Scott E. Crouter

Validity of 10 electronic pedometers for measuring steps, distance, and energy cost.

PURPOSE 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). METHODS 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. RESULTS 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. CONCLUSION In general, pedometers are most accurate for assessing steps, less accurate for assessing distance, and even less accurate for assessing kilocalories.

Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk

PURPOSE 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). METHODS 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. RESULTS 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 (Cronbachs 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. CONCLUSION 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.

An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an 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 x kg(-1) x 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 x kg(-1) x 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 minutes 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.

Refined two-regression model for the ActiGraph accelerometer.

PURPOSE The purpose of this study was to refine the 2006 Crouter two-regression model to eliminate the misclassification of walking or running when starting an activity in the middle of a minute on the ActiGraph clock. METHODS Forty-eight participants (mean [SD] age = 35 [11.4] yr) performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were divided into three routines, and 20 participants performed each routine. Participants wore an ActiGraph accelerometer on the hip, and a portable indirect calorimeter was used to measure energy expenditure. Forty-five routines were used to develop the refined two-regression model, and 15 routines were used to cross validate the model. Coefficient of variation (CV) was used to classify each activity as continuous walking or running (CV < or = 10) or intermittent lifestyle activity (CV > 10). RESULTS An exponential regression equation and a cubic equation using the natural log of the 10-s counts were developed to predict METs every 10 s for walking or running and intermittent lifestyle activities, respectively. The refined method examines each 10-s epoch and all combinations of the surrounding five 10-s epochs to find the lowest CV. In the cross-validation group, the refined method was not significantly different from measured METs for any activity (P > 0.05), except cycling (P < 0.05). In addition, the 2006 and the refined two-regression models had similar accuracy and precision for estimating energy expenditure during structured activities. CONCLUSION The refined two-regression model should eliminate the misclassification of transitional minutes when changing activities that start and stop in the middle of a minute on the ActiGraph clock, thus improving the estimate of free-living energy expenditure.

Accuracy of polar S410 heart rate monitor to estimate energy cost of exercise.

PURPOSE The purpose of this study was to examine the accuracy of the Polar S410 for estimating gross energy expenditure (EE) during exercise when using both predicted and measured VO2max and HRmax versus indirect calorimetry (IC). METHODS Ten males and 10 females initially had their VO2max and HRmax predicted by the S410, and then performed a maximal treadmill test to determine their actual values. The participants then performed three submaximal exercise tests at RPE of 3, 5, and 7 on a treadmill, cycle, and rowing ergometer for a total of nine submaximal bouts. For all submaximal testing, the participant had two S410 heart rate monitors simultaneously collecting data: one heart rate monitor (PHRM) utilized their predicted VO2max and HRmax, and one heart rate monitor (AHRM) used their actual values. Simultaneously, EE was measured by IC. RESULTS In males, there were no differences in EE among the mean values for the AHRM, PHRM, and IC for any exercise mode (P > 0.05). In females, the PHRM significantly overestimated mean EE on the treadmill (by 2.4 kcal x min(-1)), cycle (by 2.9 kcal x min(-1)), and rower (by 1.9 kcal x min(-1)) (all P < 0.05). The AHRM for females significantly improved the estimation of mean EE for all exercise modes, but it still overestimated mean EE on the treadmill (by 0.6 kcal x min(-1)) and cycle (by 1.2 kcal x min(-1)) (P < 0.05). CONCLUSION When the predicted values of VO2max and HRmax are used, the Polar S410 HRM provides a rough estimate of EE during running, rowing, and cycling. Using the actual values for VO2max and HRmax reduced the individual error scores for both genders, but in females the mean EE was still overestimated by 12%.

A new 2-regression model for the Actical accelerometer

Objective: The objective of this study was to develop a new 2-regression model relating Actical activity counts to METs. Methods: Forty-eight participants (mean (SD) age 35 (11.4) years) performed 10 min bouts of various activities ranging from sedentary behaviours to vigorous physical activities. Eighteen activities were split into three routines with each routine being performed by 20 individuals. Forty-five routines were randomly selected for the development of a new 2-regression model and 15 tests were used to cross-validate the new 2-regression model and compare it against existing equations. During each routine, the participant wore an Actical accelerometer on the hip and oxygen consumption was simultaneously measured by a portable metabolic system. The coefficient of variation (CV) of four consecutive 15 s epochs was calculated for each minute. For each activity, the average CV and the counts min−1 were calculated for minutes 4–9. If the CV was ⩽13% a walk/run regression equation was used and if the CV was >13% a lifestyle/leisure time physical activity regression was used. Results: An exponential regression line (R2 = 0.912; standard error of the estimate (SEE) = 0.149) was used for activities with a CV⩽13%, and a cubic regression line (R2 = 0.884, SEE = 0.804) was used for activities with a CV>13%. In the cross-validation group the mean estimates, using the new 2-regression model with an inactivity threshold, were within 0.56 METs of measured METs for each of the activities performed (p⩾0.05), except cycling (p<0.05). Conclusion: For most activities examined the new 2-regression model predicted METs more accurately than currently available equations for the Actical accelerometer.

Use of a Two-regression Model for Estimating Energy Expenditure in Children

PURPOSE The purpose of this study was to develop two new two-regression models (2RM), for use in children, that estimate energy expenditure (EE) using the ActiGraph GT3X: 1) mean vector magnitude (VM) counts or 2) vertical axis (VA) counts. The new 2RMs were also compared with existing ActiGraph equations for children. METHODS Fifty-seven boys and 52 girls (mean ± SD: age = 11 ± 1.7 yr, body mass index = 21.4 ± 5.5 kg·m(-2)) performed 30-min supine rest and 8 min of six different activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were split into three routines with each routine performed by 38-39 participants. Seventy-seven participants were used for the development group, and 39 participants were used for the cross-validation group. During all testing, activity data were collected using an ActiGraph GT3X, worn on the right hip, and oxygen consumption was measured using a Cosmed K4b. All energy expenditure values are expressed as MET(RMR) (activity VO(2)/resting VO(2)). RESULTS For each activity, a coefficient of variation was calculated using 10-s epochs for the VA and VM to determine whether the activity was continuous walking/running or an intermittent lifestyle activity. Separate regression equations were developed for walking/running and intermittent lifestyle activity. In the cross-validation group, the VM and VA 2RMs were within 0.8 MET(RMR) of measured MET(RMR) for all activities except Sportwall and running (all P > 0.05). The other existing ActiGraph equations had mean errors ranging from 0.0 to 2.6 MET(RMR) for the activities. CONCLUSIONS The new 2RMs for use in children with the ActiGraph GT3X provide a closer estimate of mean measured MET(RMR) than other currently available prediction equations. In addition, they improve the individual prediction errors across a wide range of activity intensities.

Estimating Physical Activity in Youth Using a Wrist Accelerometer.

PURPOSE The purpose of this study was to develop and validate methods for analyzing wrist accelerometer data in youth. METHODS A total of 181 youth (mean ± SD; age, 12.0 ± 1.5 yr) completed 30 min of supine rest and 8 min each of 2 to 7 structured activities, selected from a list of 25. Receiver operating characteristic (ROC) curves and regression analyses were used to develop prediction equations for energy expenditure (child-METs; measured activity V˙O2 divided by measured resting V˙O2) and cut points for computing time spent in sedentary behaviors (SB), light (LPA), moderate (MPA), and vigorous (VPA) physical activity. Both vertical axis (VA) and vector magnitude (VM) counts per 5 s were used for this purpose. The validation study included 42 youth (age, 12.6 ± 0.8 yr) who completed approximately 2 h of unstructured PA. During all measurements, activity data were collected using an ActiGraph GT3X or GT3X+, positioned on the dominant wrist. Oxygen consumption was measured using a Cosmed K4b. Repeated-measures ANOVA were used to compare measured versus predicted child-METs (regression only) and time spent in SB, LPA, MPA, and VPA. RESULTS All ROC cut points were similar for area under the curve (≥0.825), sensitivity (≥0.756), and specificity (≥0.634), and they significantly underestimated LPA and overestimated VPA (P < 0.05). The VA and VM regression models were within ±0.21 child-METs of mean measured child-METs and ±2.5 min of measured time spent in SB, LPA, MPA, and VPA, respectively (P > 0.05). CONCLUSIONS Compared to measured values, the VA and VM regression models developed on wrist accelerometer data had insignificant mean bias for child-METs and time spent in SB, LPA, MPA, and VPA; however, they had large individual errors.

Results from the United States’ 2014 Report Card on Physical Activity for Children and Youth

BACKGROUND The National Physical Activity Plan Alliance partnered with physical activity experts to develop a report card that provides a comprehensive assessment of physical activity among United States children and youth. METHODS The 2014 U.S. Report Card on Physical Activity for Children and Youth includes 10 indicators: overall physical activity levels, sedentary behaviors, active transportation, organized sport participation, active play, health-related fitness, family and peers, school, community and the built environment, and government strategies and investments. Data from nationally representative surveys were used to provide a comprehensive evaluation of the physical activity indicators. The Committee used the best available data source to grade the indicators using a standard rubric. RESULTS Approximately one-quarter of children and youth 6 to 15 years of age were at least moderately active for 60 min/day on at least 5 days per week. The prevalence was lower among youth compared with younger children, resulting in a grade of D- for overall physical activity levels. Five of the remaining 9 indicators received grades ranging from B- to F, whereas there was insufficient data to grade 4 indicators, highlighting the need for more research in some areas. CONCLUSIONS Physical activity levels among U.S. children and youth are low and sedentary behavior is high, suggesting that current infrastructure, policies, programs, and investments in support of childrens physical activity are not sufficient.

Comparison of incremental treadmill exercise and free range running.

PURPOSE The aim of this study was to compare physiological responses during incremental treadmill exercise and free range running. METHODS Fifteen competitive cross-country runners performed an incremental treadmill test and an unpaced 1-mile run on an indoor 200-m track. Physiological variables (VO(2peak), HR(peak), VO(2) x HR(-1)(peak), V(Epeak)) were measured using a portable metabolic analyzer. Blood lactate was measured post exercise. Outcome variables were analyzed with repeated measures ANOVA. RESULTS Although directionally similar to previous studies with cycle ergometry, the observed peak values (track vs treadmill) for VO(2) (63.0 +/- 7.4 vs 61.9 +/- 7.2 mL x kg(-1) x min(-1)), V(E) (147 +/- 37 vs 144 +/- 30 L x min(-1)), HR (188 +/- 5 vs 189 +/- 7 beats.min-1), and VO(2) x HR(-1) (22.1 +/- 4.4 vs 21.5 +/- 4.5) were not significantly different. The observed peak values for blood lactate (14.4 +/- 3.3 vs 11.7 +/- 3.0 mmol x L(-1)) were significantly (P < 0.05) different. CONCLUSIONS The results are not in full agreement with previous findings from cycling studies with the exception of post exercise blood lactate. Whether this represents a fundamental lack of effect of free range exercise or is related to mode specificity remains to be determined.