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Accuracy of physical activity and eating behavior assessment with diverse outcome measures in 50 - 74-year-old adults

Principal Investigator
Name
Alexis Ortiz
Degrees
PT, PhD
Institution
Texas Woman's University-Houston
Position Title
Professor-Research Development Officer
Email
About this CDAS Project
Study
IDATA (Learn more about this study)
Project ID
IDATA-1
Initial CDAS Request Approval
Jul 26, 2016
Title
Accuracy of physical activity and eating behavior assessment with diverse outcome measures in 50 - 74-year-old adults
Summary
The array of assessment tools to measure physical activity and eating behaviors in diverse populations is vast. However, the accuracy of these assessment tools to estimate energy expenditure is questionable even more in the elderly population. We will explore which of these combination of outcomes can help estimate more accurately total energy expenditure. Although self-reported measures of physical activity have the convenience for large-scale studies they have shown low correlations with objective measures of physical activity due to over or under estimation depending in the population of interest. Therefore, this proposal will aid determine which self-reported measures correlate with objective measures of physical activity in the elderly population. All these assessments of physical activity and eating behaviors pursue to elucidate part of the obesity epidemic in our Nation. Assessment of the different eating behaviors and physical activity patterns of this population will help predict specific categories of body composition, such as normal, overweight, and obese.
Aims

Aim: With the purpose of estimating total energy expenditure (TEE) in the fastest and least invasive manner for large scale studies we will estimate those potential outcomes that could predict TEE in individuals 50-74 years of age. Our research question to address this aim is:
What combination of self-reported physical activity (SRPA), objective physical activity (OPA), food questionnaire (FQ), and anthropometric (ant) measures can predict TEE. To accomplish this aim we will perform logistic regression with all previous measures to estimate what regression formula approximates more to TEE measured by double labeled water.
Data Analysis: Stepwise Multiple Regression will be performed to select the best predictors on total energy expenditure. Analyses will be conducted for the total population as well as by sex.

Collaborators

1. Dr. Mindy Maziarz, PhD, RDN; Department of Nutrition & Food Sciences, Texas Woman's University-Houston
2. Wanyi Wang, PhD; Office of Research & Sponsored Programs, Texas Woman's University-Houston

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