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Statistical Modeling of Dietary Intake and Weight Gain During Pregnancy Open Access


Other title
Dietary intake assessment
Functional principal component analysis
Longitudinal data
Gestational weight gain
Type of item
Degree grantor
University of Alberta
Author or creator
Che, Menglu
Supervisor and department
Linglong Kong (Mathematical and Statistical Sciences)
Yan Yuan (School of Public Health)
Examining committee member and department
Ivan Mizera (Mathematical and Statistical Science)
Linglong Kong (Mathematical and Statistical Science)
Yan Yuan (School of Public Health)
Ying Cui (Faculty of Education)
Department of Mathematical and Statistical Sciences
Date accepted
Graduation date
2016-06:Fall 2016
Master of Science
Degree level
BACKGROUND: Healthy dietary intake and appropriate weight gain are two key components of an ideal pregnancy. The objective of this thesis was to investigate the weight gain pattern of a large cohort of pregnant women and its association with dietary intakes, which may provide valuable information for the clinical intervention of inappropriate gestational weight gain. Two instruments were used to capture the dietary intakes, the level of agreement and possibility of pooling the results need to be studied. METHOD: For a validation sample of 58 child-bearing-age women, the total calories intakes captured by two instruments were compared by the Bland-Altman plot. The intakes of key nutrient captured by the interviewer-administered instrument version were predicted by the nutrient intakes from the web-based version with a regression model. Then we estimated the weight growth trajectories of each subject through functional principal component analysis techniques. The total weight gain predicted from the trajectory was then regressed on the prepregnancy body mass index, and dietary intakes and physical activities which were measured through pregnancy. RESULTS: We found that the relative bias between the two instruments were small, yet the variances in individuals could be large. Energy-adjusted intakes of macronutrients showed reasonable correlations between the two instruments (0.56 for fat, 0.73 for protein, and 0.67 for carbohydrate). LASSO regularization based multiple regression greatly improved the cross-validated R2 for folate from 0.0033 to 0.46. Our estimated weight growth trajectories showed good accuracy when compared to classic mixedeffect models with significant smaller root mean squared error. The predicted weight gain from trajectory had a strong correlation with prepregnancy body mass index, but adding the dietary intake and physical activity information did not improve the R2 of the model. CONCLUSIONS: Direct pooling of the results from the two instruments may not be feasible. But when pooling is considered, energy-adjustment for macronutrients and the LASSO-based multiple regression for micronutrients are recommended. Functional principal component analysis has significant advantages of flexibility and robustness for the weight growth trajectory modeling. We found that the weight gain during pregnancy negatively correlated to prepregnancy BMI, but the dietary intake and physical activities measured in our study did not provide useful information in predicting the weight gain.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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