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.     

Accelerometer counts and raw acceleration output in relation to mechanical loading

The purpose of this study was to assess the relationship of accelerometer output, in counts (ActiGraph GT1M) and as raw accelerations (ActiGraph GT3X+ and GENEA), with ground reaction force (GRF) in adults. Ten participants (age: 29.4 ± 8.2 yr, mass: 74.3 ± 9.8 kg, height: 1.76 ± 0.09 m) performed eight trials each of: slow walking, brisk walking, slow running, faster running and box drops. GRF data were collected for one step per trial (walking and running) using a force plate. Low jumps and higher jumps (one per second) were performed for 20 s each on the force plate. For box drops, participants dropped from a 35 cm box onto the force plate. Throughout, three accelerometers were worn at the hip: GT1M, GT3X+ and GENEA. A further GT3X+ and GENEA were worn on the left and right wrist, respectively. GT1M counts correlated with peak impact force (r = 0.85, p < 0.05), average resultant force (r = 0.73, p < 0.05) and peak loading rate (r = 0.76, p < 0.05). Accelerations from the GT3X+ and GENEA correlated with average resultant force and peak loading rate irrespective of whether monitors were worn at the hip or wrist (r > 0.82, p < 0.05, r > 0.63 p < 0.05, respectively). In conclusion, accelerometer count and raw acceleration output correlate positively with GRF and thus may be appropriate for the quantification of activity beneficial to bone. Wrist-worn monitors show a similar relationship with GRF as hip-worn monitors, suggesting that wrist-worn monitors may be a viable option for future studies looking at bone health.     

Separating Bedtime Rest from Activity Using Waist or Wrist-Worn Accelerometers in Youth

Recent interest in sedentary behavior and technological advances expanded use of watch-size accelerometers for continuous monitoring of physical activity (PA) over extended periods (e.g., 24 h/day for 1 week) in studies conducted in natural living environment. This approach necessitates the development of new methods separating bedtime rest and activity periods from the accelerometer recordings. The goal of this study was to develop a decision tree with acceptable accuracy for separating bedtime rest from activity in youth using accelerometer placed on waist or wrist. Minute-by-minute accelerometry data were collected from 81 youth (10–18 years old, 47 females) during a monitored 24-h stay in a whole-room indirect calorimeter equipped with a force platform covering the floor to detect movement. Receiver Operating Characteristic (ROC) curve analysis was used to determine the accelerometer cut points for rest and activity. To examine the classification differences, the accelerometer bedtime rest and activity classified by the algorithm in the development group (n = 41) were compared with actual bedtime rest and activity classification obtained from the room calorimeter-measured metabolic rate and movement data. The selected optimal bedtime rest cut points were 20 and 250 counts/min for the waist- and the wrist-worn accelerometer, respectively. The selected optimal activity cut points were 500 and 3,000 counts/min for waist and wrist-worn accelerometers, respectively. Bedtime rest and activity were correctly classified by the algorithm in the validation group (n = 40) by both waist- (sensitivity: 0.983, specificity: 0.946, area under ROC curve: 0. 872) and wrist-worn (0.999, 0.980 and 0.943) accelerometers. The decision tree classified bedtime rest correctly with higher accuracy than commonly used automated algorithm for both waist- and wrist-warn accelerometer (all p<0.001). We concluded that cut points developed and validated for waist- and wrist-worn uniaxial accelerometer have a good power for accurate separation of time spent in bedtime rest from activity in youth.     

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.     

Accelerometry estimates field metabolic rate in giant Australian cuttlefish Sepia apama during breeding

1. Estimating the metabolic rate of animals in nature is central to understanding the physiological, behavioural and evolutionary ecology of animals. Doubly labelled water and heart-rate methods are the most commonly used approaches, but both have limitations that preclude their application to some systems. 2. Accelerometry has emerged as a powerful tool for estimating energy expenditure in a range of animals, but is yet to be used to estimate field metabolic rate in aquatic taxa. We combined two-dimensional accelerometry and swim-tunnel respirometry to estimate patterns of energy expenditure in giant Australian cuttlefish Sepia apama during breeding. 3. Both oxygen consumption rate (Vo2) and swimming speed showed strong positive associations with body acceleration, with coefficients of determination comparable to those using similar accelerometers on terrestrial vertebrates. Despite increased activity during the day, field metabolic rate rarely approached Vo2, and night-time Vo2 was similar to that at rest. 4. These results are consistent with the life-history strategy of this species, which has a poor capacity to exercise anaerobically, and a mating strategy that is visually based. With the logistical difficulties associated with observation in aquatic environments, accelerometry is likely to prove a valuable tool for estimating energy expenditure in aquatic animals.     

Exploring the relationship between flapping behaviour and accelerometer signal during ascending flight,and a new approach to calibration

We understand little about the energetic costs of flight in free-ranging birds, in part because current techniques for estimating flight energetics in the wild are limited. Accelerometry is known to estimate energy expenditure through body movement in terrestrial animals, once calibrated using a treadmill with chamber respirometry. The flight equivalent, a wind tunnel with mask respirometry, is particularly difficult to instigate, and has not been applied to calibrate accelerometry. We take the first steps in exploring a novel method for calibrating accelerometers with flight energy expenditure. We collected accelerometry data for Harris's Hawks Parabuteo unicinctus flying to varying heights up to 4.1 m over a small horizontal distance; the mechanical energy expended to gain height can be estimated from physical first principles. The relationship between accelerometry and mechanical energy expenditure was strong, and while a simple wing flapping model confirmed that accelerometry is sensitive to both changes in wing beat amplitude and frequency, the relationship was explained predominately by changes in wing beat frequency, and less so by changes in amplitude. Our study provides initial, positive evidence that accelerometry can be calibrated with body power using climbing flights, potentially providing a basis for estimating flapping flight metabolic rate at least in situations of altitude gain.     

Measuring Free‐Living Energy Expenditure and Physical Activity with Triaxial Accelerometry

Objective: To investigate the ability of a newly developed triaxial accelerometer to predict total energy expenditure (EE) (TEE) and activity‐related EE (AEE) in free‐living conditions. Research Methods and Procedures: Subjects were 29 healthy subjects between the ages of 18 and 40. The Triaxial Accelerometer for Movement Registration (Tracmor) was worn for 15 consecutive days. Tracmor output was defined as activity counts per day (ACD) for the sum of all three axes or each axis separately (ACD‐X, ACD‐Y, ACD‐Z). TEE was measured with the doubly labeled water technique. Sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. The physical activity level was calculated as TEE × SMR^?1, and AEE was calculated as [(0.9 × TEE) ? SMR]. Body composition was calculated from body weight, body volume, and total body water using Siri's three‐compartment model. Results: Age, height, body mass, and ACD explained 83% of the variation in TEE [standard error of estimate (SEE) = 1.00 MJ/d] and 81% of the variation in AEE (SEE = 0.70 MJ/d). The partial correlations for ACD were 0.73 (p < 0.001) and 0.79 (p < 0.001) with TEE and AEE, respectively. When data on SMR or body composition were used with ACD, the explained variation in TEE was 90% (SEE = 0.74 and 0.77 MJ/d, respectively). The increase in the explained variation using three axes instead of one axis (vertical) was 5% (p < 0.05). Discussion: The correlations between Tracmor output and EE measures are the highest reported so far. To measure daily life activities, the use of triaxial accelerometry seems beneficial to uniaxial.     

Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer

Background

Few studies have compared the validity of objective measures of physical activity energy expenditure (PAEE) in pregnant and non-pregnant women. PAEE is commonly estimated with accelerometers attached to the hip or waist, but little is known about the validity and participant acceptability of wrist attachment. The objectives of the current study were to assess the validity of a simple summary measure derived from a wrist-worn accelerometer (GENEA, Unilever Discover, UK) to estimate PAEE in pregnant and non-pregnant women, and to evaluate participant acceptability.

Methods

Non-pregnant (N = 73) and pregnant (N = 35) Swedish women (aged 20–35 yrs) wore the accelerometer on their wrist for 10 days during which total energy expenditure (TEE) was assessed using doubly-labelled water. PAEE was calculated as 0.9×TEE-REE. British participants (N = 99; aged 22–65 yrs) wore accelerometers on their non-dominant wrist and hip for seven days and were asked to score the acceptability of monitor placement (scored 1 [least] through 10 [most] acceptable).

Results

There was no significant correlation between body weight and PAEE. In non-pregnant women, acceleration explained 24% of the variation in PAEE, which decreased to 19% in leave-one-out cross-validation. In pregnant women, acceleration explained 11% of the variation in PAEE, which was not significant in leave-one-out cross-validation. Median (IQR) acceptability of wrist and hip placement was 9(8–10) and 9(7–10), respectively; there was a within-individual difference of 0.47 (p<.001).

Conclusions

A simple summary measure derived from a wrist-worn tri-axial accelerometer adds significantly to the prediction of energy expenditure in non-pregnant women and is scored acceptable by participants.     

Accelerometry predicts daily energy expenditure in a bird with high activity levels

Animal ecology is shaped by energy costs, yet it is difficult to measure fine-scale energy expenditure in the wild. Because metabolism is often closely correlated with mechanical work, accelerometers have the potential to provide detailed information on energy expenditure of wild animals over fine temporal scales. Nonetheless, accelerometry needs to be validated on wild animals, especially across different locomotory modes. We merged data collected on 20 thick-billed murres (Uria lomvia) from miniature accelerometers with measurements of daily energy expenditure over 24 h using doubly labelled water. Across three different locomotory modes (swimming, flying and movement on land), dynamic body acceleration was a good predictor of daily energy expenditure as measured independently by doubly labelled water (R² = 0.73). The most parsimonious model suggested that different equations were needed to predict energy expenditure from accelerometry for flying than for surface swimming or activity on land (R² = 0.81). Our results demonstrate that accelerometers can provide an accurate integrated measure of energy expenditure in wild animals using many different locomotory modes.     

Estimating daily walking distance of captive African elephants using an accelerometer

Two central concerns for elephant husbandry and management are whether zoological enclosures are appropriately sized and the degree to which naturalistic exercise and activity are observed in such enclosures. In order to address these issues, accurate data on the daily walking distance of elephants both in situ and ex situ are necessary. We used an accelerometer, a pedometer that measures step count and activity level, to estimate walking distance in African elephants (Loxodonta africana) at the San Diego Zoo's Wild Animal Park. The accelerometer was worn simultaneously with a GPS unit that recorded actual walking distance. Estimates of walking distance were extrapolated from the accelerometer and compared with actual distances determined by GPS data. The accelerometer was found to overestimate step count, and subsequently walking distance, by including false counts of steps. Extrapolating walking distance based upon stride length measurements did not match actual GPS walking distance. However, activity level output from the accelerometer significantly correlated with actual GPS walking distance. In addition, we report that the rate of movement is comparable to that reported in other zoological settings. We provide a linear regression equation that can be utilized by other institutions to estimate daily walking distance of elephants in their collection who are outfitted with accelerometers.     

Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water

Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free‐ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free‐ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free‐ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer‐based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer‐based measures of energy expenditure strongly correlated (r² = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free‐ranging animals.     

Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users

PurposeTo assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs).MethodsSeventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr^-1) and propulsion at 4km·hr^-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.ResultsAccelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.ConclusionThe results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.     

Behaviors in rhesus monkeys (Macaca mulatta) associated with activity counts measured by accelerometer

There are large individual differences in the daily pattern and level of physical activity in humans and other species. As it is becoming apparent that activity plays an integral role in a number of physiological processes including arousal, attention, cardiovascular health and body weight regulation, there is an increased interest in quantifying activity. Nonhuman primates are particularly useful experimental models for such studies in that they exhibit a repertoire of activity more similar to humans than the activity of animals such as rodents and domestic animals. Recent studies measuring activity in nonhuman primates have used omnidirectional accelerometers, often worn on collars; however, the exact behaviors and movements detected by monkeys wearing these devices have not yet been characterized. To test the hypothesis that collar-worn accelerometers primarily detect movements that involve movement of the whole body, 16 adult female rhesus monkeys, housed individually in stainless steel cages, wore loose-fitting collars with an attached small metal box housing an activity monitor (Actical omnidirectional accelerometer; MiniMitter Inc., Bend, OR) and behavior was videotaped. Videotaped behaviors were analyzed by frame-by-frame analysis. There was a significant correlation between total (all) movement revealed by videotape analysis and activity counts detected by the accelerometers (r(s)=0.612, P=0.012), primarily reflecting a strong correlation between whole body movement and activity counts (r(s)=0.647, P=0.007). In contrast, arm movement (r(s)=-0.221, P=0.412) and head/neck movement (r(s)=0.193, P=0.474) were not correlated with activity counts. These findings support the hypothesis that activity monitor placement on a collar allows for effective quantification of whole body movement in monkeys, and indicate that behaviors such as chewing and arm movement do not significantly influence activity recorded by collar-mounted accelerometers.     

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.     

Validation of the TracmorD Triaxial Accelerometer to Assess Physical Activity in Preschool Children

Objectives: To assess validity evidence of Tracmor_D to determine energy used for physical activity in 3‐4‐year‐old children. Design and Methods: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, Tracmor_D with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. Results: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R² = 0.50, P = 0.010 and R² = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R² = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R² = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. Conclusion: Tracmor_D provides moderate‐to‐strong validity evidence that supports its use to evaluate energy used for physical activity in 3‐4‐year‐old children.     

Recording raptor behavior on the wing via accelerometry

ABSTRACT.   Measuring body movements using accelerometry data loggers is a relatively new technique, the full applicability of which has yet to be tested on volant birds. Our study illustrates the potential of accelerometry for research on large birds by using the technique to record the behavior of three species of raptors, mainly during flight. A tri-axial accelerometer was deployed on a trained Harris' Hawk ( Parabuteo unicinctus ), Tawny Eagle ( Aquila rapax ), and Griffon Vulture ( Gyps fulvus ). Comparison of flight-related variables calculated from video footage and that estimated from the acceleration data showed that the latter provided considerable and accurate information (usually <10% error) about the behavior of the birds, including wing-beat frequency and when they glided and flapped. Acceleration data permitted tentative comparisons of relative movement-specific rates of energy expenditure for the Griffon Vulture flying up versus flying down a small hill. The accelerometry data appeared to suggest, as expected, that the Griffon Vulture expended more energy flying uphill than flying back down. Our preliminary findings indicate that studies using accelerometers can likely provide information about the detailed time–energy budgets of large birds. Such information would aid in comparative analyses of behavior and energetics, and may also enhance efforts to conserve declining bird populations.     

Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant

1. Time and energy are key currencies in animal ecology, and judicious management of these is a primary focus for natural selection. At present, however, there are only two main methods for estimation of rate of energy expenditure in the field, heart rate and doubly labelled water, both of which have been used with success; but both also have their limitations. 2. The deployment of data loggers that measure acceleration is emerging as a powerful tool for quantifying the behaviour of free-living animals. Given that animal movement requires the use of energy, the accelerometry technique potentially has application in the quantification of rate of energy expenditure during activity. 3. In the present study, we test the hypothesis that acceleration can serve as a proxy for rate of energy expenditure in free-living animals. We measured rate of energy expenditure as rates of O2 consumption (VO2) and CO2 production (VCO2) in great cormorants (Phalacrocorax carbo) at rest and during pedestrian exercise. VO2 and VCO2 were then related to overall dynamic body acceleration (ODBA) measured with an externally attached three-axis accelerometer. 4. Both VO2 and VCO2 were significantly positively associated with ODBA in great cormorants. This suggests that accelerometric measurements of ODBA can be used to estimate VO2 and VCO2 and, with some additional assumptions regarding metabolic substrate use and the energy equivalence of O2 and CO2, that ODBA can be used to estimate the activity specific rate of energy expenditure of free-living cormorants. 5. To verify that the approach identifies expected trends in from situations with variable power requirements, we measured ODBA in free-living imperial cormorants (Phalacrocorax atriceps) during foraging trips. We compared ODBA during return and outward foraging flights, when birds are expected to be laden and not laden with captured fish, respectively. We also examined changes in ODBA during the descent phase of diving, when power requirements are predicted to decrease with depth due to changes in buoyancy associated with compression of plumage and respiratory air. 6. In free-living imperial cormorants, ODBA, and hence estimated VO2, was higher during the return flight of a foraging bout, and decreased with depth during the descent phase of a dive, supporting the use of accelerometry for the determination of activity-specific rate of energy expenditure.     

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.     

Assessing the validity of the accelerometry technique for estimating the energy expenditure of diving double-crested cormorants Phalacrocorax auritus

Over the past few years, acceleration-data loggers have been used to provide calibrated proxies of energy expenditure: the accelerometry technique. Relationships between rate of oxygen consumption and a derivation of acceleration data termed "overall dynamic body acceleration" (ODBA) have now been generated for a range of species, including birds, mammals, and amphibians. In this study, we examine the utility of the accelerometry technique for estimating the energy expended by double-crested cormorants Phalacrocorax auritus to undertake a dive cycle (i.e., a dive and the subsequent pause at the surface before another dive). The results show that ODBA does not calibrate with energy expenditure in diving cormorants, where energy expenditure is calculated from measures of oxygen uptake during surface periods between dives. The possible explanations include reasons why energy expenditure may not relate to ODBA but also reasons why oxygen uptake between dives may not accurately represent energy expenditure during a dive cycle.     

Assessing the development and application of the accelerometry technique for estimating energy expenditure

A theoretically valid proxy of energy expenditure is the acceleration of an animal's mass due to the movement of its body parts. Acceleration can be measured by an accelerometer and recorded onto a data logging device. Relevant studies have usually derived a measure of acceleration from the raw data that represents acceleration purely due to movement of the animal. This is termed ‘overall dynamic body acceleration’ (ODBA) and to date has proved a robust derivation of acceleration for use as an energy expenditure proxy. Acceleration data loggers are generally easy to deploy and the measures recorded appear robust to slight variation in location and orientation. This review discusses important issues concerning the accelerometry technique for estimating energy expenditure and ODBA; deriving ODBA, calibrating ODBA, acceleration logger recording frequencies, scenarios where ODBA is less likely to be valid, and the power in recording acceleration and heart rate together. While present evidence suggests that ODBA may not quantify energy expenditure during diving by birds and mammals, several recent studies have assessed changes in mechanical work in such species qualitatively through variation in ODBA during periods of submergence. The use of ODBA in field metabolic studies is likely to continue growing, supported by its relative ease of use and range of applications.