Training Load Variables Research Articles

Relationships Between Different Internal and External Training Load Variables and Elite International Women's Basketball Performance.

To investigate the relationships between internal and external training load (TL) metrics with elite international women's basketball performance. Sessional ratings of perceived exertion, PlayerLoad™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7]y, stature 186.0 [9.8]cm, body mass 77.9 [11.6]kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance. For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47-.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305-0.431). Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women's basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players' performance, especially in internal TL.

Relationship Between Subjective and External Training Load Variables in Youth Soccer Players.

To quantify and describe relationships between subjective and external measures of training load in professional youth soccer players. Data from differential ratings of perceived exertion (dRPE) and 7 measures of external training load were collected from 20 professional youth soccer players over a 46-week season. Relationships were described by repeated-measures correlation, principal component analysis, and factor analysis with oblimin rotation. Significant positive (.44 ≤ r ≤ .99; P < .001) within-individual correlations were obtained across dRPE and all external training load measures. Correlation magnitudes were found to decrease when training load variables were expressed per minute. Principal component analysis provided 2 components, which described 83.3% of variance. The first component, which described 72.9% of variance, was heavily loaded by all measures of training load, while the second component, which described 10.4% of the variance, appeared to have a split between objective and subjective measures of volume and intensity. Exploratory factor analysis identified 4 theoretical factors, with correlations between factors ranging from .5 to .8. These factors could be theoretically described as objective volume, subjective volume, objective running, and objective high-intensity measures. Removing dRPE measures from the analysis altered the structure of the model, providing a 3-factor solution. The dRPE measures are significantly correlated with a range of external training load measures and with each other. More in-depth analysis showed that dRPE measures were highly related to each other, suggesting that, in this population, they would provide practitioners with similar information. Further analysis provided characteristic groupings of variables.

Quantifying the Training and Match-Play External and Internal Load of Elite Gaelic Football Players

The current investigation quantified the training and match-play load of elite Gaelic football players across a two-season period using global positioning system technology (GPS), rating of perceived exertion (RPE) and sessional rating of perceived exertion (sRPE). Total weekly workload variables were collected across GPS, RPE, and sRPE across thirty-six elite Gaelic footballers (mean ± SD, age: 26 ± 5 years; height: 177 ± 8 cm; mass: 81 ± 7 kg) from one elite squad during a two-season observational period. External training load variables included: Total distance (m), High speed running (m; ≥ 17.1 km·h−1), Sprint distance (m; 22 km·h−1), Accelerations (n), Average metabolic power (W·kg−1), High-power distance (m; ≥ 25 W·kg−1). Internal load variables included: sRPE and RPE. Repeated measures ANOVA were used to understand the differences in loading patterns across phases, position, and week types when significant main effects were observed a Tukey’s post hoc test was applied and standardized effect sizes were calculated to understand the practical meaning of these differences. When total weekly loading across phases was considered total load was significantly greater in club 1 and provincial 1 with these phases showing the highest loading for players when compared to all other phases (p ≤ 0.001; ES: 2.95–7.22; very large). Furthermore, in-season 1 was greater for total loading when compared to in-season 2 and both championship phases (p ≤ 0.05; ES: 0.47–0.54; small). Total distance in training was greater during preseason 1 when compared to all other phases of the season (p ≤ 0.001; ES: 2.95–7.22; very large). During the in-season period, training based total distance was higher during provincial 1 when compared to other phases with similar trends across all measures (p ≤ 0.005). Finally, a positional profile for load measures was observed, with weekly context (match or non-match) having an impact on the internal and external loading players experienced across phases. The current data provide useful information for practitioners on the training periodization currently present within the elite Gaelic football training process. Specifically, the data provide positional profiles of loading across weekly and segmented phased of an elite Gaelic football season. These data can increase understanding as to the periods of increased and decreased loading across different phases of an elite Gaelic football season, while providing a framework for future analysis concerning Gaelic football periodization.

Relationships Between Training Load Variables in Professional Youth Football Players.

This study aims to investigate the relationship between subjective and external measures of load in professional youth football players whilst accounting for the effect of training theme or competition. Data from ratings of perceived exertion and global positioning system-derived measures of external training load were collected from 20 professional youth players (age=17.4±1.3 yrs) across a 46-week season. General characteristics of training sessions were categorised based on their proximity to match day. The underlying structure of the data was investigated with principal component analysis. An extraction criterion comprising eigenvalues >1 was used to identify which components to retain. Three components were retained for training performed three days prior to match day (80.2% of variance), with two components (72.9-89.7% of variance) retained for all other modes. Generally, the first component was represented by measures of volume (Total Distance, PlayerLoad and low intensity running) whilst the second and third components were characterised by measures of intensity. Identification of multiple components indicates that load monitoring should comprise multiple variables. Additionally, differences in the underlying structure across training days that reflected different goals suggest that effective monitoring should be specific to the demands of different session types.

Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required

ABSTRACT Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Monitoring physical performance and training load in young surf athletes

We aimed to study physical performance and monitor training load (both internal and external) during 12 weeks (3 times per week) and analyse the hypothetical association between physical performance and training load of competitive junior surfers. Twelve competitive surfers voluntarily participated (aged 16.00 ± 1.00y) and completed anthropometric and 8 physical performance tests including weight-bearing dorsiflexion test, functional movement screen, star excursion balance test, squat jump and countermovement jump, sprint & endurance paddling, and breath-hold capacity. Moreover, athletes were monitored by using a heart rate (HR) sensor and global position system (GPS) during each training session (n = 36). For internal load (IL), HR, rating of perceived exertion RPE, duration*RPE were used as variables and for external load (EL), duration, total distance, average and maximum speed and pace were considered for analysis. No significant correlations were found between physical performance tests and the training load variables. Significant correlations were found between IL (time*RPE), total distance (r = .58, p < .01), maximum speed (r = .43, p = .04) and duration (r = .60, p < .01). The HR was positively associated with average speed (r = .45, p = .04), pace (r = .43, p = .04), maximum speed (r = .64, p < .01). Total distance, average HR and average speed significantly predicted IL during training F(4.18) = 3.17; p = .04; R2 = .48. Data suggests that subjective instruments like RPE seems to be a good instrument to assess the training load in surf training. In terms of training for surfing, maximum speed seems to be a determinant factor in the estimation of IL perception.

Acute effects of training load on contractile properties during a competitive microcycle in elite soccer players.

The aim of this study was to examine changes in muscle contractile properties across a microcycle of training in professional soccer players during the in-season period. Nineteen professional soccer players were assessed with tensiomyography (TMG) on the biceps femoris and rectus femoris before and after 5 training sessions of an in-season microcycle. Training load was quantified during each training session. Significant differences were observed in training load variables across different training sessions, with the last training session prior to match day showing the lowest values for all training load variables. Significant pre- to post-session increases were observed in muscle stiffness of the rectus femoris and biceps femoris during the first four training sessions (effect size range, 0.5 to 0.9). However, no significant differences were observed in muscle contraction time and contraction velocity from pre- to post-session. In addition, repeated measures correlation analysis revealed significant relationships between absolute change in muscle stiffness of the rectus femoris and training duration, high-speed distance covered during training, and training average distance. The current study shows that players are physically taxed on their muscular stiffness by the training load. Post-session muscular stiffness assessment should be recommended to determine neuromuscular status and readiness in professional soccer players during the competitive season.

Methods of Monitoring Training Load and Their Association With Changes Across Fitness Measures in Hurling Players.

Malone, S, Hughes, B, Collins, K, and Akubat, I. Methods of monitoring training load and their association with changes across fitness measures in hurling players. J Strength Cond Res 34(1): 225-234, 2020-The aim of the current investigation was to assess the dose-response relationship for various methods of monitoring training load (TL) and changes in aerobic and anaerobic fitness in hurling players. Training and match load measures were collected from 30 hurling players (speed at different blood lactate [vLT, vOBLA], maximal oxygen uptake [VO2max], speed at VO2max [vVO2max], peak treadmill velocity [PTV] running economy [RE] Yo-Yo intermittent recovery test (Yo-YoIR1, Yo-YoIR2), speed at 5, 10 , 20 m, and repeated sprint ability [6 × 35 m]) before and after during a 12-week in-season period. Mean weekly training and match loads as determined by s-RPE, bTRIMP, luTRIMP, eTRIMP, iTRIMP, and gTRIMP were correlated with each other, percentage change in VO2max, vVO2max, RE, PTV, the speed at blood lactate concentrations of 2 mmol·L (vLT) and 4 mmol·L (vOBLA), and YoYoIR1, Yo-YoIR2 performance. iTRIMP showed a trivial association with s-RPE and a small to moderate association with other heart rate-based methods. Small to large changes (effect size: 0.38-1.12) were observed in aerobic and anaerobic fitness measures were observed across the 12-week period. Trivial to very large associations (r = 0.11-0.78) were observed between TL variables and changes in fitness depending on the variable analyzed. iTRIMP was shown to have large to very large associations with changes in vOBLA {r = 0.78 (95% confidence interval [CI]: 0.71-0.88)}, VO2max (r = 0.77 [95% CI: 0.68-0.82]), RE (r = 0.77 [95% CI: 0.67-0.82]), Yo-YoIR1 (r = 0.69 [95% CI: 0.51-0.72]), and RSAb (r = 0.66 [95% CI: 0.60-0.72]) with moderate associations reported between iTRIMP and other variables. All other measures of TL showed trivial to moderate associations (r = 0.11-0.66). The current investigation shows that fully individualized training load (iTRIMP) measures may potentially offer a better understanding of dose-response relationships when compared with subjective and nonindividualized measures of TL to changes in aerobic and anaerobic fitness in team sports athletes; as such, it may be suggested that practitioners should use individualized measures of TL assessment within their day-to-day practices.

Workload efficiency as a new tool to describe external and internal competitive match load of a professional soccer team: A descriptive study on the relationship between pre-game training loads and relative match load

The current study introduces a new index for external and internal workload, “workload efficiency”, and assesses in professional soccer the influence of pre-match training load on match workload efficiency. External and internal workloads were determined for 44 training sessions and 16 competitive matches using a 10-Hz global positioning system (GPS) and a 200 Hz accelerometer/heart rate monitor. Training loads were registered from day five (D-5) to day one (D-1) prior to each competitive match. Workload efficiency was calculated for each match as the ratio between overall external and internal load. A multiple stepwise regression analysis (including z-transformed variables) was used to determine training load variables that predict workload efficiency of the following matches. Training load variables of the previous days explained 26.6% of the variance in workload efficiency during the following matches. Long sprinting distance on D-3 and D-4 and total distance on D-1 positively influenced the players’ workload efficiency, whereas long training durations and high training load on D-1 showed adverse effects. The present outcomes suggest that including sprint training (high sprinting distance) four and three days prior to a match, may provide a positive stimulus for the subsequent workload efficiency in matches. The negative impact of long training duration and high training load one day before the game highlights the importance of a diligent planning of the immediate competition preparation phase. This study shows that workload efficiency is a useful metric to assess match performance and that body-worn sensor technology can be useful for tailoring training loads.

Match and Training Load Exposure and Time-Loss Incidence in Elite Rugby Union Players.

ObjectiveTo investigate the impact of match and training load on time-loss incidence in elite, professional Rugby Union players.Materials and MethodsEighty-nine Rugby Union players were monitored over two seasons of training and competition. Load was measured for all training sessions and matches using subjective [session ratings of perceived exertion (sRPE) load; RPE × session duration] and objective [global positioning systems (GPS); distance and high-speed running distance] methods and quantified using multiple approaches; absolute match and training load, acute:chronic workload ratio (ACWR), exponentially weighted moving average (EWMA) and cumulative 7, 14, 21, and 28 d sums. Mixed effect models were used to assess the effect of each variable on time-loss incidence.ResultsOf the 474 time-loss incidences that occurred across the two seasons, 50.0% were contact injuries (86.5% occurred during matches and 13.5% during training), 34.8% were non-contact injuries (31.5% occurred during matches and 68.5% during training) and 15.2% were cases of illness. The absolute match and training load variables provided the best explanation of the variance in time-loss incidence occurrence [sRPE load: p < 0.001, Akaike information criterion (AIC) = 2936; distance: p < 0.001, AIC = 3004; high-speed running distance: p < 0.001, AIC = 3025]. The EWMA approach (EWMA sRPE load: p < 0.001, AIC = 2980; EWMA distance: p < 0.001, AIC = 2980; EWMA high-speed running distance: p = 0.002, AIC = 2987) also explained more of the variance in time-loss incidence occurrence than the ACWR approach (ACWR sRPE load: p = 0.091, AIC = 2993; ACWR distance: p = 0.008, AIC = 2990; ACWR high-speed running distance: p = 0.153, AIC = 2994).ConclusionOverall, the absolute sRPE load variable best explained the variance in time-loss incidence, followed by absolute distance and absolute high-speed running distance. Whilst the model fit using the EWMA approach was not as good as the absolute load variables, it was better than when the same variables were calculated using the ACWR method. Overall, these findings suggest that the absolute match and training load variables provide the best predictors of time-loss incidence rates, with sRPE load likely to be the optimal variant of those examined here.

The Association Between the Acute:Chronic Workload Ratio and Injury and its Application in Team Sports: A Systematic Review.

There has been a recent increase in research examining training load as a method of mitigating injury risk due to its known detrimental effects on player welfare and team performance. The acute:chronic workload ratio (ACWR) takes into account the current training load (acute) and the training load that an athlete has been prepared for (chronic). The ACWR can be calculated using; (1) the rolling average model (RA) and (2) the exponentially weighted moving average model (EWMA). The primary aim of this systematic review was to investigate the literature examining the association between the occurrence of injury and the ACWR and to investigate if sufficient evidence exists to determine the best method of application of the ACWR in team sports. Studies were identified through a comprehensive search of the following databases: EMBASE, Medline, SPORTDiscus, SCOPUS, AMED and CINAHL. Extensive data extraction was performed. The methodological quality of the included studies was assessed according to the Newcastle-Ottawa Scale (NOS) for Cohort Studies. A total of 22 articles met the inclusion criteria. The assessment of article quality had an overall median NOS score of 8 (range 5-9). The findings of this review support the association between the ACWR and non-contact injuries and its use as a valuable tool for monitoring training load as part of a larger scale multifaceted monitoring system that includes other proven methods. There is support for both models, but the EWMA is the more suitable measure, in part due to its greater sensitivity. The most appropriate acute and chronic time periods, and training load variables, may be dependent on the specific sport and its structure. For practitioners, it is the important to understand the intricacies of the ACWR before deciding the best method of calculation. Future research needs to focus on the more sensitive EWMA model, for both sexes, across a larger range of sports and time frames and also combinations with other injury risk factors.

Within-Subject Correlation Between Salivary IgA and Measures of Training Load in Elite Football Players.

Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = -.39), total distance covered (r = -.55), accelerations (r = -.52), and decelerations (r = -.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = -.23), and number of sprints (r = -.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = -0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = -5.0 to -0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = -0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.

In-season internal and external training load quantification of an elite European soccer team.

Elite soccer teams that participate in European competitions need to have players in the best physical and psychological status possible to play matches. As a consequence of congestive schedule, controlling the training load (TL) and thus the level of effort and fatigue of players to reach higher performances during the matches is therefore critical. Therefore, the aim of the current study was to provide the first report of seasonal internal and external training load that included Hooper Index (HI) scores in elite soccer players during an in-season period. Nineteen elite soccer players were sampled, using global position system to collect total distance, high-speed distance (HSD) and average speed (AvS). It was also collected session rating of perceived exertion (s-RPE) and HI scores during the daily training sessions throughout the 2015–2016 in-season period. Data were analysed across ten mesocycles (M: 1 to 10) and collected according to the number of days prior to a one-match week. Total daily distance covered was higher at the start (M1 and M3) compared to the final mesocycle (M10) of the season. M1 (5589m) reached a greater distance than M5 (4473m) (ES = 9.33 [12.70, 5.95]) and M10 (4545m) (ES = 9.84 [13.39, 6.29]). M3 (5691m) reached a greater distance than M5 (ES = 9.07 [12.36, 5.78]), M7 (ES = 6.13 [8.48, 3.79]) and M10 (ES = 9.37 [12.76, 5.98]). High-speed running distance was greater in M1 (227m), than M5 (92m) (ES = 27.95 [37.68, 18.22]) and M10 (138m) (ES = 8.46 [11.55, 5.37]). Interestingly, the s-RPE response was higher in M1 (331au) in comparison to the last mesocycle (M10, 239au). HI showed minor variations across mesocycles and in days prior to the match. Every day prior to a match, all internal and external TL variables expressed significant lower values to other days prior to a match (p<0.01). In general, there were no differences between player positions.Conclusions: Our results reveal that despite the existence of some significant differences between mesocycles, there were minor changes across the in-season period for the internal and external TL variables used. Furthermore, it was observed that MD-1 presented a reduction of external TL (regardless of mesocycle) while internal TL variables did not have the same record during in-season match-day-minus.

Comparative Study of Two Intervention Programmes for Teaching Soccer to School-Age Students.

The objective of this study was to design and analyse the differences and/or similarities of two homogeneous intervention programmes (didactic units) based on two different teaching methods, Direct Instruction (DI) and Tactical Games Approach (TGA), for teaching school-age soccer. The sample was composed of 58 tasks, 29 for each intervention programme. The pedagogical and external Training Load (eTL) variables recorded in the Integral System for Training Tasks Analysis (SIATE) were studied. The two intervention programmes were compared using Chi-Square, Mann-Whitney U and the Adjusted Standardized Residuals statistical tests. Likewise, the strength of association of the variables under study was calculated using Cramer’s Phi and Cramer’s V coefficients. Both intervention programmes had the same number of tasks (n = 29), sessions (n = 12), game phases (x2= 0.000; p = 1.000), specific contents (x2 = 5.311; p = 0.968) and didactic objectives, as well as different levels of eTL (U = 145.000; p = 0.000; d = 1.357); which are necessary requirements to be considered similar. The differences and/or similarities between both intervention programmes will offer teachers guidelines to develop different didactic units using the specific DI and TGA methodologies.

Monitoring Training Load, Well-Being, Heart Rate Variability, and Competitive Performance of a Functional-Fitness Female Athlete: A Case Study.

The aim of this case study was to quantify the magnitude of internal load, acute/chronic workload ratio (ACWR), well-being perception, and heart rate variability (HRV) following 38 weeks of functional-fitness training in a female elite athlete. The internal load was obtained with session rating perceived exertion (session-RPE) while the ACWR was calculated by dividing the acute workload by the chronic workload (four-week average). Furthermore, HRV measurements were analyzed via a commercially available smartphone (HRV4training) each morning upon waking whilst in a supine position. The magnitude of internal load was: the weekly mean total during the 38 weeks was 2092 ± 861 arbitrary units (AU); during the preparation for the Open 2018 was 1973 ± 711 AU; during the Open 2018 it was 1686 ± 412 AU; and during the preparation for the Latin America Regional was 3174 ± 595 AU. The mean ACWR was 1.1 ± 0.5 and 50% of the weeks were outside of the ‘safe zone’. The well-being during the 38 weeks of training was 19.4 ± 2.3 points. There were no correlations between training load variables (weekly training load, monotony, ACWR, and HRV), and recuperation subjective variables (well-being, fatigue, sleep, pain, stress, and mood). This case study showed that the training load can be varied in accordance with preparation for a specific competition and ACWR revealed that 50% of the training weeks were outside of the ‘safe zone’, however, no injuries were reported by the athlete. The effectiveness and cost of these methods are very practical during real world functional-fitness.

The Association Between Training Load and Performance in Team Sports: A Systematic Review.

Adequate training loads promote favorable physical and physiological adaptations, reduce the likelihood of illness and injury, and, therefore, increase the possibility of success during competition. Our objective was to systematically examine the association between training load and performance outcomes in team sports. We systematically searched the PubMed, SPORTDiscus, and PsycINFO databases for original research published before July 2018. The search included terms relevant to training load, performance, and team sports. Articles were screened using pre-defined selection criteria, and methodological quality was assessed independently by two authors before data were extracted by the lead author. The electronic search yielded 5848 articles, 2373 of which were duplicates. A further 17 articles were retrieved from additional sources. In total, 26 articles met the inclusion criteria for this review, with quality scores ranging from 6 to 10 out of 11. Training exposure was more strongly associated with aerobic performance than other external training load measures. High-intensity activity (≥ 90% of maximum heart rate) was strongly associated with aerobic performance. The individualized training impulse model was strongly associated with aerobic performance, whereas various other training impulse models and perceptual training load measures showed weak associations with aerobic performance. There were no clear associations between training load and neuromuscular variables or game-related statistics. We found no consistent associations between external training load measures and performance. High-intensity internal training load appears to be the most prominent indicator of aerobic performance.

The influence of sleep and training load on illness in nationally competitive male Australian Football athletes: A cohort study over one season

ObjectivesTo determine the incidence of illness, and identify the relationship between sleep, training load and illness in nationally competitive Australian football athletes. Second, to assess multivariate effect between training load and/or sleep variables. DesignCohort study. MethodsRetrospective analyses of prospectively collected cohort data were conducted on forty-four male athletes over a 46-week season. The primary outcome was illness incidence, recorded daily by medical doctors. Independent variables were acute, chronic and acute:chronic ratios of: sleep quality, sleep quantity, internal training load and external training load defined as: total running distance, high speed running distance and sprint distance. Generalised estimating equations using Poisson (count) models were fit to examine both univariate and multivariate associations between independent variables and illness incidence. Results67 incidences of illness were recorded, with an incidence rate of 11 illnesses per 1000 running hours. Univariate analysis showed acute and chronic sleep hours and quality, as well as acute sprint and total running distance to be significantly associated with illness. Multivariate analysis identified that only acute sleep quantity was significantly, negatively associated with illness incidence (OR 0.49, CI 0.25–0.94) once all univariate significant variables were controlled for. There was no relationship between external training load and illness when sleep metrics were controlled for. ConclusionsIn a cohort of Australian football athletes, whose load was well monitored, reduced sleep quantity was associated with increased incidence of illness within the next 7 days. Monitoring sleep parameters may assist in identifying individuals at risk of illness.

Bone health in elite Kenyan runners

ABSTRACTImpact loading in athletes participating in various sports has been positively associated with increased bone mineral density (BMD), but this has not been investigated in elite Kenyan runners. Body composition and site-specific BMD measures quantified with dual x-ray absorptiometry were measured in 15 elite male Kenyan runners and 23 apparently healthy South African males of different ethnicities. Training load and biomechanical variables associated with impact loading, such as joint stiffness, were determined in the elite Kenyan runners. Greater proximal femur (PF) BMD (g · cm−2) was higher (P = 0.001, ES = 1.24) in the elite Kenyan runners compared with the controls. Six of the 15 (40%) Kenyan runners exhibited lumbar spine (LS) Z-Scores below −2.0 SD, whereas this was not found in the apparently healthy controls. PFBMD was associated with training load (r = 0.560, P = 0.003) and ankle (r = 0.710, P = 0.004) and knee (r = 0.546, P = 0.043) joint stiffness. Elite Kenyan runners exhibit greater PFBMD than healthy controls, which is associated with higher training load and higher joint stiffness. Our results reaffirm the benefits of impact loading on BMD at a weight-bearing site, while a high prevalence of low LSBMD in the elite Kenyan runners is hypothesised to be the result of a mismatch between energy intake and high training load. Future research investigating energy availability in Kenyan runners and the possible association with musculoskeletal injury should be investigated.

Importance of Various Training-Load Measures in Injury Incidence of Professional Rugby League Athletes.

To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes. TL and injury data were collected across 3 seasons (2013-2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes' corresponding injury status was marked as "available" or "unavailable." Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted. Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes. Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

Relationship Between Individualized Training Impulse and Aerobic Fitness Measures in Hurling Players Across a Training Period.

Malone, S and Collins, K. Relationship between individualized training impulse and aerobic fitness measures in Hurling players across a training period. J Strength Cond Res 30(11): 3140-3145, 2016-The current study examined the association between individual internal training load (individualized training impulse, iTRIMP) and aerobic fitness variables in male hurling players. Twenty hurling players (age, 25.5 ± 3.2 years; height, 178.9 ± 3.2 cm; body mass, 78.5 ± 4.5 kg) performed treadmill testing for V[Combining Dot Above]O2max, running economy (RE), and the speed at blood lactate concentrations of 2 mmol·L (S2) and 4 mmol·L (S4) on separate occasions before and after an 8-week training period. The Yo-Yo intermittent recovery (Yo-YoIR1, Yo-YoIR2) test performance were also assessed before and after the training period. Individualized training impulse was calculated using the blood lactate and heart rate profile of each individual player and was further assessed for each training session across the intervention period (n = 990). The results showed that iTRIMP had large to very large association with the percentage improvements in V[Combining Dot Above]O2max (r = 0.77; p = 0.002; 95% confidence interval [CI], 0.38-0.93, very large), RE (r = 0.78; p = 0.002; 95% CI, 0.40-0.93; very large), S2 (r = 0.64; p = 0.004; 95% CI, from 0.25 to 0.85; large), S4 (r = 0.78; p = 0.003; 95% CI, 0.45-0.85; very large), Yo-YoIR1 (r = 0.69; p = 0.003; 95% CI, 0.45-0.92; large), and Yo-YoIR2 (r = 0.60; p = 0.005; 95% CI, 0.45-0.92; large) performance. The study shows that iTRIMP is a means of quantifying training load in team sports and can be used to prescribe training for the maintenance or improvement of aerobic fitness during the competitive season with strong relationships seen between weekly iTRIMP measures and improvements in aerobic fitness measures.