Monitoring Training Load and Perceived Recovery in Female Basketball: Implications for Training Design

Effects of Four Weeks of In-Season Pre-Workout Supplementation on Performance, Body Composition, Muscle Damage, and Health-Related Markers in Basketball Players: A Randomized Controlled Study

This randomized, double-blinded, experimental study investigated the effects of a four-week daily pre-workout supplementation (200 mg caffeine, 3.3 g creatine monohydrate, 3.2 g β-alanine, 6 g citrulline malate, and 5 g BCAA) vs. placebo (isocaloric maltodextrin) on anaerobic (jumping, sprinting, agility, and the running-based anaerobic sprint test: RAST) and aerobic (Yo-Yo intermittent recovery test level 1) performance, as well as on body composition and selective muscle damage/health-related blood markers in well-trained basketball players during the in-season period. Eighteen basketball players (age: 24.4 ± 6.3 years, height: 185.7 ± 8.0 cm, weight: 85.7 ± 12.8 kg, body fat: 16.5 ± 4.2%) were randomly assigned into two groups: pre-workout supplement (PWS, n = 10) or placebo (PL, n = 8). PWS consumption increased aerobic performance (PWS: 8 ± 6%; PL: -2 ± 6%; p = 0.004) compared to PL. A significant decrease was observed in peak (F = 7.0; p = 0.017), average (F = 10.7; p = 0.005), and minimum power (F = 5.1; p = 0.039) following 4 weeks of supplementation in both groups. No other significant changes were observed between groups (p > 0.05). In conclusion, the consumption of the current PWS over a four-week period appears to positively influence the aerobic performance of well-trained basketball players during the in-season period. However, it does not appear to mitigate the observed decline in anaerobic power, nor does it affect performance in jumping, sprinting, and agility, or alter body composition or selective muscle damage/health-related blood markers.

Recovery in elite youth basketball players: The responsiveness of the psychophysiological measurements and the role of testosterone concentration

This study investigated the recovery responses to the Total Quality Recovery (TQR), Well-Being questionnaire (WBQ), and Heart Rate (HR) responses to Submaximal Running Test (SRT), and the influence of salivary testosterone concentration (TEST) on these responses in 25 elite youth (U15) male basketball players. TQR, WBQ, and HR measurements were assessed after 48 hours of rest (T1), 24 hours after the 1st day of training (T2) and 24 hours after the 2nd day of training (T3). Salivary sampling was conducted at T1 and T3. A significant decrease was observed for TQR (F = 4.06; p = 0.01) and for WBQ (F = 5.37; p = 0.008) from T1 to T3. No difference among the three-time points was observed for HR and HR Recovery, and the TEST concentration did not influence the results. These results show that TQR and WBQ are sensitive to acute transient alterations in training loads (TL) and may be utilized to monitor recovery in elite youth basketball players. The HR related measurements presented limited responsiveness, and the TEST seems not to influence the recovery of these players who are competing at highest performance level.

Monitoring the recovery-stress states of athletes: Psychometric properties of the acute recovery and stress scale and short recovery stress scale among Dutch and Flemish athletes

The Acute Recovery and Stress Scale (ARSS) and the Short Recovery and Stress Scale (SRSS) are recently-introduced instruments to monitor recovery and stress processes in athletes. In this study, our aims were to replicate and extend previous psychometric assessments of the instruments, by incorporating recovery and stress dimensions into one model. Therefore, we conducted five confirmatory factor analyses (CFA) and determined structural validity, internal consistency, and construct validity. Dutch and Flemish athletes (N = 385, 213 females, 170 males, 2 others, 21.03 ± 5.44 years) completed the translated ARSS and SRSS, the Recovery Stress Questionnaire for Athletes (RESTQ-Sport-76), the Rating of Perceived Exertion (RPE) and the Total Quality of Recovery (TQR). There was a good model fit for the replicated CFA, sub-optimal model fit for the models that incorporated recovery and stress into one model, and satisfactory internal consistency (α=.75 - .87). The correlations within and between the ARSS and SRSS, as well as between the ARSS/SRSS and the RESTQ-Sport-76 (r = .31 - -.77 for the ARSS, r = .28 - -.63 for the SRSS), the RPE (r = .19 - -.23), and the TQR (r = .63 - -.63) also supported construct validity. The combined findings support the use of the ARSS and SRSS to assess stress and recovery in sports-related research and practice.

Relationship Between Athlete-Reported Outcome Measures and Subsequent Match Performance in Team Sports: A Systematic Review

ABSTRACT

Sansone, P, Rago, V, Kellmann, M, and Alcaraz, PE. Relationship between athlete-reported outcome measures and subsequent match performance in team sports: A systematic review. J Strength Cond Res 37(11): 2302-2313, 2023-Athlete-reported outcome measures (AROMs; e.g., fatigue, stress, readiness, recovery, and sleep quality) are commonly implemented in team sports to monitor the athlete status. However, the relationship between AROMs and match performance indicators is unclear and warrants further investigation. This systematic review examined the relationship between precompetitive AROMs and subsequent match performances of team sport athletes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 3 (PubMed, Scopus, and Web of Science) databases were systematically searched to retrieve studies investigating the effects or association of AROMs and match: (a) technical-tactical performance (match-related statistics), (b) physical performance, (c) physiological and (d) perceptual demands, and (e) other measures of performance in adult team sport athletes. Quality assessment of included studies was performed using a modified Black and Downs checklist. Fifteen articles representing 289 team sport athletes were included. Mean quality of included studies was 7.6 ± 1.0 (of 11). Across the included studies, 22 AROMs parameters were used, and 16 different statistical approaches were identified. Approximately 11 of 15 studies used nonvalidated AROMs. Overall, associations or effects of AROMs were found consistently for match-related statistics (7/9 studies), whereas results were unclear for physical performances (3/7 studies), perceptual demands (1/2 studies), or other measures of performance (2/4 studies). Considering the importance of key match-related statistics for success in team sports, this review suggests that monitoring precompetitive AROMs has potential to provide valuable information to coaches. However, it is indispensable to validate AROMs questionnaires and to uniform data collection and statistical procedures before substantiated indications to practitioners can be made.

Investigating the effects of athlete-reported pre-training well-being and recovery on subsequent training loads in basketball players

BACKGROUND

Basketball players' external and internal training loads have been extensively monitored. However, no study has evaluated if pre-training athlete-reported conditions influence them. Therefore, this study investigated the effects of athlete-reported pre-training well-being and recovery on subsequent external load intensity, perceived exertion scores and their ratio (efficiency index) in youth basketball training.

METHODS

The external load (EL) intensity (EL∙min-1), ratings of perceived exertion (RPE) and efficiency index (EL∙min-1:RPE) of 15 youth basketball players (age: 15.2±0.3 years) were monitored during team-based training sessions. Before each session, players reported their levels of perceived recovery (using a modified 10-point Total Quality Recovery, TQR, scale), fatigue, sleep quality, muscle soreness, mood, and stress. Statistical analyses were performed via linear mixed models.

RESULTS

EL∙min-1 was higher when player reported better pre-training recovery (P= 0.001). Higher RPE scores and lower efficiency indexes were registered in players reporting better pre-training conditions, respectively. Specifically, RPE scores were higher when players reported better TQR, fatigue, muscle soreness and stress scores (all P<0.05), while training efficiency was, conversely, lower in correspondence of better TQR and sleep (all P<0.05).

CONCLUSIONS

This study identified influences of athlete-reported pre-training well-being and recovery on subsequent external intensity, RPE and efficiency index in youth basketball players. Recovery and well-being indicators could be monitored seen their influence on subsequent training loads. Current findings can be considered by basketball sport scientist when selecting athlete monitoring questionnaires and when interpreting training load outputs.

The discriminative, criterion, and longitudinal validity of small-sided games to assess physical fitness in female basketball players

The validity of small-sided games (SSG) for assessing physical fitness was evaluated in 21 female basketball players from senior (n = 8), under-18 years (n = 6), and under-16 years (n = 7) age categories. Players underwent fitness testing (countermovement jump [CMJ], agility T-test, repeated-sprint ability (RSA) test, and Yo-Yo Intermittent Recovery Test [YYIRT1]) and 3vs3-SSG before and after a 6-week preseason. Player demands were monitored during SSG using local positioning system and heart rate technology. Regarding discriminative validity, senior players produced better CMJ, agility T-test, and YYIRT1 performance (p  < 0.05, effect size [ES] = 1.72-2.25), and more distance and PlayerLoad (p  < 0.05, ES = 1.53-2.47) during SSG than under-18 players following the preseason. For criterion validity, total distance and distance completing high-intensity decelerations during SSG were significantly (p  < 0.05) correlated with CMJ (r = 0.44-0.66), YYIRT1 (r = 0.43-0.63), agility T-test (total distance only, r=-0.51), and RSA test performance (r=-0.49 to -0.52) among all players combined following the preseason. Regarding longitudinal validity, significantly better agility T-test and YYIRT1 performance (p ≤ 0.001, ES = 0.88-0.93) alongside lower heart rate during SSG (p = 0.001, ES = 0.88) were evident for all players combined following the preseason. These results partially support the validity of 3vs3-SSG to assess physical fitness in female basketball players.

Monitoring Internal Load in Women's Basketball via Subjective and Device-Based Methods: A Systematic Review

The monitoring of internal load in basketball can be used to understand the effects and potential physiological adaptations caused by external load. The main aim of this systematic review was to identify the methods and variables used to quantify internal load in female basketball. The studies included different populations and events: youth athletes, elite, and amateur players. Subjective methods included using the rating of perceived exertion (RPE) method, and sensor-based methods included monitoring the cardiac response to exercise, using heart rate (HR) as the primary metric. The results showed that the HRAvg exhibited a wider range of values during training than during competition, and different metrics were used to evaluate internal load, such as HRMax, HRmin, %HRMax, total time and % of time spent in different HR zones (2-8 zones), Banister's TRIMP, and summated HR zones. RPE and HR metrics were the most commonly used methods. However, the use of multiple metrics with little standardization resulted in significant heterogeneity among studies, limiting meaningful comparisons. The review provides a reference for current research on female basketball. Future research could address this limitation by adopting more consistent measurement protocols standardizing the use of metrics.

Effects of Playing Position and Contextual Factors on Internal Match Loads, Post-Match Recovery and Well-Being Responses of Elite Male Water Polo Players

This study aimed to investigate the effects of playing position and contextual factors (match outcome, final score difference, match location, travel duration, number of scored and conceded goals) on the internal match load, players' perceived recovery and players' well-being. The session-RPE (s-RPE), Perceived Recovery Scale (PRS) and Hooper Index (HI) of 17 male elite water polo players were monitored during all matches (regular season and play-out) of the 2021/22 Italian Serie A1 championship. Three separate, mixed linear models for repeated measures showed significant main effects: drawn compared to won matches led to higher s-RPE values (mean ± SE = 277 ± 17.6 vs. 237.3 ± 20.6), while longer travel duration (estimate = -0.148) and goals scored (estimate = -3.598) led to lower s-RPE values; balanced compared to unbalanced matches led to higher PRS values (mean ± SE = 6.8 ± 0.3 vs. 5.1 ± 0.4), while playing time (estimate = -0.041) and goals scored (estimate = -0.180) led to lower PRS values; higher scores of the HI were registered for regular season compared to the play-out (mean ± SE = 15.6 ± 0.9 vs. 13.5 ± 0.8). This study marks the importance of ecological and non-invasive monitoring tools to assess internal match load, recovery and the well-being of elite water polo players.

Investigating External and Internal Loads in Male Older Adult Basketball Players during Official Games

This study aimed at assessing the external [Player Load (PL), acceleration (ACC), changes of direction (COD), JUMP, and their relative values (PL/min; ACC/min; COD/min and JUMP/min)] and internal [percentage of the peak heart rate (%HRpeak) and the training load calculated with the session rating of perceived exertion (sRPE) method (sRPE-load)] loads of masters (senior citizen) basketball players during official games. Thirteen male basketball masters players (age: 66.6 ± 2.1 years; body mass: 89.9 ± 8.7 kg; stature: 183.7 ± 4.6 cm) were monitored during an official Lietuvos Krepsinio Veteranu Lyga (LKVL) 65-year game. Beside descriptive analysis, a chi-square goodness of fit test was adopted to assess the differences in the distribution within JUMP, ACC and COD classes of intensities (i.e., low, medium and high). The results revealed PL = 269.9 ± 83.3 AU and PL/min = 6.54 ± 1.29 AU/min. Moreover, significant differences (p < 0.001) in the distribution of the intensity classes were found for JUMP, ACC, and COD, with the lowest intensities as the most frequent. Finally, HRpeak = 81.7 ± 8.1% and sRPE-load = 148.9 ± 69.7 AU were found, with sRPE = ~3 AU. In conclusion, a low external load during an official basketball game was found compared to other basketball populations. Moreover, a high objective internal load did not correspond to a low perceived demand, which might increase the training adherence and motivation during long-term studies.

External and Internal Load Variables Encountered During Training and Games in Female Basketball Players According to Playing Level and Playing Position: A Systematic Review

Background

Despite 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.

Objective

To 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).

Methods

A 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.

Results

The 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).

Conclusions

Inconsistencies 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.

Body Fat of Basketball Players: A Systematic Review and Meta-Analysis

Background

This study aimed to provide reference values for body fat (BF) of basketball players considering sex, measurement method, and competitive level.

Methods

A systematic literature research was conducted using five electronic databases (PubMed, Web of Science, SPORTDiscus, CINAHL, Scopus). BF values were extracted, with analyses conducted using random-effects models and data reported as percentages with 95% confidence intervals (CI).

Results

After screening, 80 articles representing 4335 basketball players were selected. Pooled mean BF was 13.1% (95% CI 12.4–13.8%) for male players and 20.7% (95% CI 19.9–21.5%) for female players. Pooled mean BF was 21.4% (95% CI 18.4–24.3%) measured by dual-energy X-ray absorptiometry (DXA), 15.2% (95% CI 12.8–17.6%) via bioelectrical impedance analysis (BIA), 12.4% (95% CI 10.6–14.2%) via skinfolds and 20.0% (95% CI 13.4–26.6%) via air displacement plethysmography. Pooled mean BF across competitive levels were 13.5% (95% CI 11.6–15.3%) for international, 15.7% (95% CI 14.2–17.2%) for national and 15.1% (95% CI 13.5–16.7%) for regional-level players. As the meta-regression revealed significant effects of sex, measurement method and competitive level on BF, the meta-analysis was adjusted for these moderators. The final model revealed significant differences in BF between male and female players (p < 0.001). BF measured by DXA was significantly higher than that measured by BIA or skinfolds (p < 0.001). International-level players had significantly lower BF than national and regional-level players (p < 0.05).

Conclusions

Despite the limitations of published data, this meta-analysis provides reference values for BF of basketball players. Sex, measurement method and competitive level influence BF values, and therefore must be taken into account when interpreting results.

Recovery of the physiological status in professional basketball players using NESA neuromodulation treatment during different types of microcycles in season: A preliminary randomized clinical trial

The purpose of the study was to describe and compare recovery status after official basketball competition in players who underwent NESA neuromodulation treatment (NNT) in weeks with one or two matches. The recovery parameters of 12 professional male basketball players (mean ± SD, age: 20.6 ± 2.7 yr; height: 197.8 ± 11.7 cm; and body mass: 89.0 ± 21.2 kg) that competed in the LEB Plata (Spanish third division) were monitored 2 days after match-play over 6 weeks, and included: 1) the Hooper Test, which combines four subjective variables (sleep, stress, fatigue and soreness); 2) common biochemical markers (e.g., testosterone, cortisol and ratio T:C); and 3) lowest heart rate [HR], average HR, HR variability, sleep duration, awake time during night and onset latency before asleep). Players that completed NNT presented differences compared to the control group in sleep data. For instance, the lowest HR (p < 0.001), average HR (p < 0.001) and total awake time (p = 0.04) were significantly reduced in the NNT group. On the contrary, the control group presented greater values than the NNT group in the subjective Hooper Test, although only stress presented significant differences (Control 2.5 ± 1.2 vs. NNT cost or 3.2 ± 0.9; p = 0.01). Additionally, there were no significant differences in recovery parameters between weeks with one or two matches. In conclusion, the results suggest that players that underwent NNT tended to improve their sleep quality. Nevertheless, player's values in the biochemical markers and wellness status remained similar in both groups. The fact that no significant differences were found between weeks with one or two matches could help basketball professionals to determine that a congested schedule does not seem to negatively alter recovery status. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04939181?term=NCT04939181, NCT04939181.

Effect of Situational and Individual Factors on Training Load and Game Performance in Liga Femenina 2 Basketball Female Players

There is research that has shown how individual characteristics and performance indicators influence the load in basketball; however, studies on the influence of situational variables on performance are lacking. The aim of this study was to determine the influence of certain situational and individual variables on the training load (weekly load, game load and pre-game recovery) and the individual performance (statistical game evaluation) of female basketball players of Liga Femenina 2 during competition. The 28 games played by the 13 players of a group B team of the Liga Femenina 2 of the Spanish Basketball Federation (FEB) during the 2020/2021 season were analyzed. Data on rate of perceived exertion (RPE), perceived performance and recovery were collected through the Quanter mobile application as well as performance statistics from the FEB website. Five mixed linear analyses for repeated measures were performed to evaluate the effect of each situational and individual variable on each dependent variable (weekly load, game load, game RPE, pre-game recovery, and game statistical assessment). The results show how the weekly load increases after playing against a low-level opponent (p < 0.001). In games, the players who play the most minutes and accumulate the most load are also the most valued (p < 0.001). The pre-game recovery worsens as the season progresses (p < 0.001). After playing against a high-level opponent, the pre-game recovery values for the next game are lower (p = 0.031). The results obtained indicate that the situational and individual variables should be taken into account to monitor the workload. These results help coaches and physical trainers to better plan training weeks, taking into account the situational variables studied.

Relationship Between Game Load and Player's Performance in Professional Basketball

PURPOSE

The purpose of the study was to examine the relationships between external and internal loads, and their ratio (efficiency index), with game performance between backcourt and frontcourt professional basketball players.

METHODS

Game loads of 14 basketball players were monitored during 6 games. External load variables measured were total distance (TD); distance >18 km·h-1, commonly known as high-speed running (HSR); and number of accelerations (ACC) and decelerations (DEC) >3 m·s-2, whereas the internal load variable measured was average heart rate (HRmean). The ratio between external and internal load variables was calculated and defined through 4 efficiency indexes (TD:HRmean, HSR:HRmean, ACC:HRmean, and DEC:HRmean). Furthermore, basketball performance was quantified using game-related statistics.

RESULTS

TD presented a small association with basketball performance, whereas the other external load variables and the 4 efficiency indexes calculated showed trivial relationships with game-related statistics. Furthermore, HRmean showed the greatest (small) associations with individual performance (P = .01-.02; r = .19 to .22). Regarding specific positions, the only 2 variables that presented significant differences were DEC (P = .01; d = 0.86) and DEC:HRmean (P = .01; d = 0.81), which showed higher values in backcourt players compared with frontcourt players.

CONCLUSIONS

The results suggest that the best performances of basketball players during official competition are not associated with higher game loads. This illustrates the necessity to assess basketball performance from a holistic approach and consider more than just external and internal variables to better understand the players' performance during basketball competition.

Resilience in sports: a multidisciplinary, dynamic, and personalized perspective

Athletes are exposed to various psychological and physiological stressors, such as losing matches and high training loads. Understanding and improving the resilience of athletes is therefore crucial to prevent performance decrements and psychological or physical problems. In this review, resilience is conceptualized as a dynamic process of bouncing back to normal functioning following stressors. This process has been of wide interest in psychology, but also in the physiology and sports science literature (e.g. load and recovery). To improve our understanding of the process of resilience, we argue for a collaborative synthesis of knowledge from the domains of psychology, physiology, sports science, and data science. Accordingly, we propose a multidisciplinary, dynamic, and personalized research agenda on resilience. We explain how new technologies and data science applications are important future trends (1) to detect warning signals for resilience losses in (combinations of) psychological and physiological changes, and (2) to provide athletes and their coaches with personalized feedback about athletes' resilience.

Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players

This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, and an injury surveillance system was applied to record all MSI during the season. Anthropometric data, predicted age at peak height velocity, and skeletal age were collected. The session-RPE method was daily employed to quantify internal training/match load, and monotony, strain, and cumulative load over the weeks were calculated. A countermovement jump (CMJ) test was submitted before and after each training/match to quantify players' neuromuscular fatigue. All these data were used to predict the risk of MSI through different data mining models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM). Among them, SVM showed the best predictive ability (area under the curve = 0.84 ± 0.05). Then, Decision tree (DT) algorithm was employed to understand the interactions identified by the SVM model. The rules extracted by DT revealed how the risk of injury could change according to players' maturity status, neuromuscular fatigue, anthropometric factors, higher workloads, and low recovery status. This approach allowed to identify MSI and the underlying risk factors.

Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals

PURPOSE

The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the "ergodicity issue." Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field.

METHODS

The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level.

RESULTS

Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery.

CONCLUSIONS

The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery.

Internal and External Load Control in Team Sports through a Multivariable Model

Data related to 141 sessions of 10 semi-professional basketball players were analyzed during the competitive period of the 2018-2019 season using a multivariable model to determine possible associations between internal and external load variables and fatigue. Age, height, weight, sessional rate of perceived exertion (sRPE), summated-heart-rate-zones, heart rate variability, total accelerations and decelerations were the covariates, and post-session countermovement jump loss (10% or higher) the response variable. Based on the results observed, a rise in sRPE and accelerations and decelerations could be associated with increased lower-body neuromuscular fatigue. Observing neuromuscular fatigue was 1,008 times higher with each additional sRPE arbitrary unit (AU). Each additional high-intensity effort also increased the probability of significant levels of neuromuscular fatigue by 1,005 times. Fatigue arising from demanding sporting activities is acknowledged as a relevant inciting event leading to injuries. Thus, the methodology used in this study can be used then to monitor neuromuscular fatigue onset, also enhancing proper individual adaptations to training.

Weekly Training Demands Increase, but Game Demands Remain Consistent Across Early, Middle, and Late Phases of the Regular Season in Semiprofessional Basketball Players

PURPOSE

To compare weekly training, game, and overall (training and games) demands across phases of the regular season in basketball.

METHODS

Seven semiprofessional, male basketball players were monitored during all on-court team-based training sessions and games during the regular season. External monitoring variables included PlayerLoad™ and inertial movement analysis events per minute. Internal monitoring variables included a modified summated heart rate zones model calculated per minute and rating of perceived exertion. Linear mixed models were used to compare training, game, and overall demands between 5-week phases (early, middle, and late) of the regular season with significance set at P ≤ .05. Effect sizes were calculated between phases and interpreted as: trivial, <0.20; small, 0.20 to 0.59; moderate, 0.60 to 1.19; large, 1.20 to 1.99; very large, ≥2.00.

RESULTS

Greater (P > .05) overall inertial movement analysis events (moderate-very large) and rating of perceived exertion (moderate) were evident in the late phase compared with earlier phases. During training, more accelerations were evident in the middle (P = .01, moderate) and late (P = .05, moderate) phases compared with the early phase, while higher rating of perceived exertion (P = .04, moderate) was evident in the late phase compared with earlier phases. During games, nonsignificant, trivial-small differences in demands were apparent between phases.

CONCLUSIONS

Training and game demands should be interpreted in isolation and combined given overall player demands increased as the season progressed, predominantly due to modifications in training demands given the stability of game demands. Periodization strategies administered by coaching staff may have enabled players to train at greater intensities late in the season without compromising game intensity.

External, Internal, Perceived Training Loads and Their Relationships in Youth Basketball Players Across Different Positions

PURPOSE

To quantify external, internal, and perceived training loads and their relationships in youth basketball players across different playing positions.

METHODS

Fourteen regional-level youth male players (age: 15.2 [0.3] y) were monitored during team-based training sessions across 10 in-season weeks. The players were monitored with BioHarness-3 devices, to measure external (Impulse Load, in Newtons per second) and internal (summated-heart-rate zones [SHRZ], in arbitrary units [AU]) loads, and with the session rating of perceived exertion (sRPE, in AU) method to quantify perceived training load. Multiple linear mixed models were performed to compare training loads between playing positions (backcourt and frontcourt). Repeated-measures correlations were performed to assess the relationships between the load models, for all players and within playing positions.

RESULTS

External load (backcourt: 13,599 [2260] N·s; frontcourt: 14,934 [2173] N·s) and sRPE (backcourt: 345 [132] AU; frontcourt: 505 [158] AU) were higher in the frontcourt (P < .05, effect size: moderate), while SHRZ was similar between positions (backcourt: 239 [45] AU; frontcourt: 247 [43] AU) (P > .05; effect size: trivial). The correlations were as follows: large between the external load and SHRZ (r = .57, P < .001), moderate between SHRZ and sRPE (r = .45, P < .001), and small between the external load and sRPE (r = .26, P = .02). The correlation magnitudes were equivalent for external load-SHRZ (large) and SHRZ-sRPE (moderate) across positions, but different for the external load-sRPE correlation (small in backcourt; moderate in frontcourt).

CONCLUSIONS

In youth basketball, small-large commonalities were found between the training dose (external load) and players' responses (internal and perceived loads). Practitioners should carefully manage frontcourt players' training loads because they accumulate greater external and perceived loads than backcourt  players do.

Investigation of readiness and perceived workload in junior female basketball players during a congested match schedule

This study aimed to: a) investigate the differences in workload and readiness between two junior female national basketball teams competing at different European Championships (EC); b) compare workload, readiness and match performance for players with longer and shorter playing times, and; c) examine the relationship between workload, readiness and match performance variables. Under-18 (U18) (n = 10, height = 179.9 ± 6.6 cm, body mass = 70.2 ± 5.1 kg) and under-20 (U20) female national basketball teams (n = 11, height = 178.4 ± 8.8 cm, body mass = 73.0 ± 9.7 kg) were monitored during congested match schedules encompassing 7 matches within 9 days. Daily workload was determined via the session rating of perceived exertion (sRPE workload); readiness was measured by heart-rate variability (HRV) and well-being (WB); and match performance was assessed using the efficiency statistic and playing time. Analysis of workload and readiness during the EC showed no statistically significant between-team differences in any variables except WB for the U18 team, which was lower on Day 8 compared to the U20 team (p = 0.03; effect size [ES] = large). Players accumulating longer playing time showed a higher sRPE workload (p = 0.01, ES = moderate) and efficiency statistic (p = 0.04, ES = moderate) while no readiness variable differed significantly (p > 0.05) compared to players with shorter playing time. Trivial-to-small correlations were observed between workload, readiness and match performance variables. The study shows that junior female basketball players were able to cope with a congested schedule of 7 matches in 9 days irrespective of the competition context or individual differences in workload. Finally, combining objective and subjective methods to assess workload and readiness is recommended due to the weak relationships observed between these methods.

Pervasive intelligent multi-node health monitoring system for monitoring basketball players health and energy using IoT and 6G technology

Effective health monitoring of players in team sports like basketball allows for understanding external requirements and internal response concerning exercise and competition phases. The explosive growth of wireless devices stimulates the advancement of the internet-of-things (IoT) and 6G technologies, capable of connecting enormous and various “things” through wireless communications. Players face health issues while playing basketball are severe lower body lesions like ankle sprains, shortness of breath, teeth, head, fingers, and hand. To overcome these issues, in this paper, the Pervasive Intelligent Multi-node Health Monitoring System (PIMN-HMS) has been proposed for basketball player’s continuous health tracking based on IoT and 6G communication. With the aid of wearable monitoring sensors to gathers health information and monitor exercise records. The system consists of several sensor nodes, a network coordinator, which monitors physical movements and heart rate, and a personal server on a personal digital assistant using 6G networks. The numerical results have been performed, and the suggested PIMN-HMS model enhances the accuracy ratio of 96.7%, prediction ratio of 97.3%, low latency ratio of 11.2%, delay rate of 22.3%, and efficiency ratio of 98.7% compared to other existing models.

Not All About the Effort? A Comparison of Playing Intensities During Winning and Losing Game Quarters in Basketball

PURPOSE

To compare peak and average intensities encountered during winning and losing game quarters in basketball players.

METHODS

Eight semiprofessional male basketball players (age = 23.1 [3.8] y) were monitored during all games (N = 18) over 1 competitive season. The average intensities attained in each quarter were determined using microsensors and heart-rate monitors to derive relative values (per minute) for the following variables: PlayerLoad, frequency of high-intensity and total accelerations, decelerations, changes of direction, jumps, and total inertial movement analysis events combined, as well as modified summated-heart-rate-zones workload. The peak intensities reached in each quarter were determined using microsensors and reported as PlayerLoad per minute over 15-second, 30-second, 1-minute, 2-minute, 3-minute, 4-minute, and 5-minute sample durations. Linear mixed models and effect sizes were used to compare intensity variables between winning and losing game quarters.

RESULTS

Nonsignificant (P > .05), unclear-small differences were evident between winning and losing game quarters in all variables.

CONCLUSIONS

During winning and losing game quarters, peak and average intensities were similar. Consequently, factors other than the intensity of effort applied during games may underpin team success in individual game quarters and therefore warrant further investigation.

Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices

BACKGROUND

Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport.

OBJECTIVES

The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term 'training load' to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature.

METHODS

A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy.

RESULTS

Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study.

CONCLUSIONS

This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and methodological framework, and with only small data sets and short study periods available at this time, it is not possible to draw definitive conclusions about the true physical demands of basketball. A detailed understanding of modern technologies in basketball is also lacking, and we provide specific guidelines for defining and applying duration measurement methodologies, vetting the validity and reliability of measurement tools, and classifying competition level in basketball to address some of the identified knowledge gaps. Creating alignment in best-practice basketball research methodology, terminology and reporting may lead to a more robust understanding of the physical demands associated with the sport, thereby allowing for exploration of other research areas (e.g. injury, performance), and improved understanding and decision making in applying these methods directly with basketball athletes.

Training load, recovery and game performance in semiprofessional male basketball: influence of individual characteristics and contextual factors

This study examined the effects of individual characteristics and contextual factors on training load, pre-game recovery and game performance in adult male semi-professional basketball. Fourteen players were monitored, across a whole competitive season, with the session-RPE method to calculate weekly training load, and the Total Quality Recovery Scale to obtain pre-game recovery scores. Additionally, game-related statistics were gathered during official games to calculate the Performance Index Rating (PIR). Individual characteristics and contextual factors were grouped using k-means cluster analyses. Separate mixed linear models for repeated measures were performed to evaluate the single and combined (interaction) effects of individual characteristics (playing experience; playing position; playing time) and contextual factors (season phase; recovery cycle; previous game outcome; previous and upcoming opponent level) on weekly training load, pre-game recovery and PIR. Weekly load was higher in guards and medium minute-per-game (MPG) players, and lower for medium-experienced players, before facing high-level opponents, during later season phases and short recovery cycles (all p < 0.05). Pre-game recovery was lower in centers and high-experience players (p < 0.05). Game performance was better in high-MPG players (p < 0.05) and when facing low and medium-level opponents (p < 0.001). Interestingly, players performed better in games when the previous week's training load was low (p = 0.042). This study suggests that several individual characteristics and contextual factors need to be considered when monitoring training load (playing experience, playing position, playing time, recovery cycle, upcoming opponent level), recovery (playing experience, playing position) and game performance (opponent level, weekly training load, pre-game recovery) in basketball players during the competitive season.

Technical-tactical profile, perceived exertion, mental demands and enjoyment of different tactical tasks and training regimes in basketball small-sided games

This study aimed to evaluate the technical-tactical, perceptual and mental demands of basketball small-sided games (SSGs). Twelve male semi-professional players participated in four half-court 3vs3 SSGs characterized by different tactical tasks (offensive; defensive) and training regimes (long-intermittent; short-intermittent). The SSGs were video-recorded to perform notational analysis of technical-tactical parameters. Ratings of perceived exertion (RPE, CR-100 scale), mental effort (ME) and e njoyment were collected after completion of each SSG. Before and after the SSGs, players reported their perceived mental fatigue (MF); for this indicator, the difference between post- and pre-SSG values was calculated (ΔMF). Notational analysis evidenced a higher volume of play (ball possessions, ball possessions per minute) [large effect size (ES)], dribbles and shot attempts (moderate ES) in short-intermittent regimes compared to long-intermittent. Two-way (tactical task; training regime) repeated-measures ANOVA showed an interaction effect for RPE (moderate ES). Players reported that playing the offensive task required higher mental effort compared to playing defence (moderate ES), while no differences for mental effort were found between regimes. Enjoyment did not differ between tasks or regimes. No effects were found for ΔMF, while this indicator was significantly correlated with RPE scores (r= 0.50, large). This study suggests that, in basketball SSGs, shorter regimes induce higher technical demands, while tactical tasks influence perceived exertion responses and mental effort. Furthermore, perceived exertion appears significantly associated with variations of mental fatigue induced by training drills.