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Introduction: The parasympathetically derived marker of heart rate variability, root mean square of successive R-R differences (RMSSD), and the daily fluctuations as measured by the coefficient of variation (RMSSDCV) may be useful for tracking training adaptations in athletic populations. These vagally derived markers of heart rate variability may be especially pertinent when simultaneously considering a female athlete’s menstrual cycle. Purpose: The purpose of this study was to observe the perturbations in RMSSDCV, while considering RMSSD, across a season in the presence and absence of menses with training load in female collegiate rowers. Methods: Thirty-six (20 [1] y, 25.6 [3.4] kg·m−2) National Collegiate Athletic Association Division I female rowers were monitored for 18 consecutive weeks across a full season. Seated, ultrashortened RMSSD measurements were obtained by the rowers on at least 3 mornings per week using a smartphone photoplethysmography device. Following the RMSSD measurement, athletes indicated the presence or absence of menstruation within the application. Individual meters rowed that week and sessions rate of perceived exertion were obtained to quantify training load. Results: Longitudinal mixed-effects modeling demonstrated a significant effect of menses and time, while also considering RMSSD, such that those who were on their period had a significantly greater RMSSDCV than those who were not (11.2% vs 7.5%, respectively; P CgV (P > .05). Conclusion: The presence of menses appears to significantly impact RMSSDCV when also considering RMSSD, which may allow coaches to consider individualized training plans accordingly.

Purpose: To determine if workload and seasonal periods (preseason vs in season) are associated with the incidence of injuries and illnesses in female professional cyclists. Methods: Session rating of perceived exertion was used to quantify internal workload and was collected from 15 professional female cyclists, from 33 athlete seasons. One week (acute) workload, 4 weeks (chronic) workload, and 3 acute:chronic workload models were analyzed. Two workload models are based on moving averages of the ratios, the acute:chronic workload ratio (ACWR), and the ACWR uncoupled (ACWRuncoup). The difference between both is the chronic load; in ACWR, the acute load is part of the chronic load, and in ACWRuncoup, the acute and chronic load are uncoupled. The third workload model is based on exponentially weighted moving averages of the ratios. In addition, the athlete season is divided into the preseason and in season. Results: Generalized estimating equations analysis was used to assess the associations between the workload ratios and the occurrence of injuries and illnesses. High values of acute workload (P = .048), ACWR (P = .02), ACWRuncoup (P = .02), exponentially weighted moving averages of the ratios (P = .01), and the in season (P = .0001) are significantly associated with the occurrence of injury. No significant associations were found between the workload models, the seasonal periods, and the occurrence of illnesses. Conclusions: These findings suggest the importance of monitoring workload and workload ratios in female professional cyclists to lower the risk of injuries and therefore improve their performances. Furthermore, these results indicate that, in the preseason, additional stressors occur, which could lead to an increased risk of injuries.

Purpose: To prescribe training loads to improve performance, one must know how an athlete is responding to loading. The maximal rate of heart-rate increase (rHRI) during the transition from rest to exercise is linearly related to changes in endurance exercise performance and can be used to infer how athletes are responding to changes in training load. Relationships between rHRI and anaerobic exercise performance have not been evaluated. The objective of this study was to evaluate relationships between rHRI and anaerobic exercise performance. Methods: Eighteen recreational strength and power athletes (13 male and 5 female) were tested on a cycle ergometer for rHRI, 6-second peak power output, anaerobic capacity (30-s average power), and blood lactate concentration prior to (PRE), and 1 (POST1) and 3 (POST3) hours after fatiguing high-intensity interval cycling. Results: Compared with PRE, rHRI was slower at POST1 (effect size [ES] = −0.38, P = .045) but not POST3 (ES = −0.36, P = .11). PPO was not changed at POST1 (ES = −0.12, P = .19) but reduced at POST3 (ES = −0.52, P = .01). Anaerobic capacity was reduced at POST1 (ES = −1.24, P P P P = .11). rHRI was positively related to PPO (B = 0.19, P = .03) and anaerobic capacity (B = 0.14, P = .005) and inversely related to blood lactate concentration (B = −0.22, P = .04). Conclusions: rHRI is linearly related to acute changes in anaerobic exercise performance and may indicate how athletes are responding to training to guide the application of training loads.

Purpose: The purpose of this study was to assess neuromuscular performance capabilities over an in-season mesocycle in early-career professional soccer players and examine the relationship with training workload. Methods: Neuromuscular performance capabilities (isometric knee extensor: peak force, rate of force development, and peak twitch force) of 12 professional soccer players were assessed weekly over a 6-week period. Training and match workload were also recorded over the same period for each player (high-intensity running distance). Changes in neuromuscular performance and workload variables were analyzed. Results: There was evidence of fluctuations in neuromuscular performance capability over the mesocycle that reached statistical (P P Conclusion: Neuromuscular performance capabilities fluctuate over an in-season mesocycle and are influenced by high-intensity running workload, emphasizing the need for acute monitoring in elite soccer players.

Purpose: To compare the physical demands and performance indicators of male professional cyclists of 2 different categories (Union Cycliste Internationale WorldTour [WT] and ProTeam [PT]) during a cycling grand tour. Methods: A WT team (n = 8, 31.4 [5.4] y) and a PT team (n = 7, 26.9 [3.3] y) that completed “La Vuelta 2020” volunteered to participate. Participants’ power output (PO) was registered, and measures of physical demand and physiological performance (kilojoules spent, training stress score, time spent at different PO bands/zones, and mean maximal PO [MMP] for different exertion durations) were computed. Results: WT achieved a higher final individual position than PT (31 [interquartile range = 33] vs 71 [59], P = .004). WT cyclists showed higher mean PO and kilojoule values than their PT peers and spent more time at high-intensity PO values (>5.25 W·kg−1) and zones (91%–120% of individualized functional threshold power) (Ps P > .05), subanalyses revealed that the between-groups gap increased through the race, with WT cyclists reaching higher MMP values for ≥5-minute efforts in the second and third weeks (Ps Conclusions: Despite the multifactorial nature of cycling performance, WT cyclists spend more time at high intensities and show higher kilojoules and mean PO than their PT referents during a grand tour. Although the highest MMP values attained during the whole race might not differentiate between WT and PT cyclists, the former achieve higher MMP values as the race progresses.

Purpose: The primary aim of this study was to evaluate whether the internal (session rating of perceived exertion [sRPE] and Edwards heart-rate-based method) and external training load (jumps) affect the presession well-being perception on the day after (ie, +22 h), according to age and tactical position, in elite (ie, Serie A2) female volleyball training. Methods: Ten female elite volleyball players (age = 23 [4] y, height = 1.82 [0.04] m, body mass = 73.2 [4.9] kg) had their heart rate monitored during 13 team (115 individual) training sessions (duration: 101 [8] min). Mixed-effect models were applied to evaluate whether sRPE, Edwards method, and jumps were correlated (P ≤ .05) to Hooper index factors (ie, perceived sleep quality/disorders, stress level, fatigue, and delayed-onset muscle soreness) in relation to age and tactical position (ie, hitters, central blockers, opposites, and setters). Results: The results showed a direct relationship between sRPE (P P = .003). Conclusions: Findings indicated that female volleyball players’ internal training load influences the pretraining well-being status on the day after (+ 22 h). Therefore, coaches can benefit from this information to accurately implement periodization in a short-term perspective and to properly adopt recovery strategies in relation to the players’ well-being status.

Purpose: To analyze the relationships between the evolution of training-load values and countermovement jump (CMJ) as an indicator of stress and fatigue in a high-level 800-m runner during a whole season, including indoor (ID) and outdoor season (OD). Methods: Over 42 weeks, daily training load was quantified as the result of the product of the intensity and volume, and it was termed load index (LI). CMJ was measured in every running session after warm-up and immediately after the last effort of the session. Other jump-related variables such as CMJ height loss, average weekly CMJ, initial CMJ of the next consecutive session, and initial CMJ of the following week were studied. Results: A significant negative relationship was observed between LI and weekly CMJ (ID: r = −.68, P r = −.73, P r = −.71, P r = −.58, P r = −.52, P r = .72; P r = −.74; P r = −64, P Conclusions: CMJ may be a valid indicator of the degree of stress or fatigue generated by specific training sessions of a competitive athlete within a single session, a week, or even the following week. There could be an individual limit LI value from which the training volume does not allow a positive effect on high-speed actions such as a CMJ in the next consecutive session.

Purpose: To assess the effects of a short-term taper on the ability to perform repeated high-intensity efforts, depending on players’ fatigue level following an intensive training block. Method: After a 3-day off-season camp, 13 players followed the same 3-week preseason training block followed by a 7-day exponential taper. Performance was assessed by a repeated high-intensity effort test before and after the taper. Total sprint time, percentage of decrement, and the number of sprints equal to or higher than 90% of the best sprint were retained for analysis. Players were a posteriori classified in normal training or acute fatigue groups based on their readiness to perform prior to the taper, assessed through the magnitude of difference in psychological (Profile of Mood State Questionnaire), cardiovascular (submaximal constant-duration cycling), and neuromuscular (countermovement jump) tests between the preintensive and postintensive training blocks. Results: Training load declined by 55% (9%) during the taper (P = .001, g = −2.54). The overall group showed a small improvement in total sprint time (−3.40% [3.90%], P = .04, g = −0.39) following the taper. Relative changes tended to be higher in the acute fatigue compared with the normal training group (−5.07% [4.52%] vs −1.45% [1.88%], respectively; P = .08; d = 1.01). No taper-induced improvement was observed in percentage of decrement or number of sprints equal to or higher than 90% of the best sprint. Conclusion: A 7-day taper consisting of 55% training load reduction improved repeated high-intensity effort performance in young elite rugby union players. Pretaper level of fatigue seems to be a key determinant in the taper supercompensation process, as acutely fatigued players at the end of the intensive training block tended to benefit more from the taper.

Purpose: To assess heart rate (HR) variability responses to various markers of training load, quantify associations between HR variability and fitness, and compare responses and associations between 1-minute ultrashort and 5-minute criterion measures among a girls’ field hockey team. Methods: A total of 11 players (16.8 [1.1] y) recorded the logarithm of the root mean square of successive differences (LnRMSSD) daily throughout a 4-week training camp. The weekly mean (LnRMSSDM) and coefficient of variation (LnRMSSDCV) were analyzed. The internal training load (ITL) and external training load (ETL) were acquired with session HR and accelerometry, respectively. Speed, agility, repeated sprint ability, and intermittent fitness were assessed precamp and postcamp. Results: Similar increases in the ultrashort and criterion LnRMSSDM were observed in week 3 versus week 1 (P CV showed small ES reductions in week 2 (ES = −0.40 to −0.50), moderate reductions in week 3 (ES = −0.61 to −0.72), and small reductions in week 4 (ES = −0.42 to −0.51) versus week 1 (P > .05). Strong agreement was observed between the ultrashort and criterion values (intraclass correlation coefficient = .979). The ITL:ETL ratio peaked in week 1 (P CV, and inversely similar to LnRMSSDM. Changes in the ultrashort and criterion LnRMSSDCV from week 1 to 4 were associated with ITL (P CV in week 4 were associated (P < .05) with postcamp fitness. Conclusions: The ultrashort HR variability parameters paralleled the criterion responses, and the associations with ITL and fitness were similar in magnitude.

Purpose: To statistically evaluate the internal and external load metrics in different types of lacrosse drills. Methods: A total of 25 Division I collegiate female lacrosse players wore a heart rate monitor and a global positioning system during preseason training sessions. Seven measures determined training load, 2 internal measures and 5 external measures, across 5 different types of drills: stickwork, small-sided games, individual skills, conditioning, and team drills. Principal component analysis was used to determine which internal and external load variables were most associated with each drill type. Results: Stickwork extracted 2 principal components, explaining 45% and 17% of the variance. Small-sided games extracted 1 principal component, explaining 51% of the variance. Individual skills extracted 2 components, explaining 39% and 22% of the variance. Conditioning extracted 2 components, explaining 44% and 24% of the variance. Team drills extracted 2 components, explaining 52% and 18% of the variance. Conclusions: In 4 out of 5 training modes, the inclusion of both internal and external training-load measures was necessary to accurately decipher training load. For most drills, the first component is related to measures of external load, and the second component described the balance between internal and external load measures. Small-sided games extracted only external measures including the following: accelerations, total distance, and average speed. These results show that a combination of internal and external load measures is required to determine training load during certain training modes. This information can help coaches make decisions about desired training load for practice sessions.

Purpose: To quantify the demands of specific on- and off-court sessions, using internal and external training load metrics, in elite squash. Methods: A total of 15 professional squash players (11 males and 4 females) wore a 100-Hz triaxial accelerometer/global positioning system unit and heart rate monitor during on-court “Group,” “Feeding,” “Ghosting,” “Matchplay,” and off-court “Conditioning” sessions across a 2-week in-season microcycle. Comparisons of absolute training load (total values) and relative intensity (per minute) were made between sessions for internal (session rating of perceived exertion, differential rating of perceived exertion, TRIMP) and external (Playerload, very high–intensity movements [>3.5 m·s−2]) metrics. Results: The Group sessions were the longest (79 [12] min), followed by Feeding (55 [15] min), Matchplay (46 [17] min), Conditioning (37 [9] min), and Ghosting (35 [6] min). Time >90% maximum heart rate was the lowest during Feeding (vs all others P P > .05). Relative Playerload during Conditioning (14.3 [3.3] arbitrary unit [a.u.] per min, all P P ≥ .999). Conditioning produced the highest Playerloads (519 [153] a.u., all P P P P P Conclusions: Squash Matchplay does not systematically produce the highest training intensities and loads. Group sessions provide the highest training loads for many internal and external parameters and, therefore, play a central role within the training process. These findings facilitate planning or adjustment of intensity, volume, and frequency of sessions to achieve desirable physical outcomes.

Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi) and WI measured the day after (WIMDi+1) (r = .72, P

Purpose: A simple and 2 different exponentially weighted moving average methods were used to investigate the relationships between internal training load and elite weightlifting performance. Methods: Training impulse data (sessional ratings of perceived exertion × training duration) were collected from 21 elite weightlifters (age = 26.0 [3.2] y, height = 162.2 [11.3] cm, body mass = 72.2 [23.8] kg, previous 12-mo personal best total 96.3% [2.7%] of world record total) during the 8 weeks prior to the 2016 Olympic Games qualifying competition. The amount of training modified or cancelled due to injury/illness was also collected. The training stress balance (TSB) and acute to chronic workload ratio (ACWR) were calculated with the 3 moving average methods. Along with the amount of modified training, TSB and ACWR across the moving average methods were then examined for their relationship to competitive performance. Results: There were no consistent associations between performance and training load on the day of competition. The volatility (SD) of the ACWR in the last 21 days preceding the competition was moderately correlated with performance across moving average methods (r = −.41 to .48, P = .03–.07). TSB and ACWR volatility in the last 21 days were also significantly lower for successful performers but only as a simple moving average (P = .03 and .03, g = 1.15 and 1.07, respectively). Conclusions: Practitioners should consider restricting change and volatility in an athlete’s TSB or ACWR in the last 21 days prior to a major competition. In addition, a simple moving average seemed to better explain elite weightlifting performance than the exponentially weighted moving averages in this investigation.

Purpose: It is known that modifying the endurance-type training load of athletes may result in altered cardiac autonomic modulation that may be estimated with heart rate variability (HRV). However, the specific effects of intensive resistance-type training remain unclear. The main aim of this study was to find out whether an intensive 2-wk resistance training period affects the nocturnal HRV and strength performance of healthy participants. Methods: Young healthy men (N = 13, age 24 [2] y) performed 2-wk baseline training, 2-wk intensive training, and a 9-d tapering periods, with 2, 5, and 2 hypertrophic whole-body resistance exercise sessions per week, respectively. Maximal isometric and dynamic strength were tested at the end of these training periods. Nocturnal HRV was also analyzed at the end of these training periods. Results: As a main finding, the nocturnal root mean square of differences of successive R-R intervals decreased (P = .004; from 49 [18] to 43 [15] ms; 95% CI, 2.4–10.4; effect size = 0.97) during the 2-wk intensive resistance training period. In addition, maximal isometric strength improved slightly (P = .045; from 3933 [1362] to 4138 [1540] N; 95% CI, 5.4–404; effect size = 0.60). No changes were found in 1-repetition-maximum leg press or leg press repetitions at 80% 1-repetition maximum. Conclusions: The present data suggest that increased training load due to a short-term intensive resistance training period can be detected by nocturnal HRV. However, despite short-term accumulated physiological stress, a tendency of improvement in strength performance was detected.

The number of studies examining associations between training load and injury has increased exponentially. As a result, many new measures of exposure and training-load-based prognostic factors have been created. The acute:chronic workload ratio (ACWR) is the most popular. However, when recommending the manipulation of a prognostic factor in order to alter the likelihood of an event, one assumes a causal effect. This introduces a series of additional conceptual and methodological considerations that are problematic and should be considered. Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data. Furthermore, there are known issues with the use of ratio data and unrecognized assumptions that negatively affect the ACWR metric for use as a causal prognostic factor. ACWR use in practical settings can lead to inappropriate recommendations, because its causal relation to injury has not been established, it is an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled), it has a lack of background rationale to support its causal role, it is an ambiguous metric, and it is not consistently and unidirectionally related to injury risk. Conclusion: There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts.

Purpose: To investigate within-player effect, between-player effect, and individual response of external training load from player tracking devices on session rating of perceived exertion training load (sRPE-TL) in elite football players. Methods: The authors collected sRPE-TL from 18 outfield players in 21 training sessions. Total distance, high-speed running distance (>14.4 m/s), very high-speed running distance (>19.8 m/s), PlayerLoad™, PlayerLoad2D™, and high-intensity events (HIE > 1.5, HIE > 2.5, and HIE > 3.5 m/s) were extracted from the tracking devices. The authors modeled within-player and between-player effects of single external load variables on sRPE-TL, and multiple levels of variability, using a linear mixed model. The effect of 2 SDs of external load on sRPE-TL was evaluated with magnitude-based inferences. Results: Total distance, PlayerLoad™, PlayerLoad2D™, and HIE > 1.5 had most likely substantial within-player effects on sRPE-TL (100%–106%, very large effect sizes). Moreover, the authors observed likely substantial between-player effects (12%–19%, small to moderate effect sizes) from the majority of the external load variables and likely to very likely substantial individual responses of PlayerLoad™, high-speed running distance, very high-speed running distance, and HIE > 1.5 (19%–30% coefficient of variation, moderate to large effect sizes). Finally, sRPE-TL showed large to very large between-session variability with all external load variables. Conclusions: External load variables with low intensity-thresholds had the strongest relationship with sRPE-TL. Furthermore, the between-player effect of external load and the individual response to external load advocate for monitoring sRPE-TL in addition to external load. Finally, the large between-session variability in sRPE-TL demonstrates that substantial amounts of sRPE-TL in training sessions are not explained by single external load variables.

Purpose: To analyze training-intensity distribution (TID) using different independent monitoring systems for internal training load in a group of elite open-water swimmers. Methods: One hundred sixty training sessions were monitored in 4 elite open-water swimmers (2 females and 2 males: 23.75 [4.86] y, 62.25 [6.18] kg, 167 [6.68] cm) during 5 weeks of regular training. Heart-rate-based methods, such as time in zone (TIZ), session goal (SG), and hybrid (SG/TIZ), were used to analyze TID. Similarly to SG/TIZ, a new hybrid approach, the rating of perceived exertion (RPE)/TIZ for a more accurate analysis of TID was used. Moreover, based on the 3-zone model, the session ratings of perceived exertion of the swimmers and the coach were compared. Results: Heart-rate- and RPE-based TID methods were significantly different in quantifying Z1 (P = .012; effect size [ES] = 0.490) and Z2 (P = .006; ES = 0.778), while no difference was observed in the quantification of Z3 (P = .428; ES = 0.223). The heart-rate-based data for Z1, Z2, and Z3 were 83.2%, 7.4%, and 8.1% for TIZ; 80.8%, 8.3%, and 10.8% for SG/TIZ; and 55%, 15.6%, and 29.4% for SG. The RPE-based data were 70.9%, 19.9%, and 9.2% for RPE/TIZ% and 41.2%, 48.9%, and 9.7% for the session rating of perceived exertion. No differences were observed between the coach’s and the swimmers’ session ratings of perceived exertion in the 3 zones (Z1: P = .663, ES = −0.187; Z2: P = .110, ES = 0.578; Z3: P = .149, ES = 0.420). Conclusion: Using RPE-based TID methods, Z2 was significantly larger compared with Z1. These results show that RPE-based TID methods in elite open-water swimmers are affected by both intensity and volume.

Purpose: Although the session rating of perceived exertion (sRPE) is primarily a marker of internal training load (TL), it may be sensitive to external TL determining factors, such as duration and volume. Thus, sRPE could provide further information on accumulated fatigue not available from markers of internal TL. Therefore, the purpose of this study was to investigate sRPE during heavy training bouts at relatively constant intensity. Methods: Eleven university swimmers performed a high-volume training session consisting of 4 × 10 × 100-yd (4 × 10 × 91.4 m). Repetition lap time and heart rate were measured for each repetition and averaged for each set. Blood lactate concentration was measured after each set. At the end of each set, a 10-minute rest period was allowed, during which sRPE values were obtained, as if the training bout had ended. Results: There were no differences between sets for lap time (P = .096), heart rate (P = .717), and blood lactate concentration (P = .466), suggesting that the subjects were working at the same external and internal intensity. There was an increase (P = .0002) in sRPE between sets (first 4 [1.2], second 5 [1.3], third 7 [1.3], and fourth 8 [1.5]), suggesting that even when maintaining the same intensity, the perception of the entire workload increased with duration. Conclusions: Increases in duration, although performed with a consistent internal and external intensity, influences sRPE. These findings support the concept that sRPE may provide additional information on accumulated fatigue not available from other markers of TL.

Purpose: Criticisms of the acute to chronic workload ratio (ACWR) have been that the mathematical coupling inherent in the traditional calculation of the ACWR results in a spurious correlation. The purposes of this commentary are (1) to examine how mathematical coupling causes spurious correlations and (2) to use a case study from actual monitoring data to determine how mathematical coupling affects the ACWR. Methods: Training and competition workload (TL) data were obtained from international-level open-skill (basketball) and closed-skill (weightlifting) athletes before their respective qualifying tournaments for the 2016 Olympic Games. Correlations between acute TL, chronic TL, and the ACWR as coupled/uncoupled variations were examined. These variables were also compared using both rolling averages and exponentially weighted moving averages to account for any potential benefits of one calculation method over another. Results: Although there were some significant differences between coupled and uncoupled chronic TL and ACWR data, the effect sizes of these differences were almost all trivial (g = 0.04–0.21). Correlations ranged from r = .55 to .76, .17 to .53, and .88 to .99 for acute to chronic TL, acute to uncoupled chronic TL, and ACWR to uncoupled ACWR, respectively. Conclusions: There may be low risk of mathematical coupling causing spurious correlations in the TL–injury-risk relationship. Varying levels of correlation seem to exist naturally between acute and chronic TL variables regardless of coupling. The trivial to small effect sizes and large to nearly perfect correlations between coupled and uncoupled AWCRs also imply that mathematical coupling may have little effect on either calculation method, if practitioners choose to apply the ACWR for TL monitoring purposes.

Purpose: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators. Methods: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model. Results: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0% and 4.5%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5% of NI), individual deviation variables (5.8% of NI), and individual player markers (17.0% of NI). Conclusions: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.

The 10-km open-water swimming race is an endurance event that takes place in lakes, rivers, or sea and has been an Olympic event since 2008. The aim of the present brief report is to describe training volume and intensity distribution of elite open-water swimmers during the 2016 Olympic season, verifying if, in order to maximize performance, most of the training would be performed at low intensities.Eight elite Italian open-water swimmers (3 male and 5 female; 25 [2] y, 1.74 [0.05] m, 68.26 [8.17] kg) specialized in distances between 5 and 25 km participated in the study. Training load was determined using an online training diary. Training intensity was categorized according to the 3-zone model: Z1, light intensity; Z2, moderate intensity; and Z3, high intensity. Session rating of perceived exertion was used to quantify training-intensity distribution. This method assigns the entire session into a single intensity zone based on the rating of perceived exertion recorded 30 min posttraining.Total yearly training volume was 3576.93 (272.390) km (3220.80-4041.97), distributed across 446 (37) (397-484) sessions monitored during the 2016 Olympic season. Training-intensity distribution in each zone was 76.83% (8.11%) in Z1, 17.70% (6.79%) in Z2, and 5.47% (5.93%) in Z3.High volumes in Z1 appear to be an important training method used by elite open-water swimmers. However, future research is necessary to study the effects of different training-intensity distribution on open-water swimming performances.

Purpose: When exposed to the same external load, players receive different internal loads, resulting in varied adaptations in fitness. In adult soccer, internal training load is measured using heart rate (HR) and session rating of perceived exertion (sRPE) scales, but these have been underutilized in youth soccer. This study investigated the in-season variation in correlation between HR and sRPE estimations of training load for adolescent soccer players. Method: Fifteen male professional adolescent players were monitored for 7 months. Within-participant correlations and Bland–Altman agreement plots for HR and sRPE were calculated for each month to analyze variation over the season and for individual players to analyze the validity of the scale. Results: The monthly correlations ranged from r = .60 to r = .73 (P r = .64 (95% confidence interval, .60–.68; P Conclusion: Results showed consistently large correlations for all months. sRPE is a consistent method of measure of internal training load for the entire season for youth soccer players. Validity analysis found no bias in sRPE measurements when compared with HR for all players in the study.

© 2019 Human Kinetics, Inc. Exercise is a stressor that induces various psychophysiological responses, which mediate cellular adaptations in many organ systems. To maximize this adaptive response, coaches and scientists need to control the stress applied to the athlete at the individual level. To achieve this, precise control and manipulation of the training load are required. In 2003, the authors introduced a theoretical framework to define and conceptualize the measurable constructs of the training process. They described training load as having 2 measurable components: internal and external load. The aim of this commentary is to extend, clarify, and refine both the theoretical framework and the definitions of internal and external training load to avoid misinterpretation of this concept.

Purpose: The COVID-19 outbreak has challenged professional athletes’ training and competition routines in a way not seen before. This report aims to inform about the changes in training volume and intensity distribution and their effects on functional performance due to a 7-week home-confinement period in professional road cyclists from a Union Cycliste Internationale Pro Team. Methods: A total of 18 male professional cyclists (mean [SD] age = 24.9 [2.8] y, body mass = 66.5 [5.6] kg, maximal aerobic power = 449 [39] W; 6.8 [0.6] W/kg) were monitored during the 10 weeks before the lockdown (outdoor cycling) and the 7-week lockdown (indoor cycling turbo trainer). Data from the mean maximal power output (in watts per kilogram) produced during the best 5-minute and best 20-minute records and the training intensity distributions (weekly volumes at power-based training zones) were collected from WKO5 software. Results: Total training volume decreased 33.9% during the lockdown (P P = .38). There were large reductions in best 5-minute and best 20-minute performance (effect size > 1.36; P Conclusions: Total indoor volumes of 12 hours per week, with 6 hours per week at low intensity (Z1 and Z2) and 2 hours per week at high intensity over the threshold (Z5 and Z6), were insufficient to maintain performance in elite road cyclists during the COVID-19 lockdown. Such performance declines should be considered to enable a safe and effective return to competition.

Purpose: To provide novel insight regarding the influence of exercise modality on training load management by (1) providing a theoretical framework for the impact of physiological and biomechanical mechanisms associated with different exercise modalities on training load management in endurance exercise and (2) comparing effort-matched low-intensity training sessions performed by top-level athletes in endurance sports with similar energy demands. Practical Applications and Conclusions: The ability to perform endurance training with manageable muscular loads and low injury risks in different exercise modalities is influenced both by mechanical factors and by muscular state and coordination, which interrelate in optimizing power production while reducing friction and/or drag. Consequently, the choice of exercise modality in endurance training influences effort beyond commonly used external and internal load measurements and should be considered alongside duration, frequency, and intensity when managing training load. By comparing effort-matched low- to moderate-intensity sessions performed by top-level athletes in endurance sports, this study exemplifies how endurance exercise with varying modalities leads to different tolerable volumes. For example, the weight-bearing exercise and high-impact forces in long-distance running put high loads on muscles and tendons, leading to relatively low training volume tolerance. In speed skating, the flexed knee and hip position required for effective speed skating leads to occlusion of thighs and low volume tolerance. In contrast, the non-weight-bearing, low-contraction exercises in cycling or swimming allow for large volumes in the specific exercise modalities. Overall, these differences have major implications on training load management in sports.

Context: The application of infrared thermography to assess the effects of athletic training is increasing. It is not known if changes in skin temperature (Tsk) as assessed by infrared thermography are affected by the training load or the muscle soreness experienced by the athlete. Purpose: To describe the variations in Tsk in body areas affected by running training and examine any relationships with subjective ratings of muscle soreness. The secondary aim was to assess the feasibility of using infrared thermography for assessing training load in 2 junior male middle-distance athletes. Methods: Data were collected over a 42-d observational period with Tsk of the quadriceps, knees, shins, lateral hamstrings, biceps femoris, and Achilles tendons, and the subjective ratings of muscle soreness were taken each morning prior to any training. All training load was quantified through heart rate, running speed, and distance covered. Changes in Tsk outside the typical error were identified. Relationships between Tsk and subjective ratings of muscle soreness were also examined. Results: Over the 42-d observational period, mean Tsk of the regions of interest was reported outside the typical error on day 31 and day 22 for athletes 1 and 2, respectively. These changes in Tsk did not follow trends similar to those of to training loadings. No significant relationships were observed between Tsk of any regions of interest and muscle soreness. Conclusions: Although Tsk changed outside the typical error throughout the 42-d observational period, these changes were not reflective of training load quantified through cardiovascular strain or subjective ratings of muscle soreness.

Purpose: To compare repetition maximum (RM) to relative intensity using sets and repetitions (RISR) resistance training on measures of training load, vertical jump, and force production in well-trained lifters. Methods: Fifteen well-trained (isometric peak force = 4403.61 [664.69] N, mean [SD]) males underwent resistance training 3 d/wk for 10 wk in either an RM group (n = 8) or RISR group (n = 7). Weeks 8 to 10 consisted of a tapering period for both groups. The RM group achieved a relative maximum each day, whereas the RISR group trained based on percentages. Testing at 5 time points included unweighted (g were used to assess within- and between-groups alterations. Results: Moderate between-groups effect sizes were observed for all squat-jump and countermovement-jump conditions supporting the RISR group (g = 0.76–1.07). A small between-groups effect size supported RISR for allometrically scaled isometric peak force (g = 0.20). Large and moderate between-groups effect sizes supported RISR for rate of force development from 0 to 50 ms (g = 1.25) and 0 to 100 ms (g = 0.89). Weekly volume load displacement was not different between groups (P > .05); however, training strain was statistically greater in the RM group (P Conclusions: Overall, this study demonstrated that RISR training yielded greater improvements in vertical jump, rate of force development, and maximal strength compared with RM training, which may be explained partly by differences in the imposed training stress and the use of failure/nonfailure training in a well-trained population.

Purpose: To conduct a systematic review into the effect of training load (TL) on both performance measures and injury characteristics in rugby league players. Methods: Based on PRISMA guidelines, a systematic search of electronic databases was performed from the earliest record to December 2016. Five electronic databases (MEDLINE, SPORTDiscus, CINAHL, Web of Science, and AusSportMed) were searched using keywords within the subcategories of population (rugby league players), intervention (training quantification), and outcomes (performance or injury). Original peer-reviewed published manuscripts were considered if they had a clear quantification of TL (eg, session rating of perceived exertion), clear measures of performance (tests of physiological parameters relative to rugby league players [eg, agility]), and/or injury characteristics (eg, injury incidence). Results: Twelve studies met the inclusion criteria. The highest TL was observed in preseason training phases. Higher TL elicited greater improvements in maximal aerobic power, vertical jump (in cm), and agility scores (in s). Higher TL was associated with higher incidence of muscular strains and joint sprains, lower-limb injuries, and overexertion and overuse injuries. Conclusions: Findings suggest a dose–response relationship between TL, physical performance, and injury characteristics. However, due to study and methodological limitations, the precise nature of this relationship is, as yet, inconclusive. Therefore, the development of uniform TL definition and quantification and further research is warranted.

Purpose: To identify which combination metrics of external and internal training load (TL) capture similar or unique information for individual professional players during skills training in rugby union using principal-component (PC) analysis. Methods: TL data were collected from 21 male professional rugby union players across a competitive season. This included PlayerLoad™, total distance, and individualized high-speed distance (>61% maximal velocity; all external TL) obtained from a microtechnology device (OptimEye X4; Catapult Innovations, Melbourne, Australia) that was worn by each player and the session rating of perceived exertion (RPE) (internal TL). PC analysis was conducted on each individual to extract the underlying combinations of the 4 TL measures that best describe the total information (variance) provided by the measures. TL measures with PC loadings (PCL) above 0.7 were deemed to possess well-defined relationships with the extracted PC. Results: The findings show that from the 4 TL measures, the majority of an individual’s TL information (first PC: 55–70%) during skills training can be explained by session RPE (PCL: 0.72–0.95), total distance (PCL: 0.86–0.98), or PlayerLoad (PCL: 0.71–0.98). High-speed distance was the only variable to relate to the second PC (PCL: 0.72–1.00), which captured additional TL information (+19–28%). Conclusions: Findings suggest that practitioners could quantify the TL of rugby union skills training with one of PlayerLoad, total distance, or session RPE plus high-speed distance while limiting omitted information of the TL imposed during professional rugby union skills training.

The cortisol awakening response (CAR) is commonly used as a marker of psychological stress; however, it is unknown whether CAR is affected by regular physical-exercise-induced stress. Purpose: To assess the relationship between training load and CAR. Methods: Recreational endurance athletes were recruited from local running clubs. Subjects (n = 15) completed training logs for 2 wk, with various training loads, including psychometric analysis (Recovery-Stress Questionnaire for Athletes). Subjects provided saliva samples each day immediately after waking and 30 min postwaking. Samples were analyzed for cortisol concentration via enzyme-linked immunosorbent assay and subsequently were analyzed for CAR and CAR%. Daily training load was calculated and analyzed as training impulse. Simple linear regression was used to assess the relationship between CAR and training impulse. Results: CAR (r2 = .352, P = .025) and CAR% (r2 = .373, P = .012) both showed a significant negative relationship with training load. Conclusions: These results suggest that CAR is affected by regular exercise training loads in recreational athletes. It is recommended that future CAR research control for fitness level and exercise training load in physically active populations.

Purpose: To determine if microtechnology-derived collision loads discriminate between collision performance and compare the physical and analytical components of collision performance between positional groups. Methods: Thirty-seven professional male rugby union players participated in this study. Collision events from 11 competitive matches were coded using specific tackle and carry classifications based on the ball-carrier’s collision outcome. Collisions were automatically detected using 10 Hz microtechnology units. Collision events were identified, coded (as tackle or carry), and timestamped at the collision contact point using game analysis software. Attacking and defensive performances of 1609 collision events were analyzed. Results: Collision loads were significantly greater during dominant compared with neutral and passive collisions (P P P Conclusion: Microtechnology is a valid means of discriminating between tackle and carry performance. Thus, microtechnology-derived collision load data can be utilized to track and monitor collision events in training and games.

Perceptions of teammates and training load have been shown to influence athletes’ physical and psychological health; however, limited research has investigated these factors in relation to burnout. Athletes (N = 140) from a variety of competitive team sports, ranging in level from regional to professional, completed questionnaires measuring individual burnout, perceptions of teammates’ burnout, and training hours per week on two occasions separated by three months. After controlling for burnout at time one, training hours were associated with athletes’ burnout and perceptions of teammates’ burnout at time two. Multilevel modeling indicated actual team burnout (i.e., the average burnout score of the individual athletes in a team) and perceived team burnout were associated with individual’s own burnout. The findings highlight that burnout is dynamic and relates to physiological stressors associated with training and psychological perceptions of teammates’ burnout. Future research directions exploring potential social influences on athlete burnout are presented.