swimming

Assessment of shoulder pain and somatic dysfunction in young competitive swimmers: Preventive Osteopathic Manipulative Treatment

  • Fanny Granger, Marion Etevenard, Jean-Pascal Kily and Martin Garet*

Published on: 9th August, 2019

OCLC Number/Unique Identifier: 8212828091

Context: Shoulder pain is one of the most frequent reported complaints in intensive competitive swimming. The so-called ‘swimmers’ shoulder’ has been widely explored and has been reported sometimes without specific reference to contributing mechanisms or structures. Somatic dysfunction is defined as an impaired or altered function of related components of the somatic system and may appear in the early stage of pain feeling. Aim: To evaluate somatic dysfunctions in a group of young competitive swimmers with and without shoulder pain and its relationship with the shoulder’s mobility along with the efficacy of an osteopathic manipulative treatment (OMT) on shoulder’s mobility, pain, and comfort of swimming. Material and method: 20 competitive swimmers (14.6 ± 1.3 ys; 11.6 ± 2.4 hs.wk-1) were divided into two groups, with and without shoulder pain (SPG/CG). Before and after light touch/OMT, and 1 week later, somatic dysfunctions, shoulder’s range of mobility, pain, and swimming comfort were assessed by 2 independent osteopaths. Results: Somatic dysfunctions were observed in both groups without significant differences in the number or localization and were independent of severity of pain. In the SPG, pain decreased significantly after OMT (6.1 ± 1.9 vs. 3.9 ± 1.8; p = 0.001) and remained stable 1-week later (P = NS). Shoulder’s mobility was lower on the aching shoulder in the “shoulder pain” group when compared to the control group on flexion and abduction tests but not on extension or adduction tests. Following OMT, only abduction improved when compared to light touch. Comfort in swimming was reported as “better” in both OMT/light touch groups. Conclusion: There is no difference between light touch and OMT as both decreased pain and increased comfort in swimming but abduction range of motion only improved in the OMT group.
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AbstractDOI: 10.29328/journal.jsmt.1001045Read Full Article HTMLRead Full Article PDF

Exercise preserves pancreatic β-cell mass and function in obese OLETF rats

  • Jiawei Zhao, Zhihong Yang, Min He, Qinghua Wang and Renming Hu*

Published on: 19th June, 2018

Although exercise has been proposed to be beneficial to type 2 diabetes, its effects on β-cell function and mass remain unclear. In the present study, the effects of long-term swimming training on the function and mass of β-cells in diabetic OLETF rats were examined. At 44 weeks of age after developing diabetes, the OLETF rats were divided into two groups: a control group and an exercise group. The exercise group had a daily swimming for 12 weeks. While not found with the control rats, in the obese OLETF rats, the exercise reduced the weight gain which was associated with improved glucose tolerance and elevated circulating insulin levels as determined by the oral glucose tolerance test and insulin ELISA. The exercise improved plasma total cholesterol and triglyceride levels, and also significantly increased the islet β-cell mass and pancreatic insulin content associated with decreased β-cell apoptosis and elevated activation of the serine/threonine kinase, Akt. The present studies suggest that exercise improves diabetes symptoms via enhancement of the β-cell mass and function through decreasing glucolipotoxicity and reducing β-cell apoptosis by activating Akt in obese OLETF rats.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat
AbstractDOI: 10.29328/journal.acem.1001007Read Full Article HTMLRead Full Article PDF

Long monitoring of Birds of Elssuki Area. Sinnar State, Sudan

  • Mohamed Elmekki Ali Elbadawi Hussien*

Published on: 16th April, 2024

This study aims to make a database of the birds in the area of Elssuki, Sinnar state - Sudan. The period of study spanning from 2008 to 2023 with a mix of methods used to identify bird species in many sites in the locality, these methods include road count, line transects, and direct count besides registering every strange, rare, or unusual single species seen in the area. All these methods are used by different researchers and applied in such studies in Sudan.The study revealed that the area is one of the important areas enriched of birds in Sinnar state of 19 orders 53 families. The total number of species is 129 species. It included all birds; water birds, tree birds, diving birds, dabbling birds, swimming birds, small waders, and passerines which the most. The study concluded that there is a need for comprehensive and regular studies and short and long-term monitoring to identify, classify, and establish a database for Sudan Birds Atlas.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat
AbstractDOI: 10.29328/journal.ivs.1001040Read Full Article HTMLRead Full Article PDF

Deep Learning-Powered Genetic Insights for Elite Swimming Performance: Integrating DNA Markers, Physiological Biometrics and Performance Analytics

  • Rahul Kathuria, Reeta Devi and Asadi Srinivasulu*

Published on: 21st March, 2025

The integration of deep learning and genetic analysis has transformed the assessment of elite sports performance, particularly in competitive swimming. This study examines the fusion of deep learning techniques with DNA markers, physiological biometrics, and performance analytics to enhance the prediction and optimization of swimmer performance. A structured dataset comprising genetic sequences, physiological parameters, and biomechanical attributes was utilized to train a neural network model capable of categorizing swimmers based on genetic predisposition and athletic potential. The model achieved high classification accuracy, demonstrating a strong link between genetic markers, physiological traits, and competitive swimming outcomes. The findings emphasize the potential of AI-driven analytics in talent identification, customized training adaptations, and injury prevention. Furthermore, the study highlights the effectiveness of deep learning in analyzing complex genomic and physiological data to generate meaningful insights for performance enhancement. While the results validate the feasibility of using genetic and AI-based models for performance prediction, further studies are needed to broaden dataset diversity, integrate epigenetic influences, and test the model across varied athlete populations. This research contributes to the expanding field of AI-driven sports science and provides a solid foundation for incorporating genomics with deep learning to enhance elite athletic performance.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat
AbstractDOI: 10.29328/journal.ijbmr.1001020Read Full Article HTMLRead Full Article PDF

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