Johan Y.Y. Ng, Shan Jiang, Cecilia H.S. Chan, Amy S. Ha
{"title":"Assessing fundamental movement skills using the Test of Gross Motor Development (TGMD): Challenges and solutions to comparability and standardization","authors":"Johan Y.Y. Ng, Shan Jiang, Cecilia H.S. Chan, Amy S. Ha","doi":"10.1016/j.smhs.2025.02.007","DOIUrl":null,"url":null,"abstract":"<div><div>Proficiency in fundamental movement skills in children is linked to numerous positive physical, behavioral, and cognitive outcomes. The Test of Gross Motor Development is a widely used tool in research to measure this outcome. However, its application faces several limitations and challenges, such as subjectivity in rater judgements, limitation in data precision, and inconsistencies in score aggregations. In this commentary, we discuss these issues and explore how new technology might help address some of the challenges. Nonetheless, a consensus to how scores should be aggregated is still needed, and this could only be achieved through further discussions among researchers in the field.</div></div>","PeriodicalId":33620,"journal":{"name":"Sports Medicine and Health Science","volume":"7 4","pages":"Pages 299-301"},"PeriodicalIF":2.3000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sports Medicine and Health Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666337625000277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SPORT SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Proficiency in fundamental movement skills in children is linked to numerous positive physical, behavioral, and cognitive outcomes. The Test of Gross Motor Development is a widely used tool in research to measure this outcome. However, its application faces several limitations and challenges, such as subjectivity in rater judgements, limitation in data precision, and inconsistencies in score aggregations. In this commentary, we discuss these issues and explore how new technology might help address some of the challenges. Nonetheless, a consensus to how scores should be aggregated is still needed, and this could only be achieved through further discussions among researchers in the field.