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Int J Behav Nutr Phys Act. 2011 Aug 24;8:92. doi: 10.1186/1479-5868-8-92.

Validity of estimating minute-by-minute energy expenditure of continuous walking bouts by accelerometry.

Author information

  • 1Department of Health, Exercise & Rehabilitative Services, PO Box 5838, 175 W Mark Street, Room 358 Maxwell, Winona, MN 55987, USA. ekuffel@winona.edu

Abstract

BACKGROUND:

Objective measurement of physical activity remains an important challenge. For wearable monitors such as accelerometer-based physical activity monitors, more accurate methods are needed to convert activity counts into energy expenditure (EE).

PURPOSE:

The purpose of this study was to examine the accuracy of the refined Crouter 2-Regression Model (C2RM) for estimating EE during the transition from rest to walking and walking to rest. A secondary purpose was to determine the extent of overestimation in minute-by-minute EE between the refined C2RM and the 2006 C2RM.

METHODS:

Thirty volunteers (age, 28 ± 7.7 yrs) performed 15 minutes of seated rest, 8 minutes of over-ground walking, and 8 minutes of seated rest. An ActiGraph GT1M accelerometer and Cosmed K4b2 portable metabolic system were worn during all activities. Participants were randomly assigned to start the walking bout at 0, 20, or 40 s into the minute (according to the ActiGraph clock). Acceleration data were analyzed by two methods: 2006 Crouter model and a new refined model.

RESULTS:

The 2006 Crouter 2-Regression model over-predicted measured kcal kg-1 hr-1 during the first and last transitional minutes of the 20-s and 40-s walking conditions (P < 0.001). It also over-predicted the average EE for a walking bout (4.0 ± 0.5 kcal kg-1 hr-1), compared to both the measured kcal kg-1 hr-1 (3.6 ± 0.7 kcal kg-1 hr-1) and the refined Crouter model (3.5 ± 0.5 kcal kg-1 hr-1) (P < 0.05).

CONCLUSION:

The 2006 Crouter 2-regression model over-predicts EE at the beginning and end of walking bouts, due to high variability in accelerometer counts during the transitional minutes. The new refined model eliminates this problem and results in a more accurate prediction of EE during walking.

PMID:
21864359
[PubMed - indexed for MEDLINE]
PMCID:
PMC3170241
Free PMC Article
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