Highest oxygen consumption prediction: introducing variable theoretical proportional factors for different sports.

PURPOSE: The use of a fixed theoretical-proportional-factor (TPF15) is one of the indirect highest-oxygen-consumptions (HOC) assessment methods, but it may not accurately reflect the physiological differences across various sports (cycling-triathlon-running-football-multisport). The aim of this study is to evaluate the variability of TPF across different sports, proposing a series of sport-specific new TPF values for more accurate HOC estimation. METHODS: A sample of 340 adults (26.01 ± 7.18 years) performed a maximal-incremental-test using sport-specific-ergometers. HOC was considered for cycling  V ˙ O 2peak , whereas for the other investigated sports it was considered V ˙ O 2max . HOC was directly measured using a gas-analyzer, and TPF values were calculated using heart rate (HR): the ratio of HRmax/HRrest multiplied for the measured values of HOC. A one-way ANOVA was used to measure differences and Bland-Altman plots were constructed to compare predicted and actual  V ˙ O 2max / V ˙ O 2peak . RESULTS: Actual HOC was significantly greater than those predicted by the fixed TPF15 (P < 0.001). Sport-specific new TPF values ranged from 16.55 in multisport to 20.15 in cycling, consistently exceeding the old fixed TPF15, and predicting therefore better HOC. The new TPF exhibited a closer agreement with the directly measured V ˙ O 2max / V ˙ O 2peak  compared to the TPF15. Furthermore, the new TPF reduced the typical-measurement-error (14.94-17.78%) compared to TPF15 (15.63-24.13%). CONCLUSION: This study suggests that new TPF values predict V ˙ O 2max / V ˙ O 2peak  with higher accuracy compared to the traditional method. The use of HRmax and HRrest values allows to customize training programs for different athletes. Future research should focus on validating these findings across larger populations of athletes.

Diurnal variation of psychomotor, cognitive and physical performances in schoolchildren: sex comparison.

BACKGROUND: The present study investigated the effect of time of day (08h00 vs. 11h00 vs. 14h00 vs. 17h00) and sex (girls vs. boys) on physical (i.e., five jump test (5JT), push-ball test and 5 m shuttle run test (5mSRT)), cognitive (i.e., attention) and mental (i.e., mental flexibility) performances. METHODS: Thirty schoolchildren, equally divided in girls (n = 15; age: 9.60 ± 0.51 years) and boys (n = 15; age: 9.40 ± 0.51 years) performed the digit cancellation test, the trail making test, the 5JT, the 2 kg push-ball test and the 5mSRT in a counterbalanced and cross over study design at 08h00, 11h00, 14h00 and 17h00 with 48 h of rest in between. Additionally, rating of perceived exertion (RPE) was determined after each repetition of the 5mSRT and the average of the score (i.e., sum of RPE scores divided by 6) was determined. RESULTS: Results showed that RPE at the end of the test was significantly higher at 11h00 compared to 08h00 (p = 0.02) and 14h00 (p = 0.001) and average RPE was higher at 11h00 compared to 08h00 (p = 0.001). Likewise, attention was significantly higher at 08h00 compared to 17h00 (p = 0.001) before and after the 5mSRT test only in girls. However, 5JT performance was significantly lower at 17h00, both in girls and boys, compared to at 08h00 (p = 0.02 and p = 0.001 respectively), 11h00 (p = 0.004 and p = 0.001 respectively) and 14h00 (p = 0.001 and p = 0.001 respectively). However, push-ball (p = 0.086) and 5mSRT performances [best distance (p = 0.173), total distance (p = 0.306), mean distance (p = 0.29), fatigue index (p = 0.06)] were time of day independent. Mental flexibility was significantly higher at 08h00, 11h00 and 14h00 compared to 17h00 (p = 0.001). CONCLUSION: Mental flexibility, attention and jump performances were time of day dependent and push-ball test and 5mSRT performances did not change according to the time of day. Also, no clear sex effect was found on the diurnal variation of mental, cognitive and physical performances.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite's individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model's predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.

Aerobic capacity and respiratory indices of junior cross-country skiers and biathletes during incremental exercise testing.

The present study compared the isocapnic buffering phase (ICB), hypocapnic hyperventilation phase, ventilatory threshold (VT), respiratory compensation point (RCP), and maximum oxygen uptake (VO2max) among biathlon and cross-country ski athletes during an incremental exercise test. 37 male and 33 female Turkish National Team athletes volunteered to participate in the research. Body fat percentage, lean mass, and fat mass values of athletes were measured using the bioelectrical impedance analysis method, and oxygen consumption (VO2) was measured with a portable cardiopulmonary exercise test system with a ramp protocol on the treadmill. In VT, RCP, and VO2max phases, male athletes had higher VO2 and speed values than female athletes (p < 0.05). In contrast, they had similar values across different categories of sports (biathlon and cross-country skiing) (p > 0.05). Additionally, XC skiers and males had higher absolute (Abs) VO2 and mass-normalized (Rel) VO2 values than biathletes and females in exhaustion times and ICBs (p < 0.05). In contrast, they had similar Abs VO2 and Rel VO2 values in hypocapnic hyperventilation phases (p > 0.05). In addition, XC skiers and males had higher absolute (Abs) VO2 and relative (Rel) VO2 values than biathletes and females in exhaustion times and ICBs (p < 0.05). In contrast, they had similar Abs VO2 and Rel VO2 values in hypocapnic hyperventilation phases (p > 0.05). These results indicate significant differences in physiological profiles between male and female athletes and between XC skiers and biathletes.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach.

Background: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses. This study aims to develop a model integrating machine learning and explainable artificial intelligence (XAI) to identify novel platelet metabolomics markers of sepsis. Methods: A total of 39 participants, 25 diagnosed with sepsis and 14 control subjects, were included in the study. The profiles of platelet metabolites were analyzed using quantitative 1H-nuclear magnetic resonance (NMR) technology. Data were processed using the synthetic minority oversampling method (SMOTE)-Tomek to address the issue of class imbalance. In addition, missing data were filled using a technique based on random forests. Three machine learning models, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and kernel tree boosting (KTBoost), were used for sepsis prediction. The models were validated using cross-validation. Clinical annotations of the optimal sepsis prediction model were analyzed using SHapley Additive exPlanations (SHAP), an XAI technique. Results: The results showed that the KTBoost model (0.900 accuracy and 0.943 AUC) achieved better performance than the other models in sepsis diagnosis. SHAP results revealed that metabolites such as carnitine, glutamate, and myo-inositol are important biomarkers in sepsis prediction and intuitively explained the prediction decisions of the model. Conclusion: Platelet metabolites identified by the KTBoost model and XAI have significant potential for the early diagnosis and monitoring of sepsis and improving patient outcomes.

A crossover study on attentional focus and gross motor performance in individuals with Down syndrome.

Little is known about the effect of using an attentional focus instruction on motor performance in people with intellectual disabilities. Therefore, this study explored the effects of different attentional focus instructions on gross motor skill performances in individuals with Down syndrome. Seven community-dwelling participants (age 25.2±3.2 yrs, height 1.70±0.04 m, body mass 72.0±6.3 kg) voluntarily participated in the study. Motor performance on 5-meter running (5m sprint), vertical jump (countermovement jump with arm swing, CMJ), broad jump (standing broad jump, SBJ), forward medball throw (FMBT) or overhead medball backward throw (OMBT) and rising-up from a chair (five repetition sit-to-stand, 5STS) were recorded while performing internal-focus (IF) or external-focus (EF) instructions. EF induced significantly (p<0.05) better performance than IF in CMJ (EF: 15±9 cm; IF: 11±8 cm, median ±interquartile range), SBJ (EF: 0.8±1.05 m; IF: 0.5±1.0 m), FMBT (EF: 1.5±1.4 m; IF: 1.4±1.1 m), OMBT (EF: 4.0±1.5 m; IF: 3.6±1.1 m) and 5STS (EF: 14.2±5.4; IF:15.3±7.7 s). The time over the 5m sprint tended to be shorter with EF (4.0±2.0 s) than IF (5.05±3.3 s) but the difference did not reach the statistical significance (p = 0.29). Physical trainers and school teachers should be encouraged to manage different types of attentional focus instructions to improve cognitive and gross motor performances in persons with Down syndrome.

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction.

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.

Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy.

BACKGROUND: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). METHODS: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro-Wilk test for normality, the Kruskal-Wallis H test for group differences, and the Mann-Whitney U test with Bonferroni correction for post-hoc comparisons. RESULTS: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. CONCLUSION: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.

Moderate aerobic training counterbalances the deleterious effect of undernutrition on oxidative balance and mitochondrial markers.

The state of Maternal Protein Malnutrition (MPM) is associated with several deleterious effects, including inflammatory processes and dysregulation in oxidative balance, which can promote neurodegeneration. On the other hand, it is known that aerobic exercise can promote systemic health benefits, combating numerous chronic diseases. Therefore, we evaluate the effect of aerobic exercise training (AET) on indicators of mitochondrial bioenergetics, oxidative balance, endoplasmic reticulum stress, and neurotrophic factor in the prefrontal cortex of malnourished juvenile Wistar rats. Pregnant Wistar rats were fed with a diet containing 17% or 8% casein during pregnancy and lactation. At 30 days of life, male offspring were divided into 4 groups: Low-Protein Control (LS), Low-Protein Trained (LT), Normoprotein Control (NS), and Normoprotein Trained (NT). The trained groups performed an AET for 4 weeks, 5 days a week, 1 h a day per session. At 60 days of life, the animals were sacrificed and the skeletal muscle, and prefrontal cortex (PFC) were removed to evaluate the oxidative metabolism markers and gene expression of ATF-6, GRP78, PERK and BDNF. Our results showed that MPM impairs oxidative metabolism associated with higher oxidative and reticulum stress. However, AET restored the levels of indicators of mitochondrial bioenergetics, in addition to promoting resilience to cellular stress. AET at moderate intensity for 4 weeks in young Wistar rats can act as a non-pharmacological intervention in fighting against the deleterious effects of a protein-restricted maternal diet.

Warm-up music and low-dose caffeine enhance the activity profile and psychophysiological responses during simulated combat in female taekwondo athletes.

To assess the effects of warm-up music and low dose (3 mg·kg-1) of caffeine (CAF) on female taekwondo athlete's activity profile and psychophysiological responses during simulated combat. In a double-blinded, randomized, crossover study, 16 female athletes participated in simulated combats under one control and 5 experimental conditions [i.e., CAF alone (CAF), placebo alone (PL), CAF with music (CAF + M), PL with music (PL + M), and no supplement with music (M)]. After warming-up, athletes rated their felt arousal (FAS). Mean (HRmean) and peak (HRpeak) heart rate values were determined for each combat. After fighting, athletes rated their perceived exertion (RPE), feeling scale (FS), FAS, and physical enjoyment (PACES). Time-motion and technical-tactical variables were analyzed. CAF + M induced shorter skip and pause time, while attack time increased compared to other conditions (p < 0.05). Moreover, CAF + M increased single attacks, combined attacks, counter-attacks (p < 0.001), and defensive actions (p < 0.05) than other conditions. HRmean and HRpeak were lower under CAF + M than other conditions (p < 0.05). Additionally, higher FAS post-combat, FS, and PACES were observed under CAF + M, while RPE was lower (except CAF condition) compared to the other conditions (p < 0.05.Using CAF with warm-up music may increase combat cadence and improve the psychological state in female athletes more effectively than either strategy alone.

Anthropometric Influence on Preschool Children's Physical Fitness and Motor Skills: A Systematic Review.

Early childhood is a critical period for physical and motor development with implications for long-term health. This systematic review examined the relationship between anthropometric characteristics and measures of physical fitness and motor skills in preschool-aged children (typically 2-6 years). The search strategy was applied in four databases (PubMed, ProQuest Central, Scopus, and Web of Science) to find articles published before 11 April 2024. The results consistently demonstrated significant associations between anthropometric variables (height, weight, body mass index [BMI], body composition) and physical performance measures. Notably, height and mass were often better predictors of fitness status than BMI alone. Indicators of undernutrition (stunting, wasting) were negatively associated with motor development, emphasizing the importance of adequate nutrition. While some studies reported impaired fitness and motor skills among overweight/obese preschoolers compared to normal-weight peers, others found no differences based on weight status. Relationships between physical activity levels, anthropometrics, and motor outcomes were complex and inconsistent across studies. This review highlights key findings regarding the influence of anthropometric factors on physical capabilities in early childhood. Early identification of children with impaired growth or excessive adiposity may inform tailored interventions to promote optimal motor development and prevent issues like obesity. Creating supportive environments for healthy growth and age-appropriate physical activity opportunities is crucial during this critical developmental window.

Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis.

Endometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used. The data obtained were then classified according to age, tumor size, tumor grade and myometrial invasion. Then, by using three different machine learning methods, explainable artificial intelligence was applied to the model that best classifies these groups and possible protein biomarkers that can be used in endometrial prognosis were evaluated. The optimal model for age classification was XGBoost with AUC (98.8%), for tumor grade classification was XGBoost with AUC (98.6%), for myometrial invasion classification was LightGBM with AUC (95.1%), and finally for tumor size classification was XGBoost with AUC (94.8%). By combining the optimal models and the SHAP approach, possible protein biomarkers and their expressions were obtained for classification. Finally, EWRS1 protein was found to be common in three groups (age, myometrial invasion, tumor size). This article's findings indicate that models have been developed that can accurately classify factors including age, tumor grade, and myometrial invasion all of which are critical for determining the prognosis of endometrial cancer as well as potential protein biomarkers associated with these factors. Furthermore, we were able to provide an analysis of how the quantities of the proteins suggested as biomarkers varied throughout the classes by combining the SHAP values with these ideal models.

The contribution of energy systems during 30-second lower body Wingate anaerobic test in combat sports athletes: Intermittent versus single forms and gender comparison.

Combat sports, encompassing a range of activities from striking and grappling to mixed and weapon-based disciplines, have witnessed a surge in popularity worldwide. These sports are demanding, requiring athletes to harness energy from different metabolic pathways to perform short, high-intensity activities interspersed with periods of lower intensity. While it is established that the anaerobic alactic (ATP-PC) and anaerobic lactic systems are pivotal for high-intensity training sessions typical in combat sports, the precise contribution of these systems, particularly in varied training modalities such as single (SMT) and intermittent (IST) forms of the 30-second Wingate test, remains inadequately explored. This study aims at comparing performance outputs, physiological responses and gender differences during the SMT and IST forms of the 30-second Wingate test. Thirty-three highly trained combat sports athletes (17 women, 16 men; 10 boxing, 8 wrestling, 8 taekwondo and 7 karate) randomly performed SMT and IST. The IST consisted of three 10-second all-out attempts separated by 30 seconds of passive recovery, whereas the SMT was a single 30-second maximal effort. Resting, exercise and post-exercise oxygen uptake and peak blood lactate value were used to determine the metabolic energy demands via the PCr-LA-O2 method. The findings showed that total metabolic energy expenditure (TEE), ATP-PCr system contribution and the output of mechanical variables were higher in the IST than in the SMT form (all p<0.001). In contrast, the contribution of glycolytic and oxidative systems was higher in the SMT form (all p<0.001). However, exercise form and gender interaction were not significant (p>0.05). In combat sports, performance is not only determined by physiological and technical skills but also by metabolic energy input and efficiency. Therefore, our results can provide a comparison regarding the effects of exercise type and gender on metabolic energy metabolism to design the training of combat sports athletes.

Effects of short-term pre-competition weight loss on certain physiological parameters and strength change in elite boxers.

BACKGROUND: Athletes in certain sports aim to gain an advantage by competing in a lower body mass class instead of competing in their own body mass class. This study aims to reveal certain physiologic and strength changes in elite male boxers who lost body mass rapidly before the competition. METHODS: 30 thirty boxers who were aged between 19-24 years and having a mean age of 7.4 years participated in the study. To evaluate the effect of short-term dietary intake interventions on body composition and muscle strength before the competition, boxers were divided into three groups: control (C), exercise+diet1 (E+D1) and exercise+diet2 (E+D2) groups. The dietary habits of the participants were controlled and they participated in the training program. The data of the study consisted of variables such as body mass, height, regional muscle mass, body fat percentage, biceps and femur bicondylar circumference measurements before the competitions. Isometric strength measurements of knee extensors and flexors and shoulder internal and external rotators were also recorded. RESULTS: Physiologic parameters such as body mass change, BMI level, body fat percentage and leg muscle ratios of E+D2 were significantly decreased compared to C and E+D1 groups. Furthermore, submaximal and maximal strength production in knee extensors and flexors as well as shoulder internal and external rotators were significantly decreased in E+D2 compared to C and E+D1 groups. CONCLUSION: The tendency to lose body mass quickly in a short of time may give the desired results in terms of BMI, body mass and fat percentage, but it may cause strength losses in boxers during the competition period.

Emotional and Social Outcomes of the Teaching Personal and Social Responsibility Model in Physical Education: A Systematic Review and Meta-Analysis.

CONTEXT: In today's ever-changing world, fostering personal and social responsibility is essential for building strong and compassionate communities. This study aimed to provide a quantitative synthesis focusing on the emotional and social outcomes of Teaching Personal and Social Responsibility (TPSR) model-based Physical Education (PE) programs. METHODS: A comprehensive literature review covering the period from November 2022 to September 2023 identified 637 articles published between 2005 and 2023. Of these, 20 met the inclusion criteria. Data from these articles were coded, and a comprehensive meta-analysis was conducted, incorporating 28 effect sizes. Methodological quality was assessed using the Medical Education Research Study Quality Instrument. Hedge's g served as the effect size measure and emotional and social outcomes subgroups were consolidated. Heterogeneity was evaluated with Cochran's Q and I2. Meta-regression and ANOVA-like models addressed categorical moderators, whereas publication bias was assessed through funnel plot, failsafe number, and Egger's linear regression. RESULTS: A significant and positive effect of the TPSR model on product outcomes (Hedge's g = 0.337, 95% CI = 0.199 to 0.476) was found. Despite considerable heterogeneity (I2 = 83.830), a random effects model was justified. Assessment of publication bias indicated a low likelihood. Moderator analyses revealed that publication countries significantly influenced the effect, with stronger effects in Turkey. Publication type (article vs. thesis) also played roles in moderation. The meta-regression analyses did not reveal significant effects for the grade level, duration of intervention, publication year or sample size on the TPSR model's impact on product outcomes. The TPSR model positively impacts emotional and social outcomes in PE, enhancing children' skills and behaviour. However, variations across cultures highlight the need for further research, considering limitations like language constraints and potential biases in study selection and data extraction.

Personality and motivation of physical activity in adolescent girls: effects of perceived parental support and social physical anxiety.

In the present study, we investigated the relationship between personality and motivation for physical activity while introducing perceived parental support and social physical anxiety in adolescent girls (N = 318, Mage: 16.19 ± 0.51 years). The present study was a retrospective correlational study that was conducted to analyze of a path model. Dark triad traits: Machiavellianism, narcissism, and psychopathy, student's motivation for physical activity, social physique anxiety, and participants' perceptions of parents' behaviors were measured. The findings indicated that psychopathy and Machiavellianism were directly and indirectly associated with motivation for physical activity, but Narcissism could only directly predict the motivation for physical activity. Also, need-thwarting (the most), need-supportive and social physical anxiety could predict motivation for physical activity. This model of the result suggests that among adolescent girls, dark triad personality could, directly and indirectly, predict motivation with need-supportive and need-thwarting and also social physical anxiety. It seems that the sense of importance and more attention to oneself in adolescent girls, which exists in the narcissistic personality, can directly lead to more motivation for physical activity. Also, the duplicitous ways of Machiavellian people in pursuing their motives were confirmed in this research.

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model.

This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.

Effects of Ramadan intermittent fasting on performance, physiological responses, and bioenergetic pathway contributions during repeated sprint exercise.

Introduction: This investigation aims to elucidate the impact of Ramadan intermittent fasting on performance, physiological responses, and bioenergetic pathway contributions during repeated sprints. Methods: Fourteen active male Muslim athletes (age = 22.4 ± 1.8 years, body weight = 69.5 ± 3.8 kg, height = 176 ± 5.1 cm) executed a repeated sprint protocol, consisting of ten 20-meter sprints with 15-s passive recovery intervals, during both fasting and non-fasting conditions. The fasting session was conducted after a 12-14 h fast following Sahur (the pre-dawn meal during Ramadan). In contrast, the non-fasting session occurred before the Ramadan fasting period began, during the same hours of the day, at a time when fasting was not yet required for the athletes. Bioenergetic pathway contributions during repeated sprints were quantified using the PCr-LA-O2 method. Results: The mean sprint time during fasting sessions was 3.4 ± 0.3 s compared to 3.3 ± 0.2 s in non-fasting sessions, indicating a trend approaching the threshold of significance for slower times in the fasted state (p = 0.052, effect size (ES) = 0.34). In terms of bioenergetic contributions, the total metabolic energy expenditure (TEE) was slightly lower during fasting sessions (236.5 ± 22 kJ) compared to non-fasting sessions (245.2 ± 21.7 kJ), but this difference was not statistically significant (p = 0.102, ES = 0.40). Similarly, metabolic energy expenditure per sprint was 23.7 ± 2.2 kJ in fasting conditions compared to 24.5 ± 2.2 kJ in non-fasting conditions (p = 0.106, ES = 0.35). The oxidative energy contribution did not differ significantly between fasting (34.2 ± 4.1 kJ) and non-fasting conditions (34.2 ± 4.1 vs. 35.5 ± 5.2 kJ; p = 0.238, ES = 0.28). Similarly, lactic (60.4 ± 7.6 vs. 59.2 ± 8.3 kJ; p = 0.484, ES = 0.15); and alactic (149.3 ± 19.9 vs. 143 ± 21.5 kJ; p = 0.137, ES = 0.30) energy contributions showed no significant differences between the fasting and non-fasting sessions. The percentage of performance decrement (Pdec) and the percentage contributions of oxidative, lactic, and alactic pathways to the total energy expenditure did not differ significantly between the fasting and non-fasting conditions, indicating a similar bioenergetic profile across both conditions. Conclusion: The present findings indicate no significant differences in performance metrics and metabolic outcomes between fasted and non-fasted states. Future assessments with longer duration and higher intensity protocols may provide further insights.

Effects of Swedish Massage at Different Times of the Day on Dynamic and Static Balance in Taekwondo Athletes.

The purpose of this study is to investigate the impact of different durations of Swedish massage on the static and dynamic balance at different times of the day in taekwondo athletes. Twelve taekwondo athletes who had been practicing on a regular basis for more than 5 years participated in this study. Taekwondo athletes completed static and dynamic balance tests either after a no-massage protocol (NMP), a five-minute massage protocol (5MMP), a ten-minute massage protocol (10MMP), or a fifteen-minute massage protocol (15MMP) two times a day in the morning (08:00-12:00) and in the evening (16:00-20:00), on non-consecutive days. The findings of this study suggest that the duration of the massage has a discernible impact on dynamic balance, particularly with regard to the right foot. Taekwondo athletes who received a 10MMP or 15MMP displayed significantly improved dynamic balance compared to those in the NMP. Importantly, these improvements were independent of the time of day when the massages were administered. It underscores the potential benefits of incorporating short-duration Swedish massages into taekwondo athletes' pre-competition routines to enhance dynamic balance. These findings highlight the potential benefits of incorporating short-duration Swedish massages into taekwondo athletes' pre-competition routines to enhance dynamic balance, a critical component of their performance, regardless of the time of day.