The purpose of this study was to determine the inter- and intra-testing reliability of newly developed tests of the change of direction speed (CODS) and reactive agility (RAG) in competitive futsal players. Additionally, the developed tests were evaluated for their validity with regard to the differentiation of two performance-levels. Thirty-two professional male futsal players (age = 26.22 ± 5.22 years; body height = 182.13 ± 5.99 cm, body mass = 77.43 ± 8.00 kg) participated in the study. The sample was divided into two groups based on their level of futsal performance: A top-level-group (n = 12) and a team-level-group (n = 20). The variables included body height, mass, body mass index, a sprint over a 10-m distance (S10M), and eight newly developed futsal specific CODS and RAG tests. The CODS and RAG tests were performed by dribbling the balls (CODS_D and RAG_D) and without dribbling (CODS_T and RAG_T), and the performances on the dominant and non-dominant sides were observed separately. All CODS, and RAG tests performed on dominant side and RAG_T tests performed on the non-dominant side had good inter-testing (CV = 5–8%; ICC = 0.76–0.89) and intra-testing (CV = 4–9%; ICC = 0.77–0.91) reliability. However, RAG_D performed on the non-dominant side was not reliable (ICC = 0.60, CV = 10%). The top-level-players outperformed the team-level-players in the CODS and RAG tests that involved dribbling (t-test: 4.28 and 2.40, p < 0.05; effect sizes (ES): 0.81 and 1.5, respectively), while the team-level players achieved better results in the CODS_T (t-test: 2.08, p < 0.05; ES: 0.60). The proposed CODS and RAG tests that involved dribbling over a 3.2-m distance, especially on the dominant side, appeared to be reliable, as well as valid for distinguishing the performance level in futsal players.

Purpose: Maximal heart rate (MHR) is an important physiologic tool for prescribing and monitoring exercise in both clinical and athletic settings. However, prediction equations developed in adults may have limited accuracy in youth. The purpose of this study was to systematically review and analyze the available evidence regarding the validity of commonly used age-based MHR prediction equations among children and adolescents. Methods: Included articles were peer-reviewed, published in English, and compared measured to predicted MHR in male and female participants <18 years old. The standardized mean difference effect size (ES) was used to quantify the accuracy of age-predicted MHR values and a priori moderators were examined to identify potential sources of variability. Results: The cumulative results of 20 effects obtained from seven articles revealed that prediction equations did not accurately estimate MHR (ES= 0.44, p < .05) by 6.3 bpm (bpm). Subgroup analyses indicated that the Fox equation (MHR = 220–age) overestimated MHR by 12.4 bpm (ES = 0.95, p < .05), whereas the Tanaka equation (MHR = 208–0.7*age) underestimated MHR by 2.7 bpm (ES = −0.34, p < .05). Conclusions: Age-based MHR equations derived from adult populations are not applicable to children. However, if the use of age-based equations cannot be avoided, we recommend using the Tanaka equation, keeping in mind the range of error reported in this study. Future research should control for potential pubertal influences on sympathetic modulation during exercise to facilitate the development of more age-appropriate methods for prescribing exercise intensity.

The primary aim of this study was to determine the association between body composition and performance outcomes in youth soccer players. Twenty-five competitive male youth soccer players (age = 13.7 ± 0.8 years, height = 167.4 ± 9.7 cm, weight = 57.6 ± 12.1 kg) volunteered to participate in this study. Height and weight were used to calculate body mass index (BMI). Body fat percentage (BF%) and fat-free mass (FFM) were determined with dual-energy X-ray absorptiometry. Each athlete performed the Pacer test, vertical jump, and t-test drill. Participants were predominantly normal weight (20.4 ± 2.7 kg·m⁻²). The body composition results were 20.3 ± 4.9% for BF% and 46.5 ± 8.7 kg for FFM. The results of the performance tests indicated a mean ± standard deviation (SD) of 1418 ± 332 m for Pacer, 57.2 ± 7.4 cm for vertical jump, 11.6 ± 0.7 s for t-test. Body mass index was not associated with any performance measure (r = 0.02 to −0.21, all p > 0.05). Body fat percentage was associated with the Pacer, vertical jump, and t-test (r = −0.62, −0.57, 0.61, respectively; all p < 0.01) and remained after accounting for BMI. Fat-free mass was only significantly related to t-test (r = −0.43, p < 0.01). However, after controlling for BMI, FFM was related to all three performance tests. Body fat percentage and FFM are associated with performance in youth soccer players, with stronger relationships reported in the former metric. The findings highlight the need for accurate body composition measurements as part of an assessment battery in young athletes.

Heart rate variability (HRV) is a non-invasive biomarker that reflects autonomic nervous system dynamics, providing valuable insights into physiological adaptation, stress, and recovery in athletes. Among the various HRV metrics, the root mean square of successive differences (RMSSD) has emerged as a robust and practical measure due to its strong association with parasympathetic activity, ease of calculation, and reliability in both short- and ultra-short-term recordings. This review examines the methodological considerations for using HRV to monitor training adaptations and recovery status in athletic populations. We highlight the superiority of routine, near-daily HRV measurements over isolated assessments, emphasizing the utility of weekly averages and the coefficient of variation (CV) to capture both chronic adaptations and acute homeostatic perturbations. Additionally, we discuss the selection of HRV devices, data recording procedures, and strategies to enhance athlete compliance. While RMSSD offers significant advantages for field-based monitoring, we also address its limitations, including its sole focus on parasympathetic activity and susceptibility to external confounders. Future directions include the integration of HRV data with other physiological markers and machine learning algorithms to optimize individualized training and recovery strategies. This review provides sport scientists and practitioners with evidence-based recommendations to enhance the application of HRV in both research and real-world athletic settings.