In general, pedometers are most accurate for assessing steps, less accurate for assessing distance, and even less accurate for assessing kilocalories.

The KZ, YX200, NL, and YX701 appear to be suitable for most research purposes. Given the potential for pedometers in physical activity research, it is necessary that there be consistency across studies in the measurement of "steps per day."

Due to the variation that exists among models in regard to the internal mechanism and sensitivity, not all pedometers count steps accurately. Thus, it is important for researchers who use pedometers to assess physical activity to be aware of their accuracy and reliability.

The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities. Forty-eight participants [age 35 yr (11.4)] performed various activities chosen to represent sedentary, light, moderate, and vigorous intensities. Eighteen activities were split into three routines with each routine being performed by 20 individuals, for a total of 60 tests. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare it against already existing equations. During each routine, the participant wore an Actigraph accelerometer on the hip, and oxygen consumption was simultaneously measured by a portable metabolic system. For each activity, the coefficient of variation (CV) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity. If the CV was 10, a lifestyle/leisure time physical activity regression was used. In the cross-validation group, the mean estimates using the new algorithm (2-regression model with an inactivity threshold) were within 0.75 metabolic equivalents (METs) of measured METs for each of the activities performed (P >or= 0.05), which was a substantial improvement over the single-regression models. The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer.