The main aims of this systematic review with meta-analysis and meta-regression were to describe the effect of multidisciplinary neuromuscular and endurance interventions, including plyometric training, mixed strength and conditioning, HIIT basketball programs and repeated sprint training on youth basketball players considering age, competitive level, gender and the type of the intervention performed to explore a predictive model through a meta-regression analysis. A structured search was conducted following PRISMA guidelines and PICOS model in Medline (PubMed), Web of Science (WOS) and Cochrane databases. Groups of experiments were created according to neuromuscular power (vertical; NPV and horizontal; NPH) and endurance (E). Meta-analysis and sub-groups analysis were performed using a random effect model and pooled standardized mean differences (SMD). A random effects meta-regression was performed regressing SMD for the different sub-groups against percentage change for NPV and NPH. There was a significant positive overall effect of the multidisciplinary interventions on NPV, NPH and E. Sub-groups analysis indicate differences in the effects of the interventions on NPV and NPH considering age, gender, competitive level and the type of the intervention used. Considering the current data available, the meta-regression analysis suggests a good predictability of U-16 and plyometric training on jump performance. Besides, male and elite level youth basketball players had a good predictability on multidirectional speed and agility performance.

The aims of this study were as follows: (1) to assess how foot posture and morphology assessments change according to body mass index (BMI) status; (2) to determine which body composition parameter (BMI or waist circumference) correlates better with the foot posture index (FPI), arch height index (AHI), and midfoot width (MFW) in children. Foot morphometry (FPI, AHI, and MFW) and body composition (BMI and waist circumference (WC)) were assessed in a cross-sectional study of 575 children (mean age = 7.42 ± 1.67 years; 53.27% female). When comparing BMI groups, an increase of 8.3% in AHI and 13.6% in MFW (both p < 0.0001) was seen. In linear regression analyses, BMI and WC were positively associated with MFW explaining together 64.8% of its variance. Noteworthy, MFW is the most related to body composition parameters.Conclusion: Foot morphology assessed by FPI, AHI, and MFW differs among BMI categories in children. Noteworthy, WC correlates better with foot measures than does the more commonly used BMI, and more importantly the MFW is the foot measure best explained by children’s body weight. Since foot morphometry is different among different BMI groups, children would benefit from shoes with different patterns (thinner and wider), as well as a good system to adjust midfoot height. What is Known:• Children who are overweight and obese have flatter feet, when assessed using footprints.• Up to 72% of people have incorrectly fitted shoes. What is New:• Children with underweight have thinner and flatter feet than children with normal weight, while children with overweight and obesity have wider and higher arched feet.• Body weight is related to foot shape, which has relevance for footwear manufacturers.

In sports science, accurate tracking of athletes’ movement patterns is essential for performance analysis and injury prevention. Changes of direction (COD), frequently executed during basketball games at cutting angles of around 135° (internal angle of 45°), are essential for agility and high-level performance. Moreover, mastering effective COD mechanics is associated with a lower risk of injuries and enhanced long-term athletic success. However, manual segmentation of data from wearable sensors is labor-intensive and time-consuming, often creating bottlenecks for sports practitioners. The aim of this study was to evaluate the feasibility and accuracy of an automated algorithm for detecting COD movements in basketball and to compare its performance with manual detection methods. Data were collected from 62 basketball players, each completing two tests (V-cut test and a modified V-cut test), totaling 248 trials. The system utilizes kinematic data from an Xsens full-body kit to analyze key variables that characterize direction changes. The proposed method detects COD events with a median error of one frame and an interquartile range of two frames. The system demonstrated nearly 80% accuracy in COD detection, as validated against manual video analysis. These findings indicate that automated COD detection can significantly reduce segmentation time for practitioners while providing actionable, data-driven insights to enhance kinematic assessment during sport-specific activities.

Vasileva, F, Hristovski, R, Font-Lladó, R, Georgiev, G, Sacot, A, López-Ros, V, Calleja-González, J, Barretina-Ginesta, J, López-Bermejo, A, and Prats-Puig, A. Physical exercise-induced DNA methylation in disease-related genes in healthy adults—A systematic review with bioinformatic analysis. J Strength Cond Res 38(2): 384–393, 2024—This study aimed to systematically review the existing literature regarding physical exercise (PE) and DNA methylation (DNAm) in healthy adults. Specific goals were to (a) identify differently methylated genes (DMGs) after PE intervention, their imprinting status, chromosome and genomic location, function, and related diseases; and (b) to screen for core genes and identify methylation changes of the core genes that can be modified by PE intervention. Our search identified 2,869 articles from which 8 were finally included. We identified 1851 DMGs (p < 0.05) after PE intervention, although 45 of them were imprinted. Aerobic exercise (AE) seems to induce more DNA hypermethylation rather than hypomethylation, whereas anaerobic exercise (AN) seems to induce more DNA hypomethylation rather than hypermethylation. Aerobic exercise induced highest % of methylation changes on chromosome 6, whereas AN and mixed type (MT) on chromosome 1. Mixed type induced higher % of methylation changes close to transcription start site in comparison to AE and AN. After PE intervention, DMGs were mainly involved in fat metabolism, cell growth, and neuronal differentiation, whereas diseases regulated by those genes were mainly chronic diseases (metabolic, cardiovascular, neurodegenerative). Finally, 19 core genes were identified among DMGs, all related to protein metabolism. In conclusion, our findings may shed some light on the mechanisms explaining PE-induced health benefits such as the potential role that PE-induced DNAm may have in disease prevention and disease treatment.

The goals were to investigate in umbilical cord tissue if gestational obesity: (1) was associated with changes in DNA methylation of skeletal muscle-specific genes; (2) could modulate the co-methylation interactions among these genes. Additionally, we assessed the associations between DNA methylation levels and infant’s variables at birth and at age 6. DNA methylation was measured in sixteen pregnant women [8-gestational obesity group; 8-control group] in umbilical cord using the Infinium Methylation EPIC Bead Chip microarray. Differentially methylated CpGs were identified with Beta Regression Models [false discovery rate (FDR) < 0.05 and an Odds Ratio > 1.5 or < 0.67]. DNA methylation interactions between CpGs of skeletal muscle-specific genes were studied using data from Pearson correlation matrices. In order to quantify the interactions within each network, the number of links was computed. This identification analysis reported 38 differential methylated CpGs within skeletal muscle-specific genes (comprising 4 categories: contractibility, structure, myokines, and myogenesis). Compared to control group, gestational obesity (1) promotes hypermethylation in highly methylated genes and hypomethylation in low methylated genes; (2) CpGs in regions close to transcription sites and with high CpG density are hypomethylated while regions distant to transcriptions sites and with low CpG density are hypermethylated; (3) diminishes the number of total interactions in the co-methylation network. Interestingly, the associations between infant’s fasting glucose at age 6 and MYL6, MYH11, TNNT3, TPM2, CXCL2, and NCAM1 were still relevant after correcting for multiple testing. In conclusion, our study showed a complex interaction between gestational obesity and the epigenetic status of muscle-specific genes in umbilical cord tissue. Additionally, gestational obesity may alter the functional co-methylation connectivity of CpG within skeletal muscle-specific genes interactions, our results revealing an extensive reorganization of methylation in response to maternal overweight. Finally, changes in methylation levels of skeletal muscle specific genes may have persistent effects on the offspring of mothers with gestational obesity.