Discussion The objective was to study the main load-monitoring methods
Mário A.M. Simim, Marco Túlio de Mello, Bruno V.C. Silva, Dayane F. Rodrigues, João Paulo P. Rosa, Bruno Pena Couto and Andressa da Silva
Samuel J. Callaghan, Robert G. Lockie, Walter Yu, Warren A. Andrews, Robert F. Chipchase and Sophia Nimphius
means of assessing pace bowling loads, regardless of competition level, is counting the total number of delivers bowled. 1 – 3 This method of load monitoring also forms the basis of current guidelines for junior pace bowlers (<19 y) within Australia. 4 This external measure of load implies that the
Darren J. Burgess
Research describing load-monitoring techniques for team sport is plentiful. Much of this research is conducted retrospectively and typically involves recreational or semielite teams. Load-monitoring research conducted on professional team sports is largely observational. Challenges exist for the practitioner in implementing peer-reviewed research into the applied setting. These challenges include match scheduling, player adherence, manager/coach buy-in, sport traditions, and staff availability. External-load monitoring often attracts questions surrounding technology reliability and validity, while internal-load monitoring makes some assumptions about player adherence, as well as having some uncertainty around the impact these measures have on player performance This commentary outlines examples of load-monitoring research, discusses the issues associated with the application of this research in an elite team-sport setting, and suggests practical adjustments to the existing research where necessary.
Martin Buchheit and Ben Michael Simpson
With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players’ training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).
Filipe M. Clemente, Ana F. Silva, Cain C.T. Clark, Daniele Conte, João Ribeiro, Bruno Mendes and Ricardo Lima
October November December January February March April May Weeks, n 4 5 4 4 4 5 4 3 Sessions, n 31 31 20 29 21 31 27 13 Matches, n 2 5 5 5 6 6 6 2 Training Load Monitoring The 10-point Foster scale 14 was used to monitor the perceived effort of the players after each training session. The ratings were
Blake D. McLean, Donald Strack, Jennifer Russell and Aaron J. Coutts
-based training-load monitoring in elite team sports . Int J Sports Physiol Perform . 2017 ; 12 ( Suppl 2 ): S2136 – S2141 . PubMed ID: 27967277 doi:10.1123/ijspp.2016-0608 10.1123/ijspp.2016-0608 27967277 24. National Basketball Association . Collective Bargaining Agreement – January 19, 2017 . 2017 ; https
Christopher M. Young, Paul B. Gastin, Nick Sanders, Luke Mackey and Dan B. Dwyer
The activity profile of competition and training in elite netball has not been comprehensively reported in the literature.
To measure and analyze player load in elite netballers during matches and training sessions. The primary research question was, How does player load vary between playing positions in a match and between matches and training sessions?
Various measures of player load were recorded in 12 elite professional netballers with a mean ± SD age of 26 ± 4.9 y and height of 183.2 ± 8.7 cm. Player load was assessed using a published method that uses accelerometry. Load was represented as total load in arbitrary units (au), playing intensity (au/min), and relative time spent in each of 4 playing intensity zones (low, low to moderate, moderate, and high). Data from 15 games and up to 17 training sessions were analyzed for each player.
Player load in matches for the goal-based positions (goal shooter, goal keeper, and goal defense) tended to be lower than the attacking and wing-based positions (goal attack, wing attack, wing defense, and center). The difference was largely due to the amount of time spent in low-intensity activity. Playing intensity of matches was greater than in training sessions; however, the total time spent in moderate- to high-intensity activities was not practically different.
Accelerometry is a valuable method of measuring player load in netball, and the present results provide new information about the activity profile of different playing positions.
Avish P. Sharma, Philo U. Saunders, Laura A. Garvican-Lewis, Brad Clark, Jamie Stanley, Eileen Y. Robertson and Kevin G. Thompson
To determine the effect of training at 2100-m natural altitude on running speed (RS) during training sessions over a range of intensities relevant to middle-distance running performance.
In an observational study, 19 elite middle-distance runners (mean ± SD age 25 ± 5 y, VO2max, 71 ± 5 mL · kg–1 · min–1) completed either 4–6 wk of sea-level training (CON, n = 7) or a 4- to 5-wk natural altitude-training camp living at 2100 m and training at 1400–2700 m (ALT, n = 12) after a period of sea-level training. Each training session was recorded on a GPS watch, and athletes also provided a score for session rating of perceived exertion (sRPE). Training sessions were grouped according to duration and intensity. RS (km/h) and sRPE from matched training sessions completed at sea level and 2100 m were compared within ALT, with sessions completed at sea level in CON describing normal variation.
In ALT, RS was reduced at altitude compared with sea level, with the greatest decrements observed during threshold- and VO2max-intensity sessions (5.8% and 3.6%, respectively). Velocity of low-intensity and race-pace sessions completed at a lower altitude (1400 m) and/or with additional recovery was maintained in ALT, though at a significantly greater sRPE (P = .04 and .05, respectively). There was no change in velocity or sRPE at any intensity in CON.
RS in elite middle-distance athletes is adversely affected at 2100-m natural altitude, with levels of impairment dependent on the intensity of training. Maintenance of RS at certain intensities while training at altitude can result in a higher perceived exertion.
Harry G. Banyard, James J. Tufano, Jose Delgado, Steve W. Thompson and Kazunori Nosaka
Purpose: To compare kinetic and kinematic data from 3 different velocity-based training sessions and a 1-repetition-maximum (1RM)-percent-based training (PBT) session using full-depth, free-weight back squats with maximal concentric effort. Methods: Fifteen strength-trained men performed 4 randomized resistance-training sessions 96 h apart: PBT session involved 5 sets of 5 repetitions using 80% 1RM; load–velocity profile (LVP) session contained 5 sets of 5 repetitions with a load that could be adjusted to achieve a target velocity established from an individualized LVP equation at 80% 1RM; fixed sets 20% velocity loss threshold (FSVL20) session consisted of 5 sets at 80% 1RM, but sets were terminated once the mean velocity (MV) dropped below 20% of the threshold velocity or when 5 repetitions were completed per set; and variable sets 20% velocity loss threshold session comprised 25 repetitions in total, but participants performed as many repetitions in a set as possible until the 20% velocity loss threshold was exceeded. Results: When averaged across all repetitions, MV and peak velocity (PV) were significantly (P < .05) faster during the LVP (MV effect size [ES] = 1.05; PV ES = 1.12) and FSVL20 (MV ES = 0.81; PV ES = 0.98) sessions compared with PBT. Mean time under tension (TUT) and concentric TUT were significantly less during the LVP sessions compared with PBT. The FSVL20 sessions had significantly less repetitions, total TUT, and concentric TUT than PBT. No significant differences were found for all other measurements between any of the sessions. Conclusions: Velocity-based training permits faster velocities and avoids additional unnecessary mechanical stress but maintains similar measures of force and power output compared with strength-oriented PBT in a single training session.
Luka Svilar, Julen Castellano, Igor Jukic and David Casamichana
several load measures is required to describe the load of the 3 playing positions in basketball training sessions. The authors agree with the suggestion by Williams et al 30 that the training load monitoring process may be optimized by selecting and monitoring the most parsimonious set of variables, as