charles matthews

PhD

NCI / DCEG / Metabolic Epidemiology Branch

Senior Investigator

IDATA (Learn more about this study)

IDATA-19

Dec 28, 2017

Influence of accelerometer calibration approach on MVPA estimates for adults

The use of accelerometers in epidemiologic studies has advanced our understanding of physical activity and public health, including providing the first objective estimates of moderate-vigorous intensity physical activity (MVPA) and sedentary time in the United States, and more recent indications that light intensity physical activities also may have mortality benefits. This progress was made with low tech “first generation” devices (e.g., vertical acceleration in 60 second epochs) worn at the waist, that were calibrated in the laboratory using ambulatory activities (i.e., walking and running) to identify activity count cut-points to estimate MVPA. The “next generation” of devices (e.g., 80Hz sampling in 3-axes) and associated calibration methods have moved on from use of activity count cut-points and are more refined and robust. As next generation devices have emerged, a variety of calibration study designs using only ambulatory activities or both lifestyle and ambulatory activities are being employed. However, it is unclear how differences in the activities included in the calibration study may affect the eventual estimates of MVPA in free-living adults derived from these tools and what the possible magnitude of this difference may be. Accordingly, the purpose of this investigation is to compare MVPA estimates from three methods calibrated only to ambulatory activities to three methods calibrated to lifestyle and ambulatory activities.
METHODS
Within the Interactive Diet and Activity Tracking in AARP (iDATA) study we proposed to evaluate data collected using the activPAL and ActiGraph devices, as well as data captured using ACT24.

To compare possible differences in MVPA values by the type of calibration study/method employed we propose to compare three objective methods calibrated to ambulatory activities to three objective methods calibrated more broadly.

Two ambulatory-based methods will rely on activity count cut-points (i.e., Freedson, Sasaki), and the third will rely on measures of steps/minute to estimate MVPA (activPAL). In contrast, three distinct methods calibrated more broadly will include one method that used an activity count cut-point (Matthews), another that integrated two-regression models to capture both ambulatory and lifestyle activity (Crouter), and a third that used hybrid machine learning to estimate MVPA events (Sojourn 3x). We will also explore data from previous-day recalls to better understand the breadth of activities contributing to MVPA among our participants.

Statistical Analysis
We will describe our study population using means and frequency counts overall and by sex. To examine differences in the MVPA estimates we will calculate mean MVPA values using all valid days of observation and will examine the distributions of each method (i.e., mean, SD, median, inter-quartile range). We will plot these values by sex. Paired t-tests will be used to test for differences between methods. To better understand the specific moderate-vigorous intensity activities in which adults in this study engaged, we also will evaluate previous-day recalls from ACT24.

1) To compare MVPA estimates from three accelerometer-based methods calibrated only to ambulatory activities to three methods calibrated to lifestyle and ambulatory activities.

2) To describe self-reported MVPA values overall, by domain, as well as the most common moderate and vigorous intensity activities reported in this population.

3) Influence of Accelerometer Calibration Approach on Moderate–Vigorous Physical Activity Estimates for Adults

Charles E. Matthews, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD

Sarah Kozey Keadle, Kinesiology Department, California Polytechnic State University, San Luis Obispo, CA

David Berrigan, Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD

John Staudenmayer, Department of Mathematics and Statistics, University of Massachusetts
Amherst, MA

Pedro Saint Maurice, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD

Richard P. Troiano, Risk Factor Assessment Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD

Patty S. Freedson, Department of Kinesiology, University of Massachusetts, Amherst, MA