This study examined the effects of walking speed on the accuracy and reliability of 10 pedometers: Yamasa Skeletone (SK), Sportline 330 (SL330) and 345 (SL345), Omron (OM), Yamax Digiwalker SW-701 (DW), Kenz Lifecorder (KZ), New Lifestyles 2000 (NL), Oregon Scientific (OR), Freestyle Pacer Pro (FR), and Walk4Life LS 2525 (WL). Ten subjects (33 +/- 12 yr) walked on a treadmill at various speeds (54, 67, 80, 94, and 107 m x min-1) for 5-min stages. Simultaneously, an investigator determined steps by a hand counter and energy expenditure (kcal) by indirect calorimetry. Each brand was measured on the right and left sides. Correlation coefficients between right and left sides exceeded 0.81 for all pedometers except OR (0.76) and SL345 (0.57). Most pedometers underestimated steps at 54 m x min-1, but accuracy for step counting improved at faster speeds. At 80 m x min-1 and above, six models (SK, OM, DW, KZ, NL, and WL) gave mean values that were within +/- 1% of actual steps. Six pedometers displayed the distance traveled. Most of them estimated mean distance to within +/- 10% at 80 m x min-1 but overestimated distance at slower speeds and underestimated distance at faster speeds. Eight pedometers displayed kilocalories, but except for KZ and NL, it is unclear whether this should reflect net or gross kilocalories. If one assumes they display net kilocalories, the general trend was an overestimation of kilocalories at every speed. If one assumes they display gross kilocalorie, then seven of the eight pedometers were accurate to within +/-30% at all speeds. In general, pedometers are most accurate for assessing steps, less accurate for assessing distance, and even less accurate for assessing kilocalories.

The purpose of this study was to compare the step values of multiple brands of pedometers over a 24-h period. The following 13 electronic pedometers were assessed in the study: Accusplit Alliance 1510 (AC), Freestyle Pacer Pro (FR), Colorado on the Move (CO), Kenz Lifecorder (KZ), New-Lifestyles NL-2000 (NL), Omron HJ-105 (OM), Oregon Scientific PE316CA (OR), Sportline 330 (SL330) and 345 (SL345), Walk4Life LS 2525 (WL), Yamax Skeletone EM-180 (SK), Yamax Digi-Walker SW-200 (YX200), and the Yamax Digi-Walker SW-701 (YX701). Ten males (39.5 +/- 16.6 yr, mean +/- SD) and 10 females (43.3 +/- 16.6 yr) ranging in BMI from 19.8 to 35.4 kg.m-2 wore two pedometers for a 24-h period. The criterion pedometer (YX200) was worn on the left side of the body, and a comparison pedometer was worn on the right. Steps counted by each device were recorded at the end of the day for each of the thirteen pedometers. Subjects took an average of 9244 steps.d-1. The KZ, YX200, NL, YX701, and SL330 yielded mean values that were not significantly different from the criterion. The FR, AC, SK, CO, and SL345 significantly underestimated steps (P < 0.05) and the WL, OM, and OR significantly overestimated steps (P < 0.05) when compared with the criterion. In addition, some pedometers underestimated by 25% whereas others overestimated by 45%. 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."

Purpose: This study was designed to examine the relationship between active transportation (defined as the percentage of trips taken by walking, bicycling, and public transit) and obesity rates (BMI ≥ 30 kg · m⁻²) in different countries.Methods: National surveys of travel behavior and health indicators in Europe, North America, and Australia were used in this study; the surveys were conducted in 1994 to 2006. In some cases raw data were obtained from national or federal agencies and then analyzed, and in other cases summary data were obtained from published reports.Results: Countries with the highest levels of active transportation generally had the lowest obesity rates. Europeans walked more than United States residents (382 versus 140 km per person per year) and bicycled more (188 versus 40 km per person per year) in 2000.Discussion: Walking and bicycling are far more common in European countries than in the United States, Australia, and Canada. Active transportation is inversely related to obesity in these countries. Although the results do not prove causality, they suggest that active transportation could be one of the factors that explain international differences in obesity rates.

The purpose of this study was to determine the accuracy and reliability of the following electronic pedometers for measuring steps: Freestyle Pacer Pro (FR), Kenz Lifecorder (KZ), New Lifestyles NL-2000 (NL), Omron HJ-105 (OM), Oregon Scientific PE316CA (OR), Sportline 330 (SL330) and 345 (SL345), Walk4Life LS 2525 (WL), Yamax Skeletone EM-180 (SK), and the Yamax Digi-Walker SW-701 (DW). Ten males (34.7 +/- 12.6 yr) (mean +/- SD) and 10 females (43.1 +/- 19.9 yr) ranging in BMI from 19.8 to 33.6 kg.m-2 walked 400-m around an outdoor track while wearing two pedometers of the same model (one on the right and left sides of the body) for each of 10 models. Four pedometers of each model were assessed in this fashion. The actual steps taken were tallied by a researcher. The KZ, NL, and DW were the most accurate in counting steps, displaying values that were within +/-3% of the actual steps taken, 95% of the time. The SL330 and OM were the least accurate, displaying values that were within +/-37% of the actual steps, 95% of the time. The reliability within a single model (Cronbach's alpha) was >0.80 for all pedometers with the exception of the SL330. The intramodel reliability was exceptionally high (>0.99) in the KZ, OM, NL, and the DW. 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, then a walk/run regression equation was used, whereas 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 ≥ 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.

The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects ( n = 24 men and 24 women, mean age = 35 yr) completed a menu of activities that included sedentary, light, moderate, and vigorous intensities, and each activity was performed for 10 min. There were three different activity menus, and 20 participants completed each menu. Oxygen consumption (in ml·kg⁻¹·min⁻¹) was measured continuously, and the average of minutes 4–9 was used to represent the oxygen cost of each activity. To calculate METs, activity oxygen consumption was divided by 3.5 ml·kg⁻¹·min⁻¹ (1 MET). Accelerometer data were collected second by second using the Actigraph model 7164. For the analysis, we used the distribution of counts (10th, 25th, 50th, 75th, and 90th percentiles of a minute's second-by-second counts) and temporal dynamics of counts (lag, one autocorrelation) as the accelerometer feature inputs to the ANN. To examine model performance, we used the leave-one-out cross-validation technique. The ANN prediction of METs root-mean-squared error was 1.22 METs (confidence interval: 1.14–1.30). For the prediction of activity type, the ANN correctly classified activity type 88.8% of the time (confidence interval: 86.4–91.2%). Activity types were low-level activities, locomotion, vigorous sports, and household activities/other activities. This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that we can successfully estimate activity METs and identify activity type using ANN analytic procedures.