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Background: Accelerometer-derived total activity count is a measure of total physical activity (PA) volume. The purpose of this study was to develop age- and gender-specific percentiles for daily total activity counts (TAC), minutes of moderate-to-vigorous physical activity (MVPA), and minutes of light physical activity (LPA) in U.S. adults., Methods: Waist-worn accelerometer data from the 2003-2006 National Health and Nutrition Examination Survey were used for this analysis. The sample included adults ≥ 20 years with ≥ 10 hours accelerometer wear time on ≥ 4 days (N = 6093). MVPA and LPA were defined as the number of 1-minute epochs with counts ≥ 2020 and 100 to 2019, respectively. TAC represented the activity counts acquired daily. TAC, MVPA, and LPA were averaged across valid days to produce a daily mean., Results: Males in the 50th percentile accumulated 288 140 TAC/day, with 357 and 22 minutes/day spent in LPA and MVPA, respectively. The median for females was 235 741 TAC/ day, with 349 and 12 minutes/day spent in LPA and MVPA, respectively., Conclusions: Population-referenced TAC percentiles reflect the total volume of PA, expressed relative to other adults. This is a different approach to accelerometer data reduction that complements the current method of looking at time spent in intensity subcategories.

Background: The popularity of smartphones has led researchers to ask if they can replace traditional tools for assessing free-living physical activity. Our purpose was to establish proof-of-concept that a smartphone could record acceleration during physical activity, and those data could be modeled to predict activity type (walking or running), speed (km·h-1), and energy expenditure (METs)., Methods: An application to record and e-mail accelerations was developed for the Apple iPhone®/iPod Touch®. Twenty-five healthy adults performed treadmill walking (4.0 km·h-1 to 7.2 km·h-1) and running (8.1 km·h-1 to 11.3 km·h-1) wearing the device. Criterion energy expenditure measurements were collected via metabolic cart., Results: Activity type was classified with 99% accuracy. Speed was predicted with a bias of 0.02 km·h-1 (SEE: 0.57 km·h-1) for walking, -0.03 km·h-1 (SEE: 1.02 km·h-1) for running. Energy expenditure was predicted with a bias of 0.35 METs (SEE: 0.75 METs) for walking, -0.43 METs (SEE: 1.24 METs) for running., Conclusion: Our results suggest that an iPhone/iPod Touch can predict aspects of locomotion with accuracy similar to other accelerometer-based tools. Future studies may leverage this and the additional features of smartphones to improve data collection and compliance.

Background: The International Physical Activity Questionnaire (IPAQ) is commonly used in surveys, but reliability and validity has not been established in the Danish population., Methods: Among participants in the Danish Health Examination survey 2007-2008, 142 healthy participants (45% men) wore a unit that combined accelerometry and heart rate monitoring (Acc+HR) for 7 consecutive days and then completed the IPAQ. Background data were obtained from the survey. Physical activity energy expenditure (PAEE) and time in moderate, vigorous, and sedentary intensity levels were derived from the IPAQ and compared with estimates from Acc+HR using Spearman's correlation coefficients and Bland-Altman plots. Repeatability of the IPAQ was also assessed., Results: PAEE from the 2 methods was significantly positively correlated (0.29 and 0.49; P = 0.02 and P < 0.001; for women and men, respectively). Men significantly overestimated PAEE by IPAQ (56.2 vs 45.3 kJ/kg/day, IPAQ: Acc+HR, P < .01), while the difference was nonsignificant for women (40.8 vs 44.4 kJ/kg/day). Bland-Altman plots showed that the IPAQ overestimated PAEE, moderate, and vigorous activity without systematic error. Reliability of the IPAQ was moderate to high for all domains and intensities (total PAEE intraclass correlation coefficient = 0.58)., Conclusions: This Danish Internet-based version of the long IPAQ had modest validity and reliability when assessing PAEE at population level.

Background: The aim of this study was to validate a triaxial accelerometer setup for identifying everyday physical activity types (ie, sitting, standing, walking, walking stairs, running, and cycling)., Methods: Seventeen subjects equipped with triaxial accelerometers (ActiGraph GT3X+) at the thigh and hip carried out a standardized test procedure including walking, running, cycling, walking stairs, sitting, and standing still. A method was developed (Acti4) to discriminate between these physical activity types based on threshold values of standard deviation of acceleration and the derived inclination. Moreover, the ability of the accelerometer placed at the thigh to detect sitting posture was separately validated during free living by comparison with recordings of pressure sensors in the hip pockets., Results: Sensitivity for discriminating between the physical activity types sitting, standing, walking, running, and cycling in the standardized trials were 99%-100% and 95% for walking stairs. Specificity was higher than 99% for all activities. During free living (140 hours of measurements), sensitivity and specificity for detection of sitting posture were 98% and 93%, respectively., Conclusion: The developed method for detecting physical activity types showed a high sensitivity and specificity for sitting, standing, walking, running, walking stairs, and cycling in a standardized setting and for sitting posture during free living.

Background: This study was designed to develop a prediction algorithm that would allow the Previous Day Physical Activity Recall (PDPAR) to be equated with temporally matched data from an accelerometer., Methods: Participants (n = 121) from a large, school-based intervention wore a validated accelerometer and completed the PDPAR for 3 consecutive days. Physical activity estimates were obtained from PDPAR by totaling 30-minute bouts of activity coded as ≥4 METS. A regression equation was developed in a calibration sample (n = 91) to predict accelerometer minutes of moderate to vigorous physical activity (MVPA) from PDPAR bouts. The regression equation was then applied to a separate, holdout sample (n = 30) to evaluate the utility of the prediction algorithm., Results: Gender and PDPAR bouts accounted for 36.6% of the variance in accelerometer MVPA. The regression model showed that on average boys obtain 9.0 min of MVPA for each reported PDPAR bout, while girls obtain 4.8 min of MVPA per bout. When applied to the holdout sample, predicted minutes of MVPA from the models showed good agreement with accelerometer minutes (r = .81)., Conclusions: The prediction equation provides a valid and useful metric to aid in the interpretation of PDPAR results., (©2011 Human Kinetics, Inc.)

Background: Pedometer measurement of physical activity (PA) has been shown to be reliable and valid in industrialized populations, but its applicability in economically developing Vietnam remains untested. This study assessed the feasibility, stability and validity of pedometer estimates of PA in Vietnam., Methods: 250 adults from a population-based survey were randomly selected to wear Yamax pedometers and record activities for 7 consecutive days. Stability and concurrent validity were assessed using intraclass correlation coefficients (ICC) and Spearman correlation coefficients., Results: Overall, 97.6% of participants provided at least 1 day of usable recordings, and 76.2% wore pedometers for all 7 days. Only 5.2% of the sample participants were involved in work activities not measurable by pedometer. The number of steps increased with hours of wear. There was no significant difference between weekday and weekend in number of steps, and at least 3 days of recordings were required (ICC of the 3 days of recordings: men 0.96, women 0.97). Steps per hour were moderately correlated (men r = .42, women r = .26) with record estimates of total PA., Conclusions: It is feasible to use pedometers to estimate PA in Vietnam. The measure should involve at least 3 days of recording irrespective of day of the week., (©2011 Human Kinetics, Inc.)