The purpose of this study was to validate measures of vertical oscillation (VO) and ground contact time (GCT) derived from a commercially-available, torso-mounted accelerometer compared with single marker kinematics and kinetic ground reaction force (GRF) data. Twenty-two semi-elite runners ran on an instrumented treadmill while GRF data (1000 Hz) and three-dimensional kinematics (200 Hz) were collected for 60 s across 5 different running speeds ranging from 2.7 to 3.9 m/s. Measurement agreement was assessed by Bland-Altman plots with 95% limits of agreement and by concordance correlation coefficient (CCC). The accelerometer had excellent CCC agreement (> 0.97) with marker kinematics, but only moderate agreement, and overestimated measures between 16.27 mm to 17.56 mm compared with GRF VO measures. The GCT measures from the accelerometer had very good CCC agreement with GRF data, with less than 6 ms of mean bias at higher speeds. These results indicate a torsomounted accelerometer provides valid and accurate measures of torso-segment VO, but both a marker placed on the torso and the accelerometer yield systematic overestimations of center of mass VO. Measures of GCT from the accelerometer are valid when compared with GRF data, particularly at faster running speeds.
Ricky Watari, Blayne Hettinga, Sean Osis and Reed Ferber
Søren Brage, Niels Wedderkopp, Lars Bo Andersen and Karsten Froberg
Four Computer Science and Applications (CSA, Model 7164) accelerometers were validated against speed and heart rate in a field trial, consisting of two walking and two preset running speeds, and 3 min of running at freely chosen speeds. Fifteen children (9–11 years) were recruited from a suburban school in Denmark. Mean CSA output was calculated and converted to acceleration by calibration to sinusoidal accelerations in a mechanical setup, the latter variable being independent of frequency-based filtering. Mean CSA output and estimated acceleration both correlated significantly with speed (r 2 = 0.55 and r 2 = 0.76, respectively) and heart rate (r 2 = 0.60 and r 2 = 0.81, respectively), controlled for gender. ANOVA post hoc test failed to show significant differences in accelerometer output between running speeds. Inter-individual variability of CSA output and acceleration could not be explained by differences in step frequency in walking but running values correlated significantly with step frequency (r = −0.86 and r = −0.47 for CSA output and acceleration, respectively). Conversion of CSA output to average acceleration provides more precise estimates of intensity with less inter-individual variability than raw CSA output. Different running intensities, however, are generally not well differentiated with vertical accelerometry.
Lucie Péloquin, Pierre Gauthier, Gina Bravo, Guy Lacombe and Jean-Sébastien Billiard
The purposes of the present study were (a) to evaluate the test-retest reliability of the Price et al. (1988) 5-min walking field test, (b) to assess the validity of the test as an estimate of aerobic fitness, and (c) to derive a predictive model for estimating
Michelle M. Yore, Sandra A. Ham, Barbara E. Ainsworth, Caroline A. Macera, Deborah A. Jones and Harold W. Kohl III
In 2001, the Behavioral Risk Factor Surveillance System (BRFSS) included a new occupational physical activity (PA) question. This article evaluates the reliability of this survey question.
Forty-six subjects were followed for 3 wk, answered 3 PA surveys by telephone, and completed daily PA logs for 1 wk. Kappa statistics determined the reliability of occupational activities (sitting/standing, walking, and heavy lifting). A descriptive analysis compared the time in specific occupational activities.
Eighty percent of the respondents reported “mostly sitting or standing” at work; and test–retest reliability was moderate (k = 0.40 to 0.45). The occupationally inactive sat/stood for 85% (mean hours = 5.6) of the workday, whereas the occupationally active sat/stood for 53% (mean hours = 3.9) of the workday.
The BRFSS occupational activity question has moderate reliability, distinguishes between occupationally active and inactive persons, and can be used in surveillance systems to estimate adult occupational PA.
Renee M. Jeffreys, Thomas H. Inge, Todd M. Jenkins, Wendy C. King, Vedran Oruc, Andrew D. Douglas and Molly S. Bray
The accuracy of physical activity (PA) monitors to discriminate between PA, sedentary behavior, and nonwear in extremely obese (EO) adolescents is unknown.
Twenty-five subjects (9 male/16 female; age = 16.5 ± 2.0 y; BMI = 51 ± 8 kg/m2) wore 3 activity monitors (StepWatch [SAM], Actical [AC], Actiheart [AH]) during a 400-m walk test (400MWT), 2 standardized PA bouts of varying duration, and 1 sedentary bout.
For the 400MWT, percent error between observed and monitor-recorded steps was 5.5 ± 7.1% and 82.1 ± 38.6% for the SAM and AC steps, respectively (observed vs. SAM steps: −17.2 ± 22.2 steps; observed vs. AC steps: −264.5 ± 124.8 steps). All activity monitors were able to differentiate between PA and sedentary bouts, but only SAM steps and AH heart rate were significantly different between sedentary behavior and nonwear (P < .001). For all monitors, sedentary behavior was characterized by bouts of zero steps/counts punctuated by intermittent activity steps/counts; nonwear was represented almost exclusively by zero steps/counts.
Of all monitors tested, the SAM was most accurate in terms of counting steps and differentiating levels of PA and thus, most appropriate for EO adolescents. The ability to accurately characterize PA intensity in EO adolescents critically depends on activity monitor selection.
Jean-Philippe Heuzé and Paul Fontayne
The present report provides a summary of five studies undertaken to develop a French-language instrument to assess cohesiveness in sport teams—the “Questionnaire sur l’Ambiance du Groupe” (QAG). For the initial version of the instrument, the Group Environment Questionnaire (Carron, Widmeyer, & Brawley, 1985) was translated into French using the protocol outlined by Vallerand (1989). However, psychometric analyses undertaken in Studies 1, 2, and 3 failed to yield acceptable evidence of construct validity. Items were then revised in an attempt to make them more suitable for the French culture. Subsequent analyses in Study 4 provided support for the construct validity and reliability (internal consistency and interscale equivalence) of the QAG. In Study 5, predictive validity was demonstrated. The QAG has been found to possess satisfactory psychometric properties as a measure of cohesion in sport teams.
Jochen Klenk, Gisela Büchele, Ulrich Lindemann, Sabrina Kaufmann, Raphael Peter, Roman Laszlo, Susanne Kobel and Dietrich Rothenbacher
The aim of this study was to assess concurrent validity between activPAL and activPAL3 accelerometers in a sample of 53 community-dwelling older adults ≥ 65 years. Physical activity (PA) was measured simultaneously with activPAL and activPAL3 while performing scripted activities. The level of agreement between both devices was calculated for sitting/lying, standing, and walking. In addition, PA was measured over one week using activPAL to estimate the expected agreement with activPAL3 in real life. Overall agreement between activPAL and activPAL3 was 97%. Compared with activPAL, the largest disagreement was seen for standing, with 5% categorized as walking by activPAL3. For walking and sitting/lying, the disagreement was 2%, respectively. The expected daily differences between activPAL3 and activPAL were +15.0 min (95% CI: 11.3ߝ18.8) for walking and +29.5 min (95% CI: 6.2–52.7) for standing. ActivPAL and activPAL3 showed good agreement in older adults. However, if using these devices interchangeably, observed differences might still bias results.
Binh Ba Chu, David Lawson and Geraldine Naughton
This study considered the validation of the Computer Science Applications (CSA) activity monitor (model 7164) for predicting activity energy expenditure (EE). A group of 34 Vietnamese adolescents (aged 11 to 15) performed three 5-minute treadmill trials at 4.5, 6.6, and 8.8 km · h−1. Mean activity counts and heart rate (HR) were significantly changed with the three-speed trials (p > 0.05). An equation to predict EE (kcal · min−1) was developed from activity counts and body mass (BM) from the 24 random subjects in Vietnam and was validated on the remaining 10 subjects. This equation explained 72% of the variability in kcal · min−1 (adjusted R2 = 0.72, SEE = 0.91 kcal · min−1). Consistent with previous studies, the relatively high SEE indicates that the equation is more suited for groups of Vietnamese adolescents rather than individuals.
Gerda Jimmy, Roland Seiler and Urs Maeder
Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children.
Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model.
All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches.
The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.
Emma L. J. Eyre, Jason Tallis, Susie Wilson, Lee Wilde, Liam Akhurst, Rildo Wanderleys and Michael J. Duncan
intensities ( Rothney et al., 2008a , 2008b ). Of further concern are the predictive equations that are used to estimate physical activity which vary across software tools and are proprietary. Validation of the RT3 model and its estimates of energy expenditure suggest it underestimates energy expenditure