Статті в журналах: "Monitoring training load"

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Zeller, Sebastian, Thomas Abel, and Heiko K. Strueder. "Monitoring Training Load in Handcycling." Journal of Strength and Conditioning Research 31, no. 11 (November 2017): 3094–100. http://dx.doi.org/10.1519/jsc.0000000000001786.

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Bourdon, Pitre C., Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, et al. "Monitoring Athlete Training Loads: Consensus Statement." International Journal of Sports Physiology and Performance 12, s2 (April 2017): S2–161—S2–170. http://dx.doi.org/10.1123/ijspp.2017-0208.

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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.

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Cardinale, Marco, and Matthew C. Varley. "Wearable Training-Monitoring Technology: Applications, Challenges, and Opportunities." International Journal of Sports Physiology and Performance 12, s2 (April 2017): S2–55—S2–62. http://dx.doi.org/10.1123/ijspp.2016-0423.

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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.

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Williams, Sean, Grant Trewartha, Matthew J. Cross, Simon P. T. Kemp, and Keith A. Stokes. "Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures." International Journal of Sports Physiology and Performance 12, s2 (April 2017): S2–101—S2–106. http://dx.doi.org/10.1123/ijspp.2016-0337.

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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.

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Nielson, R., P. Glasgow, and A. Coutts. "Training load monitoring and management in athletes." Journal of Science and Medicine in Sport 22 (October 2019): S10. http://dx.doi.org/10.1016/j.jsams.2019.08.054.

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Pind, Rasmus, and Jarek Mäestu. "Monitoring training load: necessity, methods and applications." Acta Kinesiologiae Universitatis Tartuensis 23 (January 18, 2018): 7. http://dx.doi.org/10.12697/akut.2017.23.01.

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Regular physical activity and participation in organized sports is important contributor to performance and for overall health and fitness in humans of various age range. In performance related areas, every detail in the training sessions is important for the athlete to be in the best shape the chosen competition day. Sport scientists have been making hard effort to find out how the training has the influence on performance. Thus, training monitoring is important tool to evaluate an athlete’s response to training. Banister developed the ‘training impulse’ (TRIMP) as a method to quantify training load. The TRIMP consists of the exercise intensity calculated by the heart rate (HR) reserve method and the duration of exercise. Foster et al. [23] developed a modification of the rating of the perceived exertion method, which uses Rated Perceived Exertion (RPE) as a marker of training intensity within the TRIMP concept. For quantifying and calculating training load, the athlete’s RPE (1–10pt scale) is multiplied by the duration of the session. Ideally, the perceptions of training load should match between athlete and coach to have optimal adaptation. Thus, this brief review article is evaluating training monitoring opportunities without the need of expensive equipment.

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Foster, Carl, Jose A. Rodriguez-Marroyo, and Jos J. de Koning. "Monitoring Training Loads: The Past, the Present, and the Future." International Journal of Sports Physiology and Performance 12, s2 (April 2017): S2–2—S2–8. http://dx.doi.org/10.1123/ijspp.2016-0388.

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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.

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Zeghari, Lotfi, Hicham Moufti, Amine Arfaoui, and Yassir Habki. "The prevention of overtraining with the monitoring training loads: case of football." International Journal of Physical Education, Fitness and Sports 8, no. 3 (August 30, 2019): 42–50. http://dx.doi.org/10.26524/ijpefs1935.

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The aim of this paper is to use a training load quantification tool (RPE) to evaluate if the training load programmed by the coach is appropriate to the characteristics of these footballers. The study was conducted at the football section of the Sale Sports Association, Morocco, on a sample of 8 football players who practice in the club of the Association, aged between 18 and 21 years, the study was established during a mesocycle in a period from 18/03/2019 to 20/04/2019. For the quantification of the training load (TL) we chose the (RPE) tool, where each footballer must give his own perception of the effort felt in each training session, taking into consideration also the duration of the session. This will allow us to calculate the intensity of the session estimated, on a scale from 0 to 10. Based on the results of the quantification of training load for the 8 footballers, we note that in the majority of the cases, the acute load (AL) is higher than the chronic load (CL) at the end of each week. On the other hand, for the monotony index (MI) that provides information on the negative adaptations of training and overtraining, we note that it present a high value among the majority of footballers (1.8UA<2.1UA). For the average of the ratio of the training load: acute/chronic, we note that for the first three footballers the training loads are higher compared to the others. The monitoring training load help to better conceptualize the adaptations of the athlete to the training, and also allows the prediction of the performance.

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AKTÜRE, Kaan Gürbey, Mert İSKİPCİ, and Doğukan YILMAZ. "Fatigue Monitoring for Determining the Optimal Training Load." Turkiye Klinikleri Journal of Sports Sciences 13, no. 1 (2021): 133–46. http://dx.doi.org/10.5336/sportsci.2020-76262.

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Halson, Shona L. "Monitoring Training Load to Understand Fatigue in Athletes." Sports Medicine 44, S2 (September 9, 2014): 139–47. http://dx.doi.org/10.1007/s40279-014-0253-z.

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Baki, Mohd Hafizuddin, Nur Ikhwan bin Mohamad, and Ali Bin Md Nadzalan. "Monitoring Training Load on Malaysian Rugby 15s Players." Annals of Applied Sport Science 10, no. 3 (October 1, 2022): 0. http://dx.doi.org/10.52547/aassjournal.1045.

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Napier, Christopher, Megan Ryan BSc, Carlo Menon, and Max R. Paquette. "Session Rating of Perceived Exertion Combined With Training Volume for Estimating Training Responses in Runners." Journal of Athletic Training 55, no. 12 (October 16, 2020): 1285–91. http://dx.doi.org/10.4085/1062-6050-573-19.

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Context Historically, methods of monitoring training loads in runners have used simple and convenient metrics, including the duration or distance run. Changes in these values are assessed on a week-to-week basis to induce training adaptations and manage injury risk. To date, whether different measures of external loads, including biomechanical measures, provide better information regarding week-to-week changes in external loads experienced by a runner is unclear. In addition, the importance of combining internal-load measures, such as session rating of perceived exertion (sRPE), with different external-load measures to monitor week-to-week changes in training load in runners is unknown. Objective To compare week-to-week changes in the training loads of recreational runners using different quantification methods. Design Case series. Setting Community based. Patients or Other Participants Recreational runners in Vancouver, British Columbia. Main Outcome Measure(s) Week-to-week changes in running time, steps, and cumulative shock, in addition to the product of each of these variables and the corresponding sRPE scores for each run. Results Sixty-eight participants were included in the final analysis. Differences were present in week-to-week changes for running time compared with timeRPE (d = 0.24), stepsRPE (d = 0.24), and shockRPE (d = 0.31). The differences between week-to-week changes in running time and cumulative shock were also significant at the overall group level (d = 0.10). Conclusions We found that the use of an internal training-load measure (sRPE) in combination with external load (training duration) provided a more individualized estimate of week-to-week changes in overall training stress. A better estimation of training stress has significant implications for monitoring training adaptations, resulting performance, and possibly injury risk reduction. We therefore recommend the regular use of sRPE and training duration to monitor training load in runners. The use of cumulative shock as a measure of external load in some runners may also be more valid than duration alone.

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Temm, Dani A., Regan J. Standing, and Russ Best. "Training, Wellbeing and Recovery Load Monitoring in Female Youth Athletes." International Journal of Environmental Research and Public Health 19, no. 18 (September 12, 2022): 11463. http://dx.doi.org/10.3390/ijerph191811463.

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Participation in youth sports is ever-increasing, along with training and competition demands placed upon youth athletes. Young athletes may experience high training loads due to playing several sports, as well as participating in school physical education. Therefore, monitoring youth athlete load is an emerging area of research that may help limit non-functional overreaching, injury, or illness and assist with long-term athlete development. This narrative review highlights that multiple measures have been explored to monitor both internal and external load. However, the validity, reliability and practicality of these measures are often not fully understood in female youth populations. The most commonly used external monitoring methods are GPS tracking and TRIMP whereas common internal monitoring tools are questionnaires, perceived exertion rating and heart rate measures. The reporting of injuries and menstrual cycles is also crucial for providing completeness when monitoring an athlete. It has been suggested that the combination of training load, recovery and wellbeing monitoring variables is the optimal way to monitor an athlete’s fatigue levels. Whichever monitoring method is applied, in a youth population it is important that the protocol can be individualised, is inexpensive and can be easily implemented and reported so that the monitoring is sustainable.

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Bouaziz, Taieb, Emna Makni, Philippe Passelergue, Zouhair Tabka, Gérard Lac, Wassim Moalla, Karim Chamari, and Mohamed Elloumi. "Multifactorial monitoring of training load in elite rugby sevens players: cortisol/cortisone ratio as a valid tool of training load monitoring." Biology of Sport 33, no. 3 (May 3, 2016): 231–39. http://dx.doi.org/10.5604/20831862.1201812.

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King, Marguerite Helen, Nathalia Costa, Amy Lewis, Kate Watson, and Bill Vicenzino. "Throwing in the deep end: athletes, coaches and support staff experiences, perceptions and beliefs of upper limb injuries and training load in elite women’s water polo." BMJ Open Sport & Exercise Medicine 8, no. 1 (March 2022): e001214. http://dx.doi.org/10.1136/bmjsem-2021-001214.

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To understand elite athlete, coach and support staff experiences, perceptions and beliefs in women’s water polo with managing upper limb injuries and monitoring training loads. Inductive qualitative design. Twenty athletes, coaches and support staff were purposively recruited and participated in semistructured interviews. Participants either had experienced an upper limb injury or had experience managing athletes with upper limb injuries. Interviews were conducted in-person or virtually, audio-recorded, deidentified, transcribed verbatim and cleaned to ensure accuracy. Data were thematically analysed. Analysis identified five cohesive themes: (1) upper limb injury management is adequate—but prevention, communication and knowledge need improving, (2) current training load monitoring generates uncertainty and lack of consistency of processes—due to reliance on internal, and lack of external load monitoring, (3) optimal training load monitoring requires objective measurement of training load—that accurately measures the external load of athletes’ upper limbs, (4) athlete-centred philosophy matters—including athlete-centred care to facilitate individually tailored rehabilitation programmes and their inclusion in management decisions, (5) mental, social and emotional aspects of upper limb injury management matter—acknowledging feelings of loss of team inclusion, fear of missing out and frustration felt by athletes as well as the emotional labour felt by coaches when supporting athletes with an upper limb injury. Upper limb injury management and training load monitoring are evolving areas where objective measurement of training load may assist in increasing consistency of communication, collaboration and coordination between all stakeholders, and to address uncertainty. Stakeholders placed value in intangible qualities such as trust and care in their relationships with other collaborators—facilitating athlete physical, mental and emotional recovery following upper limb injuries.

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Passfield, Louis, Juan M. Murias, Massimo Sacchetti, and Andrea Nicolò. "Validity of the Training-Load Concept." International Journal of Sports Physiology and Performance 17, no. 4 (April 1, 2022): 507–14. http://dx.doi.org/10.1123/ijspp.2021-0536.

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Training load (TL) is a widely used concept in training prescription and monitoring and is also recognized as as an important tool for avoiding athlete injury, illness, and overtraining. With the widespread adoption of wearable devices, TL metrics are used increasingly by researchers and practitioners worldwide. Conceptually, TL was proposed as a means to quantify a dose of training and used to predict its resulting training effect. However, TL has never been validated as a measure of training dose, and there is a risk that fundamental problems related to its calculation are preventing advances in training prescription and monitoring. Specifically, we highlight recent studies from our research groups where we compare the acute performance decrement measured following a session with its TL metrics. These studies suggest that most TL metrics are not consistent with their notional training dose and that the exercise duration confounds their calculation. These studies also show that total work done is not an appropriate way to compare training interventions that differ in duration and intensity. We encourage scientists and practitioners to critically evaluate the validity of current TL metrics and suggest that new TL metrics need to be developed.

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Duarte, Thiago Seixas, Danilo Reis Coimbra, Renato Miranda, Heglison Custódio Toledo, Francisco Zacaron Werneck, Daniel Gustavo Schimitz de Freitas, and Mauricio Gáttas Bara Filho. "MONITORING TRAINING LOAD AND RECOVERY IN VOLLEYBALL PLAYERS DURING A SEASON." Revista Brasileira de Medicina do Esporte 25, no. 3 (June 2019): 226–29. http://dx.doi.org/10.1590/1517-869220192503195048.

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ABSTRACT Introduction Monitoring training loads, along with the recovery status, is important for preventing unwanted adaptations. Knowledge of these variables over volleyball seasons is still scarce. Objective To monitor and describe the training load and recovery status of volleyball players over a competitive season. Methods The sample consisted of 14 professional volleyball players. For the entire season, the training load was monitored daily by the SPE method during the session, and the recovery status was monitored by TQR and QBE on the first and last days of training for the week. Results There was a decrease in training load between Preparatory Period I and Competitive Period I (p = 0.03), followed by an increase in Preparatory Period II (p <0.001) and a new decrease in Competitive Periods II (p = 0.01 ) and III (p = 0.003). There was a significant reduction between Pre-TQR and QBE and Post-TQR and QBE in all mesocycles. In the Pre-TQR, there was a reduction between Preparatory Period II and Competitive Period II (p = 0.006), in the Pre-QBE, there was a reduction between Preparatory Period II and Competitive Period III (p = 0.002), and in the Post-TQR, this reduction was observed between Competitive Period I and Preparatory Period II (p = 0.03). In the Post-QBE, there was an increase between Preparatory Period I and Competitive Period I (p = 0.002), followed by a decrease in Preparatory Period II (p = 0.01). Conclusion Loads varied throughout the season, along with recovery, which varied according to the loads and characteristics of each period. Level of evidence I, Therapeutic Studies – Investigating the Results of Treatment.

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Bartík, Pavol, and Štefan Adamčák. "Monitoring the Response of Judoists´ Organisms To Training Load." European Journal of Physical Education and Sport 6, no. 4 (December 15, 2014): 208–13. http://dx.doi.org/10.13187/ejpe.2014.6.208.

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Laird, Jason, and Allan Macdonald. "Monitoring training load- The journey of an Elite Judoka." Physical Therapy in Sport 28 (November 2017): e8. http://dx.doi.org/10.1016/j.ptsp.2017.08.026.

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Teixeira, José E., Pedro Forte, Ricardo Ferraz, Miguel Leal, Joana Ribeiro, António J. Silva, Tiago M. Barbosa, and António M. Monteiro. "Monitoring Accumulated Training and Match Load in Football: A Systematic Review." International Journal of Environmental Research and Public Health 18, no. 8 (April 8, 2021): 3906. http://dx.doi.org/10.3390/ijerph18083906.

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(1) Background: Training load monitoring has become a relevant research-practice gap to control training and match demands in team sports. However, there are no systematic reviews about accumulated training and match load in football. (2) Methods: Following the preferred reporting item for systematic reviews and meta-analyses (PRISMA), a systematic search of relevant English-language articles was performed from earliest record to March 2020. The search included descriptors relevant to football, training load, and periodization. (3) Results: The literature search returned 7972 articles (WoS = 1204; Pub-Med = 869, SCOPUS = 5083, and SportDiscus = 816). After screening, 36 full-text articles met the inclusion criteria and were reviewed. Eleven of the included articles analyzed weekly training load distribution; fourteen, the weekly training load and match load distribution; and eleven were about internal and external load relationships during training. The reviewed articles were based on short-telemetry systems (n = 12), global positioning tracking systems (n = 25), local position measurement systems (n = 3), and multiple-camera systems (n = 3). External load measures were quantified with distance and covered distance in different speed zones (n = 27), acceleration and deceleration (n = 13) thresholds, accelerometer metrics (n = 11), metabolic power output (n = 4), and ratios/scores (n = 6). Additionally, the internal load measures were reported with perceived exertion (n = 16); heart-rate-based measures were reported in twelve studies (n = 12). (4) Conclusions: The weekly microcycle presented a high loading variation and a limited variation across a competitive season. The magnitude of loading variation seems to be influenced by the type of week, player’s starting status, playing positions, age group, training mode and contextual variables. The literature has focused mainly on professional men; future research should be on the youth and female accumulated training/match load monitoring.

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Murray, Andrew. "Managing the Training Load in Adolescent Athletes." International Journal of Sports Physiology and Performance 12, s2 (April 2017): S2–42—S2–49. http://dx.doi.org/10.1123/ijspp.2016-0334.

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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.

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Svilar, Luka, and Igor Jukić. "Load monitoring system in top-level basketball team." Kinesiology 50, no. 1 (2018): 25–33. http://dx.doi.org/10.26582/k.50.1.4.

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The study aimed to describe and compare the external training load, monitored using microtechnology, with the internal training load, expressed as the session rating of perceived exertion (sRPE), in elite male basketball training sessions. Thirteen professional basketball players participated in this study (age=25.7±3.3 years; body height=199.2±10.7 cm; body mass=96.6±9.4 kg). All players belonged to the same team, competing in two leagues, ACB and the Euroleague, in the 2016/2017 season. The variables assessed within the external motion analysis included: Player Load (PL), acceleration and deceleration (ACC/DEC), jumps (JUMP), and changes of direction (CoD). The internal demands were registered using the sRPE method. Pearson product-moment correlations were used to determine relationships between the variables. A significant correlation was observed between the external load variables and sRPE (range r=0.71–0.93). Additionally, the sRPE variable showed a high correlation with the total PL, ACC, DEC, and CoD. The contrary was observed with respect to the relationship between sRPE and JUMP variables: the correlation was higher for the high band and lower for the total number of jumps. With respect to the external load variables, a stronger correlation was found between PL and the total number of ACC, DEC and COD than the same variables within the high band. The only contrary finding was the correlation between PL and JUMP variables, which showed a stronger correlation for hJUMP. Tri-axial accelerometry technology and the sRPE method serve as valuable tools for monitoring the training load in basketball. Even though the two methods exhibit a strong correlation, some variation exists, likely due to frequent static movements (i.e., isometric muscle contractions) that accelerometers are not able to detect. Finally, it is suggested that both methods are to be used complementary, when possible, in order to design and control the training process as effectively as possible.

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Soligard, Torbjørn, Martin Schwellnus, Juan-Manuel Alonso, Roald Bahr, Ben Clarsen, H. Paul Dijkstra, Tim Gabbett, et al. "How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury." British Journal of Sports Medicine 50, no. 17 (August 17, 2016): 1030–41. http://dx.doi.org/10.1136/bjsports-2016-096581.

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Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport. We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.

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Sansone, Pierpaolo, Harald Tschan, Carl Foster, and Antonio Tessitore. "Monitoring Training Load and Perceived Recovery in Female Basketball: Implications for Training Design." Journal of Strength and Conditioning Research 34, no. 10 (October 2020): 2929–36. http://dx.doi.org/10.1519/jsc.0000000000002971.

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Malone, James J., Arne Jaspers, Werner Helsen, Brenda Merks, Wouter G. P. Frencken, and Michel S. Brink. "Seasonal Training Load and Wellness Monitoring in a Professional Soccer Goalkeeper." International Journal of Sports Physiology and Performance 13, no. 5 (May 1, 2018): 672–75. http://dx.doi.org/10.1123/ijspp.2017-0472.

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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.

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Pisa, Marcel Frezza, Arthur Marques Zecchin, Leonardo Gaspar Gomes, and Enrico Fuini Puggina. "External load in male professional volleyball: A systematic review." Baltic Journal of Health and Physical Activity 14, no. 2 (June 30, 2022): Article7. http://dx.doi.org/10.29359/bjhpa.14.2.07.

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Introduction. The objectives of this systematic review were to identify the volleyball external loads values in the literature and to verify the applicability of different means of quantification and monitoring of these variables during training sessions and matches. Material and methods: This systematic review was produced following the PRISMA statement recommendations, and the search for publications was carried out in the databases PubMed/NCBI, SportDiscus via EBSCOhost, SciELO. 12 studies meet the criteria and were included in this review. Results: The most used tool for quantification, monitoring and evaluation of external loads are video recording and manual or semi-automatic counting of jumps and distance covered and, more recently, the use of inertial measurements unit. The middle blocker has the highest high jump load, outside hitters jump closer to the maximum more often and setters have a high demand of medium height jumps. Conclusions: Match and training jump loads seem to be similar, and sessions that involve block or attack have a higher jump load. In professional male volleyball, training is planned with variation in training loads according to the period of the sea-son and according to the days of the week before and after games.

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Sykiotis, Stavros, Maria Kaselimi, Anastasios Doulamis, and Nikolaos Doulamis. "ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring." Sensors 22, no. 8 (April 11, 2022): 2926. http://dx.doi.org/10.3390/s22082926.

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Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal. Exceeding the limitations of recurrent models that have been widely used in sequential modeling, this paper proposes a transformer-based architecture for NILM. Our approach, called ELECTRIcity, utilizes transformer layers to accurately estimate the power signal of domestic appliances by relying entirely on attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. Another additive value of the proposed model is that ELECTRIcity works with minimal dataset pre-processing and without requiring data balancing. Furthermore, ELECTRIcity introduces an efficient training routine compared to other traditional transformer-based architectures. According to this routine, ELECTRIcity splits model training into unsupervised pre-training and downstream task fine-tuning, which yields performance increases in both predictive accuracy and training time decrease. Experimental results indicate ELECTRIcity’s superiority compared to several state-of-the-art methods.

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Miguel, Mauro, Rafael Oliveira, Nuno Loureiro, Javier García-Rubio, and Sergio J. Ibáñez. "Load Measures in Training/Match Monitoring in Soccer: A Systematic Review." International Journal of Environmental Research and Public Health 18, no. 5 (March 8, 2021): 2721. http://dx.doi.org/10.3390/ijerph18052721.

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In soccer, the assessment of the load imposed by training and a match is recognized as a fundamental task at any competitive level. The objective of this study is to carry out a systematic review on internal and external load monitoring during training and/or a match, identifying the measures used. In addition, we wish to make recommendations that make it possible to standardize the classification and use of the different measures. The systematic review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was conducted through the electronic database Web of Science, using the keywords “soccer” and “football”, each one with the terms “internal load”, “external load”, and “workload”. Of the 1223 studies initially identified, 82 were thoroughly analyzed and are part of this systematic review. Of these, 25 articles only report internal load data, 20 report only external load data, and 37 studies report both internal and external load measures. There is a huge number of load measures, which requires that soccer coaches select and focus their attention on the most useful and specific measures. Standardizing the classification of the different measures is vital in the organization of this task, as well as when it is intended to compare the results obtained in different investigations.

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Schwellnus, Martin, Torbjørn Soligard, Juan-Manuel Alonso, Roald Bahr, Ben Clarsen, H. Paul Dijkstra, Tim J. Gabbett, et al. "How much is too much? (Part 2) International Olympic Committee consensus statement on load in sport and risk of illness." British Journal of Sports Medicine 50, no. 17 (August 17, 2016): 1043–52. http://dx.doi.org/10.1136/bjsports-2016-096572.

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The modern-day athlete participating in elite sports is exposed to high training loads and increasingly saturated competition calendar. Emerging evidence indicates that inappropriate load management is a significant risk factor for acute illness and the overtraining syndrome. The IOC convened an expert group to review the scientific evidence for the relationship of load—including rapid changes in training and competition load, competition calendar congestion, psychological load and travel—and health outcomes in sport. This paper summarises the results linking load to risk of illness and overtraining in athletes, and provides athletes, coaches and support staff with practical guidelines for appropriate load management to reduce the risk of illness and overtraining in sport. These include guidelines for prescription of training and competition load, as well as for monitoring of training, competition and psychological load, athlete well-being and illness. In the process, urgent research priorities were identified.

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Teixeira, José E., Pedro Forte, Ricardo Ferraz, Miguel Leal, Joana Ribeiro, António J. Silva, Tiago M. Barbosa, and António M. Monteiro. "Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation." Applied Sciences 11, no. 11 (May 26, 2021): 4871. http://dx.doi.org/10.3390/app11114871.

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Monitoring the training load in football is an important strategy to improve athletic performance and an effective training periodization. The aim of this study was two-fold: (1) to quantify the weekly training load and recovery status variations performed by under-15, under-17 and under-19 sub-elite young football players; and (2) to analyze the influence of age, training day, weekly microcycle, training and playing position on the training load and recovery status. Twenty under-15, twenty under-17 and twenty under-19 players were monitored over a 2-week period during the first month of the 2019–2020 competitive season. Global positioning system technology (GPS) was used to collect external training loads: total distance covered, average speed, maximal running speed, relative high-speed running distance, high metabolic load distance, sprinting distance, dynamic stress load, accelerations and decelerations. Internal training load was monitored using ratings of perceived exertion (RPE) and session rating of perceived exertion (sRPE). Recovery status was obtained using the total quality recovery (TQR) scale. The results show an age-related influence for external training load (p ≤ 0.001; d = 0.29–0.86; moderate to strong effect), internal training load (p ≤ 0.001, d = 0.12–0.69; minimum to strong effect) and recovery status (p ≤ 0.001, d = 0.59; strong effect). The external training load presented differences between training days (p < 0.05, d = 0.26–0.95; moderate to strong effect). The playing position had a minimum effect on the weekly training load (p < 0.05; d = 0.06–0.18). The weekly microcycle had a moderate effect in the TD (p < 0.05, d = 0.39), RPE (p < 0.05; d = 0.35) and sRPE (p < 0.05, d = 0.35). Interaction effects were found between the four factors analyzed for deceleration (F = 2.819, p = 0.017) and between inter-day, inter-week and age for total covered distance (F = 8.342, p = 0.008). This study provided specific insights about sub-elite youth football training load and recovery status to monitor training environments and load variations. Future research should include a longer monitoring period to assess training load and recovery variations across different season phases.

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Nguyen, Vanh Khuyen, Wei Emma Zhang, and Adnan Mahmood. "Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System." ACM Transactions on Sensor Networks 17, no. 3 (June 21, 2021): 1–20. http://dx.doi.org/10.1145/3448415.

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Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

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Parson, Oliver, Siddhartha Ghosh, Mark Weal, and Alex Rogers. "An unsupervised training method for non-intrusive appliance load monitoring." Artificial Intelligence 217 (December 2014): 1–19. http://dx.doi.org/10.1016/j.artint.2014.07.010.

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Simim, Mário A. M., Marco Túlio de Mello, Bruno V. C. Silva, Dayane F. Rodrigues, João Paulo P. Rosa, Bruno Pena Couto, and Andressa da Silva. "Load Monitoring Variables in Training and Competition Situations: A Systematic Review Applied to Wheelchair Sports." Adapted Physical Activity Quarterly 34, no. 4 (October 1, 2017): 466–83. http://dx.doi.org/10.1123/apaq.2016-0149.

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The aim of this review was to identify the main variables for load monitoring in training and competition situations in wheelchair sports. Studies were identified from a systematic search of three databases (PubMed, Web of Science, and SportDiscuss), with search phrases constructed from MeSH terms, alone or in combination, limited to English-language literature, and published up to January 2016. Our main findings were that variables related to external load (distance, speed, and duration) are used to monitor load in competition. In training situations, researchers have used variables related to internal load (heart rate and VO2); in both training and competition situations, researchers used internal load measurements (training impulse and ratings of perceived exertion). We conclude that the main variables for load monitoring in competitive situations were distance, speed, and duration, whereas the variables for training situations were heart rate, VO2, training impulse, and ratings of perceived exertion.

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de Aguiar, Everton Luiz, André Eugenio Lazzaretti, Bruna Machado Mulinari, and Daniel Rodrigues Pipa. "Scattering Transform for Classification in Non-Intrusive Load Monitoring." Energies 14, no. 20 (October 18, 2021): 6796. http://dx.doi.org/10.3390/en14206796.

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Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.

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Tan, Zhukui, Bin Liu, Yutao Xu, Shengyong Feng, and Haixiang Zhao. "Application of Self-supervised Learning in Non-intrusive Load Monitoring." Journal of Physics: Conference Series 2425, no. 1 (February 1, 2023): 012037. http://dx.doi.org/10.1088/1742-6596/2425/1/012037.

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Abstract With the proposal of smart grid, the demand of both source and load for fine monitoring and control of power load is becoming increasingly prominent. Non-intrusive load monitoring is a technical means to better meet this demand. However, the research at home and abroad focuses on the existing data sets and labeled data to improve the accuracy of load identification, while the research on the training method of the model under the massive unlabeled monitoring data in the actual scene is still in a relatively blank stage. Aiming at the problem of how to make full use of unlabeled monitoring data for model training, a non-intrusive-load monitoring method based on self-supervised learning is proposed in this paper. This method designs a self-supervised learning task, so that the model can make full use of the massive unlabeled monitoring data for training, eliminating the step of manually labelling the data; Based on the encoder-decoder structure, a deep learning model is established, and the load is identified through the load characteristic vector output by the encoder, so that the method has generalization performance. In this paper, AMPds2 data set is used to verify the method, and test examples verify the effectiveness of the method.

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Ryan, Samuel, Thomas Kempton, and Aaron J. Coutts. "Data Reduction Approaches to Athlete Monitoring in Professional Australian Football." International Journal of Sports Physiology and Performance 16, no. 1 (January 1, 2021): 59–65. http://dx.doi.org/10.1123/ijspp.2020-0083.

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Purpose: To apply data reduction methods to athlete-monitoring measures to address the issue of data overload for practitioners of professional Australian football teams. Methods: Data were collected from 45 professional Australian footballers from 1 club during the 2018 Australian Football League season. External load was measured in training and matches by 10-Hz OptimEye S5 and ClearSky T6 GPS units. Internal load was measured via the session rate of perceived exertion method. Perceptual wellness was measured via questionnaires completed before training sessions with players providing a rating (1–5 Likert scale) of muscle soreness, sleep quality, fatigue, stress, and motivation. Percentage of maximum speed was calculated relative to individual maximum velocity recorded during preseason testing. Derivative external training load measures (total daily, weekly, and monthly) were calculated. Principal-component analyses (PCAs) were conducted for Daily and Chronic measures, and components were identified via scree plot inspection (eigenvalue > 1). Components underwent orthogonal rotation with a factor loading redundancy threshold of 0.70. Results: The Daily PCA identified components representing external load, perceived wellness, and internal load. The Chronic PCA identified components representing 28-d speed exposure, 28-d external load, 7-d external load, and 28-d internal load. Perceived soreness did not meet the redundancy threshold. Conclusions: Monitoring player exposure to maximum speed is more appropriate over chronic than short time frames to capture variations in between-matches training-cycle duration. Perceived soreness represents a distinct element of a player’s perception of wellness. Summed-variable and single-variable approaches are novel methods of data reduction following PCA of athlete monitoring data.

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F. Agostinho, Marcus, Alexandre Moreira, Ursula F. Julio, Gilvan S. Marcolino, Barbara M. M. Antunes, Fabio S. Lira, and Emerson Franchini. "Monitoring internal training load and salivary immuneendocrine responses during an annual judo training periodization." Journal of Exercise Rehabilitation 13, no. 1 (February 27, 2017): 68–75. http://dx.doi.org/10.12965/jer.1732850.425.

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Feijen, Stef, Angela Tate, Kevin Kuppens, Lorna A. Barry, and Filip Struyf. "Monitoring the swimmer’s training load: A narrative review of monitoring strategies applied in research." Scandinavian Journal of Medicine & Science in Sports 30, no. 11 (September 11, 2020): 2037–43. http://dx.doi.org/10.1111/sms.13798.

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Rydzik, Łukasz. "Determination of the real training load based on monitoring of K1 kickboxing bouts." Journal of Kinesiology and Exercise Sciences 32, no. 99 (October 5, 2022): 1–8. http://dx.doi.org/10.5604/01.3001.0016.0606.

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Background: Kickboxing is a combat sport in which scientific observations are regularly made to improve the quality of the training process. Heart rate monitoring is the basic form of the evaluation of training load and diagnosing the athlete's capabilities. The purpose of this study was to determine training load based on heart rate measurements in K1 kickboxers. Methods: The study was conducted on 18 kickboxers, with HR recorded over a 3-round kickboxing fight. HRmax level was calculated for each athlete according to the most recent formula. Based on these data, the percentage training load was determined according to the needs arising from the training periodization. Results: The results of the study showed that training of K1 kickboxers is based primarily on submaximal heart rates, which increase with each round of the bout (p<0.001). Conclusions: The training load for a K1 kickboxing bout based on maximum heart rate should be 95.44% HRmax in the first round, 96.23% HRmax in the second, and 97.01% HRmax in the round..

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Burgess, Darren J. "The Research Doesn’t Always Apply: Practical Solutions to Evidence-Based Training-Load Monitoring in Elite Team Sports." International Journal of Sports Physiology and Performance 12, s2 (April 2017): S2–136—S2–141. http://dx.doi.org/10.1123/ijspp.2016-0608.

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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.

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Morandi, Rodrigo F., Eduardo M. Pimenta, André G. P. Andrade, Tane K. F. Serpa, Eduardo M. Penna, Charles O. Costa, Mário N. S. O. Júnior, and Emerson S. Garcia. "Preliminary Validation of Mirrored Scales for Monitoring Professional Soccer Training Sessions." Journal of Human Kinetics 72, no. 1 (March 31, 2020): 265–78. http://dx.doi.org/10.2478/hukin-2019-0112.

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AbstractWe aimed to create a single subjective method to assess both internal training loads and subsequent fatigue. This new training-fatigue (dose-response) scale (TFS) was composed of two similar scales with the same properties, metrics and construction criteria. These two scales were designed to rate the perceived exertion (RPETFS) and perceived fatigue (RPFTFS) in professional soccer players. Twenty-two athletes participated to establish reliability, and 15 participated to establish validity. For reliability, the intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were used. For criterion validity, the Spearman’s correlation coefficient and linear regression analyses were applied. Associations between RPETFS and RPFTFS were verified by a chi square test, and a further factorial exploratory analysis was conducted. RPETFS and RPFTFS were found to be reliable (ICC 0.74 and 0.77, SEM 0.30 and 0.30, respectively) and valid. RPETFS was best explained by the internal load of the Banister training impulse (p < 0.001), while RPFTFS was best explained by the internal load of the Stagno training impulse (p < 0.001). An association was found between the scales (RPETFS and RPFTFS) in which training duration had a more substantial impact on these subjective perceptions than did training intensity (p < 0.01). RPETFS and RPFTFS scales are reliable and valid for monitoring training sessions in Brazilian professional soccer players. The simultaneous oscillations of the RPETFS and RPFTFS scores can be used by staff members to better plan weekly training programs based on dose-response ratings. Finally, training duration must be carefully controlled because it has a greater impact than intensity on subjective perceptions.

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Renaux, Douglas Paulo Bertrand, Fabiana Pottker, Hellen Cristina Ancelmo, André Eugenio Lazzaretti, Carlos Raiumundo Erig Lima, Robson Ribeiro Linhares, Elder Oroski, et al. "A Dataset for Non-Intrusive Load Monitoring: Design and Implementation." Energies 13, no. 20 (October 15, 2020): 5371. http://dx.doi.org/10.3390/en13205371.

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A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning.

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Gentil, Paulo, Vítor A. Marques, Josaphat P. P. Neto, Anna C. G. Santos, James Steele, James Fisher, Antonio Paoli, and Martim Bottaro. "Using velocity loss for monitoring resistance training effort in a real-world setting." Applied Physiology, Nutrition, and Metabolism 43, no. 8 (August 2018): 833–37. http://dx.doi.org/10.1139/apnm-2018-0011.

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The purpose of the present study was to evaluate the changes in movement velocity during resistance training with different loads while the trainees attempted to move the load at a predetermined repetition duration. Twenty-one resistance-trained men (age: 25.7 ± 5 years; height: 177.0 ± 7.2 cm; mass: 85.4 ± 13.56 kg) volunteered to participate in the study. Participants performed 2 test sessions. The first to determine 1-repetition maximum (1RM) load, and the second to evaluate velocity loss during a set to failure performed at 75% and 50% of 1RM using a 2-s concentric and 2-s eccentric repetition duration, controlled by a mobile app metronome. When using 75% 1RM there was a significant loss of movement velocity between the antepenultimate and the penultimate repetition (5.33%, p < 0.05), as well as during the penultimate and the last (22.11%, p < 0.05). At 50% of 1RM the participants performed the set until momentary failure without significant velocity loss. Monitoring velocity loss during high-load resistance training through simple methods can be an important tool for standardize the intensity of effort employed during submaximal training. This can be useful in clinical conditions where maximum exertions are contraindicated or when specific logistics are lacking.

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Mardyła, Mateusz, and Marcin Grandys. "The role of leptin in monitoring training loads during rowing: a systematic review." Journal of Kinesiology and Exercise Sciences 31, no. 95 (September 30, 2021): 45–52. http://dx.doi.org/10.5604/01.3001.0015.7605.

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Objective: In competitive sports, too small or too large loads lead to poor sports results. Situation of overload are particularly dangerous to the body, which may lead to overtraining. In this work, the literature on the possibility of assessing training loads with the use of leptin concentration measurement in men and women practicing rowing was analysed. Materials and methods: A systematic review was performed using the Scopus, Pubmed and Google Scholar databases between 1995-2020. After an initial analysis of 56 articles and taking the analysed topics into account, 25 articles were included in this review. As part of the review, data for 75 rowers were analysed. The usefulness of leptin - a hormone produced by adipose tissue - as a marker of training loads in several sports disciplines, with particular emphasis on rowing, was evaluated. Within this context, the role of leptin may be to control these loads due to its relatively high sensitivity in response to increases in training intensity or volume. The presented general characteristics of rowing and the physiological basis of exercise are the background for considerations on the possibility of using leptin as a burden marker in this discipline. Results: Due to the fact that the concentration of leptin correlates with the content of adipose tissue and BMI (Body Mass Index), its changes may inform about training loads directly related to the amount of energy expenditure. A review of the literature from the last 25 years, i.e. from the moment when this hormone was discovered, allowed to formulate the thesis that leptin may be a marker of training loads, however, determining its concentration makes sense when the same factors that may affect its secretion are taken into account each time. Conclusions: Training in rowing, that involves high training loads, causes significant changes in blood leptin levels. Training periods with high exercise load, associated with a significant increase in energy expenditure, lead to a decrease in resting leptin concentration, while periods with less load increase it. The main factor determining changes in leptin concentration during training is the amount of energy expenditure, which in the case of rowing involving very large muscle groups, is very high. Although the amount of energy expenditure in training, leading to a decrease in leptin concentration is difficult to determine, the energy expenditure cannot be less than 800 kcal in a single training unit.

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Aoki, Marcelo S., Ademir FS Arruda, Camila G. Freitas, Bernardo Miloski, Pablo R. Marcelino, Gustavo Drago, Murilo Drago, and Alexandre Moreira. "Monitoring training loads, mood states, and jump performance over two periodized training mesocycles in elite young volleyball players." International Journal of Sports Science & Coaching 12, no. 1 (December 22, 2016): 130–37. http://dx.doi.org/10.1177/1747954116684394.

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The purpose of this study was to verify the effect of the periodized training program on internal training load, mood states, and vertical jump capacity of young volleyball players. Internal training load was measured using the session rating of perceived exertion (session-RPE) method. To assess mood states, the profile of mood states questionnaire was completed once a week. The vertical jump tests were performed before and after training period. The main findings were (1) the internal training load was greater during the preparatory mesocycle than during the competitive mesocycle, for both U16 and U19 groups; (2) the U19 completed a higher training load during preparatory mesocycle than U16; (3) despite the differences in the periodized training program, the U16 group presented a higher value for the total mood disturbance and for the subscales, tension, depression, anger, and fatigue; and (4) the vertical jump performance increased from the beginning to the end of the nine-week training period for U16 and U19 groups.

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Liu, Yong Qiang. "Operative correction of judoists’ training loads on the base of on-line monitoring of heart beats rate." Physical education of students 19, no. 2 (April 28, 2015): 13–21. http://dx.doi.org/10.15561/20755279.2015.0203.

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Purpose: ensure increase of effectiveness of training process’s control by means of operative correction of training loads of different qualification judo wrestlers’ heart beats rate indicators. Material: the research was conducted on the base of Brest SCJSOR № 1. Judo wrestlers of different sport qualification (age 17-19 years old, n=15) participated in the research. Monitoring of judo wrestlers’ heart beats rate was carried out with the help of system “Polar”. Results: we have found factorial structure of functional fitness in every profile of sportsmen. Model characteristics of judo wrestlers were supplemented with the most important sides of functional fitness. Analysis of indicators of restoration effectiveness indicators (REI) in both groups of judo wrestlers showed high level of organism’s responsiveness to training load of special and power orientation in comparison with speed power load. We have worked out algorithm of operative correction of training loads by indicators of heart beats rate in training process, depending on orientation and intensity of loads’ physiological influence on judo wrestler. Conclusions: Telemetric on-line monitoring of sportsman’s heart beats rate and calculation of REI permit to objectively assess effectiveness of training’s construction and of micro-cycle in total and detect in due time the trend to development of over-loading and failure of adaptation.

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Tisna, Dhodit Rengga, M. Udin Harun Al Rasyid, and Sritrusta Sukaridhoto. "Implementation of Oxymetry Sensors for Cardiovascular Load Monitoring When Physical Exercise." EMITTER International Journal of Engineering Technology 8, no. 1 (June 2, 2020): 178–99. http://dx.doi.org/10.24003/emitter.v8i1.482.

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The performance condition of an athlete must always be maintained, one way to maintain that performance is by training. Each individual has different abilities and physiological responses in receiving the portion of the exercise. Physical exercise that exceeds the body's ability can worsen the condition of the athlete itself which can result in excessive fatigue (overtraining) or can even result in injury. Therefore a system is needed to monitor the condition of the physiological response when given the intensity of the training load so that the portion of the training provided provides positive benefits for the athlete. This system was developed using an oxymetry sensor, microcontroller and wifi module ESP8266. This system is used to collect heart rate and oxygen saturation data, then with the existing formula the heart rate value is converted to a CVL (Cardiovascular Load) value to determine the level of fatigue in athletes when given the intensity of the training load. By using a web-based application, measurement data is displayed in realtime to make it easier to see the results of monitoring. From the experimental results the system can monitor changes in the physiological condition of the athlete when given the intensity of the training load. Finally, the developed system can collect athlete's physiological data, and can store the data in a database and display it in a web application.

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Carter, Stephen J., Marissa N. Baranauskas, Tarah J. Ballinger, Laura Q. Rogers, Kathy D. Miller, and Dustin C. Nabhan. "Exercise load monitoring: integrated approaches to advance the individualisation of exercise oncology." BMJ Open Sport & Exercise Medicine 7, no. 3 (August 2021): e001134. http://dx.doi.org/10.1136/bmjsem-2021-001134.

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Whether slowing disease progression or combatting the ills of advancing age, the extensive utility of exercise training has contributed to the outright declaration by the American College of Sports Medicine that ‘exercise is medicine’. Consistent with general framework of adaptation, the advantages of exercise training are indiscriminate—benefitting even the most susceptible clinical populations. Still, the benefit of exercise training presupposes healthy adaptation wherein progressive overload matches sufficient recovery. Indeed, a difference exists between healthy adaptation and non-functional over-reaching (ie, when internal/external load exceeds recovery capacity)—a difference that may be blurred by cancer treatment and/or comorbidity. Recent advances in smartwatches make them ideally suited to non-invasively monitor the physiological stresses to exercise training. Resolving whether individuals are successfully adapting to exercise training via load monitoring bears clinical and practical relevance. While behaviour-change research aims to identify positive constructs of exercise adherence, further attention is needed to uncover how to optimise exercise prescription among cancer populations. Herein, we briefly discuss the constituents of exercise load monitoring, present examples of internal and external load and consider how such practices can be applied to cancer populations.

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Sanders, Dajo, Grant Abt, Matthijs K. C. Hesselink, Tony Myers, and Ibrahim Akubat. "Methods of Monitoring Training Load and Their Relationships to Changes in Fitness and Performance in Competitive Road Cyclists." International Journal of Sports Physiology and Performance 12, no. 5 (May 2017): 668–75. http://dx.doi.org/10.1123/ijspp.2016-0454.

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Purpose:To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists.Methods:Training data from 15 well-trained competitive cyclists were collected during a 10-wk (December–March) preseason training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas-exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister TRIMP, Edwards TRIMP, individualized TRIMP (iTRIMP), Lucia TRIMP (luTRIMP), and session rating of perceived exertion (sRPE). External load was measured using Training Stress Score (TSS).Results:Large to very large relationships (r = .54–.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol/L) were observed for all training-load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = .81 [95% CI .51–.93, r = .77 [95% CI .43–.92]) and TSS (r = .75 [95% CI .31–.93], r = .79 [95% CI .40–.94]). The strongest dose-response relationships with changes in the 8MT test were observed for iTRIMP (r = .63 [95% CI .17–.86]) and luTRIMP (r = .70 [95% CI .29–.89).Conclusions:Training-load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.

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Moreira, Alexandre, Nivaldo Ribeiro de Moura, Aaron Coutts, Eduardo Caldas Costa, Thomas Kempton, and Marcelo Saldanha Aoki. "Monitoring Internal Training Load and Mucosal Immune Responses in Futsal Athletes." Journal of Strength and Conditioning Research 27, no. 5 (May 2013): 1253–59. http://dx.doi.org/10.1519/jsc.0b013e3182653cdc.

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