Hasil untuk "Sports medicine"

C. Garber, B. Blissmer, M. Deschenes et al.

Position Stand

J. Kanaley, S. Colberg, Matthew H. Corcoran et al.

Supplemental digital content is available in the text. ABSTRACT This consensus statement is an update of the 2010 American College of Sports Medicine position stand on exercise and type 2 diabetes. Since then, a substantial amount of research on select topics in exercise in individuals of various ages with type 2 diabetes has been published while diabetes prevalence has continued to expand worldwide. This consensus statement provides a brief summary of the current evidence and extends and updates the prior recommendations. The document has been expanded to include physical activity, a broader, more comprehensive definition of human movement than planned exercise, and reducing sedentary time. Various types of physical activity enhance health and glycemic management in people with type 2 diabetes, including flexibility and balance exercise, and the importance of each recommended type or mode are discussed. In general, the 2018 Physical Activity Guidelines for Americans apply to all individuals with type 2 diabetes, with a few exceptions and modifications. People with type 2 diabetes should engage in physical activity regularly and be encouraged to reduce sedentary time and break up sitting time with frequent activity breaks. Any activities undertaken with acute and chronic health complications related to diabetes may require accommodations to ensure safe and effective participation. Other topics addressed are exercise timing to maximize its glucose-lowering effects and barriers to and inequities in physical activity adoption and maintenance.

W. Hopkins, S. Marshall, A. Batterham et al.

W. Haskell, I. Lee, R. Pate et al.

M. Nelson, W. Rejeski, S. Blair et al.

W. Hopkins

R. Pate, M. Pratt, S. Blair et al.

G. Atkinson, A. Nevill

Sandra K. Hunter, Siddhartha S Angadi, Aditi Bhargava et al.

ABSTRACT Biological sex is a primary determinant of athletic performance because of fundamental sex differences in anatomy and physiology dictated by sex chromosomes and sex hormones. Adult men are typically stronger, more powerful, and faster than women of similar age and training status. Thus, for athletic events and sports relying on endurance, muscle strength, speed, and power, males typically outperform females by 10%-30% depending on the requirements of the event. These sex differences in performance emerge with the onset of puberty and coincide with the increase in endogenous sex steroid hormones, in particular testosterone in males, which increases 30-fold by adulthood, but remains low in females. The primary goal of this consensus statement is to provide the latest scientific knowledge and mechanisms for the sex differences in athletic performance. This review highlights the differences in anatomy and physiology between males and females that are primary determinants of the sex differences in athletic performance and in response to exercise training, and the role of sex steroid hormones (particularly testosterone and estradiol). We also identify historical and nonphysiological factors that influence the sex differences in performance. Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial opportunities for high-impact studies. A major step toward closing the knowledge gap is to include more and equitable numbers of women to that of men in mechanistic studies that determine any of the sex differences in response to an acute bout of exercise, exercise training, and athletic performance.

Yunxiao Zhang, William Stone, Suryansh Kumar

Camera virtualization -- an emerging solution to novel view synthesis -- holds transformative potential for visual entertainment, live performances, and sports broadcasting by enabling the generation of photorealistic images from novel viewpoints using images from a limited set of calibrated multiple static physical cameras. Despite recent advances, achieving spatially and temporally coherent and photorealistic rendering of dynamic scenes with efficient time-archival capabilities, particularly in fast-paced sports and stage performances, remains challenging for existing approaches. Recent methods based on 3D Gaussian Splatting (3DGS) for dynamic scenes could offer real-time view-synthesis results. Yet, they are hindered by their dependence on accurate 3D point clouds from the structure-from-motion method and their inability to handle large, non-rigid, rapid motions of different subjects (e.g., flips, jumps, articulations, sudden player-to-player transitions). Moreover, independent motions of multiple subjects can break the Gaussian-tracking assumptions commonly used in 4DGS, ST-GS, and other dynamic splatting variants. This paper advocates reconsidering a neural volume rendering formulation for camera virtualization and efficient time-archival capabilities, making it useful for sports broadcasting and related applications. By modeling a dynamic scene as rigid transformations across multiple synchronized camera views at a given time, our method performs neural representation learning, providing enhanced visual rendering quality at test time. A key contribution of our approach is its support for time-archival, i.e., users can revisit any past temporal instance of a dynamic scene and can perform novel view synthesis, enabling retrospective rendering for replay, analysis, and archival of live events, a functionality absent in existing neural rendering approaches and novel view synthesis...

Ozge Ece Gunaydin, Sercan Onal Aykar, Esin Ergin et al.

Abstract Objective Thoracic mobility significantly enhances athletic ability and performance. The objective of this study was to examine the impact of thoracic mobility exercises on the physical attributes of strength, endurance, flexibility, and the speed of serve and spike in female adolescent volleyball players. Method In this study, 36 adolescent female adolescent volleyball players participated. Participants were divided into 2 groups as mobility added training and only training program. In the mobility group, thoracic mobility exercises were applied in addition to the training program for 4 weeks. The athletes were evaluated in terms of internal and external rotation strength, endurance, thoracic mobility, spike and serve speeds. A 2 × 2 ANOVA was used to compare the differences between the groups. Results In this study, significant increases were observed in strength, scapular endurance, thoracic rotation angles, smash and serve speed in both the thoracic mobility group and the control group (p < 0.05). However, it was determined that changes in some parameters differed between groups; right internal rotation strength increased only in the control group, while hyperextension increased only in the thoracic mobility group (p < 0.05). However, no group was found to be superior to the other in terms of any parameter in the intergroup comparisons (p > 0.05). Conclusion The results indicate that thoracic mobility exercises added to training programs yield similar results to the control group in terms of strength, endurance, flexibility, spike and serve speed. Future studies with longer-term interventions, dose-response design, and different sports disciplines may further contribute to the literature. Trial registration ClinicalTrials.gov NCT07203209, 24.09.2025. Retrospectively registered.

A. Fayed, N. Mansur, Kepler Alencar Mendes de Carvalho et al.

Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications. Level of evidence : Level V, letter to review.

N. Guelmami, Lamia Ben Ezzeddine, Ghouili Hatem et al.

Objective: Research in sports medicine and exercise science has experienced significant growth over recent years. With this expansion, there has been a concomitant rise in ethical challenges specific to these disciplines. While various ethical guidelines exist for numerous scientific fields, a comprehensive set tailored specifically for sports medicine and exercise science is lacking. Aiming to bridge this gap, this paper proposes a comprehensive, updated set of ethical guidelines specifically targeted at researchers in sports medicine and exercise science, providing them with a thorough framework to ensure research integrity. Methods: A collaborative approach was adopted, involving contributions from a diverse group of international experts in the field. A thorough review of existing ethical guidelines was conducted, followed by the identification and detailed examination of 15 specific ethical topics relevant to the discipline. Each topic was discussed in terms of its definition, consequences, and preventive measures. Results: The research in sports medicine and exercise science has grown significantly, bringing to the fore ethical challenges unique to these disciplines. Our comprehensive review identifies 15 key ethical challenges: plagiarism, data falsification, role of artificial intelligence chatbots in academic writing, overstating results, excessive/strategic self-citation, duplicate publications, non-disclosure of conflicts of interest, image manipulation, misuse of peer review, ghost and gift authorship, inadequate data retention, data fabrication, falsification of IRB approvals, lack of informed consent, and unethical human or animal experimentation. For each identified challenge, we propose practical solutions and best practices, enriched by the diverse perspectives of our collaborative international expert panel. This endeavor aims to offer a foundational set of ethical guidelines tailored to the nuanced needs of sports medicine and exercise science, ensuring research integrity and promoting ethical responsibility across these vital fields. Conclusion: This article represents a seminal contribution to the establishment of essential ethical guidelines specifically designed for the fields of sports medicine and exercise science. This article charts a clear course for researchers, clinicians, and policymakers by integrating these ethical principles at the heart of our scholarly and clinical activities. Consequently, it envisions a future where the principles of research integrity and ethical responsibility consistently inform every scientific discovery and every clinical engagement.

Sahibpreet Singh, Pawan Kumar

This chapter explores the complexities of sports governance, taxation, dispute resolution, and the impact of digital transformation within the sports sector. This study identifies a critical research gap regarding the integration of innovative technologies to enhance governance and talent identification in sports law. The objective is to evaluate how data-driven approaches and AI can optimize recruitment processes; also ensuring compliance with existing regulations. A comprehensive analysis of current governance structures and taxation policies,(ie Income Tax Act and GST Act), reveals preliminary results indicating that reform is necessary to support sustainable growth in the sports economy. Key findings demonstrate that AI enhances player evaluation by minimizing biases and expanding access to diverse talent pools. While the Court of Arbitration for Sport provides an efficient mechanism for dispute resolution. The implications emphasize the need for regulatory reforms that align taxation policies with international best practices, promoting transparency and accountability in sports organizations. This research contributes valuable insights into the evolving dynamics of sports management, aiming to foster innovation and integrity in the industry.

Anna Maschek, David C. Schedl

Detecting an athlete's position on a route and identifying hold usage are crucial in various climbing-related applications. However, no climbing dataset with detailed hold usage annotations exists to our knowledge. To address this issue, we introduce a dataset of 22 annotated climbing videos, providing ground-truth labels for hold locations, usage order, and time of use. Furthermore, we explore the application of keypoint-based 2D pose-estimation models for detecting hold usage in sport climbing. We determine usage by analyzing the key points of certain joints and the corresponding overlap with climbing holds. We evaluate multiple state-of-the-art models and analyze their accuracy on our dataset, identifying and highlighting climbing-specific challenges. Our dataset and results highlight key challenges in climbing-specific pose estimation and establish a foundation for future research toward AI-assisted systems for sports climbing.

Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou et al.

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Lu Zhang, Xingjian Xiong, Muxi Zhang et al.

Abstract Background Elite female basketball players experience a high incidence of lower-limb injuries, yet evidence remains limited regarding the applicability of common functional performance tests in this population. This study aimed (1) to compare the performance of the Functional Movement Screen (FMS), Landing Error Scoring System (LESS), and Y-Balance Test (YBT) between athletes with and without knee or ankle/foot injuries, and (2) to examine the correlations among these three functional performance tests. Methods Eighteen elite female basketball players from the Chinese National Team completed three functional performance tests: FMS, LESS, and YBT. Differences in total and subtest scores between injured and non-injured athletes were analyzed using independent samples t-tests and the Mann–Whitney U test. Spearman correlation analysis was conducted to examine relationships among the three tests. Results No significant differences were found in total FMS, LESS, or YBT scores between injured and non-injured athletes. However, a large effect size suggested a potential clinical trend between knee injuries and total FMS scores, and the FMS Deep Squat subtest significantly differentiated athletes with knee injuries. No significant correlations were observed among FMS, LESS, and YBT scores. Conclusion The FMS Deep Squat component may help identify knee-related functional limitations in elite female basketball players, while the three tests collectively provide complementary perspectives on movement quality assessment in applied settings. However, given the small sample and cross-sectional design, these findings should be interpreted as exploratory and warrant validation in larger, prospective studies.

Lin Hu, Haixia Feng, Jing Han et al.

Abstract Background Frailty is a syndrome as with aging in the population of type 2 diabetes mellitus (T2DM) and exercise has become an essential non-pharmacological tool especially in the pre-frail stage. Notably, the form of supervised home-based exercise program has been strongly recommended in recent years. This study aimed to verify the potential effects of the supervised home-based elastic band exercise in pre-frail older T2DM patients in China. Methods A total of 100 participants were included and randomly divided into intervention group (IG) (n = 50) and control group (CG) (n = 50). The CG received a routine care, while the IG received an extra home-based elastic band training under online and offline supervisions sustaining 12-weeks. The glycosylated hemoglobin (HbA1c), blood lipids, body composition, physical function, scales of Diabetes specificity quality of life scale (DSQL), Pittsburgh sleep quality index (PSQI) and short form geriatric depression scale (GDS-15) of the participants were evaluated before and after intervention. Results The average age of the participants were 66.01 ± 4.76 with 55% male and average BMI 24.75 ± 3.51 kg/m2. The clinical characteristics of the two groups were comparable. After 12 weeks’ training, muscle mass of the limbs (P < 0.05), physical function indicators including grip strength, chair stands (both P < 0.05), walking time (P < 0.01), HbA1c (P < 0.05), frailty score (P < 0.05), subjective sleep quality (P < 0.05), total DSQL scores (P < 0.01) and the depressive status (P < 0.01) improved significantly in IG when compared with CG. Conclusion Supervised home-based elastic band exercise could improve limb muscle mass, physical fitness, glucose and lipid control and quality of life in pre-frail older T2DM patients. Trial registration number ChiCTR2300070726; Registration date: 21/04/2023.

Ayoosh Pareek, Du Hyun Ro, J. Karlsson et al.

Machine learning (ML) is changing the way health care is practiced and recent applications of these novel statistical techniques have started to impact orthopaedic sports medicine. Machine learning enables the analysis of large volumes of data to establish complex relationships between "input" and "output" variables. These relationships may be more complex than could be established through traditional statistical analysis and can lead to the ability to predict the "output" with high levels of accuracy. Supervised learning is the most common ML approach for healthcare data and recent studies have developed algorithms to predict patient-specific outcome after surgical procedures such as hip arthroscopy and anterior cruciate ligament reconstruction. Deep learning is a higher-level ML approach that facilitates the processing and interpretation of complex datasets through artificial neural networks that are inspired by the way the human brain processes information. In orthopaedic sports medicine, deep learning has primarily been used for automatic image (computer vision) and text (natural language processing) interpretation. While applications in orthopaedic sports medicine have been increasing exponentially, one significant barrier to widespread adoption of ML remains clinician unfamiliarity with the associated methods and concepts. The goal of this review is to introduce these concepts, review current machine learning models in orthopaedic sport medicine, and discuss future opportunities for innovation within the specialty.