Citation

Salami S, Ribeiro Bandeira PF, Dehkordi PS, Sohrabi F, Martins C, Duncan MJ, Hardy LL, Shams A. Percept. Mot. Skills 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, SAGE Publishing)

DOI

10.1177/00315125231152669

PMID

36749736

Abstract

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.

Language: en

Keywords

machine learning; lifespan; motor competence; network perspective