Comparison of the ActiGraph 7164 and the ActiGraph GT1M during Self-Paced Locomotion

KOZEY, SARAH L.1; STAUDENMAYER, JOHN W.2; TROIANO, RICHARD P.3; FREEDSON, PATTY S.1

Medicine & Science in Sports & Exercise:
doi: 10.1249/MSS.0b013e3181c29e90
Basic Sciences
Abstract

Purpose: This study compared the ActiGraph accelerometer model 7164 (AM1) with the ActiGraph GT1M (AM2) during self-paced locomotion.

Methods: Participants (n = 116, aged 18-73 yr, mean body mass index = 26.1 kg·m−2) walked at self-selected slow, medium, and fast speeds around an indoor circular hallway (0.47 km). Both activity monitors were attached to a belt secured to the hip and simultaneously collected data in 60-s epochs. To compare differences between monitors, the average difference (bias) in count output and steps output was computed at each speed. Time spent in different activity intensities (light, moderate, and vigorous) based on the cut points of Freedson et al. was compared for each minute.

Results: The mean ± SD walking speed was 0.7 ± 0.22 m·s−1 for the slow speed, 1.3 ± 0.17 m·s−1 for medium, and 2.1 ± 0.61 m·s−1 for fast speeds. Ninety-five percent confidence intervals (95% CI) were used to determine significance. Across all speeds, step output was significantly higher for the AM1 (bias = 19.8%, 95% CI = −23.2% to −16.4%) because of the large differences in step output at slow speed. The count output from AM2 was a significantly higher (2.7%, 95% CI = 0.8%-4.7%) than that from AM1. Overall, 96.1% of the minutes were classified into the same MET intensity category by both monitors.

Conclusions: The step output between models was not comparable at slow speeds, and comparisons of step data collected with both models should be interpreted with caution. The count output from AM2 was slightly but significantly higher than that from AM1 during the self-paced locomotion, but this difference did not result in meaningful differences in activity intensity classifications. Thus, data collected with AM1 should be comparable to AM2 across studies for estimating habitual activity levels.

Author Information

1Department of Kinesiology, University of Massachusetts, Amherst, MA; 2Department of Math and Statistics, University of Massachusetts, Amherst, MA; and 3National Cancer Institute, Risk Factor Monitoring and Methods Branch, Bethesda, MD

Submitted for publication April 2009.

Accepted for publication September 2009.

Address for correspondence: Patty S. Freedson, Ph.D., 110 Totman Bldg, 30 Eastman Ln, Amherst, MA 01003; E-mail: psf@kin.umass.edu.

©2010The American College of Sports Medicine