Principal Component Analysis Applied to In-School Inertial Measurement Unit-Derived Data During Physical Activity: A...

(1) Background: Given the large amount of data extracted from information technologies, principal component analysis (PCA) allows the identification of the most important variables to assess physical activity (PA). The aim of this systematic review is to highlight which variables, extracted through PCA (as a data reduction technique), provide the most information about preschool and school children's PA-related behavioral patterns during school hours. (2) Methods: The search was conducted in PubMed, SCOPUS, Web of Science, and ProQuest Central according to the PRISMA guidelines and the guidelines for performing systematic reviews in sports sciences. (3) Results: From 403 studies initially identified, seven were fully reviewed, and their outcome measures were extracted and analyzed. An analysis of these seven studies (n = 8927) revealed that volume-related components explained the majority of the variance (62.8-69.0%) in PA behaviors, while intensity components contributed less (14.4-14.8%). However, confidence intervals and heterogeneity statistics (I²) were not reported in the original studies, limiting quantitative synthesis. (4) Conclusions: This systematic review demonstrates that PCA effectively identifies multidimensional patterns in children's PA and motor development, with volume-related dimensions consistently dominating the variance structure across diverse populations and settings.