Internal and External Loads in U16 Women’s Basketball Players Participating in U18 Training Sessions: A Case Study

Relationship between internal and external load in elite female youth basketball players

Rodríguez-Fernández, A, Flórez-Gil, E, Scanlan, A, and Vaquera, A. The impact of court orientation and bout-ending mechanism on the external and internal loads of female basketball players during small-sided games. J Strength Cond Res 39(9): e1081-e1090, 2025-The aims were to examine the impact of court orientation and bout-ending mechanism on external and internal loads during small-sided basketball games (SSG) in female players and examine performance-related fatigue associated with SSG formats. Twelve female basketball players participated in 4 SSG formats over a 4-week period. External (measured using local positioning system) and internal (measured using heart rate) load variables were collected. Countermovement jump and 20-m sprint were assessed pre and post each SSG. Each SSG involved 3vs3 and 3 bouts. Small-sided games formats included: (a) fixed time (3 minutes) in half-court orientation (15 × 14 m with 1 basket); (b) fixed time (3 minutes) in stretched-court orientation (7.5 × 28 m with 2 baskets); (c) fixed point (first to 5 points) in half-court orientation; and (d) fixed point (first to 5 points) in stretched-court orientation. Stretched-court SSG formats resulted in significantly greater external loads ( p < 0.01, effect size [ES] = 1.89-4.42). Countermovement jump height increased ( p < 0.05; ES = 0.40-0.43) across all SSG except the stretched-court fixed-point format, with 20-m sprint time unaffected across all SSG ( p ≥ 0.05; ES = 0.11-0.27). Stretched-court 3vs3 SSG formats elicit greater external loads than half-court, whereas fixed-time and fixed-point bout-ending formats yield comparable demands.

The aim of this study is to compare the movement characteristics and heart rate (HR) response of hearing-impaired futsal players based on their playing positions during a simulated game. The study involved twelve players from the Turkish Deaf Men's Futsal National Team, and PlayerLoad (PL) metrics such as total PL (PLTOTAL), PL per minute (PL·min⁻¹), peak PL (PLPEAK), low to very high PL bands (PLLOW-PLVHIGH), and external load variables such as acceleration (ACC), deceleration (DEC), explosive efforts (EXPEFF), and right/left change of direction (COD-R, COD-L), were analyzed using inertial movement analysis (IMA). Additionally, the internal load metrics, including minimum, average, and maximum HR (HRMIN, HRAVE, HRMAX), percentage of HRMAX (%HRMAX) and percentage of time spent in different HR zones (HRZONE1 to HRZONE4) were continuously monitored. IMA variables and HR metrics, based on playing position, were analysed with the Kruskal-Wallis H test. Group comparisons were conducted using the Mann-Whitney U test, and Bonferroni correction was applied. As a result, PL variables change with notable distinctions between defenders and wingers. Additionally, the PLVHIGH stands out as the only significant difference when comparing wingers and pivots. Wingers generally exhibit different external load, including ACC and COD-L, compared to defenders. Additionally, differences were observed in COD-L between pivots and defenders, as well as in COD-R between pivots and wingers. In conclusion, external and internal load metrics during the simulated game vary depending on the players’ positions in hearing-impaired futsal players, highlighting the importance for coaches to consider this diversity in their athlete monitoring approaches.

Konarski, JM, Konarska, A, Strzelczyk, R, Skrzypczak, M, and Malina, RM. Internal and external loads during Hockey 5's competitions among U16 players. J Strength Cond Res 35(11): 3199-3206, 2021-The purpose of the study was to characterize the external and internal loads associated with Hockey 5's, the modified small-sided format of competition for U16 field hockey introduced in 2014. Subjects were 10 males, 15.2-16.3 years, who were field players on the U16 Polish National Team. A GPSports Athlete Tracking System was used to estimate external loads (sprints, total distance), and a Polar Team2 unit was used to monitor internal loads (heart rate [HR] and energy expenditure [EE]) during active time of play for 8 field players in 2 international matches. Players covered, on average, a distance of 2,217 ± 178 m during a match including 614 ± 12 m sprinting at 8.1 ± 0.8 km·h-1 with a speedmax of 23.1 ± 1.1 km·h-1. HRmean was 170.5 ± 6.6 b·min-1, whereas HRmax was 184.9 ± 6.4 b·min-1. Speed, sprinting, and distance covered did not differ among periods, whereas HR was greater during the second and third periods. HRmax, %HRmax, and EE varied between forwards and defenders, and among periods. Effort expended by field players during Hockey 5's games can be characterized as heavy. The results highlight the need for preparation in both aerobic and anaerobic activities, which include a major speed component and adequate time for rapid recovery to facilitate the adaptation of youth players to the demands of the modified game. Although not directly comparable, comparison of internal loads during Hockey 5's and 11-a-side matches suggests more intensive activity of field players in the former.

Sport coaches increasingly rely on external load metrics for designing effective training programs. However, their accuracy in estimating internal load is inconsistent, and their ability to predict autonomic nervous system (ANS) deterioration is unknown. This study aimed to evaluate the relationships between internal and external training load metrics and ANS recovery and function in college football players. Football athletes were recruited from a D1 college in the southeastern US and prospectively followed for 27 weeks. Internal load was estimated via exercise cardiac load (ECL; average training heartrate (HR) × session duration) and measured with an armband monitor equipped with electrocardiographic capabilities (Warfighter MonitorTM (WFM), Tiger Tech Solutions, Miami, FL, USA). External load was estimated via the summation and rate of acceleration and decelerations as measured by a triaxial accelerometer using the WFM and an accelerometer-based (ACCEL) device (Catapult Player Load, Catapult Sports, Melbourne, Australia) worn on the mid-upper back. Baseline HR, HR variability (HRV) and HR recovery served as the indicators for ANS recovery and function, respectively. For HRV, two, time-domain metrics were measured: the standard deviation of the NN interval (SDNN) and root mean square of the standard deviation of the NN interval (rMSSD). Linear regression models evaluated the associations between ECL, ACCEL, and the indicators of ANS recovery and function acutely (24 h) and cumulatively (one- and two-week). Athletes (n = 71) were male and, on average, 21.3 ± 1.4 years of age. Acute ECL elicited stronger associations for 24 h baseline HR (R2 0.19 vs. 0.03), HR recovery (R2 0.38 vs. 0.07), SDNN (R2 0.19 vs. 0.02) and rMSSD (R2 0.19 vs. 0.02) compared to ACCEL. Similar results were found for one-week: 24 h baseline HR (R2 0.48 vs. 0.05), HR recovery (R2 0.55 vs. 0.05), SDNN (R2 0.47 vs. 0.05) and rMSSD (R2 0.47 vs. 0.05) and two-week cumulative exposures: 24 h baseline HR (R2 0.52 vs. 0.003), HR recovery (R2 0.57 vs. 0.05), SDNN (R2 0.52 vs. 0.003) and rMSSD (R2 0.52 vs. 0.002). Lastly, the ACCEL devices weakly correlated with ECL (rho = 0.47 and 0.43, p < 0.005). Our findings demonstrate that ACCEL poorly predicted ANS deterioration and underestimated internal training load. ACCEL devices may "miss" the finite window for preventing ANS deterioration by potentially misestimating training loads acutely and cumulatively.

PurposeThis study explored how heat stress affects training and match load in professional soccer by monitoring ten elite players during sessions under normal (18–24°C) and high (34–45°C) temperatures.MethodsTen outfield men’s soccer players from a professional team competing in the United Arab Emirates (UAE) Pro League participated in the study. A repeated-measures study design was employed to analyse the training load demands on the same players under normal (18–24°C) and high (34–45°C) temperature conditions throughout the training camp period. External loads, such as total distance (TD), high metabolic load distance (HMLD), mechanical work (MW), and maximal velocity (MaxV), as well as internal load, via Edwards’ Training Impulse (TRIMP), were analysed.ResultsThe study found that heat influenced training and match loads to varying degrees. On match day (MD), TD per minute (TD · min–1) decreased slightly (effect size [ES] = –0.55), with larger reductions observed on MD-2 (ES = –2.14) and MD-1 (ES = –1.59). Specifically, the reduction in TD.min-1 was greatest on MD-2 and MD-1, while only a small decrease was observed on MD. HMLD per minute (HMLD · min–1) also showed a significant reduction, with a moderate decrease on MD-1 (ES = –1.03) and MD (ES = –0.78). MW per minute (MW · min–1) was notably lower on MD-2 (ES = –1.50), moderately reduced on MD-1 (ES = –0.84), and slightly reduced on MD (ES = –0.45). Maximal velocity (MaxV) slightly increased on MD (ES = 0.47). TRIMP increased across all days, indicating a higher internal load under heat, with a moderate increase on MD-2 (ES = 0.77), MD-1 (ES = 0.73), and MD (ES = 0.83).ConclusionsThe study showed the different effects of heat on external and internal training loads, suggesting that while external loads decrease due to the physiological strain of heat, internal load compensates by increasing. This response may indicate a greater effort to maintain performance levels despite heat stress. These findings show that heat-induced changes in training load can help implement strategies for optimising athlete performance and recovery during periods of heat exposure.

The aim of this study was to analyse the relationship between External Load (EL) and internal load (IL). Thirteen male basketball players competing at professional level in First Spanish Division (ACB) during six friendly games throughout the 2020/2021 preseason were monitored. The EL variables collected were movement load (ML), movement intensity (MI), box score time (BST), and total duration (TD)] while IL variables monitored were heart rate (HR), respiratory rate (RR), training impulse (TRIMP) and time invested in five HR zones. Very large to almost perfect correlation (r= 0.77-0.91) exists between EL variables except TD. In addition, HR, TRIMP and RR present large to very large correlation (r= 0.55-0.79) with all EL variables except TD. Monitoring HR-based variables would present general information and an estimated prediction of players EL which could allow basketball practitioners to prioritize time invested players internal/external load.

The ability to maintain a high intensity of exercise over several repetitions depends on recovery from previous exercises. This study aimed to identify the effects of different recovery times on internal and external load during small-sided soccer games. An increase in recovery time will increase the external training load and decrease the internal exercise load, which will result in a greater physical impact of the exercise. Cross-sectional study. Level 2. Twenty male semiprofessional soccer players participated in the present study. They performed the same exercise (5-a-side game format) continuously (1 × 18 minutes) and repeatedly/fractionated (3 × 6 minutes) with different recovery times (30 seconds, 1 minute, 1.5 minutes, and 2 minutes). Their internal load (ie, average heart rate (HR) and maximum HR) and external load (ie, total distance, maximum speed, and ratio meters) were measured using an HR band and an inertial device equipped with a global positioning system, respectively. The manipulation of recovery times induced differences in the internal and external load. For the same total duration, the external and internal load indicators exhibited higher values during the fractionated method, particularly with short recovery periods. The application of small-sided soccer games with different recovery times induced varying responses in training load. To maintain high physical performance and high training load, the fractional method with short recovery periods (ie, 30 seconds) should be used. In contrast, to carefully manage players' efforts and decrease response to training load, continuous or fractional methods with longer recovery periods (ie, 1-2 minutes) should be used. The proper prescription of recovery time between exercises facilitates enhanced training efficiency and optimized performance.

BackgroundThe long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions.MethodsFourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1–7), competition 1 (C1, weeks 8–14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15–23), and competition 2 (C2, weeks 24–29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height.ResultsC2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p < 0.001). Jump count was negatively correlated with jump height (ρ = −0.089, p = 0.006) and the percentage of jumps exceeding 80% of maximal height (ρ = −0.388, p < 0.001). The internal and external training load variables were similar among different playing positions.ConclusionThe participants exhibited significantly higher internal training load in C2 and higher jump height after P1. A high jump count was associated with higher internal training load and lower jump height. Excessive jumps may result in fatigue and reduce height.

Objectives To examine the association between external and internal loads encountered during training and games and sleep duration and quality in adult, semi-professional, female basketball players. Design An exploratory, observational, longitudinal study. Methods Seven players from the same team had their external (PlayerLoad [PL] and relative PL [PL·min−1]) and internal load (session-rating of perceived exertion-load [sRPE-load] and percentage of heart rate peak [%HRpeak]) monitored across pre-season training, in-season training, and games during an entire season. Sleep duration (total sleep time [TST]) and quality (sleep efficiency [SE] and wake after sleep onset [WASO]) were monitored each night using wrist-worn activity monitors. External and internal loads were grouped according to session type as pre-season training, in-season training, and games. The sleep data were grouped as nights immediately following pre-season training, in-season training, or games. Generalized linear mixed models were used for analyses. Results A significant association was observed between external game loads (PL and PL·min−1) and WASO on nights following games (PL: β = − 0.003, 95% confidence intervals [CI] = − 0.005, − 0.002, p &lt; 0.001; PL·min−1: β = − 0.266, 95% CI = − 0.416, − 0.115, p = 0.001). No other significant associations were found between external and internal load and sleep variables. Conclusions While these findings indicate higher external loads during games may partially contribute to lower WASO, these relationships were relatively weak in magnitude with the loads encountered during training and games predominantly lacking associations with sleep among adult, semi-professional, female basketball players.

This study investigated the relationships between internal and external training load metrics across a 2-week 'in-season' microcycle in squash. 134 on-court and 32 off-court 'conditioning' sessions were completed by fifteen elite squash players with an average (±SD) of 11 ± 3 per player. During every session, external load was captured using a tri-axial accelerometer to calculate Playerload; i.e., the instantaneous rate of change of acceleration across 3-dimensional planes. Internal load was measured using heart rate (HR), global (sRPE) and differential RPE (dRPE-Legs, dRPE-Breathing). Additionally, HR was used to calculate Banister's, Edward's and TEAM TRIMPs. Across 166 training sessions, Playerload was moderately correlated with TRIMP-Banister (r = 0.43 [95% CI: 0.29-0.55], p < 0.001) and TRIMP-Edwards (r = 0.50 [0.37-0.61], p < 0.001). Association of Playerload with TRIMP-TEAM (r = 0.24 [0.09-0.38], p = 0.001) was small. There was a moderate correlation between sRPE and Playerload (r = 0.46 [0.33-0.57], p < 0.001). Association of sRPE was large with TRIMP-Banister (r = 0.68 [0.59-0.76], p = 0.001), very large with TRIMP-Edwards (r = 0.79 [0.72-0.84], p < 0.001) and moderate with TRIMP-TEAM (r = 0.44 [0.31-0.56], p < 0.001). Both dRPE-Legs (r = 0.95 [0.93-0.96], p < 0.001) and dRPE-Breathing (r = 0.92 [0.89-0.94], p < 0.001) demonstrated nearly perfect correlations with sRPE and with each other (r = 0.91 [0.88-0.93], p < 0.001). Collection of both internal and external training load data is recommended to fully appreciate the physical demands of squash training. During a training microcycle containing a variety of training sessions, interpreting internal or external metrics in isolation may underestimate or overestimate the training stress a player is experiencing.

BackgroundLimited research has investigated the association between training load and performance of basketball players during games. Little is known about how different indicators of player performance are affected by internal and external loads. The purpose of this study was to determine whether external and internal loads influence basketball players’ performance during games.MethodThis longitudinal study involved 20 professional male basketball players from a single team, classified as first-level athletes by the Chinese Basketball Association. During 34 games, external load was measured as PlayerLoad using micro-sensors, while internal load was assessed using session rating of perceived exertion (sRPE). Player performance was quantified using three metrics: Efficiency, Player Index Rating (PIR), and Plus-Minus (PM). Pearson correlation coefficients were calculated to assess the strength of the relationships between training loads and performance metrics. Linear mixed-effects models were applied to further analyze the influence of internal and external loads on basketball performance.ResultsPearson correlation analysis revealed moderate positive correlations between both sRPE and PlayerLoad with Efficiency and PIR. Specifically, sRPE (r = 0.52) and PlayerLoad (r = 0.54) were both significantly correlated with Efficiency. For PIR, sRPE (r = 0.50) and PlayerLoad (r = 0.56) also demonstrated moderate correlations. These correlations were further substantiated by linear mixed-effects models, which showed that sRPE (β = 2.21, p < 0.001) and PlayerLoad (β = 1.87, p = 0.004) had significant independent effects on Efficiency. Similarly, sRPE (β = 2.15, p < 0.001) and PlayerLoad (β = 2.36, p < 0.001) significantly predicted PIR. Additionally, a significant interaction effect between PlayerLoad and sRPE was found on Plus-Minus (β = -2.49, p < 0.001), indicating that the combination of high physical and psychological loads negatively impacted overall team performance. However, the correlation strengths for Plus-Minus were relatively low (sRPE: r = 0.16; PlayerLoad: r = 0.10).ConclusionBoth external and internal loads positively contribute to performance, the integration of objective (accelerometry) and subjective (sRPE) measures of load provides a comprehensive understanding of the physiological and psychological demands on athletes, contributing to more effective training regimens and performance optimization.

BackgroundDespite the growing global participation of females in basketball and number of studies conducted on the topic, no research has summarized the external and internal load variables encountered by female basketball players during training and games.ObjectiveTo collate existing literature investigating external and internal load variables during training and games in female basketball players according to playing level (club, high-school, representative, collegiate, semi-professional, and professional) and playing position (backcourt and frontcourt players).MethodsA systematic review of the literature was performed using PubMed, SPORTDiscus, and Web of Science to identify studies published from database inception until June 11, 2021. Studies eligible for inclusion were observational and cross-sectional studies, published in English, reporting external and/or internal load variables during training sessions and/or games. Methodological quality and bias were assessed for each study prior to data extraction using a modified Downs and Black checklist. Weighted means according to playing level and playing position were calculated and compared if a load variable was reported across two or more player samples and were consistent regarding key methodological procedures including the seasonal phase monitored, minimum exposure time set for including player data (playing time during games), approach to measure session duration, and approach to measure session intensity.ResultsThe search yielded 5513 studies of which 1541 studies were duplicates. A further 3929 studies were excluded based on title and abstract review, with 11 more studies excluded based on full-text review. Consequently, 32 studies were included in our review. Due to the wide array of methodological approaches utilized across studies for examined variables, comparisons could only be made according to playing level for blood lactate concentration during games, revealing backcourt players experienced higher lactate responses than frontcourt players (5.2 ± 1.9 mmol·L−1 vs. 4.4 ± 1.8 mmol·L−1).ConclusionsInconsistencies in the methods utilized to measure common load variables across studies limited our ability to report and compare typical external and internal loads during training and games according to playing level and position in female basketball players. It is essential that standardized methodological approaches are established for including player data as well as measuring session duration (e.g., total time, live time) and intensity (e.g., consistent rating of perceived exertion scales, intensity zone cut points) in future female basketball research to permit meaningful interpretation and comparisons of load monitoring data across studies.

The aim of this study was to identify the relationships between continuously measured internal and external load variables during volleyball competition. A total of 8 male elite volleyball athletes (Belgian Liga A and Liga B) were monitored during official competition matches. In total, 63 individual measurements are included in this study. The authors used heart-rate (HR) data as internal load and accelerometer-based activity as external load. Data were recorded at a sampling frequency of 1Hz using wearable technology during official competition. Workload during continuous game play and individual jumps performed while serving and spiking (selected by means of video analysis) were studied using correlation analysis and dynamic time-series modeling. Significant linear correlations were observed between peak acceleration and maximal HR of individual serves (ρ = .62; P = 1.6e-5) and spikes (ρ = .49; P = 1.2e-3) that were performed during the warm-up. These same actions performed during the match did not show significant correlations. The correlation between the mean HR and mean activity throughout the entire match was also found to be significant (ρ = .67; P = 2.0e-9). With respect to the time-series models, the mean value for the goodness of fit (RT2) between HR and activity was equal to .83 and .67 for the individual actions and the entire matches, respectively. The results show that there are strong relationships between internal and external load during volleyball competition. Second-order transfer function models are capable of explaining the main dynamics of HR (internal load) in response to accelerometer-based activity (external load). Time-series analysis of continuously measured workload is proposed for use in practice.

Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal-Wallis or Kolmogorov-Smirnov tests for training impulse and KDE scores, respectively. Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.