Ue calibration is described as more of a validity issue. Principles for design of accelerometry-based validation studies are described to provide a guide for future calibration research. The population must be representative of the intended population in terms of demographics and size, monitors must be representative of the population of monitors that would be available, and activities in the protocol must be representative of the types of activities performed by the intended population. It is also important to employ appropriate analytical strategies in this type of research. A case study employing the appropriate design principles is included to demonstrate how results can vary depending on the type of analyses that are used. Direct comparisons are made between a mixed model regression approach and an approach based on receiver operator characteristic curves....

Methods The three examples illustrate different uses of equivalence tests. Example 1 uses PA data to evaluate an activity monitor’s equivalence to a known criterion. Example 2 illustrates the equivalence of two field-based measures of physical fitness with no known reference method. Example 3 uses regression to evaluate an activity monitor’s equivalence across a suite of 23 activities. Results The examples illustrate the appropriate reporting and interpretation of results from equivalence tests. In the first example, the mean criterion measure is significantly within ±15% of the mean PA monitor. The mean difference is 0.18 METs and the 90% confidence interval of −0.15 to 0.52 is inside the equivalence region of −0.65 to 0.65. In the second example, we chose to define equivalence for these two measures as a ratio of mean values between 0.98 and 1.02. The estimated ratio of mean V˙O2 values is 0.99, which is significantly (P = 0.007) inside the equivalence region. In the third example, the PA monitor is not equivalent to the criterion across the suite of activities. The estimated regression intercept and slope are −1.23 and 1.06. Neither confidence interval is within the suggested regression equivalence regions. Conclusions When the study goal is to show similarity between methods, equivalence testing is more appropriate than traditional statistical tests of differences (e.g., ANOVA and t-tests)....

WELK, G. J, S. N. BLAIR, K. WOOD, S. JONES, and R. W. THOMPSON. A comparative evaluation of three accelerometry-based physical activity monitors. Med. Sci. Sports Exerc., Vol. 32, No. 9, Suppl., pp. S489–S497, 2000. Accelerometry-based activity monitors offer promise for the assessment of free-living physical activity. They provide an objective record of frequency, intensity, and duration of physical activity with minimal burden on participants. The purpose of this study was to evaluate the absolute and relative validity of three contemporary activity monitors (Computer Science and Applications, Inc. [CSA], Tritrac, and Biotrainer) under both laboratory and field conditions. Fifty-two participants completed two 30-min choreographed routines designed to simulate a variety of lifestyle physical activities. Three different treadmill paces were completed in both routines to evaluate reliability and validity under laboratory conditions. Six different lifestyle activities were also examined to evaluate the validity of the monitors under field conditions. During each routine, the activity levels of participants were monitored with the three activity monitors as well as by indirect calorimetry systems. The correlations between the monitors and measured O2 were higher for treadmill activity (mean r = 0.86) compared with lifestyle activity (mean r = 0.55). Correlations among the different monitors were high for both treadmill (r = 0.86) and lifestyle activities (r = 0.70), suggesting that the monitors provide similar information under both conditions. Under laboratory conditions, the CSA yielded accurate predictions of energy expenditure (EE), whereas the Tritrac and Biotrainer tended to overestimate the EE (101–136% of measured value). The Tritrac, however, was found to have less error in individual estimates of EE. Under field conditions, all of the monitors underestimated EE (range: 42–67% of measured value). The observed differences among the monitors were attributed primarily to differences in the accuracy of the calibration equations rather than to the monitors themselves. Further research is needed to better understand how to use these devices for field-based assessments of physical activity.

WELK, G. J, J. A. DIFFERDING, R. W. THOMPSON, S. N. BLAIR, J. DZIURA, and P. HART. The utility of the Digi-Walker step counter to assess daily physical activity patterns. Med. Sci. Sports Exerc., Vol. 32, No. 9, Suppl., pp. S481–S488, 2000. The Digi-Walker step counter is a promising and cost-effective tool to measure physical activity under free-living conditions. Two specific studies were conducted to evaluate the number of steps required to meet current physical activity guidelines. Thirty-one adults (17 men, 14 women) served as participants. In study 1, we determined the number of steps to complete a mile under two different conditions and three paces. In study 2, we conducted a field trial to examine the relationship between daily step counts and other indices of physical activity. Participants in this study wore a Digi-Walker for 2 consecutive weeks and completed the 7-d physical activity recall (PAR) after each week. In study 1, there were no differences in step counts by site, but steps were inversely related to pace, with values ranging from 1330 to 1996. Individual step counts at a specific pace were negatively correlated with height, weight, leg length, and stride length and were positively correlated with body fatness. In study 2, participants had average daily step counts of 11,603 when structured vigorous activity was included and 8265 when only light and moderate activity were measured. Modest correlations were found between step counts and estimated energy expenditure. Similar correlations were observed when step counts were related to minutes of activity per day and minutes of sitting per day. Pedometers provide a useful indicator of daily step counts but variability in activity patterns make it difficult to establish step count guidelines that correspond with other public health guidelines.