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Abstract Background Monitoring of training load is done to improve physical performance and minimize the incidence of injuries. The study examined the correlation between accumulated training load parameters based on periods with maturity (i.e., maturity offset and peak height velocity -PHV- and wellness variables -e.g., stress and sleep quality-). The second aim was to analyze the multi-linear regression between the above indicators. Methods Twenty elite young U14 soccer players (M = 13.26 ± 0.52 years, 95% CI [13.02, 13.51]) were evaluated over 26 weeks (early, mid, and end-season) to obtain stress, sleep quality, and measures of workload in the season (accumulated acute workload [AW], accumulated chronic workload [CW], accumulated acute: chronic workload ratio [ACWLR], accumulated training monotony [TM], accumulated training strain [TS]). Results The analysis revealed a moderate, statistically significant negative correlation between sleep quality and training monotony (r = -0.461, p 0.05). In the multi-linear regression analysis, maturity, PHV, sleep, and stress collectively accounted for variances of 17% in AW, 17.1% in CW, 11% in ACWLR, 21.3% in TM, and 22.6% in TS. However, individual regression coefficients for these predictors were not statistically significant (p > 0.05), indicating limited predictive power. Conclusion The study highlights the impact of sleep quality on training monotony, underscoring the importance of managing training load to mitigate the risks of overtraining. The non-significant regression coefficients suggest the complexity of predicting training outcomes based on the assessed variables. These insights emphasize the need for a holistic approach in training load management and athlete wellness monitoring.

Abstract The aim of this study was to analyze the relationships among physical cognitive ability, academic performance, and physical fitness regarding age and sex in a group of 187 students (53.48% male, 46.52% female) from one town of Norwest of Jaén, Andalusia (Spain), aged between 9 and 15 years old (M = 11.97, SD = 1.99). The D2 attention test was used in order to analyze selective attention and concentration. Physical fitness, reflected on maximal oxygen uptake (VO2max), was evaluated using the 6 min Walking Test (6MWT). The analysis taken indicated a significant relationship between physical fitness level, attention, and concentration, as in the general sample looking at sex (finding differences between boys and girls in some DA score in almost all age categories [p 0.05). In sum, the present study revealed that students with better aerobic fitness can present better-processed elements and smaller omission errors. Moreover, girls and older students seem to present better cognitive functioning scores than boys and younger. Our findings suggest that more research is necessary to elucidate the cognitive function between ages, sexes, and physical fitness and anthropometry levels of students.

Abstract Background This study aimed to compare training load parameters, delayed onset muscle soreness (DOMS), and fatigue status between season periods (1st and 2nd halves) in U14 soccer players and to analyze the relationships between training load parameters based on season periods (1st and 2nd halves) with peak height velocity (PHV), DOMS, and fatigue status in under-14 (U14) young elite soccer players. Additionally, it was intended to analyze if fatigue, DOMS and PHV could explain training load parameters across the season. Methods Twenty U14 players that competed in the national league participated in this study. The players were monitored during the whole season (26 weeks), and evaluations were carried out at the end of the in-season. Anthropometric and body composition parameters and the maturity offset of each player were utilized to compute each player's age at PHV. Players reported their levels of DOMS and fatigue status using Hooper index questionnaires. The internal load was monitored using the rating of perceived exertion (RPE). Acute weekly internal load (AW), chronic weekly internal load (CW), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) were also obtained. Results The main results showed that TM was higher in the 2nd half, while CW, AW and DOMS were higher in the 1st half of the season. Moreover, the main correlations showed a positive correlation between PHV and TS (2nd half of the season) and between fatigue and TM (1st half of the season). Conclusion In conclusion, variations in well-being status and PHV cannot explain the variations in internal training loads in elite U14 soccer players. In addition, internal training load indices during the first half of the competitive season can promote a fundamental base for progression loads during the second period of the competitive season.

Abstract Objectives The purpose of the present study was two-fold: (1) To analyse physical fitness changes of youth football players after a full-season; and (2) to examine whether physical fitness changes are explainable by estimated maturity status, 2digit:4digit ratio (2D:4D) from each hand and training load (TL) measures. Methods Twenty-seven youth elite Under-15 football players were daily monitored for training load measures during 38 weeks. At the beginning and at the end of the season, all players were assessed for physical fitness. Also, the maturity status estimation and the length of the second and fourth digits of both hands were collected at the beginning of the season. Results Significant differences were found for all physical fitness measures after the season. The second and fourth digits of left and right hands had negative moderate correlations with change of direction (COD) changes (r=-.39 to − 0.45 | p = .05 to 0.02). Also, the maturity offset measure had negative moderate correlations with COD changes (r=-.40 | p = .04). From the reported significant correlations, the maturity offset, Left 4D, Right 2D and Right 4D significantly predicted the Mod.505 COD test changes (β = 0.41, p = .04; β = -0.41, p = .04; β = -0.45, p = .02; and β = -0.44, p = .03, respectively). Conclusion The maturity offset and the 2D:4D measures have the potential to predict COD performance changes over-time in youth football players. Given the lack of associations between the maturity estimation, 2D:4D and training load measures, with the overall physical fitness measures, coaches should rely only at COD changes.

Abstract This study aimed to analyze the reliability of the tests included in the motor competence assessment (MCA) battery and compare the effects of the number of trials per test. Thirty female volleyball players (14.6 ± 1.3 years of age) were tested. The participants performed two or three trials of each test. Intra-class correlation (ICC) was calculated, and a paired sample t-test analyzed the variations between trials (1st vs. 2nd vs. 3rd). Results revealed a significant difference between the first and the second trials for jumping sideways [t(29) = -4.108, p

Abstract Background The purpose of this study was to determine the eating behaviors, obesity and physical activity status of children of similar ages living in rural and urban areas and to examine these relationships during the coronavirus disease (COVID-19) pandemic process. Method The research was conducted using the scanning model. The research group consists of children living in rural and urban areas in Turkey. The sample of the study consists of a total of 733 adolescent participants, 351 females (47.9%) and 382 males (52.1%). After anthropometric measurements were made, the Physical Activity Questionnaire for older children and the Yale Food Addiction Scale for children 2.0 were used to determine the food addiction and physical activity status of children during the COVID19 pandemic process. Since the groups were homogeneously distributed, independent samples t-test and Pearson correlation test were used. Result In terms of food addiction and physical activity levels, children living in the urban have higher scores than children living in rural areas. In addition, children living in the urban were taller and have higher body mass values than those in rural areas. In terms of physical activity level and food addiction levels, while girls living in the urban had higher activity levels than those living in rural areas, no statistically significant difference was found between the physical activity levels of boys. When evaluated in terms of general and gender, it was determined that children living in rural areas were overweight and obese at a higher rate. Obese children had higher levels of food addiction and lower physical activity levels than non-obese children. Conclusion In order to prevent childhood obesity, the level of food addiction should be reduced as well as increasing the level of physical activity. This study is limited in terms of cross-sectional evaluation. Future research can experimentally reveal how much obesity is reduced by methods such as exercise and diet interventions.

Abstract Objectives The aims of this study were to analyze the relationships between minutes of play (MP) and maturity status, fitness, and hormonal levels and to explain how those measures influence the time of play. Methods Twenty-six youth soccer players U15 participated in this study over a full-season period. Anthropometric measures, maturity status, growth hormone (GH), insulin-like growth factor and physical levels such as maximal oxygen uptake (VO2max), fatigue index, countermovement jump (CMJ) performance were collected. At the end-season, players were assessed in 6 different tests over four days. Results VO2max largely correlated with GH (r = 0.57) and CMJ (r = 0.51). Also, GH largely correlated with CMJ (r = 0.55). MP had moderate correlations with VO2max (r = 0.44) and CMJ (r = 0.42). Multiple linear regression with maturation, physical fitness and hormonal levels explained R2 of 0.62 of the MP (F (8, 17) = 3.47, p = 0.015). Although each independent variable alone was not able to determine the playing time, when using the interactions, the model significantly explained the MP. Conclusions The combination of maturity status, physical fitness, and hormonal levels seem to play a determinant role in explaining the match participation in youth soccer players.