Comparison of two different physical activity monitors
1 Diet and Human Performance Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, 308 Center Rd., Beltsville, MD, 20705, USA
2 Department of Health, Physical Education, Recreation, and Dance, University of Idaho, P.O. Box 442401, Moscow, ID, 83844, USA
3 Biometrical Consulting Service, Agricultural Research Service, United States Department of Agriculture, 10300 Baltimore Ave., Building 005, Beltsville, MD, 20705, USA
4 Food Surveys Research Group, Beltsville Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, 10300 Baltimore Ave., Building 005, Beltsville, MD, 20705, USA
BMC Medical Research Methodology 2007, 7:26 doi:10.1186/1471-2288-7-26Published: 25 June 2007
Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible.
56 women and men were fitted with two different activity monitors, the Actigraph™ (Actigraph LLC; AGR) and the Actical™ (Mini-Mitter Co.; MM) for 15 days. Both activity monitors were fixed to an elasticized belt worn over the hip, with the anterior and posterior position of the activity monitors randomized. Differences between activity monitors and the validity of brand inter-conversion were measured by t-tests, Pearson correlations, Bland-Altman plots, and coefficients of variation (CV).
The AGR detected a significantly greater amount of daily PA (216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min, P < 0.0001). The average difference between activity monitors expressed as a CV were 3.1 and 15.5% for log-transformed and raw data, respectively. When a conversion equation was applied to convert datasets from one brand to another, the differences were no longer significant, with CV's of 2.2 and 11.7%, log-transformed and raw data, respectively.
Although activity monitors predict PA on the same scale (counts/min), the results between these two brands are not directly comparable. However, the data are comparable if a conversion equation is applied, with better results for log-transformed data.