Background: It has been suggested that higher levels of fundamental motor skills (FMS) promote the physical health of preschool-aged children. The impacts of structured and unstructured interventions on FMS in children aged 10-16 years have been widely acknowledged in previous studies. However, there is a lack of relevant studies in preschool-aged children.

Objective: This meta-analysis aimed to compare the effects of structured and unstructured interventions on FMS in preschool-aged children.

Methods: The PubMed, Web of Science, and Google Scholar databases were searched from inception to 1 November 2023 to identify experiments describing structured and unstructured interventions for FMS in preschool-aged children. The Downs and Black Checklist was used to assess the risk of bias. A random effects model was used for the meta-analysis to evaluate the pooled effects of interventions on FMS. Subgroup analyses based on the duration and characteristics of the intervention were conducted to identify sources of heterogeneity.

Results: A total of 23 studies with 4,068 participants were included. There were 12 studies examining structured interventions, 9 studies examining unstructured interventions, and 6 studies comparing structured vs. unstructured interventions. The risk of bias in the included studies was generally low. All interventions significantly improved FMS in preschool-aged children compared to control treatments ( < 0.05). Structured interventions had more significant effects on locomotor skills (LMSs) in preschool-aged children than unstructured interventions (Hedges'  = 0.44,  = 0.04). The effects of structured interventions were strongly influenced by the total intervention duration, such that long-term interventions were more effective (Hedge's  = 1.29,  < 0.001).

Conclusion: Structured interventions play a crucial role in enhancing FMS among young children, especially when considering LMSs. These interventions require consistent and repeated practice over time to reach proficiency.

Systematic Review Registration: PROSPERO, identifier number CRD42023475088, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023475088.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11242925PMC
http://dx.doi.org/10.3389/fpubh.2024.1345566DOI Listing

Publication Analysis

Top Keywords

unstructured interventions
24
structured unstructured
20
interventions fms
16
preschool-aged children
12
fms preschool-aged
12
interventions
9
effects structured
8
fundamental motor
8
motor skills
8
risk bias
8

Similar Publications

Transitioning to residency: a qualitative study exploring residents' perspectives on strategies for adapting to residency.

BMC Med Educ

January 2025

Center for Education Development and Research in Health Professions (CEDAR), Lifelong Learning, Education and Assessment Research Network (LEARN), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Background: The transition to residency (TTR) goes along with new opportunities for learning and development, which can also be challenging, despite the availability of preparation courses designed to ease the transition process. Although the TTR highly depends on the organization, individual combined with organizational strategies that advance adaptation are rarely investigated. This study explores residents' strategies and experiences with organizational strategies to help them adapt to residency.

View Article and Find Full Text PDF

Local-non-local complementary learning network for 3D point cloud analysis.

Sci Rep

January 2025

School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China.

Point cloud analysis is integral to numerous applications, including mapping and autonomous driving. However, the unstructured and disordered nature of point clouds presents significant challenges for feature extraction. While both local and non-local features are essential for effective 3D point cloud analysis, existing methods often fail to seamlessly integrate these complementary features.

View Article and Find Full Text PDF

Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine-based approach in diverse healthcare systems.

Clin Transl Sci

January 2025

Division of Digestive and Liver Diseases, Department of Medicine, Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York, New York, USA.

Nonalcoholic fatty liver disease (NAFLD) is the most common global cause of chronic liver disease and remains under-recognized within healthcare systems. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to recognize and stratify at-risk patients accurately.

View Article and Find Full Text PDF

Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differences and lack of well-structured resources. To address this challenge, this work proposes a topic modeling framework for unstructured health-related documents in Afaan Oromo using latent dirichlet allocation (LDA) algorithms.

View Article and Find Full Text PDF

Objective: The resurgence of syphilis in the United States presents a significant public health challenge. Much of the information needed for syphilis surveillance resides in electronic health records (EHRs). In this manuscript, we describe a surveillance platform for automating the extraction of EHR data, known as SmartChart Suite, and the results from a pilot.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!