Managing gestational weight gain (GWG) offers lifelong health benefits in both mothers and their offspring (e.g., reducing risk of preeclampsia, development of metabolic syndrome, obesity, cardiovascular disease). Because overweight and obese pregnant women (OW/OBPW) often exceed GWG guidelines and have difficulty with managing weight, there is a critical need to identify effective weight management interventions for this population. An individually-tailored intervention that provides OW/OBPW with support for managing GWG on a weekly basis and adapts to their unique needs over pregnancy may be a highly promising way to prevent high GWG. We have synergistically integrated methods/key concepts from the behavioral sciences and control systems engineering to construct a framework for an individually-tailored, behavioral intervention (e.g., components of education, goal-setting, self-monitoring, and engaging in healthy eating/ physical activity [PA] behaviors) to control GWG in OW/OBPW. This intervention has several unique features: (a) individualized treatment to manage GWG on a weekly basis over pregnancy, (b) a validated differential equation model for energy balance to predict GWG trajectories over pregnancy and provide feedback in real- time to adapt treatment as needed, (c) e-health technology to promote self-monitoring and collect data on weight, dietary intake, PA, and psychological factors, and (d) control systems engineering to relate intensive data collected on each participant and dynamical systems modeling to optimize this intervention;in other words, manage GWG in OW/OBPW as effectively and efficiently as possible. The proposed research aims are to first, establish feasibility of delivering this individually-tailored intervention for managing GWG in OW/OBPW by conducting two studies to examine viability of delivering intervention dosages and component sequencing, procedures for self-monitoring of GWG, dietary intake, and PA with e-health technology mechanisms, randomization/retention/data collection procedures with treatment and control groups, and to establish user acceptability. Second, control systems engineering will be used to relate intensive data collected on each participant to a dynamical model that considers how changes in GWG responds to changes in energy intake, PA, and planned/self-regulatory behaviors. We will then make modifications to the intervention and identify a customized intervention plan for each woman;resulting in an optimized (effective and efficient) intervention. We will test the efficacy of this optimized intervention for managing GWG in OW/OBPW in a future randomized controlled trial. Our long-range goal is to make this intervention available to all pregnant women (via e- health technology) to improve the health of mothers and infants and impact the etiology of obesity and cardiovascular disease at a critical time in the life cycle. Thi research compliments the over-reaching goal of NIH to improve maternal/infant health and it is consistent with NHLBI's mission to promote research to reduce the burden of heart, lung, and blood diseases and their related comorbidities worldwide.
Managing gestational weight gain (GWG) offers lifelong health benefits in both mothers and their offspring;however, overweight and obese pregnant women (OW/OBPW) have particular difficulty controlling GWG and therefore requires an intensive and hands-on approach. The proposed research aims to establish feasibility of delivering an individually-tailored intervention that adapts to the unique needs and challenges of OW/OBPW and will utilize control systems engineering to optimize this intervention;in other words, make this intervention manage GWG in OW/OBPW as effectively and efficiently as possible. This research compliments the over- reaching goal of NIH to improve maternal/infant health and it is consistent with NHLBI's mission to promote research to reduce the burden of heart, lung, and blood diseases and their related comorbidities worldwide.
|Downs, Danielle Symons (2016) Obesity in Special Populations: Pregnancy. Prim Care 43:109-20, ix|
|Guo, Penghong; Rivera, Daniel E; Downs, Danielle S et al. (2016) Semi-physical Identification and State Estimation of Energy Intake for Interventions to Manage Gestational Weight Gain. Proc Am Control Conf 2016:1271-1276|
|Downs, Danielle Symons; Devlin, Courtenay A; Rhodes, Ryan E (2015) The Power of Believing: Salient Belief Predictors of Exercise Behavior in Normal Weight, Overweight, and Obese Pregnant Women. J Phys Act Health 12:1168-76|
|Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E et al. (2014) Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions. Proc Am Control Conf 2014:4198-4203|
|Downs, Danielle Symons; Savage, Jennifer S; Rauff, Erica L (2014) Falling Short of Guidelines? Nutrition and Weight Gain Knowledge in Pregnancy. J Womens Health Care 3:|
|Savage, Jennifer S; Downs, Danielle Symons; Dong, Yuwen et al. (2014) Control systems engineering for optimizing a prenatal weight gain intervention to regulate infant birth weight. Am J Public Health 104:1247-54|
|Dong, Yuwen; Rivera, Daniel E; Downs, Danielle S et al. (2013) Hybrid Model Predictive Control for Optimizing Gestational Weight Gain Behavioral Interventions. Proc Am Control Conf :1970-1975|