Related Experiment Videos
Factors Associated with Sedentary Behavior in Pregnant Women: Insights from Multiple Linear Regression and Random
Lei Zeng1, Rongrong Han1, Jiayuan Ma2
1School of Nursing, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Purpose:
High levels of sedentary behavior (sitting for more than 6 hours a day) during pregnancy are a public health concern. Understanding the prevalence, patterns, and associated factors of sedentary behavior is important to develop targeted interventions, yet contemporary evidence remains limited. This study aimed to investigate the prevalence and patterns of sedentary behavior and identify its associated factors among pregnant women using the Systems of Sedentary Behavior framework.
Patients And Methods:
A cross-sectional study was conducted in Guangzhou, China, from October 2024 to April 2025. We recruited 655 pregnant women from a single tertiary hospital who completed the sedentary behavior subscale and physical activity subscale of the Pregnancy Physical Activity Questionnaire, the Pregnancy Discomforts Questionnaire, the Physical Activity Neighborhood Environment Scale, and a sociodemographic and obstetrics data sheet. Multiple linear regression and random forest models were conducted to explore the associated factors.
Results:
The prevalence of high sedentary behavior in pregnant women was 57.3%, with a mean sedentary time of 44.11 (27.71) hours per week. Occupational sedentary behavior consumed the most time. Both models identified household physical activity, pregnancy discomfort, access to destinations, occupation, and insurance as significant factors. Additionally, age and residence were specific to the multiple linear regression model, while income was unique to the random forest model.
Conclusion:
Healthcare providers should focus on younger, employed, high-income, and urban women who are at high risk for high sedentary behavior. Increasing household physical activity and alleviating pregnancy-related discomfort may be associated with less sedentary time, while improving access to exercise facilities and insurance coverage may also warrant consideration; however, these should be framed as potential targets requiring further prospective evaluation.
Related Concept Videos
Regression Toward the Mean
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Statistical Methods for Analyzing Epidemiological Data