Hasil untuk "Sports medicine"

D. Mahler, V. Froelicher, N. Miller et al.

J. Taylor, A. Wright, S. Dischiavi et al.

Kazutaka Fukushima, Kazuyuki Kamahara, Anna Tomori et al.

Objectives To describe the characteristics of COVID-19 among elite Japanese athletes and their return-to-play (RTP) time.Methods We retrospectively reviewed clinical records at the Japan Institute of Sports Sciences between June 2022 and May 2023. Elite athletes who underwent periodic health examinations were examined by a physician to confirm COVID-19 history, symptoms and the RTP time.Results Of 994 athletes, 456 had a COVID-19 history (mean±SD, 23.3±4.6 years; 56% male). Most infections occurred during the sixth wave (Omicron variant), followed by the seventh and eighth waves, with 88% recorded after the fifth wave. Indoor athletes were more frequently affected than outdoor athletes (306 vs 150, p<0.05). Badminton athletes were the most commonly affected athletes (16%), followed by volleyball (10%) and handball (7%). Among those with a history of COVID-19, 89% reported symptoms, while 11% were asymptomatic. Fever was the most common symptom (80%), followed by sore throat (58%) and cough (44%). The median (IQR) RTP time was 10 (7–14) days. Overall, 472 athletes resumed play within 28 days, while 20 returned after 28 days. RTP delays were more frequent before Omicron (9/59 athletes) than after (11/433 athletes, p<0.05).Conclusion COVID-19 was more common among indoor sports athletes, primarily during the Omicron wave, with most cases being symptomatic but resolving quickly. These findings likely reflect factors such as close-contact training, immune changes during intensive training and international travel and may help characterise COVID-19 outcomes in elite athletes.

Julianna Podolec, Silvia Ciraolo, Joanna Wojda et al.

Purpose of Research: The research aims to provide an in-depth understanding of CLOVES syndrome, detailing its clinical features, epidemiology, etiology, and diagnostic criteria. It focuses on the role of PIK3CA gene mutations, challenges in diagnosis, and treatment options, including PI3K/AKT/mTOR pathway inhibitors. Research Materials and Methods: This article is based on a review of the current literature and clinical reports from various sources. The methodology includes a collection and synthesis of clinical data, imaging findings, and genetic analyses from published case studies and medical literature. The primary materials used in the research include: clinical case reports and studies, imaging studies, genetic analysis, therapeutic interventions, epidemiological data, literature review and data analysis. Basic Results: CLOVES syndrome affects fewer than 200 individuals worldwide, with symptoms appearing at birth or early childhood. It can lead to serious complications such as nerve compression, deep vein thrombosis, and pulmonary embolism. Diagnosis involves genetic testing and imaging, and sirolimus shows potential in managing symptoms. Conclusions: CLOVES syndrome is a rare, non-hereditary overgrowth disorder caused by a PIK3CA gene mutation. Early diagnosis and a multidisciplinary approach are vital for managing this complex condition and improving patient outcomes.

Wojciech Urban, Kornelia Karamus, Rafał Wojciech Rejmak et al.

Introduction   Irritable bowel syndrome (IBS) is a prevalent condition involving the gut–brain interaction where individuals commonly experience recurring abdominal pain, changes in bowel movements, and frequently bloating.   Aim The aim of this study was to review literature studies on IBS and non-pharmacological methods for treating this condition.    Method Data for the article were retrieved by using Pub Med setting the time descriptor to 2019-2024.   Conclusions Treating irritable bowel syndrome (IBS) continues to be challenging, as each patient needs a tailored approach. Once the correct IBS subtype is diagnosed, treatment should target the primary symptoms, such as bloating or diarrhea. Non-pharmacological treatment plays a initial, crucial and  rapidly developing role in IBS therapy.

Kai Biedermann, Gian-Andri Baumann, Christina M. Spengler et al.

Introduction Advanced Footwear Technology (AFT) enhances running economy, which is partly attributed to midsole foam properties such as high resilience (the ability of a material to absorb and recover energy under elastic deformation) and high compliance (the tolerance of a material to deformation). While compliant midsoles are known to improve running economy over non-compliant ones, the impact of further increasing compliance in already compliant midsoles remains unclear. Also unclear is the effect of increased compliance on the perception of effort and comfort during running, as this might transform a feeling of cushioning into one of instability. This study aimed to address these issues by comparing three current AFT models with similar resilience but varying compliance: Nike AlphaFly 2 (NAF), Nike VaporFly 3 (NVF), and On Cloudboom Echo 3 (CBE), with the NAF being the most compliant shoe, and thus the one with the highest level of energy return (11.1J) compared with both the NVF (6.5 J) CBE (6.0 J). Methods Sixteen well-trained runners (age 31 ± 5 years, height 178 ± 9 cm, body mass 66 ± 10 kg, body fat 14 ± 4%, V̇O2peak 59 ± 4 ml･kg-1･min-1) performed, on different days, sub-maximal running for 6 min at 16 km·h-1 (80 ± 7% V̇O2peak) on a treadmill and a 400-m track. Treadmill tests included two runs each in NAF and NVF, while track tests included three runs each in NAF, NVF and CBE, with shoe order varied systematically. Gas exchange was continuously monitored while perceived exertion and comfort were rated post-run using a 100mm visual analogue scale. Spatiotemporal data, including impact loading, ground contact time, and cadence, were assessed using accelerometry. Results The NVF improved running economy compared with the NAF (-0.8 ± 0.3 ml･kg-1･min-1, P < 0.05) and CBE (-0.7 ± 0.2 ml･kg-1･min-1, P < 0.05). These findings were corroborated by lower heart rate and ventilation with NVF, present during both treadmill and overground running. However, there was no correlation between the shoe differences seen on both surfaces. No significant differences were found between the shoes concerning perceived effort and comfort. Participants experienced lower impact magnitudes in the NVF (5.4 ± 1.5 g) compared with the NAF (5.6 ± 1.5 g, P < 0.05) and CBE (5.5 ± 1.5 g, P < 0.05). No changes in spatiotemporal data were associated with the differences in running economy between the shoes. Discussion/Conclusion These findings indicate that improvements in running economy with AFT are not a matter of endlessly pursuing increased compliance and energy return. Furthermore, perception seems to be unaffected by higher midsole compliance when different shoe models are tested, suggesting that many other factors are at play. As perception of exertion did not differ between shoes despite noticeable differences in physiological variables, it remains to be seen whether such minute differences are relevant for performance, or whether perhaps longer trials are needed to detect differences in exertion.

Hao Xu, Arbind Agrahari Baniya, Sam Well et al.

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.

Tica Lin, Ruxun Xiang, Gardenia Liu et al.

Video storytelling is essential for sports performance analysis and fan engagement, enabling sports professionals and fans to effectively communicate and interpret the spatial and temporal dynamics of gameplay. Traditional methods rely on manual annotation and verbal explanations, placing significant demands on creators for video editing skills and on viewers for cognitive focus. However, these approaches are time-consuming and often struggle to accommodate individual needs. SportsBuddy addresses this gap with an intuitive, interactive video authoring tool. It combines player tracking, embedded interaction design, and timeline visualizations to seamlessly integrate narratives and visual cues within game contexts. This empowers users to effortlessly create context-driven video stories. Since its launch, over 150 sports users, including coaches, athletes, content creators, parents and fans, have utilized SportsBuddy to produce compelling game highlights for diverse use cases. User feedback highlights its accessibility and ease of use, making video storytelling and insight communication more attainable for diverse audiences. Case studies with collegiate teams and sports creators further demonstrate SportsBuddy's impact on enhancing coaching communication, game analysis, and fan engagement.

Pinar Bisgin, Tom Strube, Niklas Tschorn et al.

Noisy labels pose significant challenges for AI model training in veterinary medicine. This study examines expert assessment ambiguity in canine auscultation data, highlights the negative impact of label noise on classification performance, and introduces methods for label noise reduction. To evaluate whether label noise can be minimized by incorporating multiple expert opinions, a dataset of 140 heart sound recordings (HSR) was annotated regarding the intensity of holosystolic heart murmurs caused by Myxomatous Mitral Valve Disease (MMVD). The expert opinions facilitated the selection of 70 high-quality HSR, resulting in a noise-reduced dataset. By leveraging individual heart cycles, the training data was expanded and classification robustness was enhanced. The investigation encompassed training and evaluating three classification algorithms: AdaBoost, XGBoost, and Random Forest. While AdaBoost and Random Forest exhibited reasonable performances, XGBoost demonstrated notable improvements in classification accuracy. All algorithms showed significant improvements in classification accuracy due to the applied label noise reduction, most notably XGBoost. Specifically, for the detection of mild heart murmurs, sensitivity increased from 37.71% to 90.98% and specificity from 76.70% to 93.69%. For the moderate category, sensitivity rose from 30.23% to 55.81% and specificity from 64.56% to 97.19%. In the loud/thrilling category, sensitivity and specificity increased from 58.28% to 95.09% and from 84.84% to 89.69%, respectively. These results highlight the importance of minimizing label noise to improve classification algorithms for the detection of canine heart murmurs. Index Terms: AI diagnosis, canine heart disease, heart sound classification, label noise reduction, machine learning, XGBoost, veterinary cardiology, MMVD.

Keivan Shariatmadar, Ahmad Osman

The integration of Artificial Intelligence (AI) into sports officiating represents a paradigm shift in how decisions are made in competitive environments. Traditional manual systems, even when supported by Instant Video Replay (IVR), often suffer from latency, subjectivity, and inconsistent enforcement, undermining fairness and athlete trust. This paper introduces 'FST.ai' -- which is developed under the 'R3AL.ai' project, which serves as its Principal Investigator: r3al.ai -- a novel AI-powered framework designed to enhance officiating in Sport Taekwondo, particularly focusing on the complex task of real-time head kick detection and scoring. Leveraging computer vision, deep learning, and edge inference, the system automates the identification and classification of key actions, significantly reducing decision time from minutes to seconds while improving consistency and transparency. Importantly, the methodology is not limited to Taekwondo. The underlying framework -- based on pose estimation, motion classification, and impact analysis -- can be adapted to a wide range of sports requiring action detection, such as judo, karate, fencing, or even team sports like football and basketball, where foul recognition or performance tracking is critical. By addressing one of Taekwondo's most challenging scenarios -- head kick scoring -- we demonstrate the robustness, scalability, and sport-agnostic potential of 'FST.ai' to transform officiating standards across multiple disciplines.

Yuhan Tang

Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescriptions from the aggregated NHANES profiles of 119,555 participants by minimizing the Magni glucose risk-reward function. The model encodes patient status and generates lifestyle medicine prescriptions, which are trained using a mixed-action Soft Actor-Critic algorithm. The task is treated as a single-step contextual bandit. The model is validated against lifestyle medicine prescriptions issued by three certified physicians from Xiangya Hospital. These results demonstrate that offline mixed-action SAC can generate risk-aware lifestyle medicine prescriptions from cross-sectional NHANES data, warranting prospective clinical validation.

S. Cowan, J. Kemp, C. Ardern et al.

Objectives We aimed to determine (1) the proportion of women authors overall, in first (lead) and last (senior) author positions, (2) the proportion of women research participants and (3) the association between women in first and/or last author positions and the proportion of women research participants in original research articles and editorials/opinion pieces in four sport and exercise medicine/physiotherapy journals. Methods The journals evaluated were the British Journal of Sports Medicine, Journal of Orthopaedic and Sports Physical Therapy, Physical Therapy in Sport and International Journal of Sports Physical Therapy. We reviewed all original research articles and editorials/opinion pieces published in 2008, 2009, 2018 and 2019. For each, we aimed to determine the gender/sex of all authors (through gender pronouns, Google Scholar, ResearchGate, institutional profiles, personal websites, photographs and/or social media), and the gender/sex of study participants reported as ‘female’ or ‘male’ or ‘women’ or ‘men’ or ‘girls’ or ‘boys’. Results We included 952 original studies and 219 editorials/opinion pieces. There were 5146 authors of original studies and 706 authors of editorials/opinion pieces. Compared with 2008/2009, the proportion of women as first and last authors was 3.6% (33.0% compared with 29.4%) and 4.8% (33.2% compared with 27.4%) higher respectively in 2018/2019. On average, the proportion of women participants in original studies remained largely unchanged over the 10-year period, only 10% of all participants were women in studies. Conclusion Women are strikingly under-represented in first and last author positions, as are women participants in sports and exercise medicine/physiotherapy journals.

C. Bolling, W. van Mechelen, H. Pasman et al.

It is possible to prevent sports injuries. Unfortunately, the demonstrated efficacy and effectiveness of injury prevention approaches are not translated into lasting real-world effects. Contemporary views in sports medicine and injury prevention suggest that sports injuries are ‘complex’ phenomena. If the problem we aim to prevent is complex, then the first step in the ‘sequence of prevention’ that defines the ‘injury problem’ already needs to have considered this. The purpose of this paper is to revisit the first step of the ‘sequence of prevention’, and to explore new perspectives that acknowledge the complexity of the sports injury problem. First, this paper provides a retrospective of the ‘sequence of prevention’, acknowledging contemporary views on sports injuries and their prevention. Thereafter, from the perspective of the socioecological model, we demonstrate the need for taking into account the complex nature of sports injuries in the first step. Finally, we propose an alternative approach to explore and understand injury context through qualitative research methods. A better understanding of the injury problem in context will guide more context-sensitive studies, thus providing a new perspective for sports injury prevention research.

Blinov V. Dmitry, Antonina G. Solopova, Elena V. Gameeva et al.

Introduction. Surgical treatment of vulvar cancer (VC) entails mental and somatic disturbances due to pain, body image changes, and sexual dysfunction, which are closely associated with impaired social functioning and reduced overall quality of life. However, the results evaluating the impact of rehabilitation programmes on various components of quality of life in these patients remain limited. Aim. to evaluate the effectiveness of rehabilitation programs in relation to the psycho-emotional sphere during 36 months following surgical treatment of early-stage VC. Materials and methods. The randomized controlled study included female patients with VC, divided into two parallel groups of those who received a personalized program of comprehensive rehabilitation (VC-1) and rehabilitation according to the general principles regulated in the national clinical guidelines (VC-2). 36 patients each were randomly assigned to VC-1 and VC-2 groups. The control group included 80 women without female cancer. The VC-2 group was recommended physical activity, psychological support, and anti-edema therapy for lymphostasis. The personalized rehabilitation program in the VC-1 group additionally included lifestyle modification, cognitive-behavioral therapy, intimate hygiene training, magnesium, vitamin B6 and folic acid supplementation, correction of sexual disorders, phytotherapy and physiotherapy from the 3rd month, and climatotherapy and landscape therapy from the 6th month. The “Well-being, Activity, Mood” (WAM) questionnaire was administered at the preoperative visit, 1 week, 1, 3, 6, 12, 24 and 36 months after surgery. Scores were presented as Me [Q25; Q75], differences were considered significant at p 0.05. Results. In the control group, scores on all WAM domains were within normal values throughout the study. In the 1st week after the surgery, well-being and activity decreased to unfavorable values in both VC-1 and VC-2 groups. Mood, however, showed significant positive dynamics compared to baseline. Subsequently, the improvement in the WAM domains was significantly faster and more pronounced in the VC-1 group than in the VC-2 group, reaching the range of favorable values by the 12th month, but not reaching the control group. Conclusion. The personalized comprehensive rehabilitation program showed efficacy on well-being, activity and mood on the WAM questionnaire compared to basic rehabilitation. However, rehabilitation measures should be continued one year after surgery.

Aleksandra Szypuła, Beniamin Michalik, Iwona Galasińska et al.

Introduction: Polyphenolic compounds are widespread in nature, contained in large quantities especially in fruits, vegetables, cereals and nuts. In recent years numerous attempts have been made to characterize their biological effects, with particular emphasis on their protective potential against the development of chronic diseases, including cancer. Purpose of the study: The aim of the study was to collect and analyze publications on the use of polyphenols in the prevention and treatment of cancer, with particular emphasis on breast cancer, colon cancer, prostate cancer and lung cancer - the four most common cancers in the Polish population. Material and methods: A review of the literature available on PubMed was performed using various combinations of keywords: 'polyphenols' + 'cancer', 'polyphenols' + 'oncology', ‘polyphenols’ + ‘chemoprevention’, ‘polyphenols’ + ‘chemotherapy’, 'polyphenols' + 'lung cancer', 'polyphenols' + 'breast cancer', ‘polyphenols’ + ‘colorectal cancer’, 'polyphenols' + 'prostate cancer'/'prostate gland cancer'. Results: The analysis of the collected publications allows to conclude that polyphenols exert multidirectional anticancer effect through antioxidant, antiproliferative, anti-inflammatory, immunomodulatory and prosenescence activity. This effect has been demonstrated, among others, in relation to cellular models of colorectal cancer, prostate cancer, breast cancer, cervical cancer and lung cancer. Polyphenols also reduce side effects associated with the use of conventional anticancer therapies and increase their effectiveness. Conclusions: Polyphenols - both as independent compounds and components of complex anticancer treatment regimens - have chemopreventive and potentially chemotherapeutic effects. By influencing various stages of carcinogenesis - initiation, promotion and progression of cancer - they constitute a promising direction for the development of new, more effective forms of therapy and prevention of cancer.

Christopher W Digiovanni, C Niek van Dijk, Mark Glazebrook et al.

Hui Liu, Jiacheng Gu, Xiyuan Huang et al.

Accurate sports prediction is a crucial skill for professional coaches, which can assist in developing effective training strategies and scientific competition tactics. Traditional methods often use complex mathematical statistical techniques to boost predictability, but this often is limited by dataset scale and has difficulty handling long-term predictions with variable distributions, notably underperforming when predicting point-set-game multi-level matches. To deal with this challenge, this paper proposes TM2, a TCDformer-based Momentum Transfer Model for long-term sports prediction, which encompasses a momentum encoding module and a prediction module based on momentum transfer. TM2 initially encodes momentum in large-scale unstructured time series using the local linear scaling approximation (LLSA) module. Then it decomposes the reconstructed time series with momentum transfer into trend and seasonal components. The final prediction results are derived from the additive combination of a multilayer perceptron (MLP) for predicting trend components and wavelet attention mechanisms for seasonal components. Comprehensive experimental results show that on the 2023 Wimbledon men's tournament datasets, TM2 significantly surpasses existing sports prediction models in terms of performance, reducing MSE by 61.64% and MAE by 63.64%.

Kuangzhi Ge, Lingjun Chen, Kevin Zhang et al.

Recently, significant advances have been made in Video Large Language Models (Video LLMs) in both academia and industry. However, methods to evaluate and benchmark the performance of different Video LLMs, especially their fine-grained, temporal visual capabilities, remain very limited. On one hand, current benchmarks use relatively simple videos (e.g., subtitled movie clips) where the model can understand the entire video by processing just a few frames. On the other hand, their datasets lack diversity in task format, comprising only QA or multi-choice QA, which overlooks the models' capacity for generating in-depth and precise texts. Sports videos, which feature intricate visual information, sequential events, and emotionally charged commentary, present a critical challenge for Video LLMs, making sports commentary an ideal benchmarking task. Inspired by these challenges, we propose a novel task: sports video commentary generation, developed $\textbf{SCBench}$ for Video LLMs. To construct such a benchmark, we introduce (1) $\textbf{SCORES}$, a six-dimensional metric specifically designed for our task, upon which we propose a GPT-based evaluation method, and (2) $\textbf{CommentarySet}$, a dataset consisting of 5,775 annotated video clips and ground-truth labels tailored to our metric. Based on SCBench, we conduct comprehensive evaluations on multiple Video LLMs (e.g. VILA, Video-LLaVA, etc.) and chain-of-thought baseline methods. Our results found that InternVL-Chat-2 achieves the best performance with 5.44, surpassing the second-best by 1.04. Our work provides a fresh perspective for future research, aiming to enhance models' overall capabilities in complex visual understanding tasks. Our dataset will be released soon.

László Csató, Sergey Ilyin

UEFA declares that it is committed to respecting the fundamental values of sports. However, the qualification rules of the post-2024 UEFA Champions League are shown to be unfair: a game with misaligned incentives was narrowly avoided in the 2023/24 German Bundesliga. We develop a mathematical model to reveal how incentives for losing can be reduced or eliminated. Since UEFA repeatedly commits the same theoretical mistake in designing the qualification system of its competitions, governing bodies in sports are called to work more closely together with the scientific community.

Alistair J. McBurnie, Damian J. Harper, Paul A. Jones et al.

High-intensity horizontal decelerations occur frequently in team sports and are typically performed to facilitate a reduction in momentum preceding a change of direction manoeuvre or following a sprinting action. The mechanical underpinnings of horizontal deceleration are unique compared to other high-intensity locomotive patterns (e.g., acceleration, maximal sprinting speed), and are characterised by a ground reaction force profile of high impact peaks and loading rates. The high mechanical loading conditions observed when performing rapid horizontal decelerations can lead to tissue damage and neuromuscular fatigue, which may diminish co-ordinative proficiency and an individual’s ability to skilfully dissipate braking loads. Furthermore, repetitive long-term deceleration loading cycles if not managed appropriately may propagate damage accumulation and offer an explanation for chronic aetiological consequences of the ‘mechanical fatigue failure’ phenomenon. Training strategies should look to enhance an athlete’s ability to skilfully dissipate braking loads, develop mechanically robust musculoskeletal structures, and ensure frequent high-intensity horizontal deceleration exposure in order to accustom individuals to the potentially damaging effects of intense decelerations that athletes will frequently perform in competition. Given the apparent importance of horizontal decelerations, in this Current Opinion article we provide considerations for sport science and medicine practitioners around the assessment, training and monitoring of horizontal deceleration. We feel these considerations could lead to new developments in injury-mitigation and physical development strategies in team sports.