Analytics (Dec 2023)
Exploring Infant Physical Activity Using a Population-Based Network Analysis Approach
Abstract
Background: Physical activity (PA) is an important aspect of infant development and has been shown to have long-term effects on health and well-being. Accurate analysis of infant PA is crucial for understanding their physical development, monitoring health and wellness, as well as identifying areas for improvement. However, individual analysis of infant PA can be challenging and often leads to biased results due to an infant’s inability to self-report and constantly changing posture and movement. This manuscript explores a population-based network analysis approach to study infants’ PA. The network analysis approach allows us to draw conclusions that are generalizable to the entire population and to identify trends and patterns in PA levels. Methods: This study aims to analyze the PA of infants aged 6–15 months using accelerometer data. A total of 20 infants from different types of childcare settings were recruited, including home-based and center-based care. Each infant wore an accelerometer for four days (2 weekdays, 2 weekend days). Data were analyzed using a network analysis approach, exploring the relationship between PA and various demographic and social factors. Results: The results showed that infants in center-based care have significantly higher levels of PA than those in home-based care. Moreover, the ankle acceleration was much higher than the waist acceleration, and activity patterns differed on weekdays and weekends. Conclusions: This study highlights the need for further research to explore the factors contributing to disparities in PA levels among infants in different childcare settings. Additionally, there is a need to develop effective strategies to promote PA among infants, considering the findings from the network analysis approach. Such efforts can contribute to enhancing infant health and well-being through targeted interventions aimed at increasing PA levels.
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