Quantification Of Training Load Research Articles

Biomechanical load quantification of national and regional soccer players with an inertial sensor setup during a jump, kick, and sprint task: assessment of discriminative validity

Training load quantification methods may help optimize soccer performance. However, whole-body indicators potentially underestimate biomechanical load. A new inertial sensor setup allows joint-specific biomechanical load quantification. Good discriminative validity further supports the use of this method, and therefore the purpose of this study is to assess the discriminative validity of this method during soccer-specific activities. Twelve national and sixteen regional soccer players wore an inertial sensor setup and performed countermovement jumps, soccer kicks, and 30 m sprints. Between-group differences in angular acceleration-based biomechanical load indicators Knee Load, Hip Load, and performance were assessed using MANOVAs and Cohen’s effect sizes. Furthermore, relationships with performance were explored. National players showed higher Knee Load during jumping (mean difference: 0.11 A.U., ES = 0.93, p = 0.02), kicking (mean difference: 1.94 A.U., ES = 0.94; p = 0.02), and almost during sprinting (mean difference: 12.85, ES = 0.77; p = 0.05). Hip Load did not differ between groups across all tasks, although national players outperformed regional players on all tests. Significant relationships between Knee Load (rjump = 0.41, rkick = 0.65), Hip Load (rjump = 0.42), and performance were observed with 95% confidence intervals ranging from trivial to large. The results confirm discriminative validity of Knee Load for jumping and kicking, but not for sprinting and Hip Load in general. The confidence intervals of the established relationships suggest that the biomechanical loads might not entirely explain between-group differences in performance. The results can be used as reference values for biomechanical load quantification in the field.

Haemoglobin mass responses and performance outcomes among high-performance swimmers following a 3-week live-high, train-high camp at 2320m.

Greater quantification and characterisation of training load (TL) throughout Live-high, train-high (LHTH) altitude (ALT) training is required to identify periodisation strategies that may lead to physiological and performance improvements in swimmers. This study aimed to examine the physiological responses and performance outcomes of 14 high-performance swimmers (FINA points: 836.0 ± 35.1) following 3weeks of LHTH at 2320m, while characterising the training load periodisation strategy adopted during the intervention. Haemoglobin (Hb) mass was measured pre-, 7 and 14days post-ALT via CO rebreathing. Performance in each athlete's primary event at national standard meets were converted to FINA points and compared from pre-to-post-ALT. TL was quantified at sea level (SL) and ALT through session rating of perceived exertion (RPE), where duration of each session was multiplied by its RPE for each athlete, with all sessions totalled to give a weekly TL. Pre-to-post-ALT changes were evaluated using repeated-measures ANOVA. Hb mass increased significantly from 798 ± 182g pre-ALT to 828 ± 187g at 7days post (p = 0.013) and 833 ± 205g 14days post-ALT (p = 0.026). Weekly TL increased from SL (3179 ± 638 au) during week one (4797 ± 1349 au, p < 0.001) and week two (4373 ± 967 au, p < 0.001), but not week three (3511 ± 730au, p = 0.149). No evidence of improved SL swimming performance was identified. A periodisation strategy characterised by a sharp spike in TL followed by a slight de-load towards the end of a LHTH intervention led to improved physiological characteristics but no change in the competitive performance of high-performance swimmers.

Daily cardiac autonomic responses during the Tour de France in a male professional cyclist.

Heart rate variability (HRV) is a common means of monitoring responses to training, yet in professional cycling, one may question its usefulness, particularly during multi-day competitions such as Grand Tours. This study aims to report and analyze HRV responses in a male professional cyclist over a season, including the Tour de France. A professional cyclist recorded resting and exercise inter-beat intervals during 5 months, comprising a training period with two altitude sojourns and two competition blocks, including the Tour de France. Resting recordings lasted 5 min in the supine position and were used for computation of mean heart rate (HR), root mean square of the successive differences (RMSSDs), and power in the low- and high-frequency bands (LF and HF, respectively). Training load quantification was based on recorded HR during exercise and expressed as training impulses (TRIMPSs). LF (3,319 ± 2,819 vs. 1,097 ± 1,657 ms2), HF (3,590 ± 1858 vs. 1,267 ± 1,683 ms2), and RMSSD (96 ± 26 vs. 46 ± 30 ms) were higher and HR (47 ± 4 vs. 54 ± 2 bpm) was lower during the training period when compared to the two competition blocks. The coefficient of variation (CV) was significantly lower during the training period than during the two competition blocks for RMSSD (26 vs. 72%), LF (85 vs. 160%), and HF (58 vs. 141%). The present study confirms that monitoring daily HRV responses during training periods is valuable in professional cycling, but questions its usefulness during the Tour de France. Moreover, the previous suggestion that CV in RMSSD would help to predict poor performance was not confirmed in a professional cyclist.

The Quantification of Physical Performance and Internal Training Load in Youth Male Soccer Players During Preseason.

The monitoring of training loads and quantification of physical performance are common practices in youth soccer academies to support coaches in prescribing and programming training for individuals. The interaction between training load and physical performance is unknown during a preseason period in youth soccer players. The current study assessed changes in training load and physical assessments across a 4-week preseason period. The relationship between physical performance and match playing time in youth male soccer players was also investigated. The training loads of 25 professional youth academy male soccer players were monitored throughout a 4-week preseason period. Assessments of power, agility, speed, and aerobic capacity were undertaken in the first training session. Session ratings of perceived exertion (sRPE) and well-being questionnaires were collected during all training sessions and preseason matches. Playing time during subsequent competitive matches was recorded. T test and 30-m-sprint assessments, conducted on the first day of preseason, were predictors of sRPE throughout preseason (t test χ2/df = 2.895, poor adjustment; 30-m sprint χ2/df = 1.608, good adjustment). YoYo Test performance was related with changes in perceived fatigue (χ2/df = 0.534, very good adjustment). Faster players reported higher values of sRPE, and players with higher aerobic capacity reported higher levels of fatigue across preseason. Well-being, perceived fatigue and soreness, and sRPE decreased across preseason. Greater match durations were related to higher levels of fatigue during preseason (P < .05). The current study highlights the relationship between training load, physical assessments, and playing time. Coaches and practitioners can use physical test data at the start of preseason as an indication of players that report higher sRPE, perceived fatigue, and reduced well-being across preseason, supporting decisions around individualized training prescriptions.

Discriminative validity of summarized hip and knee angular accelerations for lower extremity training load quantification in male soccer players during a standardised training drill

ABSTRACTThis study assessed the discriminative validity of summarized hip and knee angular accelerations during a standardized training drill. Twenty-eight soccer players performed a standardized training drill that mimics game demands. Discriminative validity was examined by assessment of between-group differences of summarized preferred kicking leg hip and knee angular accelerations, and Playerload between national and regional soccer players for the full training drill, and parts based on locomotor intensity, or additional pass and jumping header activities. Furthermore, relationships were assessed between the summarized hip and knee angular accelerations and conventional load indicators derived from a local positioning measurement system, such as high-intensity running distance and Playerload. National players had higher summarized hip (Mean difference: 62.7 A.U. ES = 0.77, p = 0.049) and knee (Mean difference: 137.1 A.U. ES = 1.06, p = 0.008) angular accelerations. Significant interaction effects were observed during high-intensity running (Hip: 0.2 A.U./m, ES = 0.98, p = 0.005; Knee: 0.61 A.U./m, ES = 1.52, p < 0.001), and sprinting (Hip: 0.3 A.U./m, ES = 1.01, p < 0.02; Knee: 0.56 A.U./m, ES = 1.57, p < 0.001). Between-group differences were not present for additional passing or jumping header activities. Compared to summarized hip and knee angular accelerations, Playerload had less ability to discriminate between players and activities. Moreover, the lower extremity training load indicators were unrelated to conventional load indicators. Together these results confirm discriminative validity of summarized hip and knee angular acceleration training load indicators during a standardised training drill.

Quantification of External Training Load among Elite-Level Goalkeepers within Competitive Microcycle

This study aimed to evaluate the external training load (ETL) of elite-level goalkeepers considering days before match day (MD minus) and playing status in subsequent matches. The ETL of three goalkeepers from the Croatian highest national football competition were analyzed, quantifying goalkeeping-specific physical performance variables (i.e., distances covered, acceleration frequencies, dives, jumps). Data were collected using a 10 Hz global-positioning system and 100 Hz accelerometer technology (Vector G7, Catapult Sports Ltd., Melbourne, Australia) from 67 training sessions. Significant daily differences for almost all physical performance variables were found (all small-to-medium effect sizes (ESs)). Specifically, total distance, total and high-intensity dives, high-intensity accelerations and decelerations, and explosive efforts were greatest on MD-3 and lowest on MD-2 and MD-1. Nonstarters performed more medium jumps on MD-4 (large ES); low jumps on MD-3 (medium ES); total, right-, and left-side dives and low jumps on MD-2 (all small-to-medium ESs); and left-side dives and low and medium jumps on MD-1 (all small-to-medium ESs) compared to the starters. These findings demonstrated that (i) elite-level goalkeepers experienced the greatest ETL on MD-3 and the lowest on MD-2 and MD-1 and that (ii) starters’ and nonstarters’ ETLs were similar on MD-4 and MD-3, while nonstarters compared to the starters presented slightly greater ETLs on MD-2 and MD-1. This study highlighted the differing daily training demands placed on elite-level goalkeepers, offering valuable insights for their preparation.

Semiautomatic Training Load Determination in Endurance Athletes

Background: Despite endurance athletes recording their training data electronically, researchers in sports cardiology rely on questionnaires to quantify training load. This is due to the complexity of quantifying large numbers of training files. We aimed to develop a semiautomatic postprocessing tool to quantify training load in clinical studies. Methods: Training data were collected from two prospective athlete’s heart studies (Master Athlete’s Heart study and Prospective Athlete Heart study). Using in-house developed software, maximal heart rate (MaxHR) and training load were calculated from heart rate monitored during cumulative training sessions. The MaxHR in the lab was compared with the MaxHR in the field. Lucia training impulse score, based on individually based exercise intensity zones, and Edwards training impulse, based on MaxHR in the field, were compared. A questionnaire was used to determine the number of training sessions and training hours per week. Results: Forty-three athletes recorded their training sessions using a chest-worn heart rate monitor and were selected for this analysis. MaxHR in the lab was significantly lower compared with MaxHR in the field (183 ± 12 bpm vs. 188 ± 13 bpm, p &lt; .01), but correlated strongly (r = .81, p &lt; .01) with acceptable limits of agreement (±15.4 bpm). An excellent correlation was found between Lucia training impulse score and Edwards training impulse (r = .92, p &lt; .0001). The quantified number of training sessions and training hours did not correlate with the number of training sessions (r = .20) and training hours (r = −.12) reported by questionnaires. Conclusion: Semiautomatic measurement of training load is feasible in a wide age group. Standard exercise questionnaires are insufficiently accurate in comparison to objective training load quantification.

Training Load Quantification in Women's Elite Football: A Season-Long Prospective Cohort Study.

The aim of this study was to investigate (1)if there are differences in training load and intensity between the different training days within a microcycle and (2)if training load and intensity within the different training days are stable over the course of a season. Data were collected over a full season from a team in the women's premier division in Norway. External load (total distance, high-speed-running distance, sprint distance, and the combined number of accelerations and decelerations [ACCDEC]) was assessed using a 10-Hz GPS system with a built-in accelerometer. Internal load was assessed through session rating of perceived exertion, which was multiplied with session duration (session rating of perceived exertion-load). Training days were classified in relation to their proximity to the upcoming match day (MD): MD - 4, MD - 3, MD - 2, and MD - 1. Contents on these days were standardized according to a weekly periodization model followed by the coaching staff. Differences between training days were analyzed using a linear mixed-effects model. All training days were significantly different from each other across multiple variables. ACCDEC values were highest on MD - 4 (147.5 [13.0] ACCDEC count), and all distance variables were highest on MD - 3. All measures of training load were significantly reduced from MD - 3 to MD - 2 (effect size [ES] = 1.0-4.1) and from MD - 2 to MD - 1 (ES = 1.6-4.3). A significant negative effect across the season was observed for session rating of perceived exertion-load and ACCDEC (ES = 0.8-2.1). These results provide evidence that elite female football teams can be successful in differentiating training load between training days when implementing a weekly periodization approach.

Quantification of training load across two competitive seasons in elite senior and youth male soccer players from an English Premiership club.

This study aimed to compare the daily training load (TL) in first-team and U-18 soccer players from an English Premiership club. 36 first-team (age 23.2 ± 5.9 years, weight 75.2 ± 8.1 kg, height 1.83 ± 0.06 m), and 22 U-18 players (age 17.5 ± 1.1 years, weight 71.1 ± 8.2 kg, height 1.78 ± 0.08 m) participated. GPS metrics were measured during all pitch training sessions throughout the 2020-21 and 2021-22 seasons. Linear mixed-effect model analyses revealed that, irrespective of training day, U-18 players covered greater total and explosive distance than first-team players, and performed a higher number of accelerations and decelerations, whereas first-team players covered greater sprint distance. Irrespective of the team, all examined variables were greater at match-day (MD)-3, while the number of accelerations and decelerations were higher at MD-4. Significant team-by-training day interactions revealed that U-18 players covered greater total and high-intensity distances than first-team players at MD-4, MD-2, and MD-1, whereas first-team players covered greater total and high-intensity distances at MD-3. Sprint distance was greater for first-team players at MD-3 and MD-4, while explosive distance was greater for U-18 players at MD-2. Also, U-18 players performed a higher number of accelerations than first-team players at MD-3 and MD-2, and a higher number of decelerations at MD-4. The present results provide novel information on TL patterns in English Premiership soccer and contribute to understanding how training methods to physically develop players are implemented in different countries and leagues.

Construct Validity and Test-Retest Reliability of Hip Load Compared With Playerload During Football-Specific Running, Kicking, and Jumping Tasks.

To determine the test-retest reliability of the recently developed Hip Load metric, evaluate its construct validity, and assess the differences with Playerload during football-specific short-distance shuttle runs. Eleven amateur football players participated in 2 identical experimental sessions. Each session included 3 different shuttle runs that were performed at 2 pace-controlled running intensities. The runs consisted of only running, running combined with kicks, and running combined with jumps. Cumulative Playerload and Hip Loads of the preferred and nonpreferred kicking leg were collected for each shuttle run. Test-retest reliability was determined using intraclass correlations, coefficients of variation, and Bland-Altman analyses. To compare the load metrics with each other, they were normalized to their respective values obtained during a 54-m run at 9km/h. Sensitivity of each load metric to running intensity, kicks, and jumps was assessed using separate linear mixed models. Intraclass correlations were high for the Hip Loads of the preferred kicking leg (.91) and the nonpreferred kicking leg (.96) and moderate for the Playerload (.87). The effects (95% CIs) of intensity and kicks on the normalized Hip Load of the kicking leg (intensity: 0.95 to 1.50, kicks: 0.36 to 1.59) and nonkicking leg (intensity: 0.96 to 1.53, kicks: 0.06 to 1.34) were larger than on the normalized Playerload (intensity: 0.12 to 0.25, kicks: 0.22 to 0.53). The inclusion of Hip Load in training load quantification may help sport practitioners to better balance load and recovery.

Utilizing Countermovement Jumps As A Measure Of Match Preparedness In NCAA Division I Women’s Soccer

PURPOSE: Training load quantification and monitoring is used in collegiate athletics to help coaches and supporting staff to determine whether players are prepared for upcoming matches. The purpose of this study was to assess the within-subjects correlation of matchday countermovement jump (CMJ) height and the 7-day sum of training load variables preceding matchday in NCAA Division I women’s soccer athletes. The authors hypothesized that matchday CMJ height would show negative moderate correlations with the 7-day sum of training load variables. METHODS: A total of 23 female soccer players (20 ± 1.1 years; 1.70 ± 0.07 m; 64.6 ± 7.0 kg) participated in this study. Data from seven matches from the 2020 season were included in this analysis. Repeated measures correlations (rmcorr) were used to determine the within-subjects correlations of matchday CMJ height and total distance covered (TD), training load score (TLS; using training impulse [TRIMP] calculations), number of sprints, and distanced covered at high-speed (< 9.32 mph; HODO). Alpha was set a priori at p ≤ 0.05 and the data was analyzed using R Studio (version 1.4.1106, R Studio, Boston, MA, USA) executing R (version 4.0.4, University of California, Berkeley, CA, USA). RESULTS: The correlations between training load variables and matchday CMJ height were not statistically significant (p > 0.05). The matchday CMJ height showed very weak nonsignificant relationships with TD (r = -0.071, p = 0.450, 95% CI = -0.254, 0.116), TLS (r = -0.016, p = 0.874, 95% CI = -0.191, 0.223), number of sprints (r = -0.116, p = 0.218, 95% CI = -0.296, 0.071), and HODO (r = -0.011, p = 0.904, 95% CI = -0.175, 0.197). CONCLUSIONS: The results suggest that the CMJ height may not be a good indicator of matchday preparedness in collegiate Division I women’s soccer as it did not correlate with the 7-day sum of training load variables preceding matchday. Future research should aim to address other strategies to assess matchday preparedness in collegiate women’s soccer in order to enhance training periodization to better prepare the athletes for competition.

A Proposal of Speed Zone Classification in Basketball: A New-Criteria Based on Maximum Registered Values

The quantification of training load has become mandatory for coaches and team staff. Within the analyzed variables, distance covered at different speed zones is one of the most studied. However, there is no consensus in the definition of speed zones in the published articles about each team sport, so it makes difficult a comparison between them. Therefore, the purposes of this article were to establish a criteria standard in the classification of speed zones in team sports and to analyze its practical application in basketball. Five speed categories were established for basketball following the methodology described above: 0-10km/h, &gt;10-13.8 km/h, &gt;13.8-17.6 km/h, &gt;17.6-21.5 km/h, and &gt;21.5 km/h. The results showed differences between periods in high-speed displacements and sprints, maximum speed, total decelerations, and accelerations. In conclusion, the categorization of workload based on standard deviation according to maximum speed could be a viable option to individualize the analysis of distance covered per speeds in team sports such as basketball. Its use may lead to a better understanding and contextualization of the locomotion data in specific sports and teams.

Quantification of in-season training load in Division I male collegiate soccer players

Quantification of in-season training load in Division I male collegiate soccer players

Differences in Immune Response During Competition and Preparation Phase in Elite Rowers.

Background: Metabolic stress is high during training and competition of Olympic rowers, but there is a lack of biomedical markers allowing to quantify training load on the molecular level. We aimed to identify such markers applying a complex approach involving inflammatory and immunologic variables.Methods: Eleven international elite male rowers (age 22.7 ± 2.4 yrs.; VO2max 71 ± 5 ml·min−1·kg−1) of the German National Rowing team were monitored at competition phase (COMP) vs. preparation phase (PREP), representing high vs. low load. Perceived stress and recovery were assessed by a Recovery Stress Questionnaire for Athletes (RESTQ-76 Sport). Immune cell activation (dendritic cell (DC)/macrophage/monocytes/T-cells) was evaluated via fluorescent activated cell sorting. Cytokines, High-Mobility Group Protein B1 (HMGB1), cell-free DNA (cfDNA), creatine kinase (CK), uric acid (UA), and kynurenine (KYN) were measured in venous blood.Results: Rowers experienced more general stress and less recovery during COMP, but sports-related stress and recovery did not differ from PREP. During COMP, DC/macrophage/monocyte and T-regulatory cells (Treg-cell) increased (p = 0.001 and 0.010). HMGB1 and cfDNA increased in most athletes during COMP (p = 0.001 and 0.048), while CK, UA, and KYN remained unaltered (p = 0.053, 0.304, and 0.211). Pro-inflammatory cytokines IL-1β (p = 0.002), TNF-α (p < 0.001), and the chemokine IL-8 (p = 0.001) were elevated during COMP, while anti-inflammatory Il-10 was lower (p = 0.002).Conclusion: COMP resulted in an increase in biomarkers reflecting tissue damage, with plausible evidence of immune cell activation that appeared to be compensated by anti-inflammatory mechanisms, such as Treg-cell proliferation. We suggest an anti-inflammatory and immunological matrix approach to optimize training load quantification in elite athletes.

Session Rating of Perceived Exertion Is Different for Similar Intensity and Duration Prescribed Low-Intensity Sessions and Has a Different Effect on Performance in Young Cross-Country Skiers.

Pind, R, Purge, P, Mäestu, E, Vahtra, E, Hofmann, P, and Mäestu, J. Session rating of perceived exertion is different for similar intensity and duration prescribed low-intensity sessions and has a different effect on performance in young cross-country skiers. J Strength Cond Res 37(1): 187-193, 2023-The aim of this study was to analyze whether the rating of perceived exertion (RPE) and the resulting internal load (sRPE) could differ among subjects when volume and intensity are matched in young cross-country (XC) skiers. Second, we analyzed whether subjects with a high or low RPE response and sRPE have different adaptation after a 1-week low-intensity high-volume training period. Well-trained national level XC skiers (n = 13) aged 13.4 ± 1.9 years took part in a 1-week training period with double poling performance tested pretraining and after 1-week training. All subjects trained under a similar training program. Rating of perceived exertion was measured after each training session on a 10-point scale, and the total training load was calculated by the sRPE method by multiplying training session's duration and the RPE. Subjects were divided into Glow (n = 6) or Ghigh (n = 7) groups based on median rating of RPE (3.78 ± 1.24) in low-intensity trainings. Total training load in low-intensity trainings was higher in Ghigh compared with Glow (4,010 ± 765 vs. 2,499 ± 193 arbitrary units, respectively), although total training time with (16.3 ± 0.8 hours vs. 16.0 ± 0.6 hours) or without (13.1 ± 0.9 hours vs. 13.3 ± 0.7 hours, p > 0.05, respectively to Glow and Ghigh) high-intensity sessions was not different. No significant differences were found for time spent in different heart rate zones between the 2 groups. Increases in performance were significant for both groups (p< 0.05), while the effect was large in Ghigh (+12.5%; ES = 0.74), and medium in Glow (+10.7%; ES = 0.34). Differences in the internal load and the further quantification of training load for different target intensity sessions could provide important practical information to stimulate expected adaptation in young endurance athletes.

Quantifying sprint kayak training on a flowing river: Exploring the utility of novel power measures and its relationship to measures of relative boat speed

ABSTRACT Quantification of external training load for sprint kayak athletes can be challenging due to the influence of the water flow on boat velocity in a flowing river environment. Therefore, this study examined the utility of novel measures of power output (PO) and its relationship to measures of relative boat speed when training on a flowing river. Twelve (8 males, 4 female) well-trained sprint kayak athletes completed 4 separate on-water sessions comprising one time-trial session (2 × 1000-m maximal efforts) and three repeated sprint kayak training sessions (5 x split 1000-m [2 × 500-m up and down the river] submaximal efforts) in their individual (K1) kayak. For each session, a Kayak Power Meter recorded athletes’ PO, and a SpeedCoach device recorded relative land-speed via a Global Positioning System (GPS) (S GPS), and relative water-speed via an impeller mounted under the boat hull (S IMP). Non-linear least squares regression were used to evaluate the curvilinear relationship between PO and speed (S GPS and S IMP) data. The exponents of velocity in the PO-S IMP relationship (2.87 females, 2.94 males) were closer to theoretical values (3.00) and showed greater model accuracy (root mean squared error (RMSE) = 20–26 W) than the PO-S GPS relationships (speed exponents = 1.58–2.02, RMSE = 31–40 W). Overall, PO measures could better account for the influence of water flow compared to traditional S GPS measures, and therefore, may be more suitable for quantifying athletes’ external load in their training environment. Highlights Since traditional S GPS and time-to-completion measures do not adjust for the water flow, these measures appear limited for prescribing and monitoring sprint kayak training within flowing river environments. The prescription of paddling PO across a wide spectrum of relative PO values elicited similar internal and external athlete responses, regardless of the direction travelled on a flowing river (i.e. upstream or downstream). The relationship between PO and S IMP during on-water sprint kayaking appears similar to those observed in rowing, where every percent change in boat speed measured relative to water (S IMP) requires a 2.87 and 2.94-fold percent change in paddling PO in female and male sprint kayak athletes, respectively. Continued evaluation of the PO-speed relationship for individual athletes may provide further insight into modelling performance and training targets for sprint kayak athletes.

Internal training and match load quantification of one-match week schedules in female first league Portugal soccer team

Internal training and match load quantification of one-match week schedules in female first league Portugal soccer team

Increases in RPE Rating Predict Fatigue Accumulation Without Changes in Heart Rate Zone Distribution After 4-Week Low-Intensity High-Volume Training Period in High-Level Rowers.

Purpose: The aim of this study was to investigate the interaction of training load quantification using heart rate (HR) and rating of perceived exertion (RPE)-based methodology, and the relationship between internal training load parameters and subjective training status (Fatigue) in high-level rowers during volume increased low-intensity training period.Methods: Training data from 19 high-level rowers (age 23.5 ± 5.9 years; maximal oxygen uptake 58.9 ± 5.8 ml·min−1·kg−1) were collected during a 4-week volume increased training period. All individual training sessions were analyzed to quantify training intensity distribution based on the HR time-in-zone method (i.e., HR Z1, HR Z2, and HR Z3) determined by the first and second ventilatory thresholds (VT1/VT2). Internal training load was calculated using session RPE (sRPE) to categorize training load by effort (i.e., sRPE1, sRPE2, and sRPE3). The Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) questionnaire was implemented after every week of the study period.Results: No differences were found between the respective HR and effort-based zone distributions during the baseline week (p > 0.05). Compared to HR Z1, sRPE1 was significantly lower in weeks 2–4 (p < 0.05), while sRPE2 was higher in weeks 2–3 compared to HR Z2 (p < 0.05) and, in week 4, the tendency (p = 0.06) of the higher amount of sRPE3 compared to HR Z3 was found. There were significant increases in RESTQ-Sport stress scales and decreases in recovery scales mostly during weeks 3 and 4. Increases in the Fatigue scale were associated with the amounts of sRPE2 and sRPE3 (p = 0.011 and p = 0.008, respectively), while no associations with Fatigue were found for HR-based session quantification with internal or external training load variables.Conclusion: During a low-intensity 4-week training period with increasing volume, RPE-based training quantification indicated a shift toward the harder rating of sessions with unchanged HR zone distributions. Moderate and Hard rated sessions were related to increases in Fatigue. Session rating of perceived exertion and effort-based training load could be practical measures in combination with HR to monitor adaptation during increased volume, low-intensity training period in endurance athletes.

Quantification of internal and external training load during a training camp in senior international female footballers

ABSTRACT Objective This study aimed to quantify the internal and external training load profile of an international training camp and identify any differences between positional groups or starting status. Methods 18 players were sampled using a global positioning system (GPS) and internal training load (TL) via session rate of perceived exertion (RPE). Results Internal and external training load variation was present across all six individual training sessions. Results indicated match day −7 (MD-7), had significantly (P= 0.001) less training volume (3339.5 m) than all other training sessions. MD-5 and MD-2 were the most intense (387.5 and 201 m very high speed running (VHSR) and 187.5 and 49 m sprint distance (SPD) respectively in combination with large volume (5933.5 and 5151.5 m). Differences in playing position and starting status were observed in MD-2. Forwards (FW) covered significantly greater distances of VHSR (P= 0.008) and SPD (P= 0.008) in comparison to midfielders (MF) whilst starters reported significantly (P = 0.013) higher internal training loads (TL). Conclusion This study provides the first report on training load in elite female footballers during an international training camp including a competitive fixture highlighting the undulation of training loads across the individual training sessions, playing positions and starting status.

The Influence of Different Training Load Quantification Methods on the Fitness-Fatigue Model.

Numerous methods exist to quantify training load (TL). However, the relationship with performance is not fully understood. Therefore the purpose of this study was to investigate the influence of the existing TL quantification methods on performance modeling and the outcome parameters of the fitness-fatigue model. During a period of 8 weeks, 9 subjects performed 3 interval training sessions per week. Performance was monitored weekly by means of a 3-km time trial on a cycle ergometer. After this training period, subjects stopped training for 3 weeks but still performed a weekly time trial. For all training sessions, Banister training impulse (TRIMP), Lucia TRIMP, Edwards TRIMP, training stress score, and session rating of perceived exertion were calculated. The fitness-fatigue model was fitted for all subjects and for all TL methods. The error in relating TL to performance was similar for all methods (Banister TRIMP: 618 [422], Lucia TRIMP: 625 [436], Edwards TRIMP: 643 [465], training stress score: 639 [448], session rating of perceived exertion: 558 [395], and kilojoules: 596 [505]). However, the TL methods evolved differently over time, which was reflected in the differences between the methods in the calculation of the day before performance on which training has the biggest positive influence (range of 19.6d). The authors concluded that TL methods cannot be used interchangeably because they evolve differently.