Background Physical activity is important in both prevention and treatment of many common diseases, but sports injuries can pose serious problems. Objective To determine whether physical activity exercises can reduce sports injuries and perform stratified analyses of strength training, stretching, proprioception and combinations of these, and provide separate acute and overuse injury estimates. Material and methods PubMed, EMBASE, Web of Science and SPORTDiscus were searched and yielded 3462 results. Two independent authors selected relevant randomised, controlled trials and quality assessments were conducted by all authors of this paper using the Cochrane collaboration domain-based quality assessment tool. Twelve studies that neglected to account for clustering effects were adjusted. Quantitative analyses were performed in STATA V.12 and sensitivity analysed by intention-to-treat. Heterogeneity (I2) and publication bias (Harbord's small-study effects) were formally tested. Results 25 trials, including 26 610 participants with 3464 injuries, were analysed. The overall effect estimate on injury prevention was heterogeneous. Stratified exposure analyses proved no beneficial effect for stretching (RR 0.963 (0.846–1.095)), whereas studies with multiple exposures (RR 0.655 (0.520–0.826)), proprioception training (RR 0.550 (0.347–0.869)), and strength training (RR 0.315 (0.207–0.480)) showed a tendency towards increasing effect. Both acute injuries (RR 0.647 (0.502–0.836)) and overuse injuries (RR 0.527 (0.373–0.746)) could be reduced by physical activity programmes. Intention-to-treat sensitivity analyses consistently revealed even more robust effect estimates. Conclusions Despite a few outlying studies, consistently favourable estimates were obtained for all injury prevention measures except for stretching. Strength training reduced sports injuries to less than 1/3 and overuse injuries could be almost halved.
<h4>Background</h4>This study aims to assess the intrinsic links between physical exercise and multidimensional poverty among rural older people, focusing on the mediating mechanisms of social and cultural capital, to inform responses to active ageing.<h4>Methods</h4>This study utilized the adult and household databases of the China Household Tracking Survey (CFPS) in 2018 and 2022. Stata17.0 was used to screen and process the data, and the A-F double critical value method was adopted to measure and structurally decompose the multi-dimensional poverty among the elderly of Chinese household farmers. The association between physical exercise participation and multi-dimensional poverty among rural elderly people was estimated using the ordinary Least squares (OLS) method. The mediating effects of social capital and cultural capital were explored by using the stepwise regression method. Finally, the heterogeneous manifestations of the impact of physical exercise participation on multi-dimensional poverty among rural elderly people were investigated by using the grouped regression method.<h4>Results</h4>This study found that physical exercise participation has a significant role in reducing multidimensional poverty in old age among family farmers (β = -0.0149, p < 0.001), and this finding remains valid after a series of robustness tests such as full model estimation (β = -0.009, p < 0.001), propensity score matching test (ATT value of -0.017, -0.015, and -0.017, respectively) and other series of robustness tests, the conclusion still holds. The results of the mediating effect showed that social capital and cultural capital mediated the effect of physical exercise on multidimensional poverty. Heterogeneity analyses showed that the effects of physical exercise participation on multidimensional poverty were more pronounced among females, unmarried, in the eastern region, and among the elderly with a high life expectancy.<h4>Conclusion</h4>Participation in physical exercise can effectively alleviate multidimensional poverty, which is reflected in the enhancement of health level, economic status, living standard, and subjective perception sub-dimensions. Engaging in physical exercise can alleviate poverty through two channels: accumulating social capital and enhancing cultural capital. The construction and maintenance of rural sports facilities should be strengthened, a 'healthy labour model' combining physical exercise and agricultural production should be constructed, special sports poverty-alleviation initiatives should be implemented for "vulnerable" elderly people, and a 'Healthy Villages' project should be launched to promote the participation of all people in poverty alleviation. The 'Healthy Villages' programme has been launched to promote the participation of the entire population in physical exercise.
Kornelia Kaźmierkiewicz, Martyna Chojnacka, Marta Ewelina Lis
et al.
Introduction and aim of the study: Neurodegenerative diseases, including Alzheimer's disease, represent a significant health challenge. Therapies are being sought that could delay the development of these diseases and also mitigate their course. Fulvic acid, which is an organic humic compound with antioxidant and anti-inflammatory properties, has attracted increasing interest in the context of treating neurodegenerative diseases. Our study aims to evaluate the effects of fulvic acid on neurodegenerative diseases, mainly Alzheimer's disease, and to determine its therapeutic potential.
Materials and methods: The paper is based on an analysis of studies available in databases such as PubMed, Google Scholar, ResearchGate, and other scientific databases. Clinical trials, preclinical studies, and review papers on the use of fulvic acid in the context of Alzheimer's disease were searched.
Conclusions: Fulvic acid, due to its anti-inflammatory and neuroprotective abilities, shows promising potential in the treatment of Alzheimer's disease, especially in terms of slowing down the loss of cognitive function and protecting against neurodegeneration. It is advisable to conduct further studies aimed at a more thorough evaluation of the efficacy and safety of fulvic acid in the context of the treatment of Alzheimer's disease and other neurodegenerative diseases.
Multi-object tracking (MOT) is essential for sports analytics, enabling performance evaluation and tactical insights. However, tracking in sports is challenging due to fast movements, occlusions, and camera shifts. Traditional tracking-by-detection methods require extensive tuning, while segmentation-based approaches struggle with track processing. We propose McByte, a tracking-by-detection framework that integrates temporally propagated segmentation mask as an association cue to improve robustness without per-video tuning. Unlike many existing methods, McByte does not require training, relying solely on pre-trained models and object detectors commonly used in the community. Evaluated on SportsMOT, DanceTrack, SoccerNet-tracking 2022 and MOT17, McByte demonstrates strong performance across sports and general pedestrian tracking. Our results highlight the benefits of mask propagation for a more adaptable and generalizable MOT approach. Code will be made available at https://github.com/tstanczyk95/McByte.
Robust ball tracking under occlusion remains a key challenge in sports video analysis, affecting tasks like event detection and officiating. We present TOTNet, a Temporal Occlusion Tracking Network that leverages 3D convolutions, visibility-weighted loss, and occlusion augmentation to improve performance under partial and full occlusions. Developed in collaboration with Paralympics Australia, TOTNet is designed for real-world sports analytics. We introduce TTA, a new occlusion-rich table tennis dataset collected from professional-level Paralympic matches, comprising 9,159 samples with 1,996 occlusion cases. Evaluated on four datasets across tennis, badminton, and table tennis, TOTNet significantly outperforms prior state-of-the-art methods, reducing RMSE from 37.30 to 7.19 and improving accuracy on fully occluded frames from 0.63 to 0.80. These results demonstrate TOTNets effectiveness for offline sports analytics in fast-paced scenarios. Code and data access:\href{https://github.com/AugustRushG/TOTNet}{AugustRushG/TOTNet}.
Sports video understanding requires perceiving high-speed dynamics, complex rules, and long temporal contexts. Yet, current Multimodal Large Language Models (MLLMs) remain narrowly focused on single sports, specific tasks, or training-free paradigms. We introduce DeepSport, the first end-to-end trained MLLM for multi-task, multi-sport video understanding. DeepSport shifts from passive frame processing to active, iterative reasoning, dynamically extracting frames to "think with videos." To train our model, we curate a unified 78k-sample dataset via a rigorous three-step text-and-vision distillation pipeline. We then employ a progressive two-stage training strategy: a Sports Curriculum Supervised Fine-Tuning phase to build foundational perception, followed by Agentic Reinforcement Learning with a novel tool-use reward. Extensive experiments on a comprehensive 6.7k benchmark demonstrate that DeepSport achieves state-of-the-art performance, outperforming powerful proprietary and open-source models, while utilizing significantly fewer frames. Furthermore, it exhibits strong zero-shot transferability to unseen sports and broad motion recognition tasks, establishing a highly efficient and generalized foundation for complex video reasoning.
Video event detection has become a cornerstone of modern sports analytics, powering automated performance evaluation, content generation, and tactical decision-making. Recent advances in deep learning have driven progress in related tasks such as Temporal Action Localization (TAL), which detects extended action segments; Action Spotting (AS), which identifies a representative timestamp; and Precise Event Spotting (PES), which pinpoints the exact frame of an event. Although closely connected, their subtle differences often blur the boundaries between them, leading to confusion in both research and practical applications. Furthermore, prior surveys either address generic video event detection or broader sports video tasks, but largely overlook the unique temporal granularity and domain-specific challenges of event spotting. In addition, most existing sports video surveys focus on elite-level competitions while neglecting the wider community of everyday practitioners. This survey addresses these gaps by: (i) clearly delineating TAL, AS, and PES and their respective use cases; (ii) introducing a structured taxonomy of state of the art approaches including temporal modeling strategies, multimodal frameworks, and data-efficient pipelines tailored for AS and PES; and (iii) critically assessing benchmark datasets and evaluation protocols, highlighting limitations such as reliance on broadcast quality footage and metrics that over reward permissive multilabel predictions. By synthesizing current research and exposing open challenges, this work provides a comprehensive foundation for developing temporally precise, generalizable, and practically deployable sports event detection systems for both the research and industry communities.
AimThis research sought to identify the association between sports participation and resilience in children and adolescents as a means to enhance mental health.MethodsA comprehensive survey was carried out, encompassing primary, middle, and high school students from chosen educational institutions. The analytical sample comprised 67,281 students of school age. Sports participation and resilience were evaluated using validated assessment tools, while relevant covariates, such as sex and school grade, were assessed through self-reported questionnaires. Generalized Linear Models were applied to ascertain the association between sports participation and resilience for the entire sample, and separately for subgroups divided by gender or school grade, after controlling for covariates.ResultsAmong the 67,281 school students, males constituted 51.9% of the sample. Approximately 47.1% of the entire sample reported no sports participation, and the average resilience score was 24.7. The regression model analysis revealed that, in the entire sample, increased in sports participation was linked to higher resilience scores (odds ratio [OR] for 1–3 times per month: 1.20, 95%CI: 1.16–1.24; OR for 1–2 times per week: 1.38, 95%CI: 1.33–1.43; OR for 3 times or more per week: 1.72, 95%CI: 1.65–1.79). Analyses stratified by gender and school grade indicated that sports participation was consistently associated with greater resilience.ConclusionThis study provides cross-sectional evidence supporting the positive association between sports participation and the resilience of children and adolescents, underscoring the potential of encouraging sports participation as a strategy for promoting mental health resilience. The findings presented herein should be subject to further confirmation or refutation in future research endeavors.
Out-of-hospital cardiac arrest (OHCA) mostly occurs in crowded public places outside hospitals, such as public sports facilities, airports, railway stations, subway stations, and shopping malls. The emergency department of Liaocheng People's Hospital in Shandong Province admitted one patient with OHCA in August 2021, who suddenly suffered a loss of consciousness and cardiac arrest during exercise after dinner. Witnesses immediately gave continuous chest compressions and artificial respiration and called our hospital's emergency department (at 120). Arriving at the emergency department, we continued to provide chest compressions and ventilator-assisted ventilation after performing endotracheal intubation. We administered adrenaline for cardiac excitation, dopamine for maintained blood pressure, sodium bicarbonate to correct the acidosis, and multiple electric defibrillations. However, the patient's cardiac Doppler ultrasound indicated poor cardiac contractions, and extracorporeal membrane oxygenation (ECMO) was started immediately. We performed coronary angiography for the patient with ECMO support, indicating that the patient had an 80% critical stenosis of the left main coronary artery and an 80%–90% stenosis in the middle section of the left anterior descending artery with an aneurysm. Fortunately, there was no obvious stenosis in the right coronary artery. The patient was transferred to the intensive care unit and received comprehensive treatment, including anticoagulation, myocardial nutritional support, improvement of cardiac function, continuous renal replacement therapy, organ function protection, anti-inflammatory treatment, and rehabilitation. Coronary artery bypass grafting was performed after the patient's condition stabilized, and he was finally discharged. ECMO support therapy for patients with cardiac arrest can be considered when economic conditions permit. It is very important to conduct the necessary examinations in the early stage of resuscitation with ECMO support to clarify the cause of the cardiac arrest and to treat it accordingly.
Diseases of the circulatory (Cardiovascular) system
Umile Giuseppe Longo, Alessandro Mazzola, Stefano Campi
et al.
<i>Background and Objectives:</i> Knee osteoarthritis is a serious burden for modern countries. Timing of surgery and treatment choice are still a matter of controversy in the orthopedic literature. The purpose of this study was to ascertain the incidence and hospitalization trends of high tibial osteotomy in Italy from 2001 to 2016. <i>Materials and Methods:</i> Data are sourced from the National Hospital Discharge Reports (SDO) of the Italian Ministry of Health between 2001 and 2016. <i>Results:</i> A total of 34,402 high tibial osteotomies were performed over the study period in Italy. The cumulative incidence was 3.6 cases per 100,000 residents. The age classes 50–54, 55–59 showed the higher number of procedures. In pediatric patients (0–19 years), high tibial osteotomies are also largely performed. The majority of patients having surgery were men with a M/F ratio of 1.5. The mean age of patients was 44.2 ± 19.2 years. Males were significantly younger than females (43.3 ± 20.7 vs. 45.6 ± 17.7). The average length of hospitalization was 6.1 ± 7.3 days. Over the course of the analysis, a declining trend in hospital stay length was seen. The main primary diagnosis codes were “Varus knee” (736.42 ICD-9-CM code, 33.9%), “Osteoarthrosis, localized, primary, leg region” (715.16 ICD-9-CM code, 9.5%). <i>Conclusions:</i> Over the study period, high tibial osteotomies in Italy almost halved. Varus deformity and knee osteoarthritis are the leading causes requiring high tibial osteotomy. Except for the pediatric setting, results showed that from the 20–24 age class to the 50–54 age class, there was an increasing request for knee osteotomy, whereas in those aged >60 years, the incidence progressively decreased. The evident decline in HTO performed over the years in Italy seems to reflect a minor role for knee osteotomy in the management of knee OA, as it seems to be primarily reserved for younger male patients.
Multi-agent trajectory data collected from domains such as team sports often suffer from missing values due to various factors. While many imputation methods have been proposed for spatiotemporal data, they are not well-suited for multi-agent sports scenarios where player movements are highly dynamic and inter-agent interactions continuously evolve. To address these challenges, we propose MIDAS (Multi-agent Imputer with Derivative-Accumulating Self-ensemble), a framework that imputes multi-agent trajectories with high accuracy and physical plausibility. It jointly predicts positions, velocities, and accelerations through a Set Transformer-based neural network and generates alternative estimates by recursively accumulating predicted velocity and acceleration values. These predictions are then combined using a learnable weighted ensemble to produce final imputed trajectories. Experiments on three sports datasets demonstrate that MIDAS significantly outperforms existing baselines in both positional accuracy and physical plausibility. Lastly, we showcase use cases of MIDAS, such as approximating total distance and pass success probability, to highlight its applicability to practical downstream tasks that require complete tracking data.