This study examines the influence of muscle strength on performance metrics in volleyball, addressing the need to understand how specific and non-specific strength training impacts athletic capabilities. A cohort of athletes underwent various strength tests, including squats (SQs), bench presses (BPs), and deadlifts (DLs), to assess their force and power during various forms of test. Lactate thresholds (LT₁ and LT₂) were also measured to evaluate aerobic and anaerobic capacities. The median speed at LT₁ was 1.80 m/s, and LT₂ was determined at a mean velocity of 2.56 m/s. Notably, a correlation was found between SQ performance and total points scored (p = 0.040, r = 0.285), while upper body strength was strongly correlated with performance metrics, showing significant relationships with points scored (p = 0.0001, r = 0.690) and peak power during BPs (p = 0.0001, r = 0.587). The findings suggest that targeted strength training enhances physical capabilities and improves technical and tactical performance in volleyball, highlighting the critical role of muscle strength in athlete selection and training strategies.

Introduction: Food allergies and intolerances represent a growing pediatric health concern. While stress-related outcomes have received increasing attention, fatigue symptoms in children with food hypersensitivity remain insufficiently characterized. The aim of the study was to evaluate perceived stress and fatigue in children with food hypersensitivity. Material and Methods: We conducted an observational cross-sectional study including 339 children aged 1–18 years with specialist-confirmed food allergy, food intolerance, or mixed pathology. Data were collected through structured parent-reported interviews incorporating the Perceived Stress Questionnaire (PSQ) and the Fatigue Assessment Scale (FAS). Results: Children with food allergy had significantly higher perceived stress and fatigue scores compared to those with food intolerance (p < 0.05), with the highest levels observed in the mixed pathology group. Both perceived stress and fatigue scores increased with age, with adolescents showing the highest values. A moderate positive correlation was identified between stress and fatigue (r = 0.49, p < 0.0001). In multivariable analyses, higher stress and fatigue scores were significantly associated with age and diagnostic category, including mixed pathology, after adjustment for sex and anthropometric indicators. Higher scores were also associated with the presence of multiple clinical symptoms, such as sleep disturbances and concentration difficulties. Conclusions: Food hypersensitivity in children is associated with a significant psychological burden characterized by elevated perceived stress and fatigue, particularly in adolescents and in those with more complex diagnostic profiles. These findings highlight the importance of multidisciplinary management strategies integrating accurate diagnosis, nutritional counseling, and psychosocial support in order to address the broader impact of pediatric food hypersensitivity.

Volleyball physical performance relies on the interaction between mechanical power, metabolic efficiency, and ventilatory regulation during repeated high-intensity actions. This study examined mechanical and cardiopulmonary responses during three consecutive 15 s countermovement jump bouts in female volleyball players, using simultaneous cardiopulmonary exercise testing. Eighteen female athletes (18–28 years) completed the protocol with 60 s active recovery between efforts. Mechanical performance showed a progressive decline (p < 0.01), with jump height decreasing from 20.59 ± 3.04 cm to 19.30 ± 3.23 cm and power output from 15.80 ± 2.61 to 14.83 ± 2.25 W/kg (p = 0.001). Oxygen uptake (VO₂) increased from 16.40 ± 6.73 to 20.87 ± 6.08 mL/min/kg (p = 0.002), while respiratory exchange ratio (RER) rose above 1.0, suggesting a growing anaerobic contribution. VE/VO₂ and PetO₂ also increased significantly (p < 0.001), indicating ventilatory adjustment to metabolic stress. Despite these adaptations, recovery between efforts appeared incomplete, reflected by persistent ventilatory and metabolic activation. These findings suggest moderate oxidative efficiency and partial fatigue compensation under short recovery conditions. The testing model may serve as a practical approach to evaluate the interplay between mechanical and metabolic performance and to refine individualized conditioning strategies in volleyball players.

Backround and Objectives: Basketball performance is shaped by repeated high-intensity actions interspersed with brief recovery. Conventional continuous or strictly incremental testing may not fully capture short active-recovery dynamics relevant to stop-and-go sports. Material and Methods: This study applied a VT₂-referenced progressive–intermittent treadmill protocol and focused on 60-s active-recovery kinetics to describe effort tolerance in an applied basketball setting. Basketball players from Mureș County completed anthropometry (24 h pre-test, fasted) and a single laboratory visit. Pre-test training and diet were standardized for 48 h (submaximal training; predominantly carbohydrate intake). CPET was performed in 3-min stages (6.5 km·h⁻¹ start; +0.7 km·h⁻¹ per stage) and stopped at RER = 1.00 and/or blood lactate = 4.0 mmol·L⁻¹ (operational VT2). After 3 min active recovery, participants completed six 60-s high-speed bouts separated by 60-s active recovery intervals (AR1–AR6), with intensities prescribed at 120–180% of VT2-derived speed, followed by an 8-min active recovery. For each AR interval, linear regression over 0–60 s yielded slopes for VO₂, VO₂/HR, VCO₂, V̇E, VE/VO₂, VE/VCO₂, and PetCO₂. Results: VT₁ was determined at 2.29 m·s⁻¹ (VO₂ 32 mL·min⁻¹·kg⁻¹) and VT2 at 3.07 m·s⁻¹ (VO₂ 42 mL·min⁻¹·kg⁻¹). Maximal intermittent speed was 5.33 m·s⁻¹ (VO₂ 45.5 mL·min⁻¹·kg⁻¹; RER 1.06; PetCO₂ 38 mmHg). VO₂ differed across successive bouts (p = 0.0001), while PetCO₂ showed a small downward drift across repetitions. Peak indices (max speed, VE/VCO₂max, PetCO₂max, VEmax) were associated with phase-specific recovery slopes across early, mid, and late recovery periods (false discovery rate–adjusted correlations). Lactate decreased over 8 min, but lactate change rates were not associated with peak indices. Conclusions: The VT₂-referenced progressive–intermittent protocol appears feasible in basketball players and provides phase-dependent recovery information that complements conventional peak CPET outcomes, with potential relevance for applied team settings.

Background/Objectives: Artificial intelligence (AI) tools are increasingly used for dietary assessment, but their reliability in pediatric nutrition remains uncertain. This study compared AI systems and human evaluators in pediatric menu assessment against expert-defined reference ratings. Methods: This observational cross-sectional study used anonymized dietary and clinical information from three healthy pediatric cases. A multidisciplinary panel of pediatric nutrition specialists established standard evaluations. Assessments were completed by 84 AI evaluations, 116 nutrition specialists, 56 pediatric-focused physicians, and 88 physicians from other specialties. Outcomes included absolute error in total energy estimation, deviations in portion and qualitative ratings, standardized scores, and binary adequacy accuracy. Groups were compared using ANOVA or Kruskal–Wallis tests with post hoc analyses. Results: A total of 344 assessments were included. Energy-estimation error differed between groups (Kruskal–Wallis = 12.85, p = 0.0016), but this analysis was based on a limited and highly unbalanced subset of 77 assessments. AI had the largest mean absolute error (260.3 ± 194.3 kcal), followed by nutrition specialists (148.4 ± 81.5 kcal); physicians with other specialties had the lowest error (60.4 ± 53.3 kcal); these subgroup comparisons should be interpreted cautiously because of the small and unequal numbers of available energy estimates. AI showed greater portion-rating deviation (0.488; 95% CI: 0.38–0.60) than nutrition specialists (0.310; 95% CI: 0.20–0.42; p = 0.0019, q = 0.0059) and pediatric-focused physicians (0.286; 95% CI: 0.16–0.41; p = 0.0181, q = 0.019). Conversely, AI showed the lowest deviations for variety (0.37 ± 0.49) and processing (0.33 ± 0.47), compared with 1.15–1.36 and 1.22–1.57, respectively, among human groups (both p = 0.0001). Conclusions: AI may support structured pediatric menu screening for descriptive qualitative features; however, lower precision for energy and portion assessment supports, under these specific conditions, its use as an adjunct, for qualified nutrition professionals.