Predicting Physical Activity Energy Expenditure Using Accelerometry in Adults From Sub‐Sahara Africa

Lack of physical activity may be an important etiological factor in the current epidemiological transition characterized by increasing prevalence of obesity and chronic diseases in sub‐Sahara Africa. However, there is a dearth of data on objectively measured physical activity energy expenditure (PAEE) in this region. We sought to develop regression equations using body composition and accelerometer counts to predict PAEE. We conducted a cross‐sectional study of 33 adult volunteers from an urban (n = 16) and a rural (n = 17) residential site in Cameroon. Energy expenditure was measured by doubly labeled water (DLW) over a period of seven consecutive days. Simultaneously, a hip‐mounted Actigraph accelerometer recorded body movement. PAEE prediction equations were derived using accelerometer counts, age, sex, and body composition variables, and cross‐validated by the jack‐knife method. The Bland and Altman limits of agreement (LOAs) approach was used to assess agreement. Our results show that PAEE (kJ/kg/day) was significantly and positively correlated with activity counts from the accelerometer (r = 0.37, P = 0.03). The derived equations explained 14–40% of the variance in PAEE. Age, sex, and accelerometer counts together explained 34% of the variance in PAEE, with accelerometer counts alone explaining 14%. The LOAs between DLW and the derived equations were wide, with predicted PAEE being up to 60 kJ/kg/day below or above the measured value. In summary, the derived equations performed better than existing published equations in predicting PAEE from accelerometer counts in this population. Accelerometry could be used to predict PAEE in this population and, therefore, has important applications for monitoring population levels of total physical activity patterns.     

Triaxial accelerometry for assessment of physical activity in young children

Objective: The purpose of the present study was to derive linear and non‐linear regression equations that estimate energy expenditure (EE) from triaxial accelerometer counts that can be used to quantitate activity in young children. We are unaware of any data regarding the validity of triaxial accelerometry for assessment of physical activity intensity in this age group. Research Methods and Procedures: EE for 27 girls and boys (6.0 ± 0.3 years) was assessed for nine activities (lying down, watching a video while sitting and standing, line drawing for coloring‐in, playing blocks, walking, stair climbing, ball toss, and running) using indirect calorimetry and was then estimated using a triaxial accelerometer (ActivTracer, GMS). Results: Significant correlations were observed between synthetic (synthesized tri‐axes as the vector), vertical, and horizontal accelerometer counts and EE for all activities (0.878 to 0.932 for EE). However, linear and non‐linear regression equations underestimated EE by >30% for stair climbing (up and down) and performing a ball toss. Therefore, linear and non‐linear regression equations were calculated for all activities except these two activities, and then evaluated for all activities. Linear and non‐linear regression equations using combined vertical and horizontal acceleration counts, synthetic counts, and horizontal counts demonstrated a better relationship between accelerometer counts and EE than did regression equations using vertical acceleration counts. Adjustment of the predicted value by the regression equations using the vertical/horizontal counts ratio improved the overestimation of EE for performing a ball toss. Discussion: The results suggest that triaxial accelerometry is a good tool for assessing daily EE in young children.     

Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry

Reduced physical activity is an important feature of Chronic Obstructive Pulmonary Disease (COPD). Various activity monitors are available but their validity is poorly established. The aim was to evaluate the validity of six monitors in patients with COPD. We hypothesized triaxial monitors to be more valid compared to uniaxial monitors. Thirty-nine patients (age 68±7 years, FEV(1) 54±18%predicted) performed a one-hour standardized activity protocol. Patients wore 6 monitors (Kenz Lifecorder (Kenz), Actiwatch, RT3, Actigraph GT3X (Actigraph), Dynaport MiniMod (MiniMod), and SenseWear Armband (SenseWear)) as well as a portable metabolic system (Oxycon Mobile). Validity was evaluated by correlation analysis between indirect calorimetry (VO(2)) and the monitor outputs: Metabolic Equivalent of Task [METs] (SenseWear, MiniMod), activity counts (Actiwatch), vector magnitude units (Actigraph, RT3) and arbitrary units (Kenz) over the whole protocol and slow versus fast walking. Minute-by-minute correlations were highest for the MiniMod (r?=?0.82), Actigraph (r?=?0.79), SenseWear (r?=?0.73) and RT3 (r?=?0.73). Over the whole protocol, the mean correlations were best for the SenseWear (r?=?0.76), Kenz (r?=?0.52), Actigraph (r?=?0.49) and MiniMod (r?=?0.45). The MiniMod (r?=?0.94) and Actigraph (r?=?0.88) performed better in detecting different walking speeds. The Dynaport MiniMod, Actigraph GT3X and SenseWear Armband (all triaxial monitors) are the most valid monitors during standardized physical activities. The Dynaport MiniMod and Actigraph GT3X discriminate best between different walking speeds.     

Physical activity assessment with accelerometers: an evaluation against doubly labeled water

This review focuses on the ability of different accelerometers to assess daily physical activity as compared with the doubly labeled water (DLW) technique, which is considered the gold standard for measuring energy expenditure under free-living conditions. The PubMed Central database (U.S. NIH free digital archive of biomedical and life sciences journal literature) was searched using the following key words: doubly or double labeled or labeled water in combination with accelerometer, accelerometry, motion sensor, or activity monitor. In total, 41 articles were identified, and screening the articles' references resulted in one extra article. Of these, 28 contained sufficient and new data. Eight different accelerometers were identified: 3 uniaxial (the Lifecorder, the Caltrac, and the CSA/MTI/Actigraph), one biaxial (the Actiwatch AW16), 2 triaxial (the Tritrac-R3D and the Tracmor), one device based on two position sensors and two motion sensors (ActiReg), and the foot-ground contact pedometer. Many studies showed poor results. Only a few mentioned partial correlations for accelerometer counts or the increase in R(2) caused by the accelerometer. The correlation between the two methods was often driven by subject characteristics such as body weight. In addition, standard errors or limits of agreement were often large or not presented. The CSA/MTI/Actigraph and the Tracmor were the two most extensively validated accelerometers. The best results were found for the Tracmor; however, this accelerometer is not yet commercially available. Of those commercially available, only the CSA/MTI/Actigraph has been proven to correlate reasonably with DLW-derived energy expenditure.     

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.     

Cardiorespiratory and metabolic responses associated with children's physical activity during self-paced games

The aim of this study was to explore the possibility of identifying clusters of children's games based on estimated energy expenditures and (or) intensity when performed in a guided active play format. The study also investigated whether the identified active play game clusters were repeatable when the games were performed on different days. Children (9.7?± 1.1?years; n?= 12) were assessed for oxygen consumption, heart rate, energy expenditure (EE), and metabolic equivalent (MET) on a treadmill (at 4, 6, and 8?km·h(-1) (0% grade)). HR and ActiGraph GT1M accelerometer (ACC) generated linear regression equations were used to estimate EE. The ACC (3?s epochs) were used for estimating METs in assigning percent time at medium-vigorous physical activity (%MVPA) of 10 self-paced games. The results showed a consistent range of EEs (ACC-equation) from 13.57?kcal·(5?min)(-1) to 25.00?kcal·(5?min)(-1) (p?< 0.05); EEs (HR-equation) from 29.72 to 42.49?kcal·(5?min)(-1) (p?< 0.05); and %MVPA from 10% to 34% (p?< 0.05) (from ACC equations) across all games. These were reproducible from day to day (p?> 0.05). This study confirms the existence of active play children's game clusters that might be useful in formatting guided active play in a dose-response manner for children.     

Estimation of resistance exercise energy expenditure using triaxial accelerometry

Recently, it was demonstrated that a uniaxial accelerometer worn at the hip could estimate resistance exercise energy expenditure. As resistance exercise takes place in more than 1 plane, the use of a triaxial accelerometer may be more effective in estimating resistance exercise energy expenditure. The aims of this study were to estimate the energy cost of resistance exercise using triaxial accelerometry and to determine the optimal location for wearing triaxial accelerometers during resistance exercise. Thirty subjects (15 men and 15 women; age = 21.7 ± 1.0 years) performed a resistance exercise protocol consisting of 2 sets of 8 exercises (10RM loads). During the resistance exercise protocol, subjects wore triaxial accelerometers on the wrist, waist, and ankle; a heart rate monitor; and a portable metabolic system. Net energy expenditure was significantly correlated with vertical (r = 0.67, p < 0.001), horizontal (r = 0.43, p = 0.02), third axis (r = 0.36, p = 0.048), and sum of 3 axes (r = 0.50, p = 0.005) counts at the waist, and horizontal counts at the wrist (r = -0.40, p = 0.03). Regression analysis using fat-free mass, sex, and the sum of accelerometer counts at the waist as variables was used to develop an equation that explained 73% of the variance of resistance exercise energy expenditure. A triaxial accelerometer worn at the waist can be used to estimate resistance exercise energy expenditure but appears to offer no benefit over uniaxial accelerometry. The use of accelerometers in estimating resistance exercise energy expenditure may prove useful for individuals and athletes who participate in resistance training and are focused on maintaining a tightly regulated energy balance.     

Actigraph accelerometer-defined boundaries for sedentary behaviour and physical activity intensities in 7 year old children

Background

Accurate objective assessment of sedentary and physical activity behaviours during childhood is integral to the understanding of their relation to later health outcomes, as well as to documenting the frequency and distribution of physical activity within a population.

Purpose

To calibrate the Actigraph GT1M accelerometer, using energy expenditure (EE) as the criterion measure, to define thresholds for sedentary behaviour and physical activity categories suitable for use in a large scale epidemiological study in young children.

Methods

Accelerometer-based assessments of physical activity (counts per minute) were calibrated against EE measures (kcal.kg⁻¹.hr⁻¹) obtained over a range of exercise intensities using a COSMED K4b² portable metabolic unit in 53 seven-year-old children. Children performed seven activities: lying down viewing television, sitting upright playing a computer game, slow walking, brisk walking, jogging, hopscotch and basketball. Threshold count values were established to identify sedentary behaviour and light, moderate and vigorous physical activity using linear discriminant analysis (LDA) and evaluated using receiver operating characteristic (ROC) curve analysis.

Results

EE was significantly associated with counts for all non-sedentary activities with the exception of jogging. Threshold values for accelerometer counts (counts.minute⁻¹) were <100 for sedentary behaviour and ≤2240, ≤3840 and ≥3841 for light, moderate and vigorous physical activity respectively. The area under the ROC curves for discrimination of sedentary behaviour and vigorous activity were 0.98. Boundaries for light and moderate physical activity were less well defined (0.61 and 0.60 respectively). Sensitivity and specificity were higher for sedentary (99% and 97%) and vigorous (95% and 91%) than for light (60% and 83%) and moderate (61% and 76%) thresholds.

Conclusion

The accelerometer cut points established in this study can be used to classify sedentary behaviour and to distinguish between light, moderate and vigorous physical activity in children of this age.     

Prediction Models Discriminating between Nonlocomotive and Locomotive Activities in Children Using a Triaxial Accelerometer with a Gravity-removal Physical Activity Classification Algorithm

The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R² = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R² = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were −0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.     

Population-Referenced Percentiles for Waist-Worn Accelerometer-Derived Total Activity Counts in U.S. Youth: 2003 – 2006 NHANES

Background

The total activity volume performed is an overall measure that takes into account the frequency, intensity, and duration of activities performed. The importance of considering total activity volume is shown by recent studies indicating that light physical activity (LPA) and intermittent moderate-to-vigorous physical activity (MVPA) have health benefits. Accelerometer-derived total activity counts (TAC) per day from a waist-worn accelerometer can serve as a proxy for an individual''s total activity volume. The purpose of this study was to develop age- and gender-specific percentiles for daily TAC, minutes of MVPA, and minutes of LPA in U.S. youth ages 6 – 19 y.

Methods

Data from the 2003 – 2006 NHANES waist-worn accelerometer component were used in this analysis. The sample was composed of youth aged 6 – 19 years with at least 4 d of ≥ 10 hours of accelerometer wear time (N = 3698). MVPA was defined using age specific cutpoints as the total number of minutes at ≥4 metabolic equivalents (METs) for youth 6 – 17 y or minutes with ≥2020 counts for youth 18 – 19 y. LPA was defined as the total number of minutes between 100 counts and the MVPA threshold. TAC/d, MVPA, and LPA were averaged across all valid days.

Results

For males in the 50^th percentile, the median activity level was 441,431 TAC/d, with 53 min/d of MVPA and 368 min/d of LPA. The median level of activity for females was 234,322 TAC/d, with 32 min/d of MVPA and 355 min/d of LPA.

Conclusion

Population referenced TAC/d percentiles for U.S. youth ages 6-19 y provide a novel means of characterizing the total activity volume performed by children and adolescents.     

Evaluation of low-intensity physical activity by triaxial accelerometry

Objective: To develop regression‐based equations that estimate physical activity ratios [energy expenditure (EE) per minute/sleeping metabolic rate] for low‐to‐moderate intensity activities using total acceleration obtained by triaxial accelerometry. Research Methods and Procedures: Twenty‐one Japanese adults were fitted with a triaxial accelerometer while also in a whole‐body human calorimeter for 22.5 hours. The protocol time was composed of sleep (8 hours), four structured activity periods totaling 4 hours (sitting, standing, housework, and walking on a treadmill at speeds of 71 and 95 m/min, 2 × 30 minutes for each activity), and residual time (10.5 hours). Acceleration data (milligausse) from the different periods and their relationship to physical activity ratio obtained from the human calorimeter allowed for the development of EE equations for each activity. The EE equations were validated on the residual times, and the percentage difference for the prediction errors was calculated as (predicted value ? measured value)/measured value × 100. Results: Using data from triaxial accelerations and the ratio of horizontal to vertical accelerations, there was relatively high accuracy in identifying the four different periods of activity. The predicted EE (882 ± 150 kcal/10.5 hours) was strongly correlated with the actual EE measured by human calorimetry (846 ± 146 kcal/10.5 hours, r = 0.94 p < 0.01), although the predicted EE was slightly higher than the measured EE. Discussion: Triaxial accelerometry, when total, vertical, and horizontal accelerations are utilized, can effectively evaluate different types of activities and estimate EE for low‐intensity physical activities associated with modern lifestyles.     

Predicting maximal aerobic capacity (VO2max) from the critical velocity test in female collegiate rowers

The objective of this study was to examine the relationship between the critical velocity (CV) test and maximal oxygen consumption (VO2max) and develop a regression equation to predict VO2max based on the CV test in female collegiate rowers. Thirty-five female (mean ± SD; age, 19.38 ± 1.3 years; height, 170.27 ± 6.07 cm; body mass, 69.58 ± 0.3 1 kg) collegiate rowers performed 2 incremental VO2max tests to volitional exhaustion on a Concept II Model D rowing ergometer to determine VO2max. After a 72-hour rest period, each rower completed 4 time trials at varying distances for the determination of CV and anaerobic rowing capacity (ARC). A positive correlation was observed between CV and absolute VO2max (r = 0.775, p < 0.001) and ARC and absolute VO2max (r = 0.414, p = 0.040). Based on the significant correlation analysis, a linear regression equation was developed to predict the absolute VO2max from CV and ARC (absolute VO2max = 1.579[CV] + 0.008[ARC] - 3.838; standard error of the estimate [SEE] = 0.192 L·min(-1)). Cross validation analyses were performed using an independent sample of 10 rowers. There was no significant difference between the mean predicted VO2max (3.02 L·min(-1)) and the observed VO2max (3.10 L·min(-1)). The constant error, SEE and validity coefficient (r) were 0.076 L·min(-1), 0.144 L·min(-1), and 0.72, respectively. The total error value was 0.155 L·min(-1). The positive relationship between CV, ARC, and VO2max suggests that the CV test may be a practical alternative to measuring the maximal oxygen uptake in the absence of a metabolic cart. Additional studies are needed to validate the regression equation using a larger sample size and different populations (junior- and senior-level female rowers) and to determine the accuracy of the equation in tracking changes after a training intervention.     

Field validation of the MTI Actigraph and BodyMedia armband monitor using the IDEEA monitor

Objective: Accelerometers offer considerable promise for improving estimates of physical activity (PA) and energy expenditure (EE) in free‐living subjects. Differences in calibration equations and cut‐off points have made it difficult to determine the most accurate way to process these data. The objective of this study was to compare the accuracy of various calibration equations and algorithms that are currently used with the MTI Actigraph (MTI) and the Sensewear Pro II (SP2) armband monitor. Research Methods and Procedures: College‐age participants (n = 30) wore an MTI and an SP2 while participating in normal activities of daily living. Activity patterns were simultaneously monitored with the Intelligent Device for Estimating Energy Expenditure and Activity (IDEEA) monitor to provide an accurate estimate (criterion measure) of EE and PA for this field‐based method comparison study. Results: The EE estimates from various MTI equations varied considerably, with mean differences ranging from ?1.10 to 0.46 METS. The EE estimates from the two SP2 equations were within 0.10 METS of the value from the IDEEA. Estimates of time spent in PA from the MTI and SP2 ranged from 34.3 to 107.1 minutes per day, while the IDEEA yielded estimates of 52 minutes per day. Discussion: The lowest errors in estimation of time spent in PA and the highest correlations were found for the new SP2 equation and for the recently proposed MTI cut‐off point of 760 counts/min (Matthews, 2005). The study indicates that the Matthews MTI cut‐off point and the new SP2 equation provide the most accurate indicators of PA.     

Energy Expenditure Compared to Physical Activity Measured by Accelerometry and Self-Report in Adolescents: A Validation Study

Background

Physical inactivity is responsible for 5.3 million deaths annually worldwide. To measure physical activity energy expenditure, the doubly labeled water (DLW) method is the gold standard. However, questionnaires and accelerometry are more widely used. We compared physical activity measured by accelerometer and questionnaire against total (TEE) and physical activity energy expenditure (PAEE) estimated by DLW.

Methods

TEE, PAEE (TEE minus resting energy expenditure) and body composition were measured using the DLW technique in 25 adolescents (16 girls) aged 13 years living in Pelotas, Brazil. Physical activity was assessed using the Actigraph accelerometer and by self-report. Physical activity data from accelerometry and self-report were tested against energy expenditure data derived from the DLW method. Further, tests were done to assess the ability of moderate-to-vigorous intensity physical activity (MVPA) to predict variability in TEE and to what extent adjustment for fat and fat-free mass predicted the variability in TEE.

Results

TEE varied from 1,265 to 4,143 kcal/day. It was positively correlated with physical activity (counts) estimated by accelerometry (rho  = 0.57; p = 0.003) and with minutes per week of physical activity by questionnaire (rho  = 0.41; p = 0.04). An increase of 10 minutes per day in moderate-to-vigorous intensity physical activity (MVPA) relates to an increase in TEE of 141 kcal/day. PAEE was positively correlated with accelerometry (rho  = 0.64; p = 0.007), but not with minutes per week of physical activity estimated by questionnaire (rho  = 0.30; p = 0.15). Physical activity by accelerometry explained 31% of the vssariability in TEE. By incorporating fat and fat-free mass in the model, we were able to explain 58% of the variability in TEE.

Conclusion

Objectively measured physical activity significantly contributes to the explained variance in both TEE and PAEE in Brazilian youth. Independently, body composition also explains variance in TEE, and should ideally be taken into account when using accelerometry to predict energy expenditure values.     

Metabolic equivalents during scooter exercise

The purpose of this study was to determine the metabolic equivalents (METs) for scooter exercise (riding a scooter, scootering) and to examine the energy expenditure and the heart rate response, so that the results can be used in health promotion activities. Eighteen young adults (10 males and 8 females) participated in scootering on a treadmill at three different speeds for six minutes each. Before, during, and after the exercise, pulmonary ventilation, oxygen uptake (VO(2)), carbon dioxide product, respiratory exchange ratio (R), and heart rate (HR) were measured. These measurements kept steady states from the 3rd to 6th minute of each different speed session. The MET values acquired during scootering at 80 m.min(-1), 110 m.min(-1), and 140 m.min(-1) were 3.9, 4.3, and 5.0, respectively. Calculated using VO(2) (ml.kg(-1).min(-1))x[4.0+R], the energy consumption for scootering at each speed was 67.0+/-10.6, 73.3+/-10.2, and 84.8+/-7.9 cal.kg(-1).min(-1), respectively. The regression equation between scootering speed (X, m.min(-1)) and VO(2) (Y, ml.kg(-1).min(-1)) is Y=0.062X+8.655, and the regression equation between HR (X, beats.min(-1)) and VO(2)reserve (Y, %) is Y=0.458X-11.264. These equations can be applied to both females and males. Thus, scootering at 80 to 140 m.min(-1) might not be sufficient to improve the cardiorespiratory fitness of young male adults similar to the participants, but it may contribute many healthy benefits to most female adults and even male adults, and improve their health and fitness at the faster speeds.     

Validation and Calibration of Physical Activity Monitors in Children

Objective: This study was designed to validate accelerometer-based activity monitors against energy expenditure (EE) in children; to compare monitor placement sites; to field-test the monitors; and to establish sedentary, light, moderate, and vigorous threshold counts. Research Methods and Procedures: Computer Science and Applications Actigraph (CSA) and Mini-Mitter Actiwatch (MM) monitors, on the hip or lower leg, were validated and calibrated against 6-hour EE measurements by room respiration calorimetry, activity by microwave detector, and heart rate by telemetry in 26 children, 6 to 16 years old. During the 6 hours, the children performed structured activities, including resting metabolic rate (RMR), Nintendo, arts and crafts, aerobic warm-up, Tae Bo, treadmill walking and running, and games. Activity energy expenditure (AEE) computed as EE − RMR was regressed against counts to derive threshold counts. Results: The mean correlations between EE or AEE and counts were slightly higher for MM-hip (r = 0.78 ± 0.06) and MM-leg (r = 0.80 ± 0.05) than CSA-hip (r = 0.66 ± 0.08) and CSA-leg (r = 0.73 ± 0.07). CSA and MM performed similarly on the hip (inter-instrument r = 0.88) and on the lower leg (inter-instrument r = 0.89). Threshold counts for the CSA-hip were <800, <3200, <8200, and ≥8200 for sedentary, light, moderate, and vigorous categories, respectively. For the MM-hip, the threshold counts were <100, <900, <2200, and ≥2200, respectively. Discussion: The validation of the CSA and MM monitors against AEE and their calibration for sedentary, light, moderate, and vigorous thresholds certify these monitors as valid, useful devices for the assessment of physical activity in children.     

Measurement of peak impact loads differ between accelerometers – Effects of system operating range and sampling rate

A wide variety of accelerometer systems, with differing sensor characteristics, are used to detect impact loading during physical activities. The study examined the effects of system characteristics on measured peak impact loading during a variety of activities by comparing outputs from three separate accelerometer systems, and by assessing the influence of simulated reductions in operating range and sampling rate. Twelve healthy young adults performed seven tasks (vertical jump, box drop, heel drop, and bilateral single leg and lateral jumps) while simultaneously wearing three tri-axial accelerometers including a criterion standard laboratory-grade unit (Endevco 7267A) and two systems primarily used for activity-monitoring (ActiGraph GT3X+, GCDC X6-2mini). Peak acceleration (gmax) was compared across accelerometers, and errors resulting from down-sampling (from 640 to 100 Hz) and range-limiting (to ±6 g) the criterion standard output were characterized. The Actigraph activity-monitoring accelerometer underestimated gmax by an average of 30.2%; underestimation by the X6-2mini was not significant. Underestimation error was greater for tasks with greater impact magnitudes. gmax was underestimated when the criterion standard signal was down-sampled (by an average of 11%), range limited (by 11%), and by combined down-sampling and range-limiting (by 18%). These effects explained 89% of the variance in gmax error for the Actigraph system. This study illustrates that both the type and intensity of activity should be considered when selecting an accelerometer for characterizing impact events. In addition, caution may be warranted when comparing impact magnitudes from studies that use different accelerometers, and when comparing accelerometer outputs to osteogenic impact thresholds proposed in literature.     

A comparison of methods for fitting allometric equations to field metabolic rates of animals

We re-examined data for field metabolic rates of varanid lizards and marsupial mammals to illustrate how different procedures for fitting the allometric equation can lead to very different estimates for the allometric coefficient and exponent. A two-parameter power function was obtained in each case by the traditional method of back-transformation from a straight line fitted to logarithms of the data. Another two-parameter power function was then generated for each data-set by non-linear regression on values in the original arithmetic scale. Allometric equations obtained by non-linear regression described the metabolic rates of all animals in the samples. Equations estimated by back-transformation from logarithms, on the other hand, described the metabolic rates of small species but not large ones. Thus, allometric equations estimated in the traditional way for field metabolic rates of varanids and marsupials do not have general importance because they do not characterize rates for species spanning the full range in body size. Logarithmic transformation of predictor and response variables creates new distributions that may enable investigators to perform statistical analyses in compliance with assumptions underlying the tests. However, statistical models fitted to transformations should not be used to estimate parameters of equations in the arithmetic domain because such equations may be seriously biased and misleading. Allometric analyses should be performed on values expressed in the original scale, if possible, because this is the scale of interest.     

Validation and calibration of an accelerometer in preschool children

Objective: Obesity rates in young children are increasing, and decreased physical activity is likely to be a major contributor to this trend. Studies of physical activity in young children are limited by the lack of valid and acceptable measures. The purpose of this study was to calibrate and validate the ActiGraph accelerometer for use with 3‐ to 5‐year‐old children. Research Methods and Procedures: Thirty preschool children wore an ActiGraph accelerometer (ActiGraph, Fort Walton Beach, FL) and a Cosmed portable metabolic system (Cosmed, Rome, Italy) during a period of rest and while performing three structured physical activities in a laboratory setting. Expired respiratory gases were collected, and oxygen consumption was measured on a breath‐by‐breath basis. Accelerometer data were collected at 15‐second intervals. For cross‐validation, the same children wore the same instruments while participating in unstructured indoor and outdoor activities for 20 minutes each at their preschool. Results: In calibrating the accelerometer, the correlation between V?o ₂ (ml/kg per min) and counts was r = 0.82 across all activities. The only significant variable in the prediction equation was accelerometer counts (R² = 0.90, standard error of the estimate = 4.70). In the cross‐validation, the intraclass correlation coefficient between measured and predicted V?o ₂ was R = 0.57 and the Spearman correlation coefficient was R = 0.66 (p < 0.001). Cut‐off points for moderate‐ and vigorous‐intensity physical activity were identified at 420 counts/15 s (V?o ₂ = 20 mL/kg per min) and 842 counts/15 s (V?o ₂ = 30 mL/kg per min), respectively. When these cutpoints were applied to the cross‐validation data, percentage agreement, kappa, and modified kappa for moderate activity were 0.69, 0.36, and 0.38, respectively. For vigorous activity, the same measures were 0.81, 0.13, and 0.62. Discussion: Accelerometer counts were highly correlated with V?o ₂ in young children. Accelerometers can be appropriately used as a measure of physical activity in this population.     

Weight-Loss Induced Changes in Physical Activity and Activity Energy Expenditure in Overweight and Obese Subjects before and after Energy Restriction

Activity energy expenditure (AEE) is the component of daily energy expenditure that is mainly influenced by the amount of physical activity (PA) and by the weight of the body displaced. This study aimed at analyzing the effect of weight loss on PA and AEE. The body weight and PA of 66 overweight and obese subjects were measured at baseline and after 12 weeks of 67% energy restriction. PA was measured using a tri-axial accelerometer for movement registration (Tracmor) and quantified in activity counts. Tracmor recordings were also processed using a classification algorithm to recognize 6 common activity types engaged in during the day. A doubly-labeled water validated equation based on Tracmor output was used to estimate AEE. After weight loss, body weight decreased by 13±4%, daily activity counts augmented by 9% (95% CI: +2%, +15%), and this increase was weakly associated with the decrease in body weight (R² = 7%; P<0.05). After weight loss subjects were significantly (P<0.05) less sedentary (–26 min/d), and increased the time spent walking (+11 min/d) and bicycling (+4 min/d). However, AEE decreased by 0.6±0.4 MJ/d after weight loss. On average, a 2-hour/day reduction of sedentary time by increasing ambulatory and generic activities was required to restore baseline levels of AEE. In conclusion, after weight loss PA increased but the related metabolic demand did not offset the reduction in AEE due to the lower body weight. Promoting physical activity according to the extent of weight loss might increase successfulness of weight maintenance.