ECSS Paris 2023: OP-AP43
INTRODUCTION: Basketball players frequently transition between 3×3 and 5×5 formats during competition and training. Recovery following active game intervals represents an important component of physiological adaptation and performance readiness but remains insufficiently investigated. This study examined heart rate (HR) and muscle oxygen saturation (SmO₂) recovery kinetics following 1-, 2-, 3-, and 4-minute intervals of 3×3 and 5×5 basketball performed over matched total durations. METHODS: Twenty-two professional male players (age: 24.5 ± 6.3 years, height: 195.7 ± 5.5 cm, body mass: 91.0 ± 8.8 kg) who competed internationally in both 3×3 and 5×5 basketball during the same season participated in this study. External load was assessed using 100 Hz inertial measurement units, including total distance (TD), distance rate (DR), jump count (JC), and jump rate (JR). Internal load was quantified using training impulse (TRIMP) derived from HR and exercise duration. Recovery responses were evaluated using post-exercise relative changes (Δ%) in HR and vastus lateralis muscle oxygen saturation (SmO₂). Recovery kinetics were calculated from measurements taken at 15, 30, 45, and 60 seconds following exercise cessation relative to values recorded at the onset of recovery. RESULTS: Across all intervals, distance per minute was significantly higher in 5×5 than 3×3 intervals (all t = 8.55–11.63, all p < 0.001), whereas jump rate and jump count were generally higher in 3×3, reaching significance mainly at the 1-min interval (JR: t = −5.38, p < 0.001; JC: t = −2.40, p = 0.019). Regarding internal load and recovery, TRIMP and post-exercise heart rate were consistently higher in 3×3 (TRIMP: t = −2.24 to −4.64, p ≤ 0.028; HR post: t = −3.02 to −4.87, p ≤ 0.004), while significant format differences in HR and SmO₂ recovery were mainly observed at the 1-min and 3-min intervals, with larger early recovery responses following 3×3 (t up to 5.85, p < 0.001). CONCLUSION: Although 5×5 basketball imposes greater external locomotor demands, 3×3 basketball elicits higher physiological strain and distinct recovery kinetics. These findings highlight the differing training stimuli and recovery characteristics between basketball formats, which may inform conditioning and load management strategies in elite players.
Read CV Rutenis PaulauskasECSS Paris 2023: OP-AP43
INTRODUCTION: Volleyball is a typical intermittent competitive sport, featuring frequent high-intensity jumps, rapid lateral movements, and alternating offensive and defensive transitions, which requires high-level physical fitness as the foundation for athletes to complete technical and tactical actions [1]. With the wide application of GPS and inertial motion analysis (IMA) in volleyball match analysis in recent years, the accurate monitoring of match load indicators has become possible, providing technical support for exploring the intrinsic relationship between match load and physical fitness. METHODS: 12 high level matches’ data of 14 elite female college volleyball athletes [Age: (21.3±1.7) years old; Height: (181.1±4.5) cm; Weight: (71.6±5.5) kg] were recorded by IMA units (K-sport). The paired sample T-test was used to compare the differences between different positions and different quarters, significant difference was set at P < 0.05, and very significant difference was set at P < 0.01. External match load indicators included total competition load, high-intensity competition load. Meanwhile, key physical fitness indicators of the subjects including aerobic capacity (maximal oxygen uptake, VO₂max), lower limb explosive force ( CMJ; SJ; DJ; VJ ;tested by Lifting Platform), agility (5-0-5 test，T test，4x5m circuit，Illinois), strength（squatting，bench-press，power clean，deadlift）and core stability ( tested by plank support time). RESULTS: The elite female volleyball athletes covered 74.5±12.7 min average playing time，The average total competition load of this team was 1128.0 ± 225.1 AU, the VO₂max (in absolute value) was 41.3 ± 3.1 ml/min, the relative value was 3.0 ± 0.3ml/kg/min, the average value of CMJ was 52.4 ± 4.8 cm, the average value of SJ was 50.0 ± 4.2 cm, the average value of DJ was 50.9 ± 4.9 cm, the average value of VJ was 291.4 ± 9.0 cm, the average value of Illinois was 17.8 ± 0.7 s, the average value of T-test was 11.9 ± 0.5 s, the average value of 5-0-5 test was 2.5 ± 0.1 s, the average value of 4x5m back-and-forth was 5.7 ± 0.2 s, the average value of squat was 106.5 ± 18.5 kg, the average value of high pull was 53.2 ± 5.0 kg, the average value of push press was 47.1 ± 7.3 kg, the average value of deadlift was 90.0± 10.9 kg, and the average value of plank was 180.6 ± 58.9 kg. Vertical jump has a significantly strong positive correlation with high-intensity competition load (0.603; P < 0.01).The 4x5m shuttle test has a significantly strong positive correlation with total competition load (0.682;P < 0.01).CMJ has a significantly strong positive correlation with SJ (0.890;P<0.01) and DJ (0.887;P<0.01). CONCLUSION: In summary, it can be seen that jumping ability and agility are extremely important for elite female college volleyball players. In future training, elite female volleyball players need to strengthen more jumping-related training to enhance their jumping ability and improve their athletic performance.
Read CV Junhao HeECSS Paris 2023: OP-AP43
INTRODUCTION: Training load monitoring strategies enable practitioners to understand the workload performed by soccer players. However, these strategies should include fatigue assessment to better understand the processes resulting from training (1). Fatigue can be measured using a variety of methods. One of the most widely used instruments is the Wellness questionnaire, which consists of a customized form designed to assess fatigue-related dimensions (2). Despite the extensive use of external, internal, and mental load monitoring in soccer, the relationships between these load dimensions and fatigue indicators are still not fully understood. Therefore, the present study aimed to examine the influence of training load on players’ readiness status. METHODS: A total of 25 professional male soccer players from the same team were monitored during the 24/25 competitive season (n=2,949 observations). The external load was assessed using GPS devices (10Hz). The internal load was collected using a modified 10-point Borg scale. To evaluate mental load, an adapted version of the Questionnaire for the Quantification of Mental Load in Team Sports was used. Furthermore, to assess general indicators of player well-being, the Wellness Questionnaire score (i.e., readiness) was recorded daily before training. Pearson’s correlation coefficient (r) was used to quantify the strength and direction of the linear relationships between the different external, internal and mental load variables and the readiness variables. Then, Linear Mixed Models were employed to examine the impact of external, internal and mental load variables on the readiness during the different training days of the microcycle. RESULTS: Correlations between training load variables and readiness index were moderate and negative for TD (r=-.32), Cognitive (r=-.35), Emotional (r=-.33), and Mental Fatigue (r=-.35). The most influential training load variables on fatigue variables were Decelerations on Fatigue (β=−.61,p<.001), Cognitive on DOMS (β=−.58,p<.001), Cognitive on Sleep (β=−.38,p<.001), and Mental Fatigue on Mental State (β=−.39,p<.001), Stress (β=−.31,p<.001), and Mood (β=−.25,p< .001). CONCLUSION: The findings indicated that training load variables influenced negatively and significantly on readiness, with mental fatigue emerging as the most impact variable. This study reveals which training load variables have the greatest impact on player readiness, providing insights into how training affects player fatigue. References: 1. Gabbett, T. J., Nassis, G. P., Oetter, E., Pretorius, J., Johnston, N., Medina, D., ... & Ryan, A. (2017). The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data. British J Sports Med,51(20),1451-1452. 2. McLean, B. D., Coutts, A. J., Kelly, V., McGuigan, M. R., & Cormack, S. J. (2010). Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Phys Perf, 5(3), 367-383.
Read CV José Carlos Ponce-BordónECSS Paris 2023: OP-AP43