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Literatura académica sobre el tema "Monitoring training load"

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Tesis sobre el tema "Monitoring training load"

1

Campbell, Patrick George. "Critically examining the capacity of wellness measures as a method of monitoring training load and the athlete training response." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134391/1/Patrick_Campbell_Thesis.pdf.

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Athlete load monitoring is standard in high-performance sports environments, and is principally utilised to maximise performance and physiological adaptations, while limiting the deleterious costs of training and competition. Modified wellness questionnaires are a popularly utilised method of load monitoring, and are commonly used to evaluate the athlete training response from a holistic viewpoint. However, there remains a lack of evidence to support their use. This thesis investigated the dosage effects of wellness measures on controlled acute and chronic modulations in training intensity and overall volume; and the associations between wellness and psychological states, load monitoring markers and common performance measures.
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2

Parson, Oliver. "Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/364263/.

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Non-intrusive appliance load monitoring (NIALM) is the process of disaggregating a household’s total electricity consumption into its contributing appliances. Smart meters are currently being deployed on national scales, providing a platform to collect aggregate household electricity consumption data. Existing approaches to NIALM require a manual training phase in which either sub-metered appliance data is collected or appliance usage is manually labelled. This training data is used to build models of the house- hold appliances, which are subsequently used to disaggregate the household’s electricity data. Due to the requirement of such a training phase, existing approaches do not scale automatically to the national scales of smart meter data currently being collected. In this thesis we propose an unsupervised training method which, unlike existing approaches, does not require a manual training phase. Instead, our approach combines general appliance knowledge with just aggregate smart meter data from the household to perform disaggregation. To do so, we address the following three problems: (i) how to generalise the behaviour of multiple appliances of the same type, (ii) how to tune general knowledge of appliances to the specific appliances within a single household using only smart meter data, and (iii) how to provide actionable energy saving advice based on the tuned appliance knowledge. First, we propose an approach to the appliance generalisation problem, which uses the Tracebase data set to build probabilistic models of household appliances. We take a Bayesian approach to modelling appliances using hidden Markov models, and empirically evaluate the extent to which they generalise to previously unseen appliances through cross validation. We show that learning using multiple appliances vastly outperforms learning from a single appliance by 61–99% when attempting to generalise to a previously unseen appliance, and furthermore that such general models can be learned from only 2–6 appliances. Second, we propose an unsupervised solution to the model tuning problem, which uses only smart meter data to learn the behaviour of the specific appliances in a given house-hold. Our approach uses general appliance models to extract appliance signatures from a household’s smart meter data, which are then used to refine the general appliance models. We evaluate the benefit of this process using the Reference Energy Disaggregation Data set, and show that the tuned appliance models more accurately represent the energy consumption behaviour of a given household’s appliances compared to when general appliance models are used, and furthermore that such general models can per- form comparably to when sub-metered data is used for model training. We also show that our tuning approach outperforms the current state of the art, which uses a factorial hidden Markov model to tune the general appliance models. Third, we apply both of these approaches to infer the energy efficiency of refrigerators and freezers in a data set of 117 households. We evaluate the accuracy of our approach, and show that it is able to successfully infer the energy efficiency of combined fridge freezers. We then propose an extension to our model tuning process using factorial hidden semi-Markov models to model households with a separate fridge and freezer. Finally, we show that through this extension our approach is able to simultaneously tune the appliance models of both appliances. The above contributions provide a solution which satisfies the requirements of a NIALM training method which is both unsupervised (no manual interaction required during training) and uses only smart meter data (no installation of additional hardware is required). When combined, the contributions presented in this thesis represent an advancement in the state of the art in the field of non-intrusive appliance load monitoring, and a step towards increasing the efficiency of energy consumption within households.
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3

Akubat, Ibrahim. "Training load monitoring in soccer : the dose-response relationships with fitness, recovery and fatigue." Thesis, University of Hull, 2012. http://hydra.hull.ac.uk/resources/hull:6898.

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The congested fixture schedules in elite soccer leagues around the world has bought the issue of recovery between games and subsequent performance to the fore in soccer related research. Studies have described the time-course of recovery for numerous biochemical and physiological measures of performance, fatigue and recovery from match-play. However, the research also suggests that there is individual variation in the external load both between players and between matches. The external load measured as distance in match-play has been shown to vary by ~30% between games. However it is the internal training load that will determine the magnitude of the physiological responses on an individual basis. Therefore the major aim of this thesis was to examine the dose-response relationships between measures of training load and the physiological and biochemical responses used as markers of recovery from match-play. The thesis also assessed the relationships between these proposed markers of recovery and soccer specific performance. In meeting the aims of the thesis a number of preliminary studies were conducted. The study in section 3 assesses the extent of fixture congestion in the English Premier League. The results showed over 30% of games for the most successful teams are played with 3 days recovery time, justifying the need for investigating recovery from soccer match-play. Given the variation in soccer match-play section 4 examines the reliability and validity of the modified BEAST90 soccer simulation. A measure of performance with less variance would allow changes in soccer specific performance to be identified with greater certainty in section 7. Section 5 assesses the influence of intermittent exercise on the blood lactate response. Given that the new iTRIMP method of measuring internal training load weights exertion with the blood lactate response it was important to assess the influence exercise mode may have on the calculation of internal training load. The results showed that at higher intensities intermittent exercise produced significantly higher blood lactate responses. Section 6 assesses the dose-response relationships between training and fitness using numerous measures of internal training load over a 6 week training period. The results showed only the iTRIMP method showed a significant relationship with changes in fitness. Section 7 assesses the dose-response relationships between exertion in soccer match-play and various physiological measures of fatigue and recovery. The relationships between these measures and changes in soccer specific performance were also assessed. Finally the internal and external load were integrated and the relationships of this ratio assessed with measures of fitness and performance. The results showed that changes in any of the physiological and biochemical measures used to assess recovery did not relate to changes in performance with the exception of testosterone which showed significant positive relationships with changes in distance covered from the 1st trial of the modified BEAST protocol to 2nd. Testosterone also was the only measure to show a significant relationship during the recovery period with any measure of training load (sRPE). Finally, the novel findings of this thesis is the relationships between the integrated ratio’s of internal and external training load with measures of aerobic fitness is also presented in section 7. The studies provided in this thesis have made a major contribution in demonstrating how data that is routinely collected at elite levels of soccer can be used more appropriately. It has also shown limitations of some the methods currently employed to measure training load. Furthermore changes in many of the markers used to assess recovery of soccer players do not seem to relate to changes in soccer specific performance. This may point to a change in paradigm which is required in both research and practice.
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4

Banyard, Henry G. "Velocity-based training: Monitoring, implementation and effects on strength and power." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2164.

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Traditionally, resistance training has been prescribed using percent-based training (PBT) methods that use the loads relative to a maximal load lifted for one repetition (1RM). However, PBT does not take into account possible day-to-day fluctuations in performance that may occur from physical or psychological stressors. One approach to address this limitation is to monitor velocity changes during resistance training, based on research showing that declines in velocity are highly correlated with fatigue. Therefore, velocity-based training (VBT) methods are proposed to provide a more objective method to modify resistance training sessions based on individual differences in day-to-day performance and the rate of training adaptation. However, at the commencement of this dissertation in 2014, no previous research had examined VBT methods in comparison to PBT methods. Thus, this thesis aimed to verify the efficacy of different VBT methods using a resistance-trained population who could lift a minimum of 150% their own body mass for at least one repetition in the full-depth back squat. These parameters were chosen so that the findings of this research were applicable to strength-trained athletes who were likely to employ VBT methods in their resistance training programs. In the first of five research studies, two portable VBT devices were examined for their accuracy to assess peak velocity (PV) and mean velocity (MV) among other kinematic variables. On three separate days, ten strength-trained men performed three 1RM back squat trials that comprised loads of 20%, 40%, 60%, 80%, 90% and 100% of 1RM. Acceptable validity criteria was based on a Pearson moment correlation coefficient >0.70, coefficient of variation (CV) ≤10% and Cohen d effect size (ES) r = 0.94 – 0.97, CV = 2.9 – 5.8%) and MV (r = 0.95 – 0.99, CV = 3.2 – 4.5%) across the relative load spectrum when compared to laboratory testing equipment. Thus, for the remainder of the VBT studies in this PhD thesis project, an LT was used to report the velocity data. In the second study, a novel velocity-based load monitoring method was investigated using 17 strength-trained men who performed three 1RM trials on separate days. Specifically, the reliability and validity of the load-velocity relationship to predict the back squat 1RM was calculated by entering MV at 100% 1RM into individualised linear regression equations which were derived from the load-velocity relationship of three (20%, 40%, 60% of 1RM), four (20%, 40%, 60%, 80% 1RM), or five (20%, 40%, 60%, 80%, 90% 1RM) incremental warm-up sets. The results showed that this predicted 1RM method was moderately reliable (ICC = 0.72 – 0.92, CV = 7.4 – 12.8%), and moderately valid (r = 0.78 – 0.93, CV = 5.7 – 12.2%). However, it could not be used as a VBT method to accurately modify training loads, since it significantly over-predicted the actual 1RM (SEE = 10.6 – 17.2 kg) due to the large variability of MV at 100% 1RM (ICC = 0.42, SEM = 0.05 m·s-1, CV = 22.5%). Therefore, this 1RM prediction method was no longer utilised as a method of adjusting training load for the remainder of the project. Despite its suggested importance, research had yet to investigate if velocity was stable between training sessions, so that individualised load-velocity profiles (LVP) could be created to track changes in velocity. Thus, the third study attempted to fill this research gap, where 18 strength-trained men performed three 1RM trials, which included warm-up loads pertaining to 20%, 40%, 60%, 80%, 90% and 100% 1RM, with the velocity of each repetition assessed by LT. It was found that PV, mean propulsive velocity (MPV) and MV were all reliable (ICC > 0.70, CV ≤ 10%, ES < 0.6) for the back squat performed at 20%, 40%, 60%, 80%, and 90% 1RM but not at 100% 1RM for MPV and MV. This meant that all three concentric velocity types could be used to develop LVPs. In addition, the smallest detectable difference was established across the relative load spectrum for PV (0.11 – 0.19 m·s-1), MPV (0.08 – 0.11 m·s-1) and MV (0.06 – 0.11 m·s-1), which then allows coaches to determine meaningful changes in velocity from their athletes between training sessions. Collectively, these results showed that LVPs could be utilised as a VBT method for monitoring sessional changes in velocity and modifying resistance-training loads according to individual differences in day-to-day performance. The fourth study compared the kinetic and kinematic data from three different VBT sessions and a PBT session in order to provide programmatic guidance to strength coaches who may choose to implement these novel methods to adjust resistance training load or volume. Fifteen strength-trained men performed four randomised resistance-training sessions 96 hours apart, which included a PBT session involving five sets of five repetitions at 80% 1RM, a LVP session (verified from Study 3) consisting of five sets of five repetitions with a load that could be adjusted to achieve a target velocity from an individualised LVP regression equation at 80% 1RM, a fixed sets 20% velocity loss threshold FSVL20 session that contained five sets at 80% of 1RM but sets were terminated once MV dropped below 20% of the maximal attainable MV from the first set or when five repetitions were completed, a variable sets 20% velocity loss threshold VSVL20 session that comprised 25 repetitions in total but participants performed as many repetitions in a set until the 20% velocity loss threshold was exceeded or 25 repetitions was completed. During the LVP and FSVL20 sessions, individuals performed repetitions with faster (p < 0.05) sessional MV (ES = 0.81 – 1.05) and PV (ES = 0.98 – 1.12), avoided additional mechanical stress with less time under tension but maintained similar force and power outputs when compared to the PBT session. Therefore, the LVP and FSVL20 methods could be employed in a strength-oriented training phase to diminish fatigue-induced decreases in movement velocity that can occur in PBT. The VBT method employed in the fifth and final study was derived from the results of Study 4. Both the LVP and FSVL20 methods permitted faster repetition velocities throughout a training session compared to PBT, but it was decided that the FSVL20 method could decrease total training volume and reduce the training stimulus, which may be unwarranted. Therefore, in Study 5, the effects of the LVP-VBT approach (VBT) versus PBT on changes in strength, power and sports performance measures following six weeks of back squat training were examined. The study involved 24 strength-trained men who performed back squat training three times per week in a daily undulating format. The training protocols were matched for sets and repetitions but differed in the assigned training load. PBT group trained with relative loads varying from 59% – 85% 1RM, whereas the VBT group trained with loads that could be adjusted to achieve a target velocity from an individualised LVP that corresponded with 59% – 85% 1RM. Pre- and post-training assessments included 1RM, 30% of 1RM countermovement jump (CMJ), 20-m sprint, and 505 change of direction test (COD). Overall, the VBT group performed repetitions with faster velocities during training (p < 0.05, MV = 0.76 m·s-1 vs. 0.66 m·s-1) that were perceived as less difficult (p < 0.05, rating of perceived exertion = 5.1 vs. 6.0), and utilized marginally lower training loads (p < 0.05, ~1.7%1RM) compared to PBT. Both VBT and PBT methods were effective for significantly enhancing 1RM (VBT: 11.3% vs. PBT: 12.5%), CMJ peak power (VBT: 7.4% vs. PBT: 6.0%), 20-m sprint (VBT: -1.9% vs. PBT: -0.9%), and COD (VBT: -5.4% vs. PBT: -3.6%). No significant differences were observed between groups for any testing assessment but likely favourable training effects were observed in 1RM for PBT group, whilst VBT group had likely favourable improvements in 5-m sprint time, and possibly favourable improvements in 10-m sprint time, and COD time. These findings suggest that both VBT and PBT methods are similarly effective; however, PBT may provide a slight 1RM strength advantage whilst VBT may be preferred by some individuals, since it permits faster training velocities, is perceived as less difficult, and is a more objective method for adjusting training load to account for individual differences in the rate of training adaptation. In conclusion, VBT (LVP approach) and PBT are similarly effective for promoting significant improvements in strength, power and sports performance tasks in strength-trained participants. However, even though the LVP-based VBT method did not provide significant increases in strength and power adaptations compared to PBT, it provided similar improvements while avoiding additional mechanical loading which may be important for the better management of training load, particularly with athletes who partake in numerous training modalities which can influence fatigue and recovery. That being said, if all repetitions are performed with maximal intended velocity but not to concentric muscular failure, a well-planned, periodised resistance training program with regular training frequency and progressive overload that accounts for bouts of recovery will provide adequate stimulus to significantly enhance strength, power and performance tasks like sprinting and changes in direction. Future training studies may look to examine the efficacy of VBT methods using multiple exercises (upper and lower body), and with different populations including women, adolescents, older adults, and potentially individuals during rehabilitation from injury so that training progress can be objectively monitored. Furthermore, future studies could look to incorporate multiple VBT methods into a training program such as the LVP method to modify resistance training load and the velocity loss thresholds method to control resistance training volume.
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5

Fuller, Melanie. "Injury surveillance and monitoring during transitions in dance training and careers including end-user perceptions towards training load practices." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/207339/1/Melanie_Fuller_Thesis.pdf.

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This research investigated injuries across and within a training year and career phases in ballet and contemporary dance. The perceptions of artistic and health professionals regarding training practices were also explored. In tertiary dance, 50% of students were injured in the first seven weeks of the program, and certain weeks across the program resulted in higher injury rates. Across one semester, spikes in stress leading to performances, and spikes in load and injury to recommence technique training were observed. Artistic staff were perceived to be responsible for planning training, providing insights for future research into injury prevention in dance.
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6

Marshall, Ian Richard. "Monitoring individual training load and stress during practices and match-play in female collegiate soccer players." Diss., [Missoula, Mont.] : The University of Montana, 2008. http://etd.lib.umt.edu/theses/available/etd-05222008-121317/.

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7

Sams, Matthew L. "Comparison of Static and Countermovement Jump Variables in Relation to Estimated Training Load and Subjective Measures of Fatigue." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etd/2411.

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The purpose of this study was to compare changes in static and countermovement jump variables across a competitive season of collegiate soccer to estimated training load and subjective measures of fatigue. Monitoring data from 21 male collegiate soccer players were retrospectively examined. Nine vertical jump sessions occurred across the season in addition to daily training load assessment and daily mood-state assessment. Group average changes from the first testing session were calculated and compared to the group average training load for the 7 days preceding each vertical jump testing session for static and countermovement jump height and allometrically scaled peak power. Statistical analysis demonstrated strong relationships between changes in vertical jump height for both conditions, allometrically scaled peak power for static jumps, and estimated training load. The results indicate changes in static jump height and allometrically scaled peak power may be more useful athlete fatigue monitoring tools than countermovement jump variables.
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8

Markwick, William. "Training load quantification in professional Australian basketball and the use of the reactive strength index as a monitoring tool." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1709.

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Study 1: The intraday reliability of the reactive strength index (RSI) calculated from a drop jump in professional men’s basketball. Purpose: To evaluate the reliability of the reactive strength index (RSI) and jump height (JH) performance from multiple drop heights with elite basketball players. Methods: Thirteen professional basketball players (mean ±SD: age 25.8 ± 3.5 y, height 1.96 ± 0.07 m, mass 94.8 ± 8.2 kg) completed 3 maximal drop jump attempts on to a jump mat at 4 randomly assigned box heights and 3 counter movement jump (CMJ) trials. Results: No statistical difference was observed between three trials for both the RSI and JH variable at all the tested drop heights. The RSI for drop jump heights from 20 cm resulted in a coefficient of variation (CV) = 3.1% and an intraclass correlation (ICCα) =0.96, 40 cm resulted in a CV = 3.0% and an ICCα = 0.95, 50 cm resulted in a CV = 2.1% and an ICCα = 0.99. The JH variable at the 40 cm drop jump height resulted in the highest reliability CV = 2.8% and an ICCα = 0.98. Conclusion: When assessing the RSI the 20, 40 and 50 cm drop heights are recommended with this population. When assessing large groups it appears that only one tria Study 2: Does session RPE relate with reactive strength qualities? A case study investigation within the National Basketball League This investigation aimed to establish the relationship between training loads derived from the sessional rating of perceived exertion (sRPE) and the reactive strength index (RSI) over a 27-week competitive season in elite basketball players. Fourteen professional male basketball players (26 ± 3.6 years; 95.8 ± 9.0 kg; 197.3 ± 7.3 cm) participated in this study. Training load data were modeled against the RSI over a 27-week competitive season with the use of a linear mixed model. The relationship between RSI and training load was only significantly different from baseline (Week 1) at Week 24 (p < 0.05) and Week 26 (p < 0.01). These primarily findings suggest that sRPE and RSI have a weak relationship, whilst the RSI does not appear to accurately reflect the changes in training load that occur during an in-season periodized training program in professional male basketball.
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9

Antualpa, Kizzy Fernandes. "O efeito da estratégia de intensificação e tapering nas respostas hormonais, comportamentais, de desempenho, e na imunidade da mucosa oral em jovens atletas de ginástica rítmica." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/39/39132/tde-21112017-103644/.

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A Ginástica Rítmica (GR) é uma modalidade caracterizada pela a participação de jovens atletas no treinamento sistematizado. Este estudo examinou o efeito de um período de intensificação (IT, 4 semanas, após um período de treinamento habitual, TH) seguido de um período de tapering (TP, 2 semanas) nos hormônios salivares (testosterona - T e cortisol - C), imunoglobulina salivar A (SIgA), severidade das infecções do trato respiratório superior (ITRS), bem-estar (WB), e no desempenho físico e técnico em 23 atletas de ginástca rítmica ( < 11 anos de idade [G1], < 13 anos de idade [G2] e > 13 anos de idade [G3]). A percepção subjetiva de esforço da sessão (PSE da sessão) foi utilizada para quantificar a carga interna de treinamento (CIT) e a razão aguda:crônica da carga de treinamento (ACT). Os questionários WB e WURSS-21 foram preenchidos diariamente. A coleta de saliva e os testes de desempenho físico e técnico foram realizados no início da IT (T1), após IT (T2) e após TP (T3). Foi observada maior CIT para TI em comparação com TP (ES = 2,37). A ACT nas semanas de IT variou de 1,2 (0,3) a 1,4 (0,3). Não foram observadas alterações significantes foi para a concentração de T (206 ± 39, 221 ± 35, 216 ± 51 ?mol/L, para T1, T2 e T3, respectivamente [grupo inteiro]; p = 0,16), concentração de C (5,7 ± 1,0, 5,8 ± 0,8, 5,0 ± 0,7 ?mol/L; p = 0,07) e índice de bem-estar (WB - 19 ± 3, 19 ± 2, 19 ± 2; p = 0,44). O WB para G3 foi significativamente menor em comparação a G1 e G2. Uma maior concentração absoluta de SIgA (SIgAabs [ug/ml]) (F = 7,6; 20 p = 0,001) para pós-IT (234 ± 104) vs pré-IT (173 ± 91) e pós-TP (182 ± 70) e uma maior taxa de secreção de SIgA (SIgAtaxa [ug/min]) (F = 3,4; p = 0,04]) para pós-IT (69 ± 28) vs pré-IT (55 ± 27) e Pós-TP (58 ± 22) foi observada. Quanto à severidade dos sintomas de ITRS, nenhuma alteração significante foi observada (?2 = 2,81; p = 0,24). Houve melhora no desempenho físico para abdominais de T2 a T3 (ES = 0,80) e T1 a T3 (ES = 0,78) e para RFms (flexões de cotovelo) (T2-T3, ES = 0,61; T1-T3, ES = 0,55). Uma melhora desempenho técnico de T1 para T3 (ES = 2,32) também foi observada. Estes resultados sugerem que uma IT de 4 semanas seguida por TP (2 semanas) parece ser uma abordagem útil para melhorar o desempenho físico e técnico em jovens ginastas de GR, mantendo a percepção de WB, das repostas hormonais, podendo inclusive, induzir adaptações positivas nos sistemas orgânicos, em particular a função da imunidade da mucosa oral de ginastas prépuberes. Ademais, os presentes resultados podem indicar que a razão ACT de 1,2-1,4 possa servir como um valor de referência para organizar de forma efetiva e segura a intensificação das cargas de treinamento<br>Rhythmic Gymnastics (RG) is a modality characterized by participation of young athletes in systematized training. This study examined the effect of an intensification period (IT; 4 weeks; after a habitual training period; HT) followed by a tapering period (TP; 2 weeks) on salivary hormones (testosterone - T and cortisol - C), salivary immunoglobulin A (SIgA), severity of upper respiratory tract infections (URTI), wellbeing (WB), and physical and technical performance in 23 rhythmic gymnasts (RG; Under-11 group [G1], Under-13 group [G2], and > 13 group [G3]). The session-rating of perceived exertion (RPE session) was used to quantify the daily internal training load (ITL) and the acute:chronic workload ratio (ACW). The WB and WURSS-21 questionnaire were completed daily. Saliva sampling, physical and technical performance tests were carried out at the beginning of the IT (T1), after IT (T2), and after TP (T3). A higher ITL was observed for IT compared to TP (ES=2.37). The ACW for the IT weeks varied from 1.2 (0.3) to 1.4 (0.3). No significant change was detected for T concentration (206 ± 39, 221 ± 35, 216 ± 51 ?mol/L, for T1, T2, and T3, respectively [whole group]; p = 0.16), C concentration (5.7 ± 1.0, 5.8 ± 0.8, 5.0 ± 0.7 ?mol/L; p = 0.07), and WB (19 ± 3, 19 ± 2, 19 ± 2; p = 0.44). A significant lower WB score was observed for the G3. A higher SIgA absolute concentration (SIgAabs [ug/ml) (F=7.6; 20 p=0.001) for post-IT (234±104) vs pre-IT (173±91), and post-TP (182±70), and a higher SIgA secretion rate (SIgArate [ug/min]) (F=3.4; p=0.04]) for post-IT (69±28) vs pre-IT (55±27), and post-TP (58±22) were observed. No significant change was observed for severity of URTI symptoms (?2=2.81; p=0.24). Physical performance increased for sit-ups from T2 to T3 (ES = 0.80), and T1 to T3 (ES = 0.78) and for pushups (T2-T3; ES = 0.61; T1-T3; ES = 0.55). Technical performance also increased from T1 to T3 (ES = 2,32). These results suggest that a 4-week IT followed by TP (2-week) seems to be a useful approach to improve physical and technical performance of youth RG, while maintaining the perception of WB, the hormonal milieu, even affording to induce positive adaptations in body systems, in particular, the mucosal immune function, in youth RG. In addition, the results may indicate the ACW ratio of 1.2-1.4 might be used to organize an effective and safety intensification of training loads
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Kotani, Yosuke. "Factors related to creating force-velocity and load-velocity profiles with the squat jump." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2487.

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It is well recognized that the ability to rapidly generate high levels of force is an important factor underpinning vertical jump performance. Recently, it has become popular to use forcevelocity (FV) and load-velocity (LV) profiles created from progressively loaded squat jumps (SJs) to guide the resistance training process. By creating FV profiles, the coach can determine if the athlete has a force or velocity deficit and then create an individualized training program based upon the determined deficit. Alternatively, the LV profile can be used to monitor performance and as a method for prescribing training load. While several recent studies have explored the efficacy of the use of training methods based on the FV or LV profile, there is limited research examining the various methodologies and technologies that are commonly used to create these profiles with SJs. Therefore, the primary purpose of this thesis project was to investigate the reliability and validity of various methods for assessing the FV and LV profile during the SJ. In Study 1, progressively loaded SJs were performed on a force plate in order to determine the overall reliability of the FV and LV profiles. After this was completed, Study 2 was used to compare three methods of determining the FV and LV profiles. Specifically, the FV and LV profiles created from data collected with the PUSH BandTM 2.0 (PUSH) and GymAware (GYM) were compared with the profiles created from data collected with a force plate in order to determine if the velocities determined with these methods agreed. In Study 3, the FV and LV profiles created with two accelerometers (Bar Sensei [BS] and Beast Sensor [BEAST]) were compared with a laboratory based four-linear position transducer system (4- LPT) in order to determine if the velocities measured with these devices agreed and were reliable. In Study 4, the mode of exercise (free-weight or Smith Machine) was compared to determine if there was agreement between the FV and LV profiles created with each mode. Finally, in Study 5, SJs that were performed with free-weights and the Smith Machine were compared in order to determine if there was agreement between the FV and LV profiles created with a force plate, the PUSH, and GYM. The primary finding of this thesis was that the FV and LV profiles created from SJs with a force plate were unreliable regardless of which mode of exercise was utilized. Additionally, the PV quantified with the GYM agreed with the PV determined with the force plate regardless of whether the SJ was performed with free-weights or a Smith Machine. Conversely, the PV determined with the PUSH did not agree with the force plate regardless of which mode of exercise was performed. Finally, the PVs determined with the BEAST agreed with the 4-LPT system, whilst the BS did not. Based upon these findings, it is important that coaches are aware that there is a lack of reliability with these profiles and that velocities measured by different devices do not always agree.
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