Diana M Thomas1, Emily Oken2, Sheryl L Rifas-Shiman2, Martha Téllez-Rojo3, Allan Just4, Katherine Svensson4, Andrea L Deierlein5, Paula C Chandler-Laney6, Richard C Miller7, Christopher McNamara8, Suzanne Phelan9, Shaw Yoshitani1, Nancy F Butte10, Leanne M Redman11. 1. Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA. 2. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA. 3. Program Evaluation and Biostatistics, Instituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico. 4. Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 5. School of Public Health, New York University, New York, New York, USA. 6. Department of Nutrition, University of Alabama, Birmingham, Alabama, USA. 7. Department of Obstetrics and Gynecology, Saint Barnabas Medical Center, Livingston, New Jersey, USA. 8. Medical Student Research Institute, Saint George's University, Grenada. 9. Department of Kinesiology, California Polytechnic State University, San Luis Obispo, California, USA. 10. USDA/Agricultural Research Service, Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA. 11. Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.
Abstract
OBJECTIVE: Prepregnancy weight may not always be known to women. A model was developed to estimate prepregnancy weight from measured pregnancy weight. METHODS: The model was developed and validated using participants from two studies (Project Viva, n = 301, model development; and Fit for Delivery [FFD], n = 401, model validation). Data from the third study (Programming Research in Obesity, Growth, Environment and Social Stressors [PROGRESS]), which included women from Mexico City, were used to demonstrate the utility of the newly developed model to objectively quantify prepregnancy weight. RESULTS: The model developed from the Project Viva study validated well with low bias (R2 = 0.95; y = 1.02x - 0.69; bias = 0.68 kg; 95% CI: -4.86 to 6.21). Predictions in women from FFD demonstrated good agreement (R2 = 0.96; y = 0.96x + 4.35; bias = 1.60 kg; 95% CI: -4.40 to 7.54; error range = -11.25 kg to 14.73 kg). High deviations from model predictions were observed in the Programming Research in PROGRESS (R2 = 0.81; y = 0.89x + 9.61; bias = 2.83 kg; 95% CI: -7.70 to 12.31; error range = -39.17 kg to 25.73 kg). The model was programmed into software (https://www.pbrc.edu/research-and-faculty/calculators/prepregnancy/). CONCLUSIONS: The developed model provides an alternative to determine prepregnancy weight in populations receiving routine health care that may not have accurate knowledge of prepregnancy weight. The software can identify misreporting and classification into incorrect gestational weight gain categories.
OBJECTIVE: Prepregnancy weight may not always be known to women. A model was developed to estimate prepregnancy weight from measured pregnancy weight. METHODS: The model was developed and validated using participants from two studies (Project Viva, n = 301, model development; and Fit for Delivery [FFD], n = 401, model validation). Data from the third study (Programming Research in Obesity, Growth, Environment and Social Stressors [PROGRESS]), which included women from Mexico City, were used to demonstrate the utility of the newly developed model to objectively quantify prepregnancy weight. RESULTS: The model developed from the Project Viva study validated well with low bias (R2 = 0.95; y = 1.02x - 0.69; bias = 0.68 kg; 95% CI: -4.86 to 6.21). Predictions in women from FFD demonstrated good agreement (R2 = 0.96; y = 0.96x + 4.35; bias = 1.60 kg; 95% CI: -4.40 to 7.54; error range = -11.25 kg to 14.73 kg). High deviations from model predictions were observed in the Programming Research in PROGRESS (R2 = 0.81; y = 0.89x + 9.61; bias = 2.83 kg; 95% CI: -7.70 to 12.31; error range = -39.17 kg to 25.73 kg). The model was programmed into software (https://www.pbrc.edu/research-and-faculty/calculators/prepregnancy/). CONCLUSIONS: The developed model provides an alternative to determine prepregnancy weight in populations receiving routine health care that may not have accurate knowledge of prepregnancy weight. The software can identify misreporting and classification into incorrect gestational weight gain categories.
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