Salami Sedigheh, Ribeiro Bandeira Paulo Felipe, Dehkordi Parvaneh Shamsipour, Sohrabi Fatemeh, Martins Clarice, Duncan Michael J, Hardy Louise L, Shams Amir
Department of Motor Behavior, Faculty of Sport Sciences, 48408Alzahra University, Tehran, Iran.
Departamento de Educação Física, 226206Universidade Regional do Cariri, Crato, Brazil.
Percept Mot Skills. 2023 Apr;130(2):658-679. doi: 10.1177/00315125231152669. Epub 2023 Feb 7.
Motor competence (MC) has been extensively examined in children and adolescents, but has not been studied among adults nor across the lifespan. The Test of Motor Competence (TMC) assesses MC in people aged 5-85 years. Among Iranians, aged 5-85 years, we aimed to determine the construct validity and reliability of the TMC and to examine associations between TMC test items and the participants' age, sex, and body mass index (BMI). We conducted confirmatory factor analysis (CFA) to evaluate the TMC's factorial structure by age group and for the whole sample. We explored associations between the TMC test items and participant age, sex, and BMI using a network analysis machine learning technique (Rstudio and qgraph). CFA supported the construct validity of a unidimensional model for motor competence for the whole sample (RMSEA = 0.003; CFI = 0.998; TLI = 0.993) and for three age groups (RMSEA <0.08; CFI and TLI >0.95). Network analyses showed fine motor skills to be the most critical centrality skills, reinforcing the importance of fine motor skills for performing and participating in many daily activities across the lifespan. We found the TMC to be a valid and reliable test to measure MC across Iranians' lifespan. We also demonstrated the advantages of using a machine learning approach via network analysis to evaluate associations between skills in a complex system.
运动能力（MC）已在儿童和青少年中得到广泛研究，但尚未在成年人中进行研究，也未在整个生命周期中进行研究。运动能力测试（TMC）评估5至85岁人群的MC。在5至85岁的伊朗人中，我们旨在确定TMC的结构效度和信度，并研究TMC测试项目与参与者的年龄、性别和体重指数（BMI）之间的关联。我们进行了验证性因素分析（CFA），以按年龄组和对整个样本评估TMC的因子结构。我们使用网络分析机器学习技术（Rstudio和qgraph）探索了TMC测试项目与参与者年龄、性别和BMI之间的关联。CFA支持了整个样本（RMSEA = 0.003；CFI = 0.998；TLI = 0.993）以及三个年龄组（RMSEA <0.08；CFI和TLI >0.95）的运动能力单维模型的结构效度。网络分析表明，精细运动技能是最关键的中心技能，这强化了精细运动技能在整个生命周期中执行和参与许多日常活动的重要性。我们发现TMC是一种有效且可靠的测试，可用于测量伊朗人整个生命周期的MC。我们还展示了通过网络分析使用机器学习方法来评估复杂系统中技能之间关联的优势。
