Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach.

Hashem Salarzadeh Jenatabadi,Che Wan Jasimah Bt Wan Mohamed Radzi,Nadia Samsudin

doi:10.3390/ijerph17145201

Hashem Salarzadeh Jenatabadi, Che Wan Jasimah Bt Wan Mohamed Radzi + Show 1 more

Open Access

https://doi.org/10.3390/ijerph17145201

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Abstract

As postpartum obesity is becoming a global public health challenge, there is a need to apply postpartum obesity modeling to determine the indicators of postpartum obesity using an appropriate statistical technique. This research comprised two phases, namely: (i) development of a previously created postpartum obesity modeling; (ii) construction of a statistical comparison model and introduction of a better estimator for the research framework. The research model displayed the associations and interactions between the variables that were analyzed using the Structural Equation Modeling (SEM) method to determine the body mass index (BMI) levels related to postpartum obesity. The most significant correlations obtained were between BMI and other substantial variables in the SEM analysis. The research framework included two categories of data related to postpartum women: living in urban and rural areas in Iran. The SEM output with the Bayesian estimator was 81.1%, with variations in the postpartum women’s BMI, which is related to their demographics, lifestyle, food intake, and mental health. Meanwhile, the variation based on SEM with partial least squares estimator was equal to 70.2%, and SEM with a maximum likelihood estimator was equal to 76.8%. On the other hand, the output of the root mean square error (RMSE), mean absolute error (MSE) and mean absolute percentage error (MPE) for the Bayesian estimator is lower than the maximum likelihood and partial least square estimators. Thus, the predicted values of the SEM with Bayesian estimator are closer to the observed value compared to maximum likelihood and partial least square. In conclusion, the higher values of R-square and lower values of MPE, RMSE, and MSE will produce better goodness of fit for SEM with Bayesian estimators.

Highlights

High-income countries are often plagued with an obesity pandemic [1]
Mean absolute percentage error (MPE) for the Bayesian estimator is lower than the maximum likelihood and partial least square estimators
The predicted values of the Structural Equation Modeling (SEM) with Bayesian estimator are closer to the observed value compared to maximum likelihood and partial least square

Summary

Introduction

High-income countries are often plagued with an obesity pandemic [1]. The populations in low- and middle-income countries that are affected by obesity and overweight problems has risen, especially in urbanized areas [2]. Based on the world population in 2016, an estimated 2.1 billion people were found to be overweight, of which 650 million were obese [5]. It is predicted that half of the population will be affected by obesity and overweight by 2030 [6]. According to the WHO, worldwide obesity has nearly tripled since 1975, with 39% of the adult population being overweight, and 13% suffering from obesity [1]

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