A "Model-on-Demand" Methodology For Energy Intake Estimation to Improve Gestational Weight Control Interventions.

Energy intake underreporting is a frequent concern in weight control interventions. In prior work, a series of estimation approaches were developed to better understand the issue of underreporting of energy intake; among these is an approach based on semi-physical identification principles that adjusts energy intake self-reports by obtaining a functional relationship for the extent of underreporting. In this paper, this global modeling approach is extended, and for comparison purposes, a local modeling approach based on the concept of Model-on-Demand (MoD) is developed. The local approach displays comparable performance, but involves reduced engineering e ort and demands less a priori information. Cross-validation is utilized to evaluate both approaches, which in practice serves as the basis for selecting parsimonious yet accurate models. The effectiveness of the enhanced global and MoD local estimation methods is evaluated with data obtained from Healthy Mom Zone, a novel gestational weight intervention study focused on the needs of obese and overweight women.

State Estimation Under Correlated Partial Measurement Losses: Implications for Weight Control Interventions.

The growing prevalence of obesity and related health problems warrants immediate need for effective weight control interventions. Quantitative energy balance models serve as powerful tools to assist in these interventions, as a result of their ability to accurately predict individual weight change based on reliable measurements of energy intake and energy expenditure. However, the data collected in most existing weight interventions is self-monitored; these measurements often have significant noise or experience losses resulting from participant non-adherence, which in turn, limits accurate model estimation. To address this issue, we develop a Kalman filter-based estimation algorithm for a practical scenario where on-line state estimation for weight, or energy intake/expenditure is still possible despite correlated partial data losses. To account for non-linearities in the models, an algorithm based on extended Kalman filtering is also developed for sequential state estimation in the presence of missing data. Simulation studies are presented to illustrate the performance of the algorithms and the potential benefits of these techniques in real-life interventions.

Perceived Exertion during Exercise Is Associated with Children's Energy Intake.

PURPOSE: To examine the individual-level factors that predict energy intake (EI) after imposed exercise (EX) and sedentary time (SED) in children. METHODS: Healthy-weight children ages 9-12 yr (n = 20) reported to the laboratory for one baseline and two experimental visits (EX and SED) each separated by 1 wk in a randomized crossover design. Percent body fat, weight (kg), and height (m) were used to calculate fat-mass index (FM index) and fat-free mass index (FFM index; kg·m). On the EX day, children exercised at 70% estimated V˙O2peak for 30 min on a cycle ergometer, whereas cardiovascular responses and RPE were measured. Objective EI (kcal) was measured at identical meals (breakfast, lunch, snack, and dinner) on the EX and SED days. RESULTS: Total EI was not statistically different between the EX and SED days (t = 1.8, P = 0.09). FFM index was positively associated with EI on the EX day (r = 0.54, P < 0.05). RPE was also positively associated with EI on the EX day (r = 0.82, P < 0.001). Together, FFM index and RPE explained 77% of the variability in EX day EI (F(2,17) = 26.4, P < 0.001). For each unit increase in RPE, children consumed approximately 270 more calories on the EX day. A similar pattern of associations was observed on the SED day. CONCLUSIONS: FFM index was positively associated with EI on the EX day. Despite experiencing the same 70% relative exercise intensity, increased perceived difficulty predicted greater EI on both the EX and SED day. These findings demonstrate a role for both FFM and RPE in explaining EI variability in children.

Impact of imposed exercise on energy intake in children at risk for overweight.

BACKGROUND: Exercise not only has a direct effect on energy balance through energy expenditure (EE), but also has an indirect effect through its impact on energy intake (EI). This study examined the effects of acute exercise on daily ad libitum EI in children at risk for becoming overweight due to family history. METHODS: Twenty healthy-weight children (ages 9-12 years, 12 male/8 female) with at least one overweight biological parent (body mass index ≥ 25 kg/m2) participated. Children reported to the laboratory for one baseline and two experimental visits (EX = exercise, SED = sedentary) each separated by 1 week in a randomized crossover design. Two hours into the EX day session, children exercised at 70 % estimated VO2max for 30 min on a cycle ergometer. Objective EI (kcal) was measured at a standard breakfast (~285 kcal) and ad libitum lunch, snack and dinner. Meals were identical on the EX and SED days. Activity-related EE (kcal) was estimated with accelerometers worn on the non-dominant wrist and ankle. Relative EI (kcal) was computed as the difference between Total EI and Activity-related EE for each testing day. Paired t-tests were performed to test differences in Total EI, Activity-related EE and Relative EI between the EX and SED days. RESULTS: Across all meals, Total EI was not statistically different between the EX and SED days (t = 1.8, p = 0.09). Activity-related EE was greater on the EX day compared to the SED day (t = 10.1, p < 0.001). By design, this difference was predominantly driven by activity during the morning (t = 20.4, p < 0.001). Because children consumed a similar number of kcal on each day, but had greater Activity-related EE on the EX day, Relative EI was lower (t = -5.15, p < 0.001) for the EX day (1636 ± 456 kcal) relative to the SED day (1862 ± 426 kcal). CONCLUSIONS: Imposed exercise was effective in reducing Relative EI compared to being sedentary. Morning exercise may help children at risk for becoming overweight to better regulate their energy balance within the course of a day.

Semi-physical Identification and State Estimation of Energy Intake for Interventions to Manage Gestational Weight Gain.

Excessive gestational weight gain (i.e., weight gain during pregnancy) is a significant public health concern, and has been the recent focus of novel, control systems-based interventions. This paper develops a control-oriented dynamical systems model based on a first-principles energy balance model from the literature, which is evaluated against participant data from a study targeted to obese and overweight pregnant women. The results indicate significant under-reporting of energy intake among the participant population. A series of approaches based on system identification and state estimation are developed in the paper to better understand and characterize the extent of under-reporting; these range from back-calculating energy intake from a closed-form of the energy balance model, to a constrained semi-physical identification approach that estimates the extent of systematic under-reporting in the presence of noise and possibly missing data. Additionally, we describe an adaptive algorithm based on Kalman filtering to estimate energy intake in real-time. The approaches are illustrated with data from both simulated and actual intervention participants.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

Hybrid Model Predictive Control for Optimizing Gestational Weight Gain Behavioral Interventions.

Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.

Pregnant women exaggerate cautious gait patterns during the transition between level and hill surfaces.

Falls are the leading cause of nonfatal injury across all age groups and a common incident for pregnant women. Thus, there is a critical demand for research to evaluate if walking strategies in pregnant women change throughout pregnancy in order to effectively intervene and minimize the incidence rate. The aim of the present study was to analyze modifications in temporal-spatial parameters as well as muscle activity during hill walking transitions in pregnant women between gestational week 20 and 32. Based upon previous literature, we hypothesized that in comparison to level walking, the transition strides of pregnant women would be distinct between trimesters in order to accommodate the physical changes within twelve weeks. Thirteen pregnant women completed a series of randomly assigned walking conditions on level and hill surfaces during gestational week 20 and 32. Our results demonstrated that pregnant women modulated their gait patterns throughout pregnancy with additional joint flexion as well as muscle activity at the ankle, knee and hip. In summary, pregnant women exaggerate cautious gait patterns by walking slower and wider with greater joint flexion and muscle activity in order to safely transition between level and hill surfaces.

Postpartum anxiety and maternal-infant health outcomes.

OBJECTIVE: Postpartum anxiety screening does not typically occur, despite changes in life roles and responsibility after childbirth. We sought to determine the prevalence of postpartum anxiety during the maternity hospitalization and its associations with maternal and child outcomes. We further aimed to compare correlates of anxiety with correlates of depression. METHODS: For a randomized controlled trial of mothers with "well" newborns ≥34 weeks' gestation comparing 2 post-hospital discharge care models, mothers completed baseline in-person interviews during the postpartum stay and telephone surveys at 2 weeks, 2 months, and 6 months to assess health care use, breastfeeding duration, anxiety, and depression. All participants intended to breastfeed. State anxiety scores ≥40 on the State Trait Anxiety Inventory (STAI) and depression scores ≥12 on the Edinburgh Postnatal Depression Survey (EPDS) were considered positive. RESULTS: A total of 192 (17%) of 1123 participating mothers had a positive baseline STAI; 62 (6%) had a positive EPDS. Primiparity was associated with a positive STAI (20% vs 15%, P = .02), but not a positive EPDS (4% vs 7%, P = .05). Positive STAI scores were associated with cesarean delivery (22% vs 15%, P = .001), reduced duration of breastfeeding (P = .003), and increased maternal, but not infant total unplanned health care utilization within 2 weeks of delivery (P = .001). Positive STAI scores occurred more frequently than positive EPDS scores at each assessment through 6 months postpartum. CONCLUSIONS: Postpartum state anxiety is a common, acute phenomenon during the maternity hospitalization that is associated with increased maternal health care utilization after discharge and reduced breastfeeding duration. State anxiety screening during the postpartum stay could improve these outcomes.

A Dynamical Systems Model for Improving Gestational Weight Gain Behavioral Interventions.

Excessive gestational weight gain (GWG) represents a major public health concern. In this paper, we present a dynamical systems model that describes how a behavioral intervention can influence weight gain during pregnancy. The model relies on the integration of a mechanistic energy balance with a dynamical behavioral model. The behavioral model incorporates some well-accepted concepts from psychology: the Theory of Planned Behavior (TPB) and the principle of self-regulation which describes how internal processes within the individual can serve to reinforce the positive outcomes of an intervention. A hypothetical case study is presented to illustrate the basic workings of the model and demonstrate how the proper design of the intervention can counteract natural trends towards declines in healthy eating and reduced physical activity during the course of pregnancy. The model can be used by behavioral scientists to evaluate decision rules for adaptive time-varying behavioral interventions, or as the open-loop model for hybrid model predictive control algorithms acting as decision frameworks for such interventions.