Comparing the performance of three generations of ActiGraph accelerometers.

ActiGraph accelerometers are a useful tool for objective assessment of physical activity in clinical and epidemiological studies. Several generations of ActiGraph are being used; however, little work has been done to verify that measurements are consistent across generations. This study employed mechanical oscillations to characterize the dynamic response and intermonitor variability of three generations of ActiGraph monitors, from the oldest 7164 (n = 13), 71256 (n = 12), to the newest GT1M (n = 12). The response due to independent radius (22.1-60.4 mm) and frequency (25-250 rpm) changes were measured, as well as intermonitor variability within each generation. The 7164 and 71256 have similar relationships between activity counts and radius (P = 0.229) but were significantly different from the GT1M (P < 0.001). The frequency responses were nonlinear in all three generations. Although the response curve shapes were similar, the differences between generations at various frequencies were significant (P < 0.017), especially in the extremes of the measurement range. Intermonitor variability was markedly reduced in the GT1M compared with the 7164 and 71256. Other measurement differences between generations include decreased peak counts and decreased sensitivity in low-frequency detection in the GT1M. The results of this study revealed an improvement of the intermonitor variability by the GT1M monitor. However, the reduced sensitivity in low-count ranges in the GT1M may not be well suited for monitoring sedentary or light-intensity movements. Furthermore, the algorithms for energy expenditure predictions developed using older 7164 monitors may need to be modified for the GT1M.

[1]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[2]  P A Willems,et al.  The mechanics of running in children , 1998, The Journal of physiology.

[3]  Scott E Crouter,et al.  A novel method for using accelerometer data to predict energy expenditure. , 2006, Journal of applied physiology.

[4]  P. D. Watson,et al.  Validity of the computer science and applications (CSA) activity monitor in children. , 1998, Medicine and science in sports and exercise.

[5]  P. Freedson,et al.  Validity of accelerometry for the assessment of moderate intensity physical activity in the field. , 2000, Medicine and science in sports and exercise.

[6]  Barry W. Fudge,et al.  Estimation of oxygen uptake during fast running using accelerometry and heart rate. , 2007, Medicine and science in sports and exercise.

[7]  Mark S Tremblay,et al.  Technical reliability assessment of three accelerometer models in a mechanical setup. , 2006, Medicine and science in sports and exercise.

[8]  J. Staudenmayer,et al.  Development of novel techniques to classify physical activity mode using accelerometers. , 2006, Medicine and science in sports and exercise.

[9]  S. Brage,et al.  Reliability and Validity of the Computer Science and Applications Accelerometer in a Mechanical Setting , 2003 .

[10]  Leena Choi,et al.  Validity of Physical Activity Intensity Predictions by ActiGraph, Actical, and RT3 Accelerometers , 2008, Obesity.

[11]  G. Welk,et al.  Reliability of accelerometry-based activity monitors: a generalizability study. , 2004, Medicine and science in sports and exercise.

[12]  Gregory J Welk,et al.  Principles of design and analyses for the calibration of accelerometry-based activity monitors. , 2005, Medicine and science in sports and exercise.

[13]  B E Ainsworth,et al.  Validity of four motion sensors in measuring moderate intensity physical activity. , 2000, Medicine and science in sports and exercise.

[14]  Robert Williams,et al.  Fully proportional actigraphy: A new instrument , 1996 .

[15]  P S Freedson,et al.  Calibration of the Computer Science and Applications, Inc. accelerometer. , 1998, Medicine and science in sports and exercise.

[16]  M. Orendurff,et al.  The effect of walking speed on center of mass displacement. , 2004, Journal of rehabilitation research and development.

[17]  P. Freedson,et al.  Validity of the Computer Science and Applications, Inc. (CSA) activity monitor. , 1995, Medicine and science in sports and exercise.

[18]  M Sun,et al.  A method for measuring mechanical work and work efficiency during human activities. , 1993, Journal of biomechanics.

[19]  S. Grant,et al.  Using the Computer Science and Applications (CSA) Activity Monitor in Preschool Children , 1999 .

[20]  Ulf Ekelund,et al.  Effect of monitor placement and of activity setting on the MTI accelerometer output. , 2003, Medicine and science in sports and exercise.

[21]  B. Ainsworth,et al.  Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. , 2000, Medicine and science in sports and exercise.

[22]  J F Nichols,et al.  Assessment of Physical Activity with the Computer Science and Applications, Inc., Accelerometer: Laboratory versus Field Validation , 2000, Research quarterly for exercise and sport.

[23]  P. Freedson,et al.  Amount of time spent in sedentary behaviors in the United States, 2003-2004. , 2008, American journal of epidemiology.

[24]  J. Shaw,et al.  Objectively Measured Sedentary Time, Physical Activity, and Metabolic Risk , 2007, Diabetes Care.

[25]  J. Curnow,et al.  Technical reliability of the CSA activity monitor: The EarlyBird Study. , 2002, Medicine and science in sports and exercise.

[26]  Sanjay Kalra,et al.  OBJECTIVELY MEASURED LIGHT‐INTENSITY PHYSICAL ACTIVITY IS INDEPENDENTLY ASSOCIATED WITH 2‐H PLASMA GLUCOSE , 2008 .