Melinda W. Valliant

Nutrition education by a registered dietitian improves dietary intake and nutrition knowledge of a NCAA female volleyball team.

Eleven female participants from a NCAA Division I volleyball team were evaluated for adequate energy and macronutrient intake during two off-seasons. Total energy and macronutrient intake were assessed by food records and results were compared against estimated needs using the Nelson equation. Dietary intervention was employed regarding the individual dietary needs of each athlete as well as a pre- and post-sports nutrition knowledge survey. Post dietary intervention, total energy, and macronutrient intake improved, as well as a significant improvement in sports nutrition knowledge (p < 0.001). Nutrition education is useful in improving dietary intake and nutrition knowledge of female athletes.

A resting metabolic rate equation including bioelectrical impedance - derived lean body mass provides a better prediction in premenopausal African American women across a spectrum of body mass indices.

The purpose of this cross-sectional study was to develop, validate, and cross-validate an equation for predicting resting metabolic rate (RMR) in African American females. Data were collected from August 2004 to January 2005. Participants in the study included 100 African American women, 18 to 40 years old, with various body mass indices. Fifty participants were randomly selected to develop the equation and the remaining 50 were employed to test the equations. Bioelectrical impedance analysis (BIA), confirmed by dual-energy x-ray absorptiometry, was used to determine fat-free mass (FFM) and fat mass (FM) and RMR was measured by indirect calorimetry (ie, measured RMR [mRMR]) with a canopy system. A forward, stepwise, multiple regression analysis was performed with mRMR as the dependent variable and age, height, weight, FM, and FFM served as independent variables. The new Wells-Valliant equation was the only equation in this investigation that did not differ significantly from mRMR. When estimating RMR, it is imperative to select an equation that provides the best estimate of RMR for the population considered. The Wells-Valliant equation developed in this study includes FM and FFM, is more accurate than previous equations in estimating RMR in African American women, and can be calculated with more readily available BIA equipment.

Comparison of Air Displacement Plethysmography and Dual-Energy X-Ray Absorptiometry for Estimation of Body Fat Percentage in National Collegiate Athletic Association Division I Athletes

..........................................................................................................ii List of Abbreviations..........................................................................................iv List of Tables & Figures.......................................................................................vii Chapter 1: Introduction........................................................................................1 Chapter 2: Literature Review..................................................................................3 Hydrostatic Weighing.................................................................................3 Bioelectrical Impedance Analysis...................................................................3 Air Displacement Plethysmography...............................................................4 Dual-Energy X-Ray Absorptiometry...............................................................6 Air Displacement Plethysmography Versus Dual-Energy X-Ray Absorptiometry.......................................................................................7 Conclusion.............................................................................................9 Chapter 3: Methods...........................................................................................10 Bod Pod................................................................................................11 DXA....................................................................................................12 Statistical Analysis...................................................................................12 Chapter 4: Results............................................................................................13 Chapter 5: Discussion........................................................................................17 Discrepancies Between Bod Pod and DXA......................................................17 Clinical Implications................................................................................18

Associations between Multiple Indices of Energy Expenditure and Body Composition

There has been an explosion of research investigating the association between various indices of physical activity and markers of health, including body composition, with much of this research characterized by subjective measures and single cross-sectional assessments. The purpose was to examine relationships between multiple indices of energy expenditure (EE) and body composition (BC) using objective, clinical tools in a dual cross-sectional design. Males (n=14) wore the SenseWear PRO2 armband (SP2) for two 3-day periods (T1, T2) separated by 6 weeks. The SP2 measured four EE indices: total energy expenditure (TEE, kcal), physical activity energy expenditure (AEE, kcal), physical activity duration (PA, mins), and number of steps (STPS). DXA measured four BC indices: bone mineral content (BMC, g), lean soft tissue (LT, g), and adipose tissue (AT, %, g). Associations between EE and BC were examined using bivariate correlations. At T1, TEE was correlated with BMC (r= 0.841, p2, TEE was correlated to BMC (r=0.596, p=0.025), while AEE was associated with LT (r=0.535; p=0.049). TEE and AEE demonstrated consistent associations with fat free mass. Despite being key aspects of recommendations for health, physical activity duration and steps were not associated with any BC index nor was either index of fat mass associated with any measure of EE.

Research Gaps and Opportunities in Sensor-Based Medical Exploration Capabilities in Extravehicular Astronaut Suits

NASA’s Human Research Program has identified the need to improve their capability to predict estimated medical risks during exploration missions, as well as the need to provide computed medical decision support while minimizing medical resource utilization. From May 2017 through October 2017, a research team with the University of Mississippi Electrical Engineering Department conducted interviews with a dozen NASA personnel from the Human Research Program, Life Support Systems Technology Development, Game Changing Development Program, the Wyle Science, Technology and Engineering Group, and MEI Technologies. This report outlines the challenges and gaps identified as a result of these interviews in the Human Research Roadmap towards implementing a sensor-based medical monitoring system in EVA suits, as well as the opportunities in pursuing solutions towards these challenges. We describe the knowledge gaps in determining a clear definition for which measurable EVA suit environment and astronaut medical conditions are mission critical. We detail the current principle and special challenges of monitoring mission critical measurables in micro-gravity and zero-gravity environments with respect to their sensing ability. We then propose a three-stage research framework for meeting these objectives that is robust in scope, yet partitioned such that innovations or setbacks in one stage will not unduly harm progress. First, identifying a set mission critical measurable will enable innovations in sensor networks in EVA suits. We discuss proposed approaches to monitoring astronaut health and environments and relevant gaps. The second stage is using data from the sensor network as inputs to algorithms for determining when mission-critical parameters have been violated, as well as priorities for reporting that information. The third research area focuses on secure and reliable delivery of sensor information to the IV crewmembers, along with rendering of mission-critical information in a Heads-Up Display (HUD) worn by the astronaut. We discuss the current status of HUD technology in EVA suits and the challenges towards advancing that technology for mission deployment. We also discuss challenges in how the astronaut and IV crewmembers will utilize logged health and tracking information operationally. Finally, the status of the work already conducted under the proposed research framework is discussed.