Megan L. Wood, Amanda Waterman, Mark Mon-Williams, Liam J B Hill
{"title":"在一项人口研究中,通过数据缩减技术确定传感器运动控制的关键运动学测量方法(出生于布拉德福德)","authors":"Megan L. Wood, Amanda Waterman, Mark Mon-Williams, Liam J B Hill","doi":"10.12688/wellcomeopenres.22486.1","DOIUrl":null,"url":null,"abstract":"Background Sensorimotor processes underpin skilled human behaviour and can thus act as an important marker of neurological status. Kinematic assessments offer objective measures of sensorimotor control but can generate countless output variables. This study sought to guide future analyses of such data by determining the key variables that capture children’s sensorimotor control on a standardised assessment battery deployed in cohort studies. Methods The Born in Bradford (BiB) longitudinal cohort study has collected sensorimotor data from 22,266 children aged 4–11 years via a computerised kinematic assessment battery (“CKAT”). CKAT measures three sensorimotor processing tasks (Tracking, Aiming, Steering). The BiB CKAT data were analysed using a “train then test” approach with two independent samples. Independent models were constructed for Tracking, Aiming, and Steering. The data were analysed using Principal Components Analysis followed by Confirmatory Factor Analysis. Results The kinematic data could be reduced to 4-7 principal components per task (decreased from >600 individual data points). These components reflect a wide range of core sensorimotor competencies including measures of both spatial and temporal accuracy. Further analyses using the derived variables showed these components capture the age-related differences reported in the literature (via a range of measures selected previously in a necessarily arbitrary way by study authors). Conclusions We identified the key variables of interest within the rich kinematic measures generated by a standardised tool for assessing sensorimotor control processes (CKAT). This work can guide future use of such data by providing a principled framework for the selection of the appropriate variables for analysis (where otherwise high levels of redundancy cause researchers to make arbitrary decisions). These methods could and should be applied in any form of kinematic assessment.","PeriodicalId":508490,"journal":{"name":"Wellcome Open Research","volume":"1 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Key kinematic measures of sensorimotor control identified via data reduction techniques in a population study (Born in Bradford)\",\"authors\":\"Megan L. Wood, Amanda Waterman, Mark Mon-Williams, Liam J B Hill\",\"doi\":\"10.12688/wellcomeopenres.22486.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background Sensorimotor processes underpin skilled human behaviour and can thus act as an important marker of neurological status. Kinematic assessments offer objective measures of sensorimotor control but can generate countless output variables. This study sought to guide future analyses of such data by determining the key variables that capture children’s sensorimotor control on a standardised assessment battery deployed in cohort studies. Methods The Born in Bradford (BiB) longitudinal cohort study has collected sensorimotor data from 22,266 children aged 4–11 years via a computerised kinematic assessment battery (“CKAT”). CKAT measures three sensorimotor processing tasks (Tracking, Aiming, Steering). The BiB CKAT data were analysed using a “train then test” approach with two independent samples. Independent models were constructed for Tracking, Aiming, and Steering. The data were analysed using Principal Components Analysis followed by Confirmatory Factor Analysis. Results The kinematic data could be reduced to 4-7 principal components per task (decreased from >600 individual data points). These components reflect a wide range of core sensorimotor competencies including measures of both spatial and temporal accuracy. Further analyses using the derived variables showed these components capture the age-related differences reported in the literature (via a range of measures selected previously in a necessarily arbitrary way by study authors). Conclusions We identified the key variables of interest within the rich kinematic measures generated by a standardised tool for assessing sensorimotor control processes (CKAT). This work can guide future use of such data by providing a principled framework for the selection of the appropriate variables for analysis (where otherwise high levels of redundancy cause researchers to make arbitrary decisions). These methods could and should be applied in any form of kinematic assessment.\",\"PeriodicalId\":508490,\"journal\":{\"name\":\"Wellcome Open Research\",\"volume\":\"1 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wellcome Open Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12688/wellcomeopenres.22486.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wellcome Open Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/wellcomeopenres.22486.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Key kinematic measures of sensorimotor control identified via data reduction techniques in a population study (Born in Bradford)
Background Sensorimotor processes underpin skilled human behaviour and can thus act as an important marker of neurological status. Kinematic assessments offer objective measures of sensorimotor control but can generate countless output variables. This study sought to guide future analyses of such data by determining the key variables that capture children’s sensorimotor control on a standardised assessment battery deployed in cohort studies. Methods The Born in Bradford (BiB) longitudinal cohort study has collected sensorimotor data from 22,266 children aged 4–11 years via a computerised kinematic assessment battery (“CKAT”). CKAT measures three sensorimotor processing tasks (Tracking, Aiming, Steering). The BiB CKAT data were analysed using a “train then test” approach with two independent samples. Independent models were constructed for Tracking, Aiming, and Steering. The data were analysed using Principal Components Analysis followed by Confirmatory Factor Analysis. Results The kinematic data could be reduced to 4-7 principal components per task (decreased from >600 individual data points). These components reflect a wide range of core sensorimotor competencies including measures of both spatial and temporal accuracy. Further analyses using the derived variables showed these components capture the age-related differences reported in the literature (via a range of measures selected previously in a necessarily arbitrary way by study authors). Conclusions We identified the key variables of interest within the rich kinematic measures generated by a standardised tool for assessing sensorimotor control processes (CKAT). This work can guide future use of such data by providing a principled framework for the selection of the appropriate variables for analysis (where otherwise high levels of redundancy cause researchers to make arbitrary decisions). These methods could and should be applied in any form of kinematic assessment.