Hasil untuk "Sports medicine"

P. Brukner, K. Khan

C. L. Otis, B. Drinkwater, M. Johnson et al.

Xiong Wang, Xiong Wang, Zhi Zhang et al.

ObjectiveThe Meyers and McKeever (MM) classification is widely used for posterior cruciate ligament (PCL) tibial avulsion fractures; however, it fails to comprehensively reflect fracture characteristics and morphology due to its exclusive reliance on plain radiographs, which may result in suboptimal treatment decisions. Computed tomography (CT) scanning and three-dimensional computed tomography (3DCT) reconstruction can provide a more detailed visualization of articular fracture configurations, enabling the development of effective treatment strategies. Therefore, we developed a novel classification system for PCL tibial avulsion fractures based on fracture characteristics on 3DCT images, systematically evaluated and compared classification accuracy and reliability with the MM classification.MethodsPatients aged 18 years or older who underwent plain radiographs and CT examinations that confirmed PCL tibial avulsion fractures from June 2020 to Jan 2025 were included. A novel 3DCT-based classification system was established by considering three key fracture characteristics: fracture displacement degree, fracture line numbers, and fracture involvement regions. To verify the reliability and accuracy of the novel 3DCT-based and MM classification systems, intra- and inter-rater reliability assessments were performed. Additionally, the consistency and discrepancy in fracture patterns between the two classification systems were systematically described.ResultsUltimately, 53 patients (40 males and 13 females) with PCL tibial avulsion fractures were enrolled in the final study (mean age 42.9, range 22–65). The novel 3DCT-based classification system consisted of four principles and seven categories. The intra-rater reliability of the MM classification demonstrated substantial agreement, whereas the 3DCT-based classification exhibited perfect agreement. The inter-rater reliability of both classifications displayed substantial agreement, and the novel classification had higher reliability values. In addition, approximately 22.6% of non-displaced fracture types, along with some type II and III fractures identified through radiographs, exhibited differing fracture characteristics when evaluated using 3DCT.ConclusionThe novel 3DCT-based classification is more reliable, simplified, and intuitive than the MM classification. This novel classification system allows for a more accurate description of these fractures and reduces the risk of misdiagnosis based on radiographs. Additionally, it provides valuable guidance for preoperative planning and the selection of appropriate treatment strategies.

Patrycja, Katarzyna Fabiś, Mateusz Zbylut et al.

Chronic heart failure is a widespread clinical condition associated with reduced exercise tolerance, impaired quality of life, and increased morbidity and mortality. Exercise-based cardiac rehabilitation represents an essential component of non-pharmacological management in patients with chronic heart failure; however, optimal training strategies and delivery models remain under discussion. This narrative review summarizes current evidence regarding the safety, efficacy, and feasibility of exercise-based rehabilitation across different heart failure phenotypes. The analysis includes conventional centre-based programs, home-based rehabilitation, telerehabilitation, and novel exercise modalities. Available evidence indicates that structured exercise training improves functional capacity, exercise tolerance, and health-related quality of life, regardless of rehabilitation setting. Combined aerobic and resistance training, as well as high-intensity interval training, appear to provide greater functional benefits than moderate continuous exercise, particularly under supervised conditions. Functional assessment using cardiopulmonary exercise testing remains the reference standard, while the six-minute walk test offers a practical alternative in routine clinical practice. Individualized, patient-centred rehabilitation strategies are crucial for optimizing outcomes and supporting long-term engagement in physical activity among patients with chronic heart failure.

Arushi Rai, Adriana Kovashka

Video-LLMs often attend to irrelevant frames, which is especially detrimental for sports coaching tasks requiring precise temporal grounding. Yet obtaining frame-level supervision is challenging: expensive to collect from humans and unreliable from other models. We improve temporal grounding without additional annotations by exploiting the observation that related tasks, such as generation and verification, must attend to the same frames. We enforce this via a self-consistency objective over select visual attention maps of tightly-related tasks. Using VidDiffBench, which provides ground-truth keyframe annotations, we first validate that attention misallocation is a significant bottleneck. We then show that training with our objective yields gains of +3.0%, +14.1% accuracy and +0.9 BERTScore over supervised finetuning across three sports coaching tasks: Exact, FitnessQA, and ExpertAF, even surpassing closed-source models.

J. Jakicic, K. Clark, E. Coleman et al.

Thomas W. Fenn, M.D., Dominic M. Farronato, M.D., Douglas K. Wells, M.D. et al.

Purpose: To evaluate the accuracy of ChatGPT’s responses to frequently asked questions (FAQs) about hamstring injuries and to determine, if prompted, whether ChatGPT could appropriately tailor the reading level to that suggested. Methods: A preliminary list of 15 questions on hamstring injuries was developed from various FAQ sections on patient education websites from a variety of institutions, from which the 10 most frequently cited questions were selected. Three queries were performed, inputting the questions into ChatGPT-4.0: (1) unprompted, naïve, (2) additional prompt specifying the response being tailored to a seventh-grade reading level, and (3) additional prompt specifying the response being tailored to a college graduate reading level. The responses from the unprompted query were independently evaluated by two of the authors. To assess the quality of the answers, a grading system was applied: (A) correct and sufficient response; (B) correct but insufficient response; (C) response containing both correct and incorrect information; and (D) incorrect response. In addition, the readability of each response was measured using the Flesch-Kinkaid Reading Ease Score (FRES) and Grade Level (FKGL) scales. Results: Ten responses were evaluated. Inter-rater reliability was 0.6 regarding grading. Of the initial query, 2 of 10 responses received a grade of A, seven were graded as B, and one were graded as C. The average cumulative FRES and FKGL scores of the initial query was 61.64 and 10.28, respectively. The average cumulative FRES and FKGL scores of the secondary query were 75.2 and 6.1, respectively. Finally, the average FRES and FKGL scores of the third query were 12.08 and 17.23. Conclusions: ChatGPT showed generally satisfactory accuracy in responding to questions regarding hamstring injuries, although certain responses lacked completeness or specificity. On initial, unprompted queries, the readability of responses aligned with a tenth-grade level. However, when explicitly prompted, ChatGPT reliably adjusted the complexity of its responses to both a seventh-grade and a graduate-level reading standard. These findings suggest that although ChatGPT may not consistently deliver fully comprehensive medical information, it possesses the capacity to adapt its output to meet specific readability targets. Clinical Relevance: Artificial intelligence models like ChatGPT have the potential to serve as a supplemental educational tool for patients with orthopaedic to aid medical-decision making. It is important that we continually review the quality of they medical information generated by these artificial models as the evolve and improve.

Kinga Racisz, Joanna Duda, Jakub Kędzia et al.

Introduction According to data, 5% of people in Europe and 6% of people in Poland are vegetarians. Therefore, an increasing percentage of pregnant women will follow a plant-based diet. During pregnancy, the need for vitamins and minerals increases, and the proper balance and possible supplementation of a vegetarian diet are crucial for proper fetal development and a pregnant woman's health. Aim of study This study aims to evaluate and compare information regarding the adequacy of a vegetarian diet for pregnant women and its impact on maternal outcomes, fetal development, newborn health and lactation. Materials and methods The search methodology incorporated the terms “vegetarian diet” or “plant-based diet” or “vegan” AND “pregnancy” or “pregnant” or “health benefits”, along with variations of these terms, found in many scientific databases. Publications issued before 2019 and case reports were excluded. Conclusion There is a consensus that a plant-based diet is safe during both pregnancy and lactation. Nevertheless, it may be associated with many nutrient deficiencies. A vegetarian diet promotes a lower incidence of excessive weight gain, which results in a less frequent occurrence of EWG-related complications. There was no higher prevalence of premature births or infant mortality. The occurrence of gestational diabetes, small gestational age/low birth weight and congenital anomalies require further study. Breastfeeding during a vegetarian diet is possible, but appropriate supplementation is recommended.

Nancy Yesenia Ortiz-Garcia, Diego Eduardo Rueda-Capristran, Ajay Kumar et al.

Abstract Background Neuromuscular diseases (NMDs) can impair respiratory muscle function, leading to increased morbidity and mortality. Respiratory muscle training (RMT) is widely used to manage these respiratory complications, but its efficacy across different NMDs remains unclear. This systematic review and meta-analysis evaluated the impact of physiotherapy interventions, specifically RMT, on respiratory muscle function in NMD patients. Methods A systematic search of multiple databases, including MEDLINE, EMBASE, Web of Science, Cochrane, CRS-Web, PEDro, LILACS, ICTPR, the China National Knowledge Infrastructure database, and ClinicalTrials.gov, was conducted up to February 2025. Randomized controlled trials (RCTs) and cohort studies evaluating RMT’s effect on lung volumes and respiratory muscle strength in NMD patients were included. Risk of bias assessment was performed using Cochrane Risk of bias tool for RCTs and Newcastle-Ottawa Scale for cohorts. Meta-analyses were performed using a random-effects model, and heterogeneity was assessed with I² statistics. Results Sixteen studies were analyzed from 9,626 screened articles. The meta-analysis demonstrated significant improvements in respiratory muscle strength, particularly in maximal inspiratory pressure (MD: 6.83 cmH₂O, 95% CI: 2.08 to 11.58, p < 0.01, I² = 3.8%) and maximal expiratory pressure (MD: 13.05 cmH₂O, 95% CI: 3.65to 22.42, p < 0.01, I² = 43%). No significant improvements were observed in forced vital capacity (MD: 3.13%, 95% CI: -8.06 to 14.34, p = 0.58), sniff nasal inspiratory pressure (MD: 1.47 cmH₂O, 95% CI: -15.45 to 18.39, p = 0.86), forced expiratory volume in one second (MD: -0.02 L, 95% CI: -0.17 to 0.13, p = 0.78), and vital capacity (MD: -0.10 L, 95% CI: -0.31 to 0.11, p = 0.33). Conclusion This review supports the role of respiratory muscle training in improving inspiratory and expiratory muscle strength in patients with neuromuscular diseases. However, variability in study methodologies and patient populations limits the statistical significance of some respiratory parameters. Future studies should aim to standardize interventions and outcome measures to provide more conclusive evidence on the efficacy of RMT.

Dennis Krämer, Anja Bosold, Martin Minarik et al.

Generative Artificial Intelligence (AI) tools such as ChatGPT, Copilot, or Gemini have a crucial impact on academic research and teaching. Empirical data on how students perceive the increasing influence of AI, which different types of tools they use, what they expect from them in their daily academic tasks, and their concerns regarding the use of AI in their studies are still limited. The manuscript presents findings from a quantitative survey conducted among sports students of all semesters in Germany using an online questionnaire. It explores aspects such as students' usage behavior, motivational factors, and uncertainties regarding the impact of AI tools on academia in the future. Furthermore, the social climate in sports studies is being investigated to provide a general overview of the current situation of the students in Germany. Data collection took place between August and November 2023, addressing all sports departments at German universities, with a total of 262 students participating. Our Findings indicate that students have a strong interest in using AI tools in their studies, expecting them to improve their overall academic performance, understand the complexity of scientific approaches, and save time. They express confidence that the proliferation of AI will not compromise their critical thinking skills. Moreover, students are positive about integrating more AI-related topics into the curriculum and about lecturers adopting more AI-based teaching methods. However, our findings also show that students have concerns about plagiarism, lecturer preparedness and their own skills and future skill development.

Enzo B Onofre, Leonardo M P Moraes, Cristina D Aguiar

This paper introduces AskSport, a question-answering web application about sports. It allows users to ask questions using natural language and retrieve the three most relevant answers, including related information and documents. The paper describes the characteristics and functionalities of the application, including use cases demonstrating its ability to return names and numerical values. AskSport and its implementation are available for public access on HuggingFace.

Hossein Feiz, David Labbé, Thomas Romeas et al.

We propose a novel framework for accurate 3D human pose estimation in combat sports using sparse multi-camera setups. Our method integrates robust multi-view 2D pose tracking via a transformer-based top-down approach, employing epipolar geometry constraints and long-term video object segmentation for consistent identity tracking across views. Initial 3D poses are obtained through weighted triangulation and spline smoothing, followed by kinematic optimization to refine pose accuracy. We further enhance pose realism and robustness by introducing a multi-person physics-based trajectory optimization step, effectively addressing challenges such as rapid motions, occlusions, and close interactions. Experimental results on diverse datasets, including a new benchmark of elite boxing footage, demonstrate state-of-the-art performance. Additionally, we release comprehensive annotated video datasets to advance future research in multi-person pose estimation for combat sports.

Chunggi Lee, Ut Gong, Tica Lin et al.

Injury prevention in sports requires understanding how bio-mechanical risks emerge from movement patterns captured in real-world scenarios. However, identifying and interpreting injury prone events from raw video remains difficult and time-consuming. We present VAIR, a visual analytics system that supports injury risk analysis using 3D human motion reconstructed from sports video. VAIR combines pose estimation, bio-mechanical simulation, and synchronized visualizations to help users explore how joint-level risk indicators evolve over time. Domain experts can inspect movement segments through temporally aligned joint angles, angular velocity, and internal forces to detect patterns associated with known injury mechanisms. Through case studies involving Achilles tendon and Anterior cruciate ligament (ACL) injuries in basketball, we show that VAIR enables more efficient identification and interpretation of risky movements. Expert feedback confirms that VAIR improves diagnostic reasoning and supports both retrospective analysis and proactive intervention planning.

Maciej Superson, Klaudia Wilk-Trytko, Katarzyna Szmyt et al.

Intruduction and purpose: Patients with severe asthma account for approximately 3% to 10% of all asthma patients. They have higher hospital utilization rates and treatment costs than patients with non-severe asthma. Previously, treatment options for these patients were limited due to unacceptable side effects. However, the advent of biologic therapies has provided promising targeted therapy for these patients. State of knowledge: Biologic therapies target inflammatory modulators that play a key role in the pathogenesis of asthma, particularly in patients with high T2 cells. These therapies have shown promising results in reducing asthma symptoms, improving lung function, decreasing the use of oral corticosteroids, and enhancing patients' quality of life. Conclusions: This article reviews the mechanism of action, efficacy, and indications of currently approved biologic drugs available in Poland, as well as potential therapeutic targets for the future.

Manuel Pinto, Inês Santos

O objetivo deste estudo foi analisar a associação entre a motivação e vários aspectos da prática de exercício físico (EF) em indivíduos que mantiveram a perda de massa corporal, e explorar o papel do gênero. A amostra foi constituída por 253 participantes, foram avaliadas as suas características sociodemográficas, a composição corporal, o EF, a motivação geral e específica para o exercício, com instrumentos validados. Utilizou-se o teste t-de student para comparar as diversas variáveis entre os participantes. Para analisar a associação entre as variáveis motivacionais e os diferentes aspectos do EF, utilizou-se o coeficiente de correlação de Pearson. Foram ainda criados tercis para as variáveis motivacionais e posteriormente comparadas as médias dos vários aspectos do EF, através do teste da ANOVA one-way. Observaram-se diferenças entre gêneros no que respeita à intensidade, duração e dispêndio energético e na maioria das variáveis motivacionais, favorecendo o gênero masculino. Identificaram-se associações positivas entre as variáveis motivacionais relacionadas com o EF, nomeadamente a motivação intrínseca e as regulações de ordem mais autônoma, e os diferentes aspectos do EF, na amostra total e no gênero feminino (p < 0,05). Em indivíduos com sucesso na manutenção da perda de massa corporal, particularmente nas mulheres, motivações mais autônomas para o EF associaram-se a maior prática de EF. Essas conclusões são significativas para programas de perda de massa corporal e promoção de saúde, indicando que a motivação autônoma pode ser um fator de sucesso na manutenção da massa corporal.

Shizhe Yuan, Li Zhou

With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accommodating diverse postures, particularly with occlusions, rapid movements, and the resource constraints of Internet of Things (IoT) devices, making it difficult to balance accuracy and real-time performance. To address these issues, we propose GTA-Net, an intelligent system for posture correction and real-time feedback in adolescent sports, integrated within an IoT-enabled environment. This model enhances pose estimation in dynamic scenes by incorporating Graph Convolutional Networks (GCN), Temporal Convolutional Networks (TCN), and Hierarchical Attention mechanisms, achieving real-time correction through IoT devices. Experimental results show GTA-Net's superior performance on Human3.6M, HumanEva-I, and MPI-INF-3DHP datasets, with Mean Per Joint Position Error (MPJPE) values of 32.2mm, 15.0mm, and 48.0mm, respectively, significantly outperforming existing methods. The model also demonstrates strong robustness in complex scenarios, maintaining high accuracy even with occlusions and rapid movements. This system enhances real-time posture correction and offers broad applications in intelligent sports and health management.

Maria Koshkina, James H. Elder

Jersey number recognition is an important task in sports video analysis, partly due to its importance for long-term player tracking. It can be viewed as a variant of scene text recognition. However, there is a lack of published attempts to apply scene text recognition models on jersey number data. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We address issues of occlusions and assess the degree to which training on one sport (hockey) can be generalized to another (soccer). For the latter, we also consider how jersey number recognition at the single-image level can be aggregated across frames to yield tracklet-level jersey number labels. We demonstrate high performance on image- and tracklet-level tasks, achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code, models, and data are available at https://github.com/mkoshkina/jersey-number-pipeline.

D. Harriss, G. Atkinson

Aleksandra Kulbat, Aleksandra Karwańska, Mateusz Kulbat et al.

Depression is a common mental health disorder that affects the majority of the population. The exact cause of depression is not fully understood, but various factors such as genetics, environmental stressors, and psychological factors are believed to play a role. Due to the complexity of the etiopathogenesis of depression, the selection of appropriate therapeutic management is sometimes complicated. Treatment for depression typically involves a combination of medication and psychotherapy. Antidepressant medication can help alleviate symptoms by regulating neurotransmitters in the brain, while psychotherapy can help individuals understand and change negative thoughts and behaviors. Physical activity, such as sport, has been shown to have a positive impact on mental health and can help alleviate symptoms of depression. Exercise releases endorphins, reducing stress and improving mood. Health education can also play a role in preventing and managing depression by raising awareness, reducing stigma, and teaching individuals coping skills to maintain good mental health.