An effective training-monitoring system (TMS) can positively influence performance through monitoring program effectiveness and reducing the risk of illness or injury. 1 However, successfully implementing a TMS can be problematic in elite sport, with issues relating to end-user buy-in and a
Emma C. Neupert, Stewart T. Cotterill, and Simon A. Jobson
Twan ten Haaf, Selma van Staveren, Danilo Iannetta, Bart Roelands, Romain Meeusen, Maria F. Piacentini, Carl Foster, Leo Koenderman, Hein A.M. Daanen, and Jos J. de Koning
monitor performance do not fit in an athlete’s training schedule. Therefore, sport scientists search for training monitoring tools that are easy to perform in training practice and are objective, inexpensive, and not demanding. 3 Reaction time is a measurement that fits these requirements and has been
Maria Francesca Piacentini and Romain Meeusen
This longitudinal case study evaluated the effectiveness of an online training-monitoring system to prevent nonfunctional overreaching (NFOR).
A female master track and field athlete was followed by means of a daily online training diary (www.spartanova.com) and a weekly profile of mood state (POMS). The online diary consists of objective training data and subjective feelings reported on a 10-cm visual analog scale. Furthermore, parameters that quantify and summarize training and adaptation to training were calculated. The novelty consists in the inclusion of a specific measuring parameter tested to detect NFOR (OR score).
During track-season preparation, the athlete was facing some major personal changes, and extratraining stress factors increased. Despite the fact that training load (TL) did not increase, the or score showed a 222% and then a 997% increase compared with baseline. POMS showed a 167% increase in fatigue, a 38% decrease in vigor, a 32% increase in depression scores, and a total mood increase of 22%, with a 1-wk shift compared with the OR score. A 41% decrease in TL restored the OR score and POMS to baseline values within 10 d.
The results demonstrate that immediate feedback obtained by “warning signals” to both athletes and coaches, based on individual baseline data, seems an optimal predictor of FOR/NFOR.
Cruz Hogan, Martyn J. Binnie, Matthew Doyle, Leanne Lester, and Peter Peeling
utility of real-time PO measures as a training monitoring and prescription tool. 20 In recent work from our laboratory, we have utilized such technology to validate an on-water GXT, where stages are incremented by PO. 13 Although this test appears to provide valid and reliable physiological outcomes, 13
Andrea Nicolò, Marco Montini, Michele Girardi, Francesco Felici, Ilenia Bazzucchi, and Massimo Sacchetti
Training monitoring is receiving renewed interest in soccer, with the aim of quantifying the load of training, maximizing performance, and minimizing the risk of injury and illness. 1 However, monitoring team sports is more challenging compared with other sports. A major challenge is how to
Kizzy Antualpa, Marcelo Saldanha Aoki, and Alexandre Moreira
training monitoring during the HT of the rhythmic gymnasts, notably, for internal training load and well-being monitoring, followed by 4 weeks of IT, which, in turn, was followed by a 2-week TP. Training was intensified by the addition of 40% of the HT volume performed by rhythmic gymnasts. The training
Carl Foster, Daniel Boullosa, Michael McGuigan, Andrea Fusco, Cristina Cortis, Blaine E. Arney, Bo Orton, Christopher Dodge, Salvador Jaime, Kim Radtke, Teun van Erp, Jos J. de Koning, Daniel Bok, Jose A. Rodriguez-Marroyo, and John P. Porcari
has recently been reviewed relative to its inherent validity and value for training monitoring. 2 , 3 Historical Background The sRPE method is an adaptation of the rating of perceived exertion (RPE) method pioneered by Sweden Gunnar Borg. 4 – 6 The Borg method began as an attempt to get beyond the HR
Marco Cardinale and Matthew C. Varley
The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost-effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies.
Shaun J. McLaren, Michael Graham, Iain R. Spears, and Matthew Weston
To investigate the sensitivity of differential ratings of perceived exertion (dRPE) as measures of internal load.
Twenty-two male university soccer players performed 2 maximal incremental-exercise protocols (cycle, treadmill) on separate days. Maximal oxygen uptake (V̇O2max), maximal heart rate (HRmax), peak blood lactate concentration (B[La]peak), and the preprotocol-to-postprotocol change in countermovement-jump height (ΔCMJH) were measured for each protocol. Players provided dRPE (CR100) for breathlessness (RPE-B) and leg-muscle exertion (RPE-L) immediately on exercise termination (RPE-B0, RPE-L0) and 30 min postexercise (RPE-B30, RPE-L30). Data were analyzed using magnitude-based inferences.
There were clear between-protocols differences for V̇O2max (cycle 46.5 ± 6.3 vs treadmill 51.0 ± 5.1 mL · kg−1 · min−1, mean difference –9.2%; ±90% confidence limits 3.7%), HRmax (184.7 ± 12.7 vs 196.7 ± 7.8 beats/min, –6.0%; ±1.7%), B[La]peak (9.7 ± 2.1 vs 8.5 ± 2.0 mmol/L, 15%; ±10%), and ΔCMJH (–7.1 ± 4.2 vs 0.6 ± 3.6 cm, –23.2%; ±5.4%). Clear between-protocols differences were recorded for RPE-B0 (78.0 ± 11.7 vs 94.7 ± 9.5 AU, –18.1%; ±4.5%), RPE-L0 (92.6 ± 9.7 vs 81.3 ± 14.1 AU, 15.3%; ±7.6%), RPE-B30 (70 ± 11 vs 82 ± 13 AU, –13.8%; ±7.3%), and RPE-L30 (86 ± 12 vs 65 ± 19 AU, 37%; ±17%). A substantial timing effect was observed for dRPE, with moderate to large reductions in all scores 30 min postexercise compared with scores collected on exercise termination.
dRPE enhance the precision of internal-load measurement and therefore represent a worthwhile addition to training-load-monitoring procedures.
David B. Pyne, Matt Spencer, and Iñigo Mujika
One of the challenges for sports scientists working in football is to balance the needs for routine fitness testing with daily fatigue and well-being monitoring to best manage the physical preparation of players. In this commentary, the authors examine contemporary issues of fitness testing in football to identify ways of improving the value of routine testing and monitoring. A testing program must be well planned and organized to ensure that the results are useful. Different tests can be employed for younger and older players. A rigorous approach to analysis and interpretation of results is desirable, and database management must address both short- and long-term requirements of players, staff, and programs.