J. C. D. L. Cruz-Márquez, A. Cruz-Campos, J. C. D. L. Cruz-Campos, María Belén Cueto-Martín, M. García-Jiménez, M. T. Campos-Blasco
{"title":"用数学模型预测运动损伤","authors":"J. C. D. L. Cruz-Márquez, A. Cruz-Campos, J. C. D. L. Cruz-Campos, María Belén Cueto-Martín, M. García-Jiménez, M. T. Campos-Blasco","doi":"10.18848/2152-7857/CGP/V03I04/53955","DOIUrl":null,"url":null,"abstract":"A number of different methodological approaches have been used to describe the inciting event for sports injuries. These include interviews of injured athletes, analysis of video recordings of actual injuries, clinical studies (clinical findings of joint damage are studied to understand the injury mechanism, mainly through plain radiography, magnetic resonance imaging, arthroscopy, and computed tomography scans), in vivo studies (ligament strain or forces are measured to understand ligament loading patterns), cadaver studies and simulation of injury situations, and measurement/estimation from \"close to injury\" situations. This chapter describes mathematical modeling approach and assesses its strengths and weaknesses in contributing to the understanding and prevention of sports injuries. This chapter demonstrates the relationship between structural measures and lower limb injuries. Sports injuries can affect any and all parts of the body depending on the particular repetitive movement performed just like any repetitive motion injury. While there are factors that raise the risk of injury, there are also elements that predispose athletes to sports injuries. Rehabilitation and preventative efforts should be centered on a thorough knowledge of risk factor etiology as well as knowledge of how such factors contribute to sports injuries. In most epidemiological studies directed toward identifying major sports injury causation factors, injured athletes have been compared with uninjured athletes through single variable techniques. However, many of the factors highlighted later in this paper through these analytical techniques either interact or are interrelated. Multivariable statistical techniques have also been used to detail risk factor interaction (Mechelen, 1992), such as discriminatory analyses and stepwise logistic regression (Dixon, 1993). In this chapter we will identify potential predictive factors that can be used in logistic regression equations, the basic concepts of this mathematical study, and equations that have been developed to what they are today.","PeriodicalId":169947,"journal":{"name":"The International Journal of Sport and Society: Annual Review","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Prediction of Sports Injuries by Mathematical Models\",\"authors\":\"J. C. D. L. Cruz-Márquez, A. Cruz-Campos, J. C. D. L. Cruz-Campos, María Belén Cueto-Martín, M. García-Jiménez, M. T. Campos-Blasco\",\"doi\":\"10.18848/2152-7857/CGP/V03I04/53955\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A number of different methodological approaches have been used to describe the inciting event for sports injuries. These include interviews of injured athletes, analysis of video recordings of actual injuries, clinical studies (clinical findings of joint damage are studied to understand the injury mechanism, mainly through plain radiography, magnetic resonance imaging, arthroscopy, and computed tomography scans), in vivo studies (ligament strain or forces are measured to understand ligament loading patterns), cadaver studies and simulation of injury situations, and measurement/estimation from \\\"close to injury\\\" situations. This chapter describes mathematical modeling approach and assesses its strengths and weaknesses in contributing to the understanding and prevention of sports injuries. This chapter demonstrates the relationship between structural measures and lower limb injuries. Sports injuries can affect any and all parts of the body depending on the particular repetitive movement performed just like any repetitive motion injury. While there are factors that raise the risk of injury, there are also elements that predispose athletes to sports injuries. Rehabilitation and preventative efforts should be centered on a thorough knowledge of risk factor etiology as well as knowledge of how such factors contribute to sports injuries. In most epidemiological studies directed toward identifying major sports injury causation factors, injured athletes have been compared with uninjured athletes through single variable techniques. However, many of the factors highlighted later in this paper through these analytical techniques either interact or are interrelated. Multivariable statistical techniques have also been used to detail risk factor interaction (Mechelen, 1992), such as discriminatory analyses and stepwise logistic regression (Dixon, 1993). In this chapter we will identify potential predictive factors that can be used in logistic regression equations, the basic concepts of this mathematical study, and equations that have been developed to what they are today.\",\"PeriodicalId\":169947,\"journal\":{\"name\":\"The International Journal of Sport and Society: Annual Review\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The International Journal of Sport and Society: Annual Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18848/2152-7857/CGP/V03I04/53955\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International Journal of Sport and Society: Annual Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18848/2152-7857/CGP/V03I04/53955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Sports Injuries by Mathematical Models
A number of different methodological approaches have been used to describe the inciting event for sports injuries. These include interviews of injured athletes, analysis of video recordings of actual injuries, clinical studies (clinical findings of joint damage are studied to understand the injury mechanism, mainly through plain radiography, magnetic resonance imaging, arthroscopy, and computed tomography scans), in vivo studies (ligament strain or forces are measured to understand ligament loading patterns), cadaver studies and simulation of injury situations, and measurement/estimation from "close to injury" situations. This chapter describes mathematical modeling approach and assesses its strengths and weaknesses in contributing to the understanding and prevention of sports injuries. This chapter demonstrates the relationship between structural measures and lower limb injuries. Sports injuries can affect any and all parts of the body depending on the particular repetitive movement performed just like any repetitive motion injury. While there are factors that raise the risk of injury, there are also elements that predispose athletes to sports injuries. Rehabilitation and preventative efforts should be centered on a thorough knowledge of risk factor etiology as well as knowledge of how such factors contribute to sports injuries. In most epidemiological studies directed toward identifying major sports injury causation factors, injured athletes have been compared with uninjured athletes through single variable techniques. However, many of the factors highlighted later in this paper through these analytical techniques either interact or are interrelated. Multivariable statistical techniques have also been used to detail risk factor interaction (Mechelen, 1992), such as discriminatory analyses and stepwise logistic regression (Dixon, 1993). In this chapter we will identify potential predictive factors that can be used in logistic regression equations, the basic concepts of this mathematical study, and equations that have been developed to what they are today.