energy balance teens: a measurement error approach to estimating energy balance in free-living adolescents

Conditions: Childhood Obesity

Summary: there is a critical need to develop an affordable, valid, and reliable techniques to assess free-living energy expenditure (ee), energy storage (es), and energy intake (ei). the purpose of this project is to develop and evaluate statistical procedures to model, quantify and adjust for the measurement error of and consumer (e.g., garmin) activity monitors and body composition scales to estimate ee and es, and use the 'calibrated' values to estimate free-living ei.

dietary intake and physical activity are important lifestyle behaviors that have a profound role in the development of my many chronic diseases, including heart disease, diabetes, kidney disease, certain cancers, and overweight/obesity. it is clear that there are a multitude of physiological, environmental, and behavioral factors that influence obesity risk, but at the most basic level body weight is determined by the energy balance of energy intake (ei) and energy expenditure (ee). standard assessment techniques of ei in population-based studies rely on individuals to self-report the foods they eat, but these estimates are typically 12-31% below expected values. this has led expert dieticians and nutritional epidemiologists to declare 'ei is inaccurately measured by self-report' and 'wholly unacceptable for scientific research.' thus, there is a critical need to develop an affordable, valid, and reliable techniques to assess free-living ee, energy storage (es), and ei. the investigator's long-term goal is to assess the components of energy balance to better inform obesity prevention and treatment. the short-term goal, and the purpose of this application, is to develop and evaluate statistical procedures to model, quantify and adjust for the measurement error of and consumer (e.g., fitbit) activity monitors and body composition scales to estimate ee and es, and use the 'calibrated' values to estimate free-living ei. the status quo as it relates to the use of non-gold standard devices is that there exists large variability compared to criterion measures that may produce erroneous estimates, particularly in mean ee, making their use at a population-level ill-advised. in contrast, the investigator's working hypothesis is that the error inherent in consumer devices can be quantified and adjusted for, allowing for the accurate assessment of ee and es. the purpose of this project is to apply measurement error techniques to a pilot sample (n=24) of free-living adolescents to improve energy balance estimates. the investigators will use a consumer physical activity monitor (garmin vivofit 4), and a consumer body composition analyzer (garmin smartscale) to estimate daily ee and change in es over two consecutive 14-day periods, separated by a 14-day washout period. the investigators will develop calibration models using simultaneously collected gold-standard techniques including doubly labeled water for ee and duel-energy x-ray absorptiometry for es as references. the investigators will use the calibrated ee and es to calculate ei using the intake-balance technique. lastly, the investigators will evaluate the feasibility and acceptability of the protocols and methodology. at the completion of the proposed study, it is the investigators expectation that they will have generated important pilot data and assessed project feasibility in adolescents for a large-scale nih r01 application. a fully powered study will improve the assessment of ee and es in free-living conditions using research grade and consumer devices, allowing for the estimation of ei with greater accuracy than currently available techniques. this project will make significant advancements on the assessment of energy balance in free-living settings.

Eligibility criteria:
inclusion criteria: * all participants will need to have an in-home wi-fi network and access to a smartphone that can operate mobile applications to automatically sync with the garmin devices * bmi percentile between 5th and 99th exclusion criteria: -bmi percentile \ 99th