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Öğe Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model(Mdpi, 2024) Aygun, Umran; Yagin, Fatma Hilal; Yagin, Burak; Yasar, Seyma; Colak, Cemil; Ozkan, Ahmet Selim; Ardigo, Luca PaoloThis 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.Öğe Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research(Mdpi, 2023) Yagin, Burak; Yagin, Fatma Hilal; Colak, Cemil; Inceoglu, Feyza; Kadry, Seifedine; Kim, JungeunAim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the black box problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p <= 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p <= 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.Öğe Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome(Peerj Inc, 2024) Yagin, Fatma Hilal; Shateri, Ahmadreza; Nasiri, Hamid; Yagin, Burak; Colak, Cemil; Alghannam, Abdullah F.Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.Öğe Effect of listening to preferred music at different frequencies during warmup on physical performance and psychophysiological responses in male athletes(Nature Portfolio, 2025) Jebabli, Nidhal; Boujabli, Manar; Ouergui, Ibrahim; Ouerghi, Nejmeddine; Yagin, Fatma Hila; Yagin, Burak; Khlifi, MariemThe present study assessed the effects of listening to preferred music during warm-up at different frequencies (440 Hz vs. 432 Hz) on the physical performance and psychophysiological responses in male athletes. In a double-blind crossover study design, twenty-two male athletes (Mean +/- SD: age: 22.4 +/- 1.6 years; body mass: 71.8 +/- 8.3 kg; height: 1.83 +/- 0.1 m; BMI: 22.7 +/- 1.9 kg. m(- 2) randomly performed the 3-min all out test (3MT), repeated sprint test (RST), squat jump (SJ) and countermovement jump (CMJ) tests after listening to preferred music at 440 Hz-432 Hz frequencies or without music during warm-up. Heart rate was measured during 3MT, Also, rating of perceived exertion (RPE) and feeling scale (FS) were measured just after 3MT and RST tests. Results showed that no music condition induced lower performance compared to listening to preferred music at 440 Hz (mean speed: p < 0.001, d = 0.82; peak speed: p < 0.001, d = 0.85; total distance covered: p < 0.001, d = 0.83; FS: p < 0.001, d = 1.21) and music 432 Hz (mean speed: p < 0.001, d = 0.75; peak speed: p < 0.001, d = 0.58; total distance covered: p < 0.001, d = 0.76, FS: p = 0.004, d = 0.77) during 3MT. In addition, a significant decrease in Total time and Fast time indices of RST, FS, SJ and CMJ values was recorded with music at 440 Hz compared to music 432 Hz (total time: p < 0.001, d = 0.65; fast time: p < 0.001, d = 0.53; FS: p = 0.018, d = 0.59; SJ: p = 0.031, d = 0.3; CMJ: p = 0.007, d = 0.41) and no music condition (total time: p < 0.001, d = 0.89; fast time: p < 0.001, d = 0.57; FS: p < 0.001, d = 1.21; SJ: p < 0.001, d = 0.64; CMJ: p < 0.001, d = 0.54). However, no significant change was observed in heart rate, fatigue index of the RST, or RPE. Listening to preferred music at a fast tempo and moderate volume during warm-up sessions improved physical performance and positive mood irrespective of whether it was tuned to 440 Hz-432 Hz. The benefits were more noticeable at 440 Hz.Öğe Effect of listening to preferred music at different frequencies during warmup on physical performance and psychophysiological responses in male athletes (vol 15, 36120 , 2025)(Nature Portfolio, 2025) Jebabli, Nidhal; Boujabli, Manar; Ouergui, Ibrahim; Ouerghi, Nejmeddine; Yagin, Fatma Hilal; Yagin, Burak; Khlifi, Mariem[No abstract available]Öğe Explainable artificial intelligence model for identifying COVID-19 gene biomarkers(Pergamon-Elsevier Science Ltd, 2023) Yagin, Fatma Hilal; Cicek, Ipek Balikci; Alkhateeb, Abedalrhman; Yagin, Burak; Colak, Cemil; Azzeh, Mohammad; Akbulut, SamiAim: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples.Methods: In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine -Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic expla-nations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19-associated biomarker candidate genes and improve the final model's interpretability.Results: For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class.Conclusions: The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.Öğe An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites(Mdpi, 2023) Yagin, Fatma Hilal; Alkhateeb, Abedalrhman; Raza, Ali; Samee, Nagwan Abdel; Mahmoud, Noha F.; Colak, Cemil; Yagin, BurakBackground: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. This study uses explainable artificial intelligence and machine learning techniques to identify discriminative metabolites for ME/CFS. Material and Methods: The model investigates a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics. Random forest methods together with other classifiers were applied to the data to classify individuals as ME/CFS patients and healthy individuals. The classification learning algorithms' performance in the validation step was evaluated using a variety of methods, including the traditional hold-out validation method, as well as the more modern cross-validation and bootstrap methods. Explainable artificial intelligence approaches were applied to clinically explain the optimum model's prediction decisions. Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis. The random forest model outperformed the other classifiers in ME/CFS prediction using the 1000-iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC. According to the obtained results, the bootstrap validation approach demonstrated the highest classification outcomes. Conclusion: The proposed model accurately classifies ME/CFS patients based on the selected biomarker candidate metabolites. It offers a clear interpretation of risk estimation for ME/CFS, aiding physicians in comprehending the significance of key metabolomic features within the model.Öğe Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics(Mdpi, 2023) Yagin, Fatma Hilal; Yasar, Seyma; Gormez, Yasin; Yagin, Burak; Pinar, Abdulvahap; Alkhateeb, Abedalrhman; Ardigo, Luca PaoloDiabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 +/- 1.88) % accuracy, (89.33 +/- 1.80) % precision, (91.24 +/- 1.67) % recall, (89.37 +/- 1.52) % F1-Score, and (97.00 +/- 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.Öğe A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients(Mdpi, 2023) Yagin, Fatma Hilal; Alkhateeb, Abedalrhman; Colak, Cemil; Azzeh, Mohammad; Yagin, Burak; Rueda, LuisColorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).Öğe Identification of metabolomics-based biomarker discovery in individuals with down syndrome utilizing kernel-tree model-enhanced explainable artificial intelligence methodology(Frontiers Media Sa, 2025) Colak, Cemil; Yagin, Fatma Hilal; Yagin, Burak; Alkhateeb, Abedalrhman; Al-Rawi, Mahmood Basil A.; Akhloufi, Moulay A.; Aghaei, MohammadrezaObjective: This study aims to develop an explainable artificial intelligence (XAI) model integrated with machine learning (ML) to comprehensively investigate metabolic differences between individuals with Down syndrome (T21) and healthy controls (D21) and to identify novel/pathway-specific biomarkers. In this study, ML classifiers including AdaBoost, LightGBM, Random Forest, KTBoost, and XGBoost are applied to metabolomics data obtained from metabolomic analyses by high-resolution liquid chromatography-mass spectrometry (LC-MS) using blood plasma samples of 316 T21 and 103 D21 individuals, and the importance of metabolites is evaluated by XAI-based SHAP analysis. The KTBoost model shows the highest classification performance with an accuracy of 90.4% and area under the curve (AUC) of 95.9%, outperforming AdaBoost, LightGBM, Random Forest, and XGBoost. Significant downregulation and upregulation of some metabolites were observed in the T21 group compared to the D21 group. Metabolites such as vitamin C, taurolithocholic acid, sphingosine, and prostaglandin A2/B2/J2 are observed at low levels in the T21 group. In contrast, metabolites such as thymidine, tau-roursodeoxycholic acid, serine, and nervonic acid are elevated. SHAP analysis revealed that L-Citrulline, Kynurenin, Prostaglandin A2/B2/J2, Urate, and Pantothenate metabolites could be novel/pathway-specific biomarkers to differentiate the T21 group. This study revealed significant metabolic alterations in individuals with T21 and demonstrated the effectiveness of the combination of ML and XAI methods to identify novel/pathway-specific biomarkers. The findings may contribute to a better understanding of Down syndrome's molecular mechanisms and the development of future diagnostic and therapeutic strategies.Öğe The level of the aggression in karate athletes with different handedness and belt grades(Mre Press, 2024) Alikhajeh, Yaser; Nabilpour, Maghsoud; Rahim, Gholam Rasul Mohammad; Ghollasimood, Mozhgan; Yagin, Fatma Hilal; Yagin, Burak; Gulu, MehmetKarate athletes with different lateral talents possess different functions in terms of skills and personality characteristics in a way that handedness can be considered an advantage. Given that there is a paucity of research in the domain of personality characteristics, handedness and belt grades, the current research aims to investigate the relationship between handedness and belt grades with aggression among karate athletes. 120 male karate athletes participated. To measure handedness, we used Annette's handedness questionnaire and to measure aggression, we used Bredemeier's aggression questionnaire. The questionnaires were distributed among participants one day before the tournament. A two-way analysis of variance (ANOVA) was used to measure the effects of belt grades and handedness on the level of aggression. The results of the study indicated that there was no statistically significant difference in the average level of aggression between left-handed and right-handed karate athletes. There was also no statistically significant difference in the average level of aggression between karate athletes with different belt grades.Öğe Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence(Frontiers Media Sa, 2025) Gormez, Yasin; Yagin, Fatma Hilal; Yagin, Burak; Aygun, Yalin; Boke, Hulusi; Badicu, Georgian; De Sousa Fernandes, Matheus SantosObjectives This study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. The inclusion of XAI methodologies facilitates a comprehensive understanding of the risk factors influencing the model predictions and thus increases transparency in the identification of obesity risk factors.Methods Six ML models were used: Bernoulli Naive Bayes, CatBoost, Decision Tree, Extra Trees Classifier, Histogram-based Gradient Boosting and Support Vector Machine. For each model, hyperparameters were tuned by random search methodology and model effectiveness was evaluated by repeated holdout testing. SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures.Results The CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. Nonetheless, other models such as Decision Tree and Histogram-based Gradient Boosting also yielded strong and competitive results. The results also highlighted age, weight, height and specific food patterns as key predictors of obesity. In terms of interpretability, LIME showed superior in fidelity, whereas SHAP showed improved sparsity and consistency across models, facilitating a comprehensive understanding of trait importance.Conclusion This research demonstrates that ML algorithms, when integrated with XAI technologies, can accurately predict obesity levels and explain important contributing risk factors. The use of SHAP and LIME increases model transparency, facilitating the identification of specific lifestyle patterns linked to obesity risk. These findings help to formulate more precise intervention techniques guided by a reliable and understandable predictive framework.Öğe Serotoninergic modulation in the brainstem and hypothalamus of female overnourished rats: impact on mitochondrial markers, oxidative stress and BDNF mRNA levels(Frontiers Media Sa, 2025) Rodrigues, Thyago de Oliveira; dos Santos Junior, Osmar Henrique; Beltrao de Lemos, Maria Daniele Teixeira; de Sousa Fernandes, Matheus Santos; Yagin, Fatma Hilal; Yagin, Burak; Das, SamarjitIntroduction Obesity is a global epidemic identified by the World Health Organization, and its complexity involves genetic, cultural, socioeconomic, and behavioral factors.Methods In this study, we used female Wistar rats, with litters standardized to nine female pups, which were divided into two groups: normally nourished or overnourished. The groups were further subdivided into control and fluoxetine-treated groups, with the pharmacological treatment maintained until the 21st day of life. At 30 days of age, euthanasia was performed, and tissues from the hypothalamus and brainstem were collected.Results We observed an increase in body weight and the Lee index in the overnourished group, but fluoxetine treatment reduced these indices. Additionally, overnourished rats consumed more palatable food. Biochemically, NADH content in the hypothalamus was altered by overnutrition but restored by fluoxetine treatment. Citrate synthase activity was reduced in the overnourished group in the hypothalamus but increased in the brainstem of fluoxetine-treated rats. The production of reactive oxygen species was higher in the overnourished group, and oxidative stress biomarkers showed increased levels of MDA and protein carbonylation in these rats. Overnutrition impaired the antioxidant activity of enzymes in both the hypothalamus and brainstem, whereas fluoxetine treatment improved this activity. BDNF expression was higher in the fluoxetine-treated groups compared to the overnourished group.Discussion These results demonstrate the detrimental effects of maternal overnutrition on the development of female offspring and the therapeutic potential of serotonergic manipulation to mitigate the early effects of obesity, with tissue-specific variations.Öğe A Thirty-Minute Nap Enhances Performance in Running-Based Anaerobic Sprint Tests during and after Ramadan Observance(Mdpi, 2022) Yagin, Fatma Hilal; Eken, Ozgur; Bayer, Ramazan; Salcman, Vaclav; Gabrys, Tomasz; Koc, Hurmuz; Yagin, BurakThe purpose of this study was to determine the impact of a 30 min nap (N30) on the Running-Based Anaerobic Sprint Test (RAST) both during and after Ramadan. Ten physically active kickboxers (age: 21.20 +/- 1.61 years, height: 174.80 +/- 4.34 cm, body mass: 73.30 +/- 7.10 kg and body mass index (BMI): 24.00 +/- 2.21 kg/m(2)) voluntarily performed the RAST test after an N30 and in a no-nap condition (NN) during two experimental periods: the last ten days of Ramadan (DR) and similar to 3 weeks after Ramadan (AR). During each DR-NN, DR-N30, AR-NN and AR-N30 protocol, kickboxers performed RAST performance. A statistically significant difference was found between Ramadan periods (DR vs. AR) in terms of max power (W) (F = 80.93; p(1) < 0.001; eta(2)(p) = 0.89), minimum power (W) (F = 49.05; p(1) < 0.001; eta(2)(p) = 0.84), average power (W) (F = 83.79; p(1) < 0.001; eta(2)(p) = 0.90) and fatigue index (%) results (F = 11.25; p(1) = 0.008; eta(2)(p) = 0.55). In addition, the nap factor was statistically significant in terms of the max power (W) (F = 81.89; p(2) < 0.001; eta(2)(p) = 0.90), minimum power (W) (F = 80.37; p(2) < 0.001; eta(2)(p) = 0.89), average power (W) (F = 108.41; p(2) < 0.001; eta(2)(p) = 0.92) and fatigue index (%) results (F = 16.14; p(2) = 0.003; eta(2)(p) = 0.64). Taking a daytime nap benefits subsequent performance in RAST. The benefits of napping were greater after an N30 opportunity for DR and AR.
