An Energy Balance Framework for Evaluating Self-Reported Methods for Estimating Weight Change in Individuals

Date

2020-08

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Abstract

Weight change is explained by energy imbalance between energy intake (EI) and energy expenditure (EE). Because changes in EI and EE affect each other in a dynamic way, energy deficit that is produced by changes of physical activity energy expenditure (PAEE) or EI are often smaller than predicted. However, there are sources of error in estimating energy deficit such as measures of PAEE and EI (both objective and subjective methods) as well as confounding factors that affect inconsistent estimates of EB. Subjective self-reported data have been used to collect data in large populations due to the advantages of requiring few resources and the data require little processing, but they have low reliability and validity compared to objectively measured methods. Thus, it is necessary to find and develop methods to improve the accuracy of self-reported EI and EE measurement with accounting for possible confounding factors such as initial body composition, PA history, energy balance status, and racial differences. We hypothesized that the accuracy of weight change estimation would be improved by accounting for the variability attributable to these factors at the individual level. Therefore, this dissertation sought to identify methods to improve accuracy of estimating energy balance using self-reported EE and EI data by evaluating how various self-reported measures account for the possible confounding factors. This dissertation 1) tested accuracy for a range of predicted weight change estimation after a prescribed exercise intervention using measured and self-reported data and comparing to the observed weight changes, 2) examined how initial energy balance before participating in the exercise intervention affects the accuracy of energy balance estimates, and 3) examined the association of racial differences with accuracy of estimation for weight changes to the intervention. This dissertation found the predicted weight change estimations among the variety of self-reported PAEE methods were fairly consistent, with the predicted weight change by PAEE estimation using RPE being the most accurate self-reported method for estimation of PAEE used to predict weight changes after the prescribed intervention, followed by the combination of objectively measured HR and self-reported RPE. In addition, individuals showed considerable variability of estimated EB status before participating in the intervention and a small amount of positive EB before participating in the prescribed activity on average. The accuracy of predicted weight changes was improved by accounting for individual variability such as initial body composition, PA level, and especially baseline EB status. Finally, this dissertation observed racial differences of body size and composition at baseline, but race did not affect prediction of weight changes after accounting for these differences in predicting change in body weight. The findings of this dissertation indicate that accuracy of self-reported measures can be improved to be more feasible for use and analysis in large population research settings, primarily reducing bias and improving precision by accounting for individual variability in baseline characteristics and using a validated prediction model. Investigators need to consider including individual variability at baseline to improve estimation of expected energy deficit and the development of effective weight management intervention programs.

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Keywords

Energy Balance, Weight Changes, Self-reported Measures, Energy Expenditure, Physical Activity, Exercise Intervention, Prescribed Activity, the TIGER study, Energy Intake, Dietary Intake, Activity Log, Rating of Perceived Exertion, Heart Rate Monitor

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