Purpose: To analyze the energetic profile of the Basketball Exercise Simulation Test (BEST). Methods: Ten male elite junior basketball players (age 15.5 [0.6] y, height 180 [9] cm, and body mass 66.1 [11.2] kg) performed a modified BEST (20 circuits consisting of jumping, sprinting, jogging, shuffling, and short breaks) simulating professional basketball game play. Circuit time, sprint time, sprint decrement, oxygen uptake (VO₂), heart rate, and blood lactate concentration (blc) were obtained. Metabolic energy and metabolic power above rest (W_tot and P_tot), as well as energy share in terms of aerobic (W_aer), glycolytic (W_blc), and high-energy phosphates (W_PCr), were calculated from VO₂ during exercise, net lactate production, and the fast component of postexercise VO₂ kinetics, respectively. Results: W_aer, W_blc, and W_PCr reflect 89% (2%), 5% (1%), and 6% (1%) of total energy needed, respectively. Assuming an aerobic replenishment of PCr energy stores during short breaks, the adjusted energy share yielded W_aer 66% (4%), W_blc 5% (1%), and W_PCr 29% (1%). W_aer and W_PCr were negatively correlated (−0.72 and −0.59) with sprint time, which was not the case for W_blc. Conclusions: Consistent with general findings on energy system interaction during repeated high-intensity exercise bouts, the intermittent profile of the BEST relies primarily on aerobic energy combined with repetitive supplementation by anaerobic utilization of high-energy phosphates.

Athlete monitoring systems (AMSs) provide a centralized platform for integrating, processing, analyzing, and graphing various monitoring data to help coaches manage the rigorous demands of elite men’s basketball players, who frequently participate in high‐stress games with minimal recovery time. This review synthesizes current challenges in deploying AMSs, underscores their role in injury prevention and performance optimization, and discusses technological advances that could enhance their utility. Key challenges include selecting appropriate monitoring methods based on human and financial resources, accuracy of data collection, real‐time data processing, and personalization of training regimens. Due to the weaknesses and limitations of each monitoring method, it is recommended that both objective (e.g., external load data, heart rate measures, and biomarkers) and subjective (athlete‐reported outcome measures) monitoring data be integrated into an AMS to provide a holistic insight of the athlete’s health and readiness. In addition, decision support systems integrated into an AMS can help coaches quickly gain an overview of their players’ current condition and make informed decisions about daily load and recovery management. In this context, future perspectives suggest the potential for AMSs to incorporate predictive analytics and artificial intelligence to further enhance decision‐making processes in elite men’s basketball. Our findings underscore the need for continued innovation and rigorous validation of AMS technologies to ensure they meet the evolving demands of professional sports environments.

Background: Artistic gymnastics is a popular Olympic discipline where female athletes compete in four and male athletes in six events with floor exercise having the longest competition duration in Women’s and Men’s artistic gymnastics (WAG, MAG). To date no valid information on the energetics of floor gymnastics is available although this may be important for specific conditioning programming. This study evaluated the metabolic profile of a simulated floor competition in sub-elite gymnasts. Methods: 17 (9 male, 8 female) sub-elite gymnasts aged 22.5 ± 2.6y took part in a floor-training-competition where oxygen uptake was measured during and until 15 min post-exercise. Additionally, resting and peak blood lactate concentration after exercise were obtained. The PCr-LA-O₂ method was used to calculate the metabolic energy and the relative aerobic (W_AER), anaerobic alactic (W_PCr) and anaerobic lactic (W_BLC) energy contribution. Further, the athletes completed a 30 s Bosco-jumping test, a countermovement jump and a drop jump. Results: The competition scores were 9.2 (CI:8.9–9.3) in WAG and 10.6 (CI:10.4–10.9) in MAG. The metabolic profile of the floor routine was mainly aerobic (58.9%, CI: 56.0–61.8%) followed by the anaerobic alactic (24.2%, CI: 21.3–27.1%) and anaerobic lactic shares (16.9%, CI:14.9–18.8%). While sex had a significant (p = .010, d = 1.207) large effect on energy contribution, this was not the case for competition duration (p = .728, d = 0.061). Relative energy contribution of WAG and MAG differed in W_AER (64.0 ± 4.7% vs. 54.4 ± 6.8%, p = .004, d = 1.739) but not in W_PCr (21.3 ± 6.1% vs. 26.7 ± 8.0%, p = .144, d = 0.801) and W_BLC (14.7 ± 5.4% vs. 18.9 ± 4.2%, p = .085, d = 0.954). Further no correlation between any energy share and performance was found but between W_PCr and training experience (r = .680, p = .044) and W_BLC and competition level (r = .668, p = .049). Conclusion: The results show a predominant aerobic energy contribution and a considerable anaerobic contribution with no significant difference between anaerobic shares. Consequently, gymnastic specific aerobic training should not be neglected, while a different aerobic share in WAG and MAG strengthens sex-specific conditioning. All in all, the specific metabolic share must secure adequate energy provision, while relative proportions of the two anaerobic pathways seem to depend on training and competition history.

Purpose: To assess the test–retest reliability of the continuous (PCr-LA-O₂) and intermittent (PCr-LA-O_2int) version of the 3-component model of energy distribution in an applied setting. Methods: Sixteen male handball players (age 23 [3] y, height 185 [7] cm, weight 85 [14] kg) completed the 30–15 Intermittent Fitness Test (30–15_IFT) twice. Performance was assessed by peak speed (speed of the last successfully completed stage of the 30–15_IFT [V_IFT], in kilometers per hour) and time to exhaustion (in seconds). Oxygen uptake (in milliliters per kilogram per minute) and blood lactate concentrations (in millimoles per liter) were obtained before, during, and until 15 minutes after exercise. Total metabolic energy (in joules per kilogram), total metabolic power (in watts per kilogram), and energy shares (in joules per kilogram and percentage) of the aerobic (energy contribution of the aerobic system [W_AERint]), anaerobic lactic, and anaerobic alactic (anaerobic alactic energy [W_PCrint]) systems were calculated using both model versions, respectively. Results: Test–retest reliability was very good for V_IFT (limits of agreement [LoA]: −1.13 to 0.63 km·h⁻¹, coefficient of variation [CV%] 1.68), time to exhaustion (LoA: −101 to 38 s, CV% 2.92), peak oxygen uptake (LoA: −2.68 to 4.04 mL·min⁻¹·kg⁻¹, CV% 1.48), and peak heart rate (−6.9 to 7.7 beats·min⁻¹, CV% 1.1), but moderate for change in blood lactate concentration (LoA: −3.84 to 4.07 mmol·L⁻¹, CV% 11.43). Reliability of the modeled total energy and its fractions were high for total metabolic energy (LoA: −1489 to 1177 J·kg⁻¹, CV% 2.88), total metabolic power (LoA: −2.0 to 1.9 W·kg⁻¹, CV% 3.58), contribution of aerobic (LoA: −1673 to 1283 J·kg⁻¹, CV% 3.62), W_AERint (LoA: −1760 to 2160 J·kg⁻¹, CV% 6.04), and moderate for anaerobic alactic (LoA: −368 to 439 J·kg⁻¹, CV% 14.85), W_PCrint (LoA: −1707 to 988 J·kg⁻¹, CV% 9.98), and energy share of anaerobic lactic concentration (LoA: −229 to 235 J·kg⁻¹, CV% 11.43). Conclusion: Considering the inherent fluctuations of the underlying energetics, the reliabilities of both versions of the 3-component model of energy distribution are acceptable for applied settings.

Purpose: To evaluate the metabolic relevance of type of locomotion in anaerobic testing by analyzing and comparing the metabolic profile of the Bosco Continuous Jumping Test (CJ30) with the corresponding profile of the Wingate Anaerobic Test (WAnT). Methods: A total of 11 well-trained, male team-sport athletes (age = 23.7 [2.2] y, height = 184.1 [2.8] cm, weight = 82.4 [6.4] kg) completed a CJ30 and WAnT each. During the WAnT, power data and revolutions per minute were recorded, and during the CJ30, jump height and jumping frequency were recorded. In addition, oxygen uptake and blood lactate concentration were assessed, and metabolic profiles were determined via the PCr-LA-O₂ method. Results: In the CJ30, metabolic energy was lower (109.3 [18.0] vs 143.0 [13.1] kJ, P < .001, d = −2.302), while peak power (24.8 [4.4] vs 11.8 [0.5] W·kg⁻¹, P < .001, d = 3.59) and mean power (20.8 [3.6] vs 9.1 [0.5] W·kg⁻¹, P < .001, d = 4.14) were higher than in the WAnT. The metabolic profiles of the CJ30 (aerobic energy = 20.00% [4.7%], anaerobic alactic energy [W_PCr] = 45.6% [4.5%], anaerobic lactic energy = 34.4% [5.2%]) and the WAnT (aerobic energy = 16.0% [3.0%], anaerobic alactic W_PCr = 34.5% [5.0%], anaerobic lactic energy = 49.5% [3.3%]) are highly anaerobic. Absolute energy contribution for the CJ30 and WAnT was equal in W_PCr (49.9 [11.1] vs 50.2 [11.2] kJ), but anaerobic lactic energy (37.7 [7.7] vs 69.9 [5.3] kJ) and aerobic energy (20.6 [5.7] vs 23.0 [4.0] kJ) were higher in the WAnT. Mechanical efficiency was substantially higher in the CJ30 (37.9% [4.5%] vs 15.6% [1.0%], P < .001, d = 6.86), while the fatigue index was lower (18.5% [3.8%] vs 23.2% [3.1%], P < .001, d = −1.38) than in the WAnT. Conclusions: Although the anaerobic share in both tests is similar and predominant, the CJ30 primarily taxes the W_PCr system, while the WAnT more strongly relies on the glycolytic pathway. Thus, the 2 tests should not be used interchangeably, and the type of locomotion seems crucial when choosing an anaerobic test for a specific sport.

Purpose: To analyze the energetic profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2 (YYIR1 and YYIR2). Methods: Intermittent running distance (IR1D and IR2D), time to exhaustion (IR1T and IR2T), and total recovery time between shuttles (IR1R and IR2R) were measured in 10 well-trained male athletes (age 24.4 [2.0] y, height 182 [1] cm, weight 75.8 [7.9] kg). Respiratory gases and blood lactate (BLC) were obtained preexercise, during exercise, and until 15 min postexercise. Metabolic energy, average metabolic power , and energy share (percentage of aerobic [W_AER], anaerobic lactic [W_BLC], and anaerobic alactic energy system [W_PCr]) were calculated using the PCr-La-O₂ method. Results: Peak oxygen consumption was possibly higher in YYIR2 (60.3 [5.1] mL·kg⁻¹·min⁻¹) than in YYIR1 (P = .116, 57.7 [4.5] mL·kg⁻¹·min⁻¹, d = −0.58). IR1D, IR1T, and IR1R were very likely higher than IR2D, IR2T, and IR2R, respectively (P < .001, 1876 [391] vs 672 [132] m, d = −2.83; P < .001, 916 [175] vs 304 [57] s, d = −3.03; and P < .001, 460 [100] vs 150 [40] s, d = −2.83). Metabolic energy was most likely lower in YYIR2 than in YYIR1 (P < .001, 493.5 [118.1] vs 984.8 [171.7] kJ, d = 3.24). Average metabolic power was most likely higher in YYIR2 than in YYIR1 (P < .001, 21.5 [1.7] vs 14.5 [2.2] W·kg⁻¹, d = 3.54). When considering aerobic phosphocreatine restoration during breaks between shuttles, W_AER (P = .693, 49% [10%] vs 48% [5%], d = −0.16) was similar, W_PCr (P = .165, 47% [11%] vs 42% [6%], d = −0.54) possibly higher, and W_BLC (P < .001, 4% [1%] vs 10% [3%], d = 1.95) almost certainly lower in YYIR1 than in YYIR2. Conclusions: W_AER and W_PCr are predominant in YYIR1 and YYIR2 with almost identical W_AER. Higher IR1D and IR1T in YYIR1 result in higher metabolic energy but lower average metabolic power and slightly lower peak oxygen consumption. Higher IR1R allows for higher reliance on W_PCr in YYIR1, while YYIR2 requires a higher fraction of W_BLC.