Journal of Exercise Science & Physical Activity Reviews

The Role of Genomics in Precision Medicine and Personalized Treatment in Diabetes Mellitus

2024-06-09T17:14:05+03:00

Diabetes mellitus, a chronic metabolic disorder marked by persistent hyperglycemia, represents a significant global health challenge. The disease manifests in various forms, including Type 1 diabetes (T1D), Type 2 diabetes (T2D), gestational diabetes, and rare monogenic types, each with distinct etiologies. Advances in omics technologies, particularly genomics, have provided significant advances in the understanding of diabetes by providing insight into its genetic and molecular basis. This study delves into the role of genomics in diabetes research, highlighting how genome-wide association studies (GWAS) have uncovered numerous genetic loci associated with T1D and T2D. The findings elucidate the hereditary basis of these conditions and propose potential targets for personalized treatments. Genomic discoveries have critical implications for risk prediction, pathophysiological insights, and the development of targeted interventions, thereby paving the way for precision medicine in diabetes care. However, challenges such as the complexity of polygenic influences and gene-environment interactions necessitate ongoing research to fully exploit the potential of genomics in combating diabetes.

Normality Distributions Commonly Used in Sport and Health Sciences

2024-05-30T21:02:59+03:00

This article provides a comprehensive exploration of univariate and multivariate hypothesis tests commonly employed to assess the normality of data distributions. Normality is a foundational assumption in various statistical analyses, rendering the evaluation of data distribution conformity to the normal distribution paramount. In this article, we discuss the principles behind univariate tests, such as the Kolmogorov-Smirnov test, Anderson-Darling test, and Shapiro-Wilk test, as well as multivariate tests, including the Mahalanobis distance and Mardia's multivariate skewness and kurtosis tests. The article aims to aid researchers and practitioners in selecting the most suitable tests for their specific data analysis requirements.

The Importance of Medical Terminology in the Health Sector

2024-06-09T17:11:43+03:00

Medical terminology, rooted in the accumulation of knowledge within various fields of medicine, plays a crucial role in effective communication among healthcare professionals and ensuring patient safety. This study aims to explore the significance of medical terminology for students and professionals working in the health sector to effectively follow developments on medical platforms and communicate efficiently. Originating from Latin and Greek roots, medical terms have evolved over time, with English emerging as a prominent language in medical discourse. Standardized and common terminology is essential to navigate the rapidly evolving landscape of scientific and technological advancements in medicine, fostering a shared understanding among professionals across different disciplines. Proficiency in medical terminology is paramount for healthcare workers to accurately describe medical conditions, treatments, and procedures, thereby minimizing the risk of miscommunication and errors in patient care. By providing a common language for documentation, coding, and analysis of medical data, medical terminology education ensures uniformity and efficiency in healthcare practices. Moreover, familiarity with medical terminology enhances interdisciplinary collaboration and enables seamless communication among members of the healthcare team. This study underscores the critical role of medical terminology in promoting effective communication, advancing patient safety, and optimizing healthcare delivery.

Positive and Negative Effects of High-Intensity Interval Training on Athletic Performance

2024-04-05T00:11:22+03:00

In recent decades, high-intensity interval training (HIIT) has become popular among athletes. This training method involves repeating short training periods with regular recovery intervals. This popularity of HIIT and its effects on athletic performance has made it a primary focus of interest among researchers, conditioners, and coaches. This study aims to examine the positive and negative impact of HIIT on athletic performance in depth and to bring the findings to the literature. In this framework, the current study will address the potential of HIIT to improve performance in different sports, its health effects, and potential risks. In this study, a review of current literature examining the positive and negative effects of HIIT on athletic performance was conducted. This study was structured to include studies and meta-analyses in various scientific databases such as "Web of Science", "PubMed", "Google Scholar" and "TR Index". For the analysis of the studies, literature was searched in these databases with keywords such as "High-Intensity Interval Training", "HIIT", "Sportive Performance", "Athletic Performance", "HIIT and Sportive Performance", "Effects of HIIT", "Sports Injuries" and "Injury Risks in Sports" and the conclusion section of the current study has created according to the results of these studies. Findings in the literature show that HIIT has the potential to improve performance in many sports. Studies have shown significant improvements in performance measures such as strength, endurance, speed, and anaerobic capacity. However, HIIT training protocols are not without negative impacts, such as overtraining risks, muscular injuries, and excessive fatigue. Both coaches and athletes should understand that HIIT is an effective way to improve performance but should avoid overdoing it in their training protocols. As such, training programs should be individualized, considering individual needs and tolerances. Furthermore, further research is recommended to test the negative impact of HIIT.

The Effect of Regular Exercise and Physical Activity in Psoriasis

2024-03-06T18:10:51+03:00

Psoriasis is an important health problem that negatively affects people's quality of life. Psor asis, a chronic skin disease, is usually characterized by redness, rash, itching, and scaling of the skin. Although psoriasis is usually treated with pharmacologic methods, recent studies suggesting that exercise has positive effects on psoriasis have increased interest in this subject and thus the positive effect of exercise on psoriasis has diversified treatment strategies. In this context, this study aimed to examine the potential effects of exercise on the course of psoriasis by comparing the results of studies on exercise and psoriasis in the literature.This review includes a systematic analysis of scientific studies related to the research topic. In this study, an extensive literature review was conducted to examine the effect of physical exercise on psoriasis. The review of studies on the effect of exercise on psoriasis was conducted in Web of Science, Pub-Med, Scopus, and Google Scholar electronic databases. Ten academic articles were included in the current study. The conclusion section of the present study was formed by compiling the results of the studies. As a result, it was found that regular exercise had positive effects on skin lesions, inflammation, and overall quality of life in individuals with psoriasis. The results of our study suggest that exercise may positively affect the course of psoriasis. Given the positive effects of regular exercise in alleviating disease progression, controlling symptoms, and improving quality of life, it is important to create more awareness among coaches, conditioners, healthcare professionals, and patients about the integration of exercise. However, exercise programs need to be personalized and tailored to the individual, considering individual differences and disease severity. Further research and clinical studies on the effect of exercise on psoriasis are needed.

Statistical Effect Sizes in Sports Science

2024-06-30T19:05:12+03:00

Understanding the impact of various interventions, training methods, and strategies is crucial in sports science. Statistical effect sizes are essential tools that quantify the magnitude of these effects, providing more insight than simple significance testing. This article explores the most commonly used effect size metrics in sports science, including Cohen's d, Hedges' g, Pearson's r, and Eta Squared (η²). By examining these metrics, we highlight their importance in assessing practical significance, comparing results across studies, and informing evidence-based practice. Furthermore, the article delves into the interpretation and application of these effect sizes, offering guidance on their use in research and practice to enhance the understanding and optimization of athletic performance and well-being. This comprehensive overview aims to equip sports scientists, coaches, and practitioners with the knowledge to apply these statistical tools effectively, ultimately improving the quality and impact of sports science research. Additionally, the article discusses the context-specific importance of these effect size measures, ensuring that readers can accurately interpret and utilize them in diverse research scenarios.

The Effect of Pilates Exercises on Health Beliefs Attitudes, Body Image and Body Composition in Sedentary Women

2024-05-30T21:05:44+03:00

The aim of this research is to examine the effects of pilates exercises on health beliefs, attitudes towards exercise, body image perception and body composition in sedentary individuals. The sample of the study consisted of 30 female participants (n=30) between the ages of 24-55 (24/55=34.83±8.91). Sample size was determined by G-power analysis. Participants were given 1-hour reformer pilates group exercises 3 times a week for a total of 12 weeks. Health Beliefs Models Scale Towards Exercise (HBM) and Body Image Scale (BIS) were used. Tanita and tape measure were used for body and girth measurements. Nonparametric Wilcoxon rank signs and Kruskal-Wallis tests were used to determine significant differences. To test correlational hypotheses, Spearman correlation analysis was used to determine strong or weak relationships between variables. In the study, .05 was determined as the limit value for significance.In the research findings, it is seen that there is a significant difference in the fat percentage of the participants before and after pilates training (z=-4.623, p<.001). HBM scale health development sub-dimension scores before pilates training [x² (sd= 4, n=30) = 6.770, p>05] and after [x² (sd= 4, n=30) = 3.190, p>05] did not show any significant difference. Between BIS and educational status, the participants' BIS scores before the training [x² (sd= 4, n=30) = 4.133, p>05] and after [x² (sd= 4, n=30) = 4.607, p>05] depended on their educational status. did not show any significant difference. When the correlation results were examined, it was seen that there was no significant relationship between the last measurements of the participants' fat percentage and the scale scores (p>.05). As a result, it was determined that the participants in the study had improvements in their variables as a result of 12 weeks of pilates exercises.

Effect of Eight Weeks of Zumba® Fitness Exercise on Some Physical Fitnesses Parameter of Sedenter Women

2024-05-30T21:04:06+03:00

This research was conducted to examine the effects of Zumba Fitness exercises performed regularly for eight weeks on some physical fitness levels in sedentary women. The sample group of the experimental study (n=14; age=19±0.9; height=159±6.70) consists of randomized sedentary volunteer female participants. In the experimental research, some physical fitness tests were applied to the participants. In assessing aerobic fitness; Harvard step test is used to evaluate strength; Hand grip test and leg strength test are used in the evaluation of body composition. Among anthropometric circumference measurements, shoulder, arm, chest, waist, abdomen, hip, thigh and calf circumference values were recorded with a tape measure. All data were analyzed in the IBM-SPSS 23 statistical program. Findings obtained in this study; When looking at the Pearson Correlation analysis results for the Pre-Post values of Body Mass Index, Body Fat Percentage, Shoulder, Arm, Chest, Waist, Abdomen, Hip, Thigh, Calf, a statistically significant relationship was found between the pre-post values of all variables (p < α=0.05). All relationships appear to be at a high level and in the same direction. When the Pearson Correlation analysis results for right hand grip, left hand grip, leg strength and Harvard step test Pre-Post values were examined, a statistically significant relationship was found between the pre-post values of all variables (p < α=0.05). The results were discussed and interpreted.

Prediction of Several Machine Learning Classification Models to Predict Type 2 Diabetes Diagnosis in Females

2024-05-30T21:01:21+03:00

Medical and biological sciences are among the many fields greatly impacted by machine learning (ML). Diabetes is a chronic disease characterized by unusually high blood sugar levels and insulin use. The analysis of diabetic patients and the diagnosis of the disease using various ML approaches is the main subject of this research. In this study, ML models were estimated using the PIMA dataset, which consists of female diabetic patients who were at least 21 years old. The relevant dataset was originally provided by the National Institute of Diabetes and Digestive and Kidney Diseases. Various diagnostic measurements are included in the dataset. Model performance was evaluated using an exploratory dataset and the 5-fold cross-validation method. The purpose of the dataset is to predict whether a patient has diabetes using diagnostic ML algorithms. In this study, the performance of classifiers such as Naive Bayes (NB), Stochastic Gradient Boosting (SGB), Extreme Gradient Boosting (XGBoost), and Logistic Regression (LR) algorithms were compared. According to the results of the performance measurements for diabetes prediction, the Stochastic Gradient Boosting (SGB) model outperformed the other ML algorithms in terms of Accuracy, Balanced Accuracy, F1-Score, Sensitivity, Specificity, Youden index, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).

Examining the Effects of Physical Activity Participation Levels of Individuals in Quality of Life

2024-03-30T22:35:42+03:00

The aim of the study was to determine the participation levels of individuals in physical activities and the effects of activities on quality of life, and to evaluate the role of physical activity on quality of life.The research is a quantitative study and is based on the descriptive survey model. The population of the study consisted of 562 adult volunteers, 254 males and 308 females. Personal data on gender, occupa-tional status, chronic disease, medication use, smoking, alcohol use, region of residence, and number of days of physical activity were collected from the individuals. In addition, the quality of life scale short form (WHOQOL-BREF) developed by the World Health Organization (WHO) was used in the study. While analyzing the data of the study, the t-test was used for pairwise comparisons, and the Anova Tukey test was used for multiple comparisons within groups. When the data obtained from the study were examined, it was found that men were better than women in the general health status sub-dimension, married individuals were better than singles in the psychological, social communication, and environment sub-dimensions, individuals who did not have chronic diseases, did not use medication, did not use alcohol, and did not smoke were found to have high scores in the sub-dimensions of the scale in their favor, and those with a high number of weekly physical activity days had higher quality of life scale scores than those with a low number of physical activity days. As a result, in this study, it was seen that many variables affect quality of life. In order to develop positive attitudes and behaviors according to these variables, it was concluded that it is very valuable to adopt awareness of a healthy long life in prosperity and peace and to convey the importance of maintaining a balanced quality of life in order to create a healthy society and a healthy future.

Dynamism in Sketch and Figure Drawing: Artistic and Illustrative Analysis of Volleyball Movements

2024-05-30T21:00:07+03:00

Works of art and scientific research are central to human behaviour. Leonardo Da Vinci's work on human anatomy explores in depth the interaction between art and science. Movement and dynamism in drawing and figure drawings make a significant contribution to the understanding of the human body and the development of sports movements in illustration. This paper explores the intersection of art, anatomy, and movement dynamics, focusing on the theoretical and practical investigations of figure and pattern drawings. The research employed descriptive analysis, creating detailed charcoal drawings and digital illustrations to capture the essence of movement. This work begins with the creation of pencil sketches of volleyball figures, and then using Adobe Illustrator CS6 program, these pencil drawings are arranged using a single diagonal line to organize the tonal values of movement with light and dark tones. Then, with a single light-coloured line on a monochromatic background, the movements are created in detail with the Adobe Photoshop program. Analyses the dynamism of movement in figure drawings with the elements of perspective, proportion and proportion, linear tonal values and contours, pose and movement and presents visual analysis. Findings demonstrate the effectiveness of combining traditional and digital techniques to represent the dynamism of sports movements, contributing to a deeper understanding of the relationship between art, anatomy, and movement. This interdisciplinary approach underscores the complexity and visual impact of pattern drawing, offering new insights into the study of movement and anatomy in art.

Examination of Optimal Nutrition Habits in Adults

2024-03-28T11:54:44+03:00

The aim of the study was to conduct a survey on optimal eating habits in adults. The study is a quantitative study and is based on the descriptive survey model. The population of the study consisted of a total of 624 adults. Of these, 314 were male and 310 were female. Demographic da-ta were collected from the participants to understand the effects of variables such as age, gender, education level, marital status, family type, employment status, BMI, and physical activity on eating habits. In addi-tion, the "Attitude Scale on Healthy Eating," (SBITQ) developed by Demir and Cicioğlu (2019), was used in the study. The scale is a 5-point Likert-type scale consisting of 4 sub-dimensions and 21 questions, in-cluding "about nutrition," " feeling towards nutrition," "positive nutrition," and "malnutrition." While analyz-ing the data of the study, the t-test was used for pairwise comparisons, and the Anova Tukey test was used for multiple comparisons within groups. When the data obtained from the study were analyzed, it was seen that there was a significant dif-ference in the sub-dimensions of optimal eating habits in adults in the variables of age, gender, educational status, marital status, family type, employment status, BMI (CDC), and educational status.In conclusion, analyses by age groups revealed significant differences in nutritional attitudes and knowledge levels among different age groups. This shows that the effect of age on nutritional habits is important and that nutritional preferences may change depending on age. It is also seen that gender plays a determining role in nutritional habits. Women were generally found to have higher nutrition scores. Similar-ly, educational level also has an effect on dietary attitudes. Individuals with higher levels of education were generally found to have more conscious eating habits. Improving dietary habits is a critical step to improving the overall health and quality of life of individuals and communities. Therefore, it is important to use the findings in the development of health policies and nutrition education programs.

Development of a Bagged CART Model for Subclassification of Diabetic Retinopathy Using Metabolomics Data

2024-06-09T17:15:41+03:00

In this study, we present the development and evaluation of a predictive model for classifying the subclasses of diabetic retinopathy—No Diabetic Retinopathy (NDR), Non-Proliferative Diabetic Retinopathy (NPDR), and Proliferative Diabetic Retinopathy (PDR)—using metabolomics data. The metabolomics dataset underwent rigorous preprocessing to address missing values, employing the Random Forest algorithm, and was subsequently normalized to ensure comparability across all samples. A bagged Classification and Regression Trees (CART) algorithm was utilized to construct the prediction model, leveraging its robustness and accuracy for classification tasks. Our model demonstrated significant potential in accurately classifying diabetic retinopathy subclasses, suggesting that metabolomics data, when combined with advanced machine learning techniques, can provide valuable insights into the progression and management of diabetic retinopathy. This study underscores the importance of integrating metabolomics biomarkers and machine learning for the advancement of personalized medicine in diabetic care.

Predicting mental impairment in sarcopenic elderly women using machine learning and association rules

2024-06-30T19:08:47+03:00

Sarcopenia, a prevalent condition in the elderly characterized by muscle mass and function deterioration, is associated with increased risks of falls, functional decline, frailty, and mortality. This study investigates the link between sarcopenia and cognitive impairment in elderly women and develops a machine-learning prediction model based on association rules to forecast mental status. A total of 67 community-dwelling women aged 60 and above participated in this cross-sectional study. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and physical activity levels were measured through self-reported activity logs and the six-minute walk test (6MWT). Regularized Class Association Rules (RCAR) were employed to create a prediction model. Results indicated that weekly walking, increased moderate physical activity, and reduced sitting time were significantly associated with lower severity of mental impairment. Specifically, women with a higher Skeletal Muscle Index (SMI) and consistent moderate physical activity demonstrated better cognitive performance. The RCAR model achieved high accuracy (94%), balanced accuracy (93.9%), sensitivity (92.9%), and specificity (94.9%) in predicting cognitive impairment. These findings emphasize the importance of physical activity in mitigating cognitive decline in sarcopenic elderly women and highlight the potential of machine-learning approaches in developing predictive models for clinical applications. Future research targeting sarcopenia could play a crucial role in improving both physical and mental health in the aging population.

Predictive model for glycemic control in patients with diabetes mellitus

2024-06-30T19:08:09+03:00

Diabetes mellitus, a chronic disease characterized by high blood sugar levels, necessitates effective glycemic control to prevent severe complications such as damage to the heart, blood vessels, eyes, kidneys, and nerves. This study aims to utilize machine learning techniques to predict glycemic control among a open Access dataset of 77,723 newly diagnosed diabetic patients in Istanbul. By employing a logistic regression model, the study identifies key features influencing glycemic control, enhancing model interpretability for clinicians. The model demonstrates robust performance with an accuracy of 0.825, precision scores of 0.86 (positive class) and 0.76 (negative class), recall values of 0.86 (positive class) and 0.77 (negative class), and corresponding F1 scores. Feature importance analysis reveals HbA1c as the dominant predictor, significantly surpassing other variables. These findings provide critical insights into the application of machine learning in diabetes management, highlighting the pivotal role of HbA1c in glycemic control prediction.

Prediction of myalgic chronic fatigue syndrome disorder with machine learning approach

2024-06-30T19:07:26+03:00

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disorder characterized by unexplained fatigue, post-exertional malaise, unrefreshing sleep, and cognitive impairment or orthostatic intolerance. Due to the absence of a recognized laboratory diagnostic test, diagnosis relies on patient history and physical examination. This study aimed to identify significant metabolomic markers and employ machine learning techniques for the classification of ME/CFS. Utilizing open-access metabolomics data from 26 ME/CFS patients and 26 controls, we implemented a comprehensive data preprocessing and modeling framework. Feature selection was performed using Random Forest, and data normalization was achieved through standardization. A Gaussian Naive Bayes model was trained and validated using 5-fold cross-validation. The model exhibited an accuracy of 0.786, sensitivity of 0.952, specificity of 0.619, and an F1 score of 0.816. These results indicate a high efficacy in identifying positive cases of ME/CFS.

Use of Logistic Regression Method in Predicting Obesity Levels with Machine Learning Method

2024-06-30T19:06:15+03:00

Obesity is a worldwide health issue due to excessive fat accumulation, especially prevalent in developing countries. It increases risks for diabetes, heart disease, and cancer, affecting multiple body systems. In 2016, 1.9 billion people were overweight, with 650 million classified as obese, emphasizing its global impact on public health. Both rich and developing nations are seeing sharp increases in their obesity rates, while low- and middle-income nations are seeing the biggest increases. This emphasizes how critical it is to create international plans for the administration and avoidance of obesity. This thorough analysis demonstrates the substantial effects of obesity on public health, health systems, and individual health. Public health policy are thus greatly influenced by studies on the causes, effects, and practical management techniques of obesity. The aim of this study is to derive classification metrics for a machine learning(ML) model suitable for classifying obesity levels of individuals and to present the corresponding accurate classification performance metric. Using the logistic regression model, the following classification performance metrics for predicting obesity levels were calculated: Area under ROC curve (AUC) is 0.980, Classification accuracy (CA) is 0.909, F1-Score is 0.911, Precision (Prec) is 0.909, Recall is 0.860, Matthews correlation coefficient (MCC) is 0.992, and Specificity (Spec) is 0.992. Notably, the classification accuracy (CA) of 90.9% indicates a significant achievement in correctly classifying the levels of obesity.This evaluation demonstrates the efficacy of the logistic regression model in distinguishing between different obesity levels, with high values in various performance metrics such as AUC and MCC underscoring the model's robustness and reliability in medical.