An individualised approach to monitoring and prescribing training in elite youth football players

The concept of how training load affects performance is founded in the notion that training contributes to two specific outcomes, these are developed simultaneously by repeated bouts of training and act in conflict of each other; fitness and fatigue (Banister et al., 1975). The ability to understand these two components and how they interact with training load is commonly termed the “dose-response relationship” (Banister, 1991). The accurate quantification of training load, fitness and fatigue are therefore of paramount importance to coaches and practitioners looking to examine this relationship. In recent years, the advancement in technology has seen a rise in the number of methodologies used to assess training load and specific training outcomes. However, there is a general lack of evidence regarding the reliability, sensitivity and usefulness of these methods to help inform the training process. The aim of this thesis was therefore to improve the current understanding around the monitoring and prescription of training, with special reference to the relationship between training load, fitness and fatigue. Chapter 4 of this thesis looked to establish test re-test reliability. Variables selected for investigation were measures of subjective wellness; fatigue, muscle soreness, sleep quality, stress levels and mood state, assessments of physical performance; countermovement jump (CMJ), squat jump (SJ) and drop jump (DJ) and the assessment of tri-axial accelerometer data; PlayerLoadTM and individual component planes anterior-posterior (PLAP), mediolateral (PLML), and vertical (PLV), were collected during a sub-maximal shuttle run. The results from this investigation suggest that a short three minute sub-maximal shuttle run can be used as a reliable method to collect accelerometer data. Additionally, assessments of CMJ height, SJ height, DJ contact time (DJ-CT) and DJ reactive strength index (DJ-RSI) were all deemed to have good reliability. In contrast, this chapter highlighted the poor test re-test reliability of the subjective wellness questionnaire. Importantly, the minimum detectable change (MDC) was also calculated for all measures within this study to provide an estimate of measurement error and a threshold for changes that can be considered ‘real’. Chapter 5 assessed the sensitivity and reproducibility of these measures following a standardised training session. To assess sensitivity, the signal-to-noise (S: N) ratio was calculated by using the post training fatigue response (signal) and the MDC derived from Chapter 4 (noise). The fatigue response was considered reproducible if the S: N ratio was greater than one following two standardised training sessions. Three measures met the criteria to be considered both sensitive and reproducible; DJ-RSI, PLML and %PLV. All other measures did not meet the criteria. Subjective ratings of fatigue, muscle soreness and sleep quality did show a sensitive response on one occasion, however, this was not reproducible. This might be due to the categorical nature of the data, making detectable group changes hard to accomplish. The subjective wellness questionnaire was subsequently adapted to include three items; subjective fatigue, muscle soreness and sleep quality on a 10-point scale. The test re-test reliability of these three questions was established in Chapter 6, demonstrating that subjective fatigue and muscle soreness have good test re-test reliability. Chapter 6 was comprised of two studies looking to simultaneously establish the dose-response relationship between training load, measures of fatigue (Part I) and measures of fitness (Part II). In Part I training load was strategically altered on three occasions during a standardised training session in a randomised crossover design. In Part II training and match load was monitored over a 6-week training period with maximal aerobic speed (MAS) assessed pre and post. A key objective for both studies was to assess differences in the training load-fitness-fatigue relationship when using various training load measures, in particular differences between arbitrary and individualised speed thresholds. Results from Part I showed a large to very large relationship between training load and subjective fatigue, muscle soreness and DJ-RSI performance. No differences were found between arbitrary and individualised thresholds. In Part II however, individual external training load, assessed via time above MAS (t>MAS), showed a very large relationship with changes in aerobic fitness. This was in contrast to the unclear relationships with arbitrary thresholds. Taking the results from both studies into consideration it was concluded that t>MAS is a key measure of training load if the objective is to assess the relationship with both fitness and fatigue concurrently with one measure. Chapter 7 subsequently looked to validate the training load-fitness-fatigue relationships established in Chapter 6 via an intervention study. The aim was to develop a novel intervention that prescribed t>MAS, in order to improve aerobic fitness, based on the findings from Chapter 6. Additionally, the fatigue response following a standardised training session was assessed pre and post intervention to evaluate the effect the predicted improvements in aerobic fitness would have on measures of fatigue. Results from Chapter 7 indicate a highly predictable improvement in aerobic fitness from the training load completed during the study, validating the use of t>MAS as a monitoring and intervention tool. Furthermore, this improvement in aerobic fitness attenuated the fatigue response following a standardised training session. The final key finding was the very strong relationship between improvements in aerobic fitness and reductions in fatigue response. This further highlights the relationship between t>MAS, fitness and fatigue. In summary, this thesis has helped further current understanding on the monitoring and prescription of training load, with reference to fitness and fatigue. Firstly, a rigorous approach was used to identify fatigue monitoring measures that are reliable, sensitive and reproducible. Secondly, the relationship between training load, fatigue and fitness was clearly established. And finally, it has contributed new knowledge to the existing literature by establishing the efficacy of a novel MAS intervention to improve aerobic fitness and attenuate a fatigue response in elite youth football players

Analysis of repeated high-intensity running performance in professional soccer

The aims of this study conducted in a professional soccer team were two-fold: to characterise repeated high-intensity movement activity profiles in official match-play; b) to inform and verify the construct validity of tests commonly used to determine repeated-sprint ability in soccer by investigating the relationship between the results from a test of repeated-sprint ability and repeated high-intensity performance in competition. High-intensity running performance (movement at velocities >19.8 km/h for a minimum of 1-s duration) in 20 players was measured using computerised time motion analysis. Performance in 80 French League 1 matches was analysed. In addition, 12 out of the 20 players performed a repeated-sprint test on a non-motorized treadmill consisting of 6 consecutive 6s sprints separated by 20s passive recovery intervals. In all players, the majority of consecutive high-intensity actions in competition were performed after recovery durations ≥61s, recovery activity separating these efforts was generally active in nature with the major part of this spent walking, and players performed 1.1±1.1 repeated high-intensity bouts (a minimum of 3 consecutive high-intensity with a mean recovery time ≤20s separating efforts) per game. Players reporting lowest performance decrements in the repeated-sprint ability test performed more high-intensity actions interspersed by short recovery times (≤20s, p<0.01 and ≤30s, p<0.05) compared to those with higher decrements. Across positional roles, central-midfielders performed a greater number of high-intensity actions separated by short recovery times (≤20s) and spent a larger proportion of time running at higher intensities during recovery periods while fullbacks performed the most repeated high-intensity bouts (statistical differences across positional roles from p<0.05 to p<0.001). These findings have implications for repeated high-intensity testing and physical conditioning regimens

Can greater muscularity in larger individuals resolve the 3/4 power-law controversy when modelling maximum oxygen uptake?

BACKGROUND: The power function relationship, MR = a.m(b), between metabolic rate (MR) and body mass m has been the source of much controversy amongst biologists for many years. Various studies have reported mass exponents (b) greater than the anticipated 'surface-area' exponent 0.67, often closer to 0.75 originally identified by Kleiber. AIM: The study aimed to provide a biological explanation for these 'inflated' exponents when modelling maximum oxygen uptake (max), based on the observations from this and previous studies that larger individuals develop disproportionately more muscle mass in the arms and legs. RESEARCH DESIGN AND SUBJECTS: A cross-sectional study of 119 professional soccer players from Croatia aged 18-34 was carried out. RESULTS: Here we confirm that the power function relationship between max and body mass of the professional soccer players results in an 'inflated' mass exponent of 0.75 (95% confidence interval from 0.56 to 0.93), but also the larger soccer players have disproportionately greater leg muscle girths. When the analysis was repeated incorporating the calf and thigh muscle girths rather than body mass as predictor variables, the analysis not only explained significantly more of the variance in max, but the sum of the exponents confirmed a surface-area law. CONCLUSIONS: These findings confirm the pitfalls of fitting body-mass power laws and suggest using muscle-girth methodology as a more appropriate way to scale or normalize metabolic variables such as max for individuals of different body sizes

THE RELATIONSHIP BETWEEN MUSCULOSKELETAL STRENGTH, PHYSIOLOGICAL CHARACTERISTICS, AND KNEE KINESTHESIA FOLLOWING FATIGUING EXERCISE

Fatiguing exercise may result in impaired functional joint stability and increased risk of unintentional injury. While there are several musculoskeletal and physiological characteristics related to fatigue onset, their relationship with proprioceptive changes following fatigue has not been examined. The purpose of this study was to establish the relationship between musculoskeletal and physiological characteristics and changes in proprioception, measured by threshold to detect passive motion (TTDPM), following fatiguing exercise. Twenty, physically active females participated (age: 28.65 ± 5.6 years, height: 165.6 ± 4.3 cm, weight: 61.8 ± 8.0 kg, BMI: 22.5± 2.3 kg/m2, BF: 23.3 ± 5.4%). During Visit 1, subjects completed an exercise history and 24-hour dietary questionnaire, and body composition, TTDPM familiarization, isokinetic knee strength, and maximal oxygen uptake/lactate threshold assessments. During Visit 2, subjects completed TTDPM and isometric knee strength testing prior to and following a fatiguing exercise protocol. Wilcoxon signed rank tests determined TTDPM and isometric knee strength changes from pre- to post- fatigue. Spearman’s rho correlation coefficients determined the relationship between strength and physiological variables with pre- to post-fatigue changes in TTDPM and with pre-fatigue and post-fatigue TTDPM in extension and flexion (α=0.05). No significant differences were demonstrated from pre-fatigue to post-fatigue TTDPM despite a significant decrease in isometric knee flexion strength (P<0.01) and flexion/extension ratio (P<0.05) following fatigue. No significant correlations were observed between strength or physiological variables and changes in TTDPM from pre- to post-fatigue in extension or flexion. Flexion/extension ratio was significantly correlated with pre-fatigue TTDPM in extension (r=-0.231, P<0.05). Peak oxygen uptake was significantly correlated with pre-fatigue (r=-0.500, P<0.01) and post-fatigue (r=-0.520, P<0.05) TTDPM in extension. No significant relationships were demonstrated between musculoskeletal and physiological characteristics and changes in TTDPM following fatigue. The results suggest that highly trained individuals may have better proprioception, and that the high fitness level of subjects in this investigation may have contributed to absence of TTDPM deficits following fatigue despite reaching a high level of perceptual and physiological fatigue. Future studies should consider various subject populations, other musculoskeletal strength characteristics, and different modalities of proprioception to determine the most important contributions to proprioceptive changes following fatigue

Does maturity estimation, 2D:4D and training load measures explain physical fitness changes of youth football players?

Objectives: The purpose of the present study was two-fold: (1) To analyse physical fitness changes of youth football players after a full-season; and (2) to examine whether physical fitness changes are explainable by estimated maturity status, 2digit:4digit ratio (2D:4D) from each hand and training load (TL) measures. Methods: Twenty-seven youth elite Under-15 football players were daily monitored for training load measures during 38 weeks. At the beginning and at the end of the season, all players were assessed for physical fitness. Also, the maturity status estimation and the length of the second and fourth digits of both hands were collected at the beginning of the season. Results: Significant differences were found for all physical fitness measures after the season. The second and fourth digits of left and right hands had negative moderate correlations with change of direction (COD) changes (r=-.39 to − 0.45 | p = .05 to 0.02). Also, the maturity offset measure had negative moderate correlations with COD changes (r=-.40 | p = .04). From the reported significant correlations, the maturity offset, Left 4D, Right 2D and Right 4D significantly predicted the Mod.505 COD test changes (β = 0.41, p = .04; β = -0.41, p = .04; β = -0.45, p = .02; and β = -0.44, p = .03, respectively). Conclusion: The maturity offset and the 2D:4D measures have the potential to predict COD performance changes over-time in youth football players. Given the lack of associations between the maturity estimation, 2D:4D and training load measures, with the overall physical fitness measures, coaches should rely only at COD changes

Does maturity estimation, 2D: 4D and training load measures explain physical fitness changes of youth football players?

Objectives: The purpose of the present study was two-fold: (1) To analyse physical fitness changes of youth football players after a full-season; and (2) to examine whether physical fitness changes are explainable by estimated maturity status, 2digit:4digit ratio (2D:4D) from each hand and training load (TL) measures. Methods: Twenty-seven youth elite Under-15 football players were daily monitored for training load measures during 38 weeks. At the beginning and at the end of the season, all players were assessed for physical fitness. Also, the maturity status estimation and the length of the second and fourth digits of both hands were collected at the beginning of the season. Results: Significant differences were found for all physical fitness measures after the season. The second and fourth digits of left and right hands had negative moderate correlations with change of direction (COD) changes (r=-.39 to − 0.45 | p = .05 to 0.02). Also, the maturity offset measure had negative moderate correlations with COD changes (r=-.40 | p = .04). From the reported significant correlations, the maturity offset, Left 4D, Right 2D and Right 4D significantly predicted the Mod.505 COD test changes (β = 0.41, p = .04; β = -0.41, p = .04; β = -0.45, p = .02; and β = -0.44, p = .03, respectively). Conclusion: The maturity offset and the 2D:4D measures have the potential to predict COD performance changes over-time in youth football players. Given the lack of associations between the maturity estimation, 2D:4D and training load measures, with the overall physical fitness measures, coaches should rely only at COD changes

Active recovery and technique performance in soccer

The present study confirms the general impression of previous studies, that active recovery has an effect on soccer players’ restitution [1; 3; 25; 8; 19]. A study by Karlsen [21] suggested that technique training in a fatigue state also will give a beneficial effect for soccer players during a match [21]. These effects seem to be even more pronounced, when it comes to restitution after a period of high intensity work. The present study there were found that soccer players can, with the influence of active recovery, maintain or improve the level of technical skills