Search Results (20,886)

Introduction: T2DM mellitus (T2DM) is a major global health issue associated with significant morbidity, mortality, and economic burden. While the role of lifestyle factors in glycemic control is well-established, the influence of oral health behaviors remains underexplored. Objective: This study aimed to investigate the interplay between lifestyle habits, oral health behaviors, and glycemic control in patients with T2DM. Methodology: A cross-sectional study was conducted on 132 patients (66 men and 66 women) with T2DM at the Pius Brînzeu Emergency Hospital in Timișoara, Romania. Data on smoking, physical activity, alcohol consumption, tooth brushing frequency, and dental visits were collected using structured questionnaires, and glycemic control was assessed through HbA1c measurements. Statistical analyses, including Pearson correlations and linear regression, were performed. Results: Among men, HbA1c levels were negatively associated with exercise frequency (ß = −0.26, p < 0.05) and education level (correlation coefficient −0.27, p < 0.05), and positively associated with dental visits and tooth brushing frequency (correlation coefficient 0.26, p < 0.05). In the combined analysis, education level positively correlated with both dental visits (correlation coefficient 0.24, p < 0.01) and alcohol consumption (correlation coefficient 0.22, p < 0.05). Conclusions: These findings underscore the importance of integrating oral health and lifestyle interventions into diabetes management to optimize patient outcomes. Full article

Introduction: Osteoarthritis (OA) is the most prevalent form of arthritis and affects over 528 million people worldwide. Degenerative joint disease involves cartilage degradation, subchondral bone remodeling, and synovial inflammation, leading to chronic pain, stiffness, and impaired joint function. Initially regarded as a “wear and tear” condition associated with aging and mechanical stress, OA is now recognized as a multifaceted disease influenced by systemic factors such as metabolic syndrome, obesity, and chronic low-grade inflammation. Recent studies have focused on the gut-joint axis to investigate how the gut microbiome modulates inflammation and pain in OA. Materials and Methods: A systematic review was conducted following the PRISMA guidelines and was registered with PROSPERO (CRD42024556265). This review included studies involving adults with symptomatic OA and analyzed the relationship between the gut microbiome and OA-related pain. Randomized and non-randomized clinical trials, case reports, editorials, and pilot studies were excluded. Searches were performed in PubMed, Cochrane Library, and Web of Science without publication date restrictions, and filtered for “observational studies”. The study selection and data extraction were performed by two independent researchers, and the risk of bias was assessed using appropriate tools. Results: Five observational studies were included in the systematic review, and three were included in the meta-analysis. Two studies reported an association between different tryptophan metabolites and pain levels in patients with OA. Two other studies demonstrated a correlation between lipopolysaccharide levels and pain in OA. A fifth study confirmed the relationship between Streptococcus relative abundance of Streptococcus spp. and knee pain. These results were not supported by a meta-analysis, which found no significant association between the presence of pain in OA and the presence of bacilli of the genus Streptococcus or plasma markers of the tryptophan pathway. Conclusions: Current evidence indicates a potential link between gut microbiome dysbiosis and OA-related pain. However, methodological limitations preclude definitive conclusions. Further research using advanced techniques and larger cohorts is needed to validate and extend these findings and elucidate the underlying mechanisms. Targeted manipulation of the gut microbiome may be a valuable strategy for pain management in OA patients. Full article

Background/Objectives: Metabolic syndrome (MetS) is a cluster of risk factors that significantly increase the risk of cardiovascular disease, including type 2 diabetes, etc. Assessing the predictive diagnostic power of anthropometric indicators for MetS is crucial for the early identification and prevention of related health issues. This study focuses on the Korean adult population while providing insights that may be applicable to broader global contexts. Therefore, this study aimed to compare the predictive diagnostic capabilities of various anthropometric indicators, including body mass index (BMI), waist-to-height ratio (WHtR), muscle mass-to-fat mass ratio (MFR), muscle mass-to-waist ratio (MWR), and body shape index (ABSI), in relation to MetS in Korean adults. Methods: Data from 13,725 participants of the Korea National Health and Nutrition Examination Survey (2008–2011) were analyzed. The diagnostic power of each indicator was assessed using the receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) values were compared. Participants were classified into normal (NG) and abnormal (AG) groups based on established cutoff values, and logistic regression analysis was performed to evaluate the odds of MetS in each group. Results: WHtR showed the highest AUC values (0.792 for men and 0.768 for women), indicating superior diagnostic accuracy compared to the other indicators (p < 0.001). Logistic regression analysis indicated that both the unadjusted and adjusted odds ratios (OR) for MetS were significantly higher in the AG than in the NG across all indicators (p < 0.001). Specifically, the adjusted OR (95% confidence interval) for WHtR in the AG was 6.793 (5.929–7.784) for men and 4.665 (4.151–5.423) for women, representing the highest values among all indicators (p < 0.001). Conclusion: Among the various anthropometric indicators, WHtR is the most reliable and practical for predicting MetS in Korean adults. It is useful for early intervention and prevention in both clinical and public health settings. Full article

Excessive exercise is a primary cause of sports injuries and sudden death. Therefore, it is vital to develop an effective monitoring technology for exercise intensity. Based on the noninvasiveness and real-time nature of an electrocardiogram (ECG), exercise ECG classification based on ECG features could be used for detecting exercise intensity. However, current R-peak detection algorithms still have limitations, especially in high-intensity exercise scenarios and in the presence of noise interference. Additionally, the features utilized for exercise ECG classification are not comprehensive. To address these issues, the following tasks have been accomplished: (1) a hybrid time–frequency-domain model, BILSTM-CNN, is proposed for R-peak detection by utilizing BILSTM, multi-scale convolution, and an attention mechanism; (2) to enhance the robustness of the detector, a preprocessing data generator and a post-processing adaptive filter technique are proposed; (3) to improve the reliability of exercise intensity detection, the accurate heart rate variability (HRV) features derived from the proposed BILSTM-CNN and comprehensive features are constructed, which include various descriptive features (wavelets, local binary patterns (LBP), and higher-order statistics (HOS)) tested by the feasibility experiments and optimized deep learning features extracted from the continuous wavelet transform (CWT) of exercise ECG signals. The proposed system is evaluated by real ECG datasets, and it shows remarkable effectiveness in classifying five types of motion states, with an accuracy of 99.1%, a recall of 99.1%, and an F1 score of 99.1%. Full article

Breast cancer (BC) is the most common cancer among women, with an incidence of 85–94 per 100,000 people annually in Europe. Despite the increasing incidence of BC, advancements in early detection and novel therapeutic approaches have improved survival rates. However, adjuvant treatments are associated with side effects, including a reduction in the left ventricular ejection fraction (LVEF), which can result in severe cardiac damage and progress to heart failure. Methods: Thirty-eight women undergoing BC treatment were included in the study. Eighteen women (47.2 ± 5.4 years old) participated in a supervised physical exercise (PE) program for 60 min/day, twice weekly, at moderate to high intensity (5 min joint mobilization, 20 min predominantly aerobic training, 30 min of strength training, and 5 min cooldown). The remaining 20 women (51.5 ± 9.5 years) were advised to remain active during treatments, but without specific supervision. In the PE group, a slight reduction in the LVEF was observed after chemotherapy (63.73 ± 3.34% vs. 61.00 ± 6.54%, p = 0.131). In contrast, the control group showed a statistically significant reduction in the LVEF (64.93 ± 4.00% vs. 60.57 ± 4.86%, p = 0.008). Although the results suggest a potential protective effect of regular physical exercise during BC treatment, the study was inconclusive regarding its role in preventing cardiac dysfunction. Further research with a larger sample size and longer follow-up is warranted. Full article

This study aimed to determine the acute effects of the French contrast method (FCM) and post-activation potentiation (PAP) protocols on 3 × 3 basketball game demands and thermal asymmetry in male basketball players and to compare these effects between protocols. Eighteen male basketball players (mean ± SD; age: 21.7 ± 1.5 years, 10.6 ± 1.9 years of experience) visited the laboratory four times, 72 h apart. The players participated in three different protocols (baseline: 3 × 3 game; FCM + 3 × 3 game; PAP + 3 × 3 game; respectively). The players’ internal and external loads were monitored, game profiles were analyzed, and thermography was applied during the protocols. The results revealed that FCM and PAP did not significantly differ in internal load; however, the significant highest total distance and distance in band 2 during the 3 × 3 basketball game was after the FCM. The 1-point attempt was significantly higher after the FCM, and turnover was significantly higher after PAP. Significant thermal asymmetry was observed in the abdominals and lower back after the FCM and PAP. The results of this study provide coaches and practitioners with detailed information regarding the game demands that can be used to improve the playing profile of 3 × 3 basketball players. Full article

BACKGROUND: Sport practice may elevate the risk of cardiovascular events, including sudden cardiac death, in athletes with undiagnosed heart conditions. In Italy, pre-participation screening includes a resting ECG and either the Harvard Step Test (HST) or maximal exercise testing (MET), but the relative efficacy of the latter two tests for detecting arrhythmias and heart conditions remains unclear. METHODS: This study examined 511 paediatric athletes (8–18 years, 76.3% male) without known cardiovascular, renal, or endocrine diseases. All athletes underwent both HST and MET within 30 days. Absolute data and data relative to theoretical peak heart rates, arrhythmias (supraventricular and ventricular) and cardiovascular diagnoses were collected. RESULTS: HST resulted in a lower peak heart rate than MET (181.1 ± 9.8 vs. 187.5 ± 8.1 bpm, p < 0.001), but led to the detection of more supraventricular (18.6% vs. 13.1%, p < 0.001) and ventricular (30.5% vs. 22.7%, p < 0.001) arrhythmias, clustering during recovery (p = 0.014). This pattern was significant in males but not females. Among athletes diagnosed with cardiovascular diseases (22.3%), HST identified more ventricular arrhythmias (26.3% vs. 18.4%, p = 0.05), recovery-phase arrhythmias (20.2% vs. 14.0%, p = 0.035), and polymorphic arrhythmias (6.1% vs. 1.8%, p = 0.025). CONCLUSIONS: HST detects arrhythmias more effectively than MET in young male athletes, especially during recovery. More ventricular arrhythmias were highlighted even in athletes with cardiovascular conditions. Full article

This article shows the results of research conducted on the corrosion resistance of the FeAl (Fe40Al5Cr0.2TiB) alloy in two variants: the alloy after casting and after homogenization annealing (1000 °C, 93 h). Analysis of the microstructure of these alloys was conducted on the light microscope, and the phase composition was determined by X-ray diffraction. Resistance to electrochemical corrosion was tested in a 5% NaCl solution using the potentiodynamic polarization technique and electrochemical impedance spectroscopy. The surface of alloys after corrosion tests was examined by scanning electron microscopy. Chemical composition tests were conducted using an energy-dispersive X-ray spectrometer. The structure analysis was made with an electron backscatter diffraction detector. Based on the studies, it was found that the corrosion resistance of the FeAl alloy after homogenization annealing was higher than that of the FeAl alloy after casting. This alloy showed a more non-homogeneous and coarse-grained microstructure compared to the alloy after homogenization annealing. The investigation of the surface condition of FeAl alloys after corrosion tests showed the presence of pits, particularly in the case of the alloy after casting. Full article

Background/Objectives: CrossFit^® is one of the most popular yet controversial training regimens. Some groups extol the positive effects of its practice, while others argue that it is unsafe and that there is limited information. The aim of this study was to investigate, through a self-reported questionnaire, the epidemiology of Portuguese CrossFit^® training practitioners. Methods: Statistical analyses, including Mann–Whitney U, chi-square, Spearman’s rho correlations, and logistic regressions, were conducted. Results: A total of 288 practitioners completed the questionnaire, with 39.9% reporting injuries. These injuries occurred mainly during training, particularly when performing gymnastics exercises. Tendon (40.9%) and shoulder (46.1%) were the most common injuries. Key risk factors for injury included male gender (OR = 2.7), years of practice (4–6 years: OR = 7.22), heavier body weight (OR = 4.76), and higher weekly training volume (4–6 times per week). Conclusions: Approximately 40% of CrossFit^® practitioners are expected to experience injuries, particularly in the shoulder and tendons. Injury risk is influenced by factors such as practice years, weekly training, sex, and body weight. These findings may help guide practitioners, coaches, and health professionals in better understanding the risk factors, mitigating injuries, and developing effective injury prevention strategies. Full article

To enhance athletic performance and reduce the risk of injury, load quantification has allowed for a better understanding of the individual characteristics of the physical demands on soccer players during training or competition. In this regard, it appears crucial to summarize scientific evidence to provide useful information and future directions related to the speed and acceleration profiles of male soccer players. This review aims to evaluate the findings reflected in the available literature on both profiles in football, synthesizing and discussing data from scientific articles, while providing insights into quantification methods, employed thresholds, tracking systems, terminology, playing position, and microcycle day. Therefore, it is hoped that this narrative review can support objective decision-making in practice for coaches, sports scientists, and medical teams regarding individualized load management and the appropriate selection of metrics, to explore current trends in soccer player profiles. Full article

Background/Objectives: The COVID-19 pandemic has led to detrimental effects on diverse aspects of the mental and physical health of the general population worldwide. The elderly are more susceptible to COVID-19 infection compared to younger age groups. In this aspect, the purpose of the current survey is to evaluate the effect of the COVID-19 pandemic on the interrelationships among the sociodemographic and anthropometric characteristics, depressive behavior, quality of life, cognition status, physical activity and nutritional status of older adults. Methods: The present study constitutes a comparative, cross-sectional study of 4162 older adults (mean age ± standard deviation: 72.13 ± 8.1 years and 75.22 ± 8.2 years in the pre- and post-COVID-19 periods, respectively, and a male/female ratio of almost 1:1). We used validated questionnaires to assess depression, cognition function, quality of life, physical activity and nutritional status of the elderly in the pre-Covid and post-Covid periods. Relevant questionnaires were also used for collecting sociodemographic data, while anthropometric data were measured using standard protocols. Results: The present study finds that the COVID-19 pandemic influenced, in an independent manner, residence location, smoking status, overweight/obesity and abdominal obesity, depressive behavior, quality of life, cognition behavior, physical activity levels and nutritional status of the elderly. The COVID-19 pandemic was also related to employment and living status as well as family economic status. Nevertheless, the above three relationships were insignificant in the multivariate analysis. Conclusions: The COVID-19 pandemic exerted deleterious effects on several aspects of the mental and physical health of the elderly, which appeared to strongly persist in the post-Covid period. Future prospective population-based and well-organized surveys should be conducted to establish whether there is a causality long-term effect of the COVID-19 pandemic on diverse aspects of the mental and physical health of the elderly. Full article

Given that performance in football depends on tactical, technical, physical, and mental skills, the purpose of this study was to investigate whether there are differences in running performance between winning and non-winning teams in the Turkish League, taking into account the influence of game location and the comparative quality of the team and its opponents. Utilizing a dataset from the 2021–2022 season provided by InStat Fitness, an optical tracking technology platform certified by FIFA, the analysis included 185 matches after adjusting for matches with red card incidents. The research employed both two-way ANCOVA and binary logistic regression analyses to explore the relationships between running performance (categorized into four intensity zones) and match results, considering factors such as match location and teams’ strength. The results of the two-way ANCOVAs indicate that running performance metrics, specifically the distances covered at different intensities, even in cases where statistically significant differences are observed, have small practical significance (partial eta squared ≤ 0.03 in all cases). Conversely, as shown by the binary logistic regression, home advantage triples the probability of winning (p < 0.001, Exp(B) = 3.119), while the increase in probability caused by team quality (p < 0.001, Exp(B) = 1.085) and the decrease caused by opponent quality (p < 0.001, Exp(B) = 0.911) are also significant. The conclusions highlight that running performance metrics are not decisive predictors of match outcomes in professional football. This suggests the importance of integrating tactical, technical, and psychological factors into team preparation and performance analysis. This study underscores the need for future research to adopt dynamic methods that reflect the game’s fluid nature and to explore these relationships across various leagues and seasons to enhance the generalizability of the findings. Full article

With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive fatigue and resultant injury, and increase efficiency and safety. However, current wearable sensing devices are often uncomfortable and imprecise. Furthermore, stable methods for fatigue detection are not yet established. To address these challenges, this paper introduces 3D printing and deep learning to design a smart wearable sensing device to detect different states of sports fatigue. First, to meet the need for comfort and improved accuracy in data collection, we utilized reverse engineering and additive manufacturing technologies. Second, we designed a prototype based on the long short-term memory (LSTM) neural network to analyze the collected bioelectrical signals for the identification of sports fatigue states and the extraction of related indicators. Finally, we conducted a large number of numerical experiments. The results demonstrated that our prototype and related equipment could collect signals and mine information as well as identify indicators associated with sports fatigue in the signals, thereby improving accuracy in the classification of fatigue states. Full article