Space and players' number constrains the external and internal load demands in youth futsal.

This study aims to quantify and compare the external and internal training load demands of sided-game drills in professional team players during the competitive season. Twenty-four male professional soccer players of the same club were enrolled in this study. Drills were categorized as large-sided games (LSG): 10vs10 (84 × 60 m or 72 × 60 m), Hexagon possession 9vs9 + 3 (36 × 48 m), Possession gate 8vs8 + 2 (36 × 44 m), Possession 7vs7 + 3 (30 × 32 m) or as Small-sided games (SSG): 6vs6 (48 × 42 m), and Possession 6vs4 (30 × 60 m). A total of 7 drills and 279 individual data points were included in this analysis. Distance covered, high-speed running (HSR), and sprinting distance were all calculated in meters per minute (m.min−1) while total accelerations (>3 m.s−2) and total decelerations (− < 3 m.s−2) were calculated in number of actions per minute (n.min−1). All external load was measured with global navigation satellite systems (GNSS) STATSports Apex units. Players’ internal load was quantified using their rating of perceived exertion (RPE). We found that distance covered (p < 0.01, large), HSR (p < 0.01, large), and sprinting distance (p < 0.01, large) changed between drills (e.g., greater in LSG formats), acceleration (p < 0.01, large) and deceleration (p < 0.01, large) demands were greater in smaller formats (e.g., SSG 6vs6, and Possession 6vs4), while RPE was lower in the Possession gate 8vs8 + 2 format (p < 0.01, large). This study found that sided-games can replicate and sometimes exceed some match-specific intensity parameters, however, HSR and sprinting were consistently lower compared to official matches.

"Monitoring athlete preparedness, including quantifying training and competition load and determining fatigue/training status, is used to complement training and recovery prescription in professional sport (Kenttä &amp; Hassmén, 2002). The overall objective of this research was to investigate contemporary athlete monitoring practices in professional Australian football (AF). The aim of study 1 was to identify the relationship between external training load and session rating of perceived exertion (s-RPE) training load and the impact that playing experience, playing position and 2-km time-trial performance had on that relationship. Microtechnology devices provided external training load (distance, mean speed, high-speed running distance, Player loadTM (PL), and Player load slowTM (PLslow)). The external training load measures had moderate to very large associations with s-RPE training load. When controlling for external training load, the 4- to 5-year players had a small increase in s-RPE training load compared to the 0- to 1- and 2- to 3-year players. Furthermore, ruckmen had moderately higher s-RPE training load than midfielders, and there was a 0.2% increase in s-RPE training load per 1 s increase in time-trial time. The aim of study 2 was to profile weekly wellness within the context of the competitive season of professional AF. Each morning before any physical training, players completed a 5-item customised self-report questionnaire (sleep quality, fatigue, stress, mood, and muscle soreness), with the mean of the individual indices used to determine overall wellness. Internal match load (s-RPE), match-to-match micro-cycle length, stage of the season and internal training load were included in multivariate linear models in order to determine their effect on weekly wellness profile. There was a lower weekly training load on a 6-day micro-cycle (mean ± s = 1813 ± 291 au) compared to a 7- (1898 ± 327 au, likely small) and 8-day (1900 ± 271 au, likely small) micro-cycle. Match load had no significant impact on weekly wellness profile, whilst there was an interaction between micro-cycle and days-post-match. There was likely to be a moderate decrease in wellness Z-score 1 d post match for an 8-day micro-cycle compared to a 6- and 7- day cycle. There was possibly a small reduction in overall wellness Z-score in the second half of the season compared to the first half of the season. Finally, training load had no effect on wellness Z-score when controlled for days-post-match, micro-cycle and stage of the season. The aim of study 3 was to assess the application of athlete self-report measures to prompt modifications to training dose by exploring its association with subsequent activity profiles. The impact of perceived wellness on a range of external load parameters, RPE and external load: RPE ratios, was explored during skill-based training in AF. Mixed-effect linear models revealed significant effects of wellness Z-score on PL and PLslow. A negative wellness Z-score corresponded to a small reduction in PL and a moderate reduction PLslow, compared to those without reduced wellness. A small reduction was also observed in the PLslow: RPE ratio models, while a small increase was seen in mean speed: RPE ratio. The aim of study 4 was to corroborate the use of particular contemporary monitoring measures by examining their effect on individual match performances. The effects of internal load parameters, combined with athlete self-reported wellness, on subjective and objective measures of match performance in 20 rounds of professional AF was examined. Acute weekly internal load (s-RPE) was determined for each independent training modality. Chronic load was calculated as the rolling 4-week mean and a training-stress balance (TSB) was ascertained by dividing the acute load (1-weekly total) by the chronic load (4-week mean) expressed as a percentage. Load from every training modality was used to calculate an overall acute load, overall chronic load, and overall TSB and only outdoor skills and conditioning sessions were used to calculate a field-based acute load, a field-based chronic load and field-based TSB. Weekly wellness was quantified as the mean of the overall daily wellness scores. An iterative linear mixed modelling approach demonstrated that load and wellness variables had minimal impact on subjective performance ratings (coaches’ votes). Conversely, objective performance, measured via Champion Data© ranking points was positively associated with load, although the magnitude of this effect was greater for field-based loads compared to overall loads. Furthermore, athletes with high loads reporting low wellness, ranked better in objective performance than those reporting high wellness with high loads. Alternatively, an increase in wellness was associated with better objective performance when accompanying lower loads. This collection of studies suggests that s-RPE has a strong relationship with measures of external load, which is moderated by playing position, experience and time-trial performance in AF and that coaches and sport scientists should give consideration to these mediators of s- RPE. It was also revealed that the weekly profile of self-reported wellness in response to matches was influenced by the match-to-match micro-cycle and stage of the season in AF. However, when factoring in these conditions, training load had minimal influence on wellness profile. As such, determination of ‘red flags’ in self-reported measures should be made against comparative weeks. Furthermore, pre-training self-reported wellness was shown to be associated with accelerometer-derived external load measures, suggesting an altered movement pattern during diminished training states. Understanding the changes in external load that might be produced, relative to the pre-training self-reported wellness, provides coaches with an opportunity to adjust prescription if warranted. Finally, the use of internal load and athlete self-report measures can be corroborated based on their relationship with an objective measure of performance in AF and the importance of a mixed-method approach to comprehensively assess athlete status is emphasised."

Fields, JB, Merigan, JM, Gallo, S, White, JB, and Jones, MT. External and internal load measures during preseason training in men collegiate soccer athletes. J Strength Cond Res 35(9): 2572-2578, 2021-Collegiate athletes are exposed to high volume loads during preseason training. Monitoring training load can inform training and recovery periods. Therefore, the purpose was to examine changes in and bidirectional relationship between external and internal load metrics in men collegiate soccer athletes (n = 20; age, 20 ± 1 year). Internal load measures of heart rate variability (HRV), salivary testosterone (T) and cortisol (C), and self-assessment wellness and ratings of perceived exertion scales were collected daily. External load measures of total distance, player load, high-speed distance, high inertial movement analysis, and repeated high-intensity efforts were collected in each training session using global positioning system/global navigation satellite system technology. A 1-way analysis of variance determined weekly changes in external load, physiological, hormonal, and subjective self-assessment measures of internal load. Bidirectional prediction of external load markers and self-assessment measures on physiological and hormonal markers of internal load were assessed by hierarchical linear regression models (p < 0.05). External load measures, C, energy, sleep, and rate of perceived exertion (RPE) decreased (p < 0.01), whereas T, T:C ratio, anger, depression, and vigor increased (p < 0.01) from week 1 to week 2. Morning C positively predicted afternoon external load and post-training RPE (p < 0.05); T:C ratio negatively predicted afternoon external load and post-training RPE (p < 0.05); and morning HRV negatively predicted post-training RPE (p = 0.031). Despite reduced hormonal stress and external load across weeks, negative perceptions of fatigue increased, suggesting fatigue patterns may have a delayed response. Load may have a more belated, chronic effect on perceptions of fatigue, whereas hormonal changes may be more immediate and sensitive to change. Practitioners may wish to use a variety of external and internal load measures to understand athletes' stress responses to training.

Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models' performance on predicting the reported RPE values for future training sessions was compared with the naive baseline's performance. Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.

The purpose of the present study was to analyze the internal and external loads on regular and floater players during standardized small-sided games (SSGs) with different numbers of players (teams of 3, 5, or 7 players). Fifteen male semi-professional football players played different SSGs maintaining the same relative area per player. Total distance (TD), distance covered at different speeds (DC), the number of accelerations and decelerations, maximal (HR<inf>max</inf>) and mean (HR<inf>mean</inf>) heart rate and rate of perceived exertion (RPE) were registered. Regular players showed greater internal and external loads in SSGs with 3 and 5 players without floaters than with floaters (ES 0.60-to-1.27). Likewise, with floaters, regular players in the SSGs with 3 performed more accelerations (ES 1.40 and 1.17) and with 7 achieved higher TD, DC>14 km·h-1, HR<inf>max</inf> and HR<inf>mean</inf> (ES 0.66-to-2.79) than any other. During SSGs with 7 players the floaters showed a higher TD and decelerations than in other SSGs (ES 0.47-to-1.70), and a higher DC (0-6.9 km·h-1,14-17.9 km·h-1) and RPE than in SSGs with 3 players (ES 0.59-to-0.89). During SSGs with 5, the floaters showed a higher TD, HR<inf>max</inf>, HR<inf>mean</inf> and RPE than in SSGs with 3 (ES 0.86-to-1.45). In all SSGs, regular players showed higher TD, DC (14-17.9 km·h-1), accelerations, decelerations and HR<inf>mean</inf> than floaters (ES 1.24-to-6.23). Coaches must carefully design SSGs because the number of players and the presence or absence of floaters can affect the external-internal load expressed.

Zhang, S, Li, M, Xing, W, Zheng, W, and Zhai, Z. Effects of player characteristics and periodization strategies on external and internal loads, wellness, and recovery in collegiate male basketball players. J Strength Cond Res XX(X): 000-000, 2025-This study investigated the effects of playing time, player role, player position, and training cycles-both microcycles (the six days leading up to a match) and mesocycles (the four weeks preceding competition)-on PlayerLoad (PL), rating of perceived exertion (RPE),well-being (WB), and total quality recovery (TQR) in highly trained collegiate male basketball players. A total of 18 highly trained collegiate male basketball players were monitored across a continuous 2-month, resulting in a total of 804 training session observations. External load was quantified as PL using wearable tracking devices, while internal load was assessed through RPE. Well-being and TQR were measured using digital self-report questionnaires. The key findings showed that high-minute players (>26 minutes) exhibited significantly higher PL (p < 0.05, effect sizes [ES] = 0.34) and RPE (p < 0.05, ES = 0.33) compared with low-minute players (<26 minutes). Positional differences were also reported, with centers reporting significantly lower RPE than forwards (p < 0.05, ES = -0.67) and higher WB than guards (p < 0.05, ES = -0.88). In the microcycle analysis, the highest PL and RPE were observed 5 to 6 days before official matches. Mesocycle analysis showed a significant increase in PL and RPE during week 2, accompanied by a concurrent decrease in TQR. These results demonstrate that playing time, player role, player position, and training cycles significantly influence PL, RPE, WB, and TQR, underscoring the importance of individualized load management within microcycle and mesocycle structures to optimize performance and recovery in collegiate male basketball players.

The aims of this study were 3-fold: firstly, to present an integrative approach to external and internal load dynamics for monitoring fitness and fatigue status of specific in-court rink hockey training sessions in a standard microcycle; secondly, to assess the differences between training sessions and matches; the third and final aim was to assess the association between external and internal load metrics. The external load, using a local positioning system, and internal load, using the declared rate of perceived exertion, were measured during 23 in-season microcycles for nine top-level players. Training load data were analysed with regard to the number of days before or after a match [match day (MD) minus or plus]. In relation to the first aim, internal and external load metrics merged into a single integrated system using pooled data z-scores provided an invisible monitoring tool that places the players in the fitness-fatigue continuum throughout the different microcycle sessions. In this regard, MD-4 and MD-1 sessions tend to place, with a low dispersion, the players in a “low external and internal load” zone. On the contrary, in MD-3 and MD-2 sessions, as well as in MD, in which higher loads were recorded, most of the players were within a “high external and internal load” zone with a tendency towards dispersion towards the fitness or fatigue zones. Finally, and with regard to the second and third aims, an inverted “U-shape” load dynamic related to the specific goals of each training session was the main finding in terms of comparison between MD; a load peak between MD-3 and MD-2 sessions and a significant decrease in all the load variables in MD-1 sessions were found; and high-to-low correlations were found between external and internal load metrics. This study presents an integrative approach to the external and internal load of players for monitoring fitness and fatigue status during a standard microcycle in rink hockey that might provide team sport staff members with a deeper understanding of load distribution in the microcycle in relation to the match.

PurposeTo compare external and internal training load markers during resistance training (RT) in normoxia (N), intermittent hypobaric hypoxia (HH), and intermittent normobaric hypoxia (NH).MethodsThirty-three volunteers were assigned an 8-week RT program in either N (690 m, n = 10), HH (2320 m, n = 10), or NH (inspired fraction of oxygen = 15.9%; ~ 2320 m, n = 13). The RT program (3x/week) consisted of six exercises, with three sets of six to 12 repetitions at ~ 70% of one repetition maximum (1RM) with the first session of each week used for analysis. 1RM in back squat and bench press was used to evaluate muscle strength before and after the program. External load was assessed by the volume load relative to body mass (RVL, kg·kg−1). Internal load was assessed by the ratings of perceived exertion (RPE) and heart rate (HR).ResultsSmaller relative improvements were found for the back squat in the N group (11.5 ± 8.8%) when compared to the NH group (22.2 ± 8.2%, P = 0.01) and the HH group (22 ± 8.1%, P = 0.02). All groups showed similar RVL, HR responses and RPE across the program (P˃0.05). However, reduced HR recovery values, calculated as the difference between the highest HR value (HRpeak) and the resting heart rate after a two min rest, were seen in the N and NH groups across the program (P<\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<$$\\end{document} 0.05).ConclusionIt seems that 8 weeks of intermittent RT in hypoxic environments could maximize time-efficiency when aiming to improve strength levels in back squat without evoking higher levels of physiological stress. Performing RT at hypobaric hypoxia may improve the cardiorespiratory response, which in turn could speed recovery.

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.

Duathlon is a rapidly promoting sport, so it is necessary to identify the factors that influence performance. This study aimed to analyse internal and external loads and the interactions between them during running legs before and after cycling in duathlon. Twenty-three male and female athletes participated in a simulated duathlon (5k-20k-5k). Internal workload was assessed using the rate of perceived exertion (RPE) and heart rate (maximum: HRMAX; average: HRAVG; R-R interval), whereas external workload was assessed using inertial sensors (Player Load by RealTrack, PLRT) attached to six body locations. The results showed that both running legs presented an increasing trend in RPE, HRMAX, and HRAVG throughout the kilometres, while the R–R interval decreased. The PLRT of the upper and lower back, knee, and ankle increased throughout the first running leg but not in the second. The PLRT was greater in the lower body than in the upper body because of energy absorption. RPE was higher in the second running leg than in the first leg. In conclusion, the cycling leg affects the internal and external load between running legs in duathlon. Load monitoring can help coaches understand duathletes’ performance and design specific training strategies to reduce fatigue during competition.

This study investigated the relationships between Rate of Perceived Exertion (RPE) and various objective internal and external training load measures across multiple drill types in youth academy soccer players, a comparative approach that has been rarely examined. Forty-six male outfield soccer players (16.3 ± 0.4 years) from two under-17 academy-level teams competing in a city-level league, training three times per week with weekend matches, were monitored over two weeks during regular training. Data included RPE (CR-10 scale), heart rate responses (HRaverage, HRmax), and external load variables (total distance per minute, average speed, distance in Z4 [15-19 km/h], and Z5 [>19 km/h]) via Polar Team Pro, measured across all drills (3v3, 6v6, 9v9, 11v11, 10x5 positional game, repeated sprint training, muscular endurance circuit training, and slalom exercise). Aerobic capacity (VIFT) was assessed separately using the 30-15 IFT. Results indicated that RPE consistently showed large positive correlations with HRaverage (e.g., 3v3: r = 0.977) and HRmax (e.g., 3v3: r = 0.778) across most drills, Conversely, relationships between RPE and the VIFT were varied, showing large negative correlations in larger-sided games (e.g., 11v11: r = -0.446; 9v9: r = -0.585), suggesting fitter players perceived less effort. Correlations between RPE and general distance/speed metrics were inconsistent, while distances covered in high-intensity speed zones (Z4, Z5) showed large positive correlations with RPE (e.g., 3v3 Z4: r = 0.830; 3v3 Z5: r = 0.710), particularly in drills like 3v3, 6v6, repeated sprint training, and slalom. In conclusion, RPE's relationship with training load is drill-specific. It possibly reflects physiological strain in small-sided games and conditioning drills, but shows more variable associations with external load and fitness in large-sided or tactical formats. Coaches should therefore combine RPE with objective metrics and interpret it in light of drill characteristics. These findings should be viewed cautiously given the short two-week monitoring period and absence of additional physiological markers.

Aviation firefighting is a strenuous occupation that requires individuals to engage in intense physical activity amidst elevated stress levels and extreme environmental conditions. Despite this, there has been limited investigation regarding the internal and external loads associated with aviation firefighting tasks, which include hose dragging, stair climbing, casualty evacuation, and fire extinguishing in airports and aircrafts. The aim of this study was to examine the internal and external loads placed on aviation firefighters. By identifying these demands, this study seeks to inform the development of targeted training strategies, improve job safety, and lower the risk of musculoskeletal injuries. Sixteen Australian aviation firefighters (35.13 ± 8.2 years) were recruited and assigned specific roles to complete an aircraft firefighting scenario. Measures of heart rate (HR), oxygen consumption (V˙O2), and rating of perceived exertion (RPE) were used to quantify internal load, while measures of completion time and distance travelled were used to quantify external load. The median scenario completion time was 21 min (IQR = 5), with each role travelling a median distance of 245-541 m. During the scenario, median average HR values ranged between 61.1 and 72.0% HRmax and median maximal HR values ranged between 77.8 and 84.4% HRmax. As the only group to record V˙O2, driver firefighters operated at a median average V˙O2 of 49% of their V˙O2max and achieved a median maximal V˙O2 of 78% of their V˙O2max. This study effectively identified the task-specific internal and external loads associated with aviation firefighting, offering valuable insights for developing specific training protocols for firefighters to ensure appropriate physical capacity to perform their job roles safely.

Heart rate (HR) monitoring is a practical method for assessing internal load (IL). However, it remains unclear for which age group HR would be an appropriate predictor of IL considering the relationship with external load (EL). Thus, this study aims to evaluate the relevance and applicability of HR monitoring by exploring the relationship between EL and IL among U16 soccer players. EL was measured using global positioning system (GPS) data, while IL was assessed through training impulse (TRIMP), Edward’s TRIMP, HR exertion, rate of perceived exertion (RPE), and session-RPE (s-RPE). Nineteen (N = 19) male footballers from an elite football academy participated, with data collected from 50 training sessions and 11 matches. In the analysis of the training sessions, TRIMP demonstrated a near-perfect correlation with total distance (TD) (p < 0.001), and eTRIMP correlated strongly with TD (r = 0.82) and player load (r = 0.79). HR exertion also correlated significantly with TD, medium-speed running, decelerations, inertial movement analysis (IMA) events, and player load (p < 0.001). In matches, a large correlation was observed between TRIMP and TD (r = 0.73), while the strongest correlation was between RPE and s-RPE with TD and PL (p < 0.001). Furthermore, TD emerged as the best GPS-derived predictor of both TRIMP and HR exertion in training contexts. These findings provide evidence for the validity and usability of heart rate-based and RPE-based measures to indicate IL in U16 soccer players. Future research should focus on contextual factors in exploring the relationship between EL and IL.

Running programs traditionally monitor external loads (eg, time and distance). Recent efforts have encouraged a more comprehensive approach to also account for internal loads (eg, intensity, measured as the session rating of perceived exertion [sRPE]). The combination of external and internal loads accounts for the possible interaction between these loads. Although weekly changes in training loads have been reported between external loads and the combination of external and internal loads during 2- and 4-week training cycles, no authors have indicated whether these differences occur during an entire cross-country season in high school runners. To compare changes in training loads, as measured by (1) external loads and (2) combined external and internal loads in high school runners during an interscholastic cross-country season. Case series. Community-based setting with daily online surveys. Twenty-four high school cross-country runners (females = 14, males = 10, age = 15.9 ± 1.1 years, running experience = 9.9 ± 3.2 years). Week-to-week percentage changes in training load were measured by external loads (time, distance) and combined external and internal loads (time × sRPE [timeRPE] and distance × sRPE [distanceRPE]). Overall, the average weekly change was 7.1% greater for distanceRPE than for distance (P = .04, d = 0.18). When the weekly running duration decreased, we found the average weekly change was 5.2% greater for distanceRPE than for timeRPE (P = .03, d = 0.24). When the weekly running duration was maintained or increased, the average weekly change was 10% to 15% greater when external and internal loads were combined versus external loads alone, but these differences were nonsignificant (P = .11-.22, d = 0.19-0.34). Progression in the training load may be underestimated when relying solely on external loads. The interaction between internal loads (sRPE) and external loads (distance or time) appears to provide a different measure of the training stresses experienced by runners than external loads alone.

The objective of this study was to estimate the oxygen uptake (&OV0312;O2) in elite youth soccer players using measures of heart rate (HR) and ratings of perceived exertion (RPEs). Forty-six regional-level male youth soccer players (∼13 years) participated in 2 VO(2)max tests. Data for HR, RPE, and VO(2) were simultaneously recorded during the VO(2)max tests with incremental running speed. Regression equations were derived from the first VO(2)max test. Two weeks later, all players performed the same VO(2)max test to validate the developed regression equations. There were no significant differences between the estimated values in the first test and actual values in the second test. During the continuous endurance exercise, the combination of percentage of maximal HR (%HRmax) and RPE measures gave similar estimation of %VO(2)max (R = 83%) in comparison to %HRmax alone (R = 81%). However, the estimation of VO(2) using combined %HRmax and RPE was not satisfactory (R = 45-46%). Therefore, the use of %HRmax (without RPE) to estimate %VO(2)max could be a useful tool in young soccer players during field-based continuous endurance testing and training. Specifically, coaches can use the %HRmax to quantify internal loads (%VO(2)max) and subsequently implement continuous endurance training at appropriate intensities. Furthermore, it seems that RPE is more useful as a measure of internal load during noncontinuous (e.g., intermittent and sprint) exercises but not to estimate %VO(2)max during continuous aerobic exercise (R = 59%).