Estimates Of Daily Energy Expenditure Research Articles

Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable

BackgroundAll behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed ‘dynamic body acceleration’ (DBA). However, converting acceleration proxies (m/s2) to standard units (watts) involves costly in-lab respirometry measurements, and there is a lack of viable substitutes for empirical calibration relationships when these are unavailable.MethodsWe used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting ‘power calibration equation’ to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber’s law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population.ResultsACTIWAKE’s DEE estimates did not differ significantly from DLW (t(19) = − 1.25; P = 0.22), while ACTIREST24’s estimates did (t(19) = − 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1–2% of mean) was lower than that in DLW (s.d. 33% of mean).ConclusionsWe found that linear locomotion-based calibration applied to the waking period, and a ‘flat’ resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.

Integration of Report-Based Methods to Enhance the Interpretation of Monitor-Based Research: Results From the Free-Living Activity Study for Health Project

Accelerometry-based monitors are commonly utilized to evaluate physical activity behavior, but the lack of contextual information limits the interpretability and value of the data. Integration of report-based with monitor-based data allows the complementary strengths of the two approaches to be used to triangulate information and to create a more complete picture of free-living physical behavior. This investigation utilizes data collected from the Free-Living Activity Study for Health to test the feasibility of annotating monitor data with contextual information from the Activities Completed Over Time in 24-hr (ACT24) previous-day recall. The evaluation includes data from 134 adults who completed the 24-hr free-living monitoring protocol and retrospective 24-hr recall. Analyses focused on the relative agreement of energy expenditure estimates between ACT24 and two monitor-based methods (ActiGraph and SenseWear Armband). Daily energy expenditure estimates from ACT24 were equivalent to the reference device-based estimate. Minute-level agreement of energy expenditure between ACT24 and device-based methods was moderate and was similar to the agreement between two different monitor-based methods. This minute-level agreement between ACT24 and device-based methods demonstrates the feasibility and utility of integrating self-report with accelerometer data to provide richer context on the monitored behaviors. This type of integration offers promise for advancing the assessment of physical behavior by aiding in data interpretation and providing opportunities to improve physical activity assessment methods under free-living conditions.

Non‐invasive measurement of metabolic rates in wild, free‐living birds using doubly labelled water

Abstract Doubly labelled water (DLW) is routinely used to measure energy expenditure and water turnover in free‐ranging animals. Standard methods involve capture, blood sampling for baseline measurement, injection with isotopic tracers, captivity for an equilibration period, post‐dose blood sampling, release and subsequent recapture for final blood sampling. Single sampling methods that minimise disturbance by reducing capture and handling time have been developed and tested. Sampling faeces rather than blood could further reduce disturbance to study animals in a range of species and study systems. However, the extent to which estimates of metabolic rate derived from blood and faecal samples diverge has not been investigated. We compared isotopic enrichment in blood and faecal samples taken concurrently from captive Southern Pied Babblers Turdoides bicolor. Isotopic enrichment levels in faeces and in blood were used to calculate initial and final ratios of for each individual. We then used these ratios to calculate daily energy expenditure (DEE) and directly compared measurements from blood samples with those from faecal samples within individuals. We found that faecal sampling resulted in estimates of DEE that agree with those based on blood sampling. Additionally, we field‐tested a faecal sampling protocol with a habituated population of babblers in the southern Kalahari Desert. During the field test, study animals were not captured or handled for either dosing or sampling. Field‐testing confirmed the practical feasibility of non‐invasive dosing and sampling techniques in free‐living animals, and we obtained measurements of DEE that we used to test an a priori prediction that DEE is inversely related to air temperature. Our data show decreasing DEE with increasing air temperature, a pattern consistent with studies testing similar predictions in birds using traditional DLW methods. We demonstrate that faecal samples can substitute for blood when measuring DEE using DLW and provide a method that will allow field‐based researchers to obtain sound physiological measurements while minimising handling and removal of study animals from their natural environments. A plain language summary is available for this article.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors.

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Energetic consequences of time‐activity budgets for a breeding seabird

AbstractHow animals allocate their time to different behaviours has important consequences for their overall energy budget and reflects how they function in their environment. This potentially affects their ability to successfully reproduce, thereby impacting their fitness. We used accelerometers to record time‐activity budgets of 21 incubating and chick‐rearing kittiwakes (Rissa tridactyla) on Puffin Island, UK. These budgets were examined on a per day and per foraging trip basis. We applied activity‐specific estimates of energy expenditure to the kittiwakes' time‐activity budgets in order to identify the costs of variation in their allocation of time to different behaviours. Estimates of daily energy expenditure for incubating kittiwakes averaged 494 ± 20 kJ d−1 while chick‐rearing birds averaged 559 ± 11 kJ d−1. Time‐activity budgets highlighted that kittiwakes did not spend a large proportion of their time flying during longer foraging trips, or during any given 24‐h period. With time spent flying highlighted as the driving factor behind elevated energy budgets, this suggests behavioural compensation resulting in a possible energetic ceiling to their activities. We also identified that kittiwakes were highly variable in the proportion of time they spent either flying or on the water during foraging trips. Such variation meant that using forage trip duration alone to predict energy expenditure gave a mean error of 19% when compared to estimates incorporating the proportion of a foraging trip spent flying. We have therefore highlighted that trip duration alone is not an accurate indicator of energy expenditure.

Morphometrics of mid-Atlantic dabbling ducks for use in thermoregulation models

ABSTRACT Bioenergetics modeling is a popular tool used by waterfowl biologists to estimate carrying capacity based on food energy availability and daily energy expenditure (DEE). For wintering waterfowl, estimates of DEE may incorporate a cost of thermoregulation (CT) component, which accounts for metabolic heat production when ambient temperatures fall below a species-specific Lower Critical Temperature (LCT). Typically, DEE estimates have utilized either a fixed CT component or a simple CT model based solely on the magnitude of the difference between ambient temperature and LCT. Using a more complex CT model that accounts for differential heat loss from individual body regions due to temperature, wind speed, and contact with air or water may provide more detailed estimates of CT and, in turn, carrying capacity. However, such models required detailed morphometrics as model inputs in addition to environmental data. We present morphometrics for 8 dabbling duck species for use in thermoregulation models, as...

How accurate is triaxial RT3TM (RT3) accelerometer for estimating energy expenditure

The participants are instructed to remove the RT3 only for sleeping, bathing and swimming activities but some studies have shown the participants remove the accelerometer for longer times. The activities performed while the accelerometer is not being worn and, or energy that was not recorded during this time has never been estimated. This study aimed to assess compliance in using the accelerometer and quantify the energy expenditure (EE) not recorded by the accelerometer during the time it was not worn in free-living young males in a consecutive 4-day period. Eleven male participants 19 to 23 years of age, 54.7 to 85.5 kg with body mass index of 19.1 to 27.6 kg.m-2 completed the study. Resting metabolic rate was measured by indirect calorimetry. Daily EE estimation was on average 23.6% higher using the Bouchard Physical Activity Records than the RT3. Accelerometers were worn for 67 to 98% of waking hours but up to 30% of the EE was not recorded due to the device not being worn by participants mainly during intense physical activity. Recording the physical activity when the accelerometer is not being worn would provide a more precise estimative of the EE.

Concurrent validation of estimated activity energy expenditure using a 3‐day diary and accelerometry in adolescents

Estimates of daily energy expenditure are important to studies of physical activity and energy balance. Objective measures are not always feasible and further research is needed to validate survey instruments and diaries. The study validates estimated activity energy expenditure (AEE) based on a 3-day diary protocol relative to AEE derived from uniaxial accelerometry in adolescents, 265 girls and 227 boys (12.5-16.4 years). Participants completed the diary and wore a GT1M Actigraph accelerometer on the same days. Height and weight were measured. Correlations between protocols were significant (P<0.001) but moderate, r=0.65 in males and r=0.69 in females. The highest correlation occurred among males on Friday, r=0.74 (P<0.01). Controlling for body mass, partial correlations between protocols decreased to 0.44 and 0.35 in males and females, respectively. About 97% of the cases fell within the limits of agreement in a Bland-Altman plot. The criterion of inclusion for the accelerometer excluded 18% of the initial sample. In summary, the 3-day diary was completed without any major problems and provided a reasonably valid alternative for assessing AEE. Concordance between methods was slightly lower for individuals with higher values of AEE.

Prediction of Bodyweight and Energy Expenditure Using Point Pressure and Foot Acceleration Measurements

Bodyweight (BW) is an essential outcome measure for weight management and is also a major predictor in the estimation of daily energy expenditure (EE). Many individuals, particularly those who are overweight, tend to underreport their BW, posing a challenge for monitors that track physical activity and estimate EE. The ability to automatically estimate BW can potentially increase the practicality and accuracy of these monitoring systems. This paper investigates the feasibility of automatically estimating BW and using this BW to estimate energy expenditure with a footwear-based, multisensor activity monitor. The SmartShoe device uses small pressure sensors embedded in key weight support locations of the insole and a heel-mounted 3D accelerometer. Bodyweight estimates for 9 subjects are computed from pressure sensor measurements when an automatic classification algorithm recognizes a standing posture. We compared the accuracy of EE prediction using estimated BW compared to that of using the measured BW. The results show that point pressure measurement is capable of providing rough estimates of body weight (root-mean squared error of 10.52 kg) which in turn provide a sufficient replacement of manually-entered bodyweight for the purpose of EE prediction (root-mean squared error of 0.7456 METs vs. 0.6972 METs). Advances in the pressure sensor technology should enable better accuracy of body weight estimation and further improvement in accuracy of EE prediction using automatic BW estimates.

Observed Herring Gull Kleptoparasitism of American Black Ducks

Abstract Herring gulls Larus argentatus were observed to kleptoparasitize American black ducks Anas rubripes feeding on fiddler crabs Uca pugnax in coastal New Jersey. Although widespread in Laridae, kleptoparasitism has never been described between these two species. Over two winters of intensive 24-hour behavioral observations, this interaction was observed on two occasions during similar tidal conditions. Although this appears to be a rare interaction with limited energetic consequences, we note that quantifying these uncommon interspecific interactions is a benefit of thorough behavior observations, which may refine estimates of daily energy expenditure.

Estimation of Daily Energy Expenditure in Pregnant and Non-Pregnant Women Using a Wrist-Worn Tri-Axial Accelerometer

BackgroundFew 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.MethodsNon-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).ResultsThere 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).ConclusionsA 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.

Assessment of habitual physical activity and energy expenditure in dialysis patients and relationships to nutritional parameters

Assessment of physical activity level and of energy expenditure is important in the clinical and nutritional care of dialysis patients, but it is not so easy to accomplish. The SenseWear™ Armband (SWA) is a novel multisensory device that is worn on the upper arm and collects a variety of physiologic data related to physical activity. Thus, duration and intensity of physical activity is recorded and expressed as METs (Metabolic Equivalent Task), and energy expenditure is estimated. The aim of our study was to assess interdialytic spontaneous physical activity in stable chronic hemodialysis (HD) patients and the relation to nutritional status and dietary nutrient intake. In 50 stable patients on maintenance hemodialysis treatment and 33 normal subjects (control group), level of spontaneous physical activity and estimated daily energy expenditure was assessed by SWA and related to biochemistry and anthropometry data, bioelectric impedance vector analysis, and energy and nutrient intake information coming from a 3-day food recall. In respect to controls, HD patients showed lower mean daily METs value (1.3 ± 0.3 vs. 1.5 ± 0.2, p < 0.01), a lower time spent on activities > 3 METs (89 ± 85 vs. 143 ± 104 min/day, p < 0.05), lower number of steps per day (5,584 ± 3,734 vs. 11,735 ± 5,130, p < 0.001), resulting in a lower estimated energy expenditure (2,190 ± 629 vs. 2,462 ± 443 Kcal/day, p < 0.05). 31 out of the 50 HD patients (62%) had a mean daily value < 1.4 METs and hence were defined as sedentary. They differed from the active patients for higher age (63 ± 12 vs. 54 ± 12 y, p < 0.01), lower energy intake (26.1 ± 6.4 vs. 32.4 ± 11.3 Kcal/day, p < 0.05) and lower phase angle (5.5 ± 1.0 vs. 6.3 ± 0.9, p < 0.05). SWA-based estimation of daily energy expenditure was negatively related to age (r = -0.31, p < 0.05), whereas positive relations were observed with BMI (r = 0.51, p < 0.001), phase angle (r = 0.40, p < 0.01), serum phosphate (r = 0.49, p < 0.001) and albumin (r = 0.41, p < 0.01). The mean daily METs values were strongly related to normalized energy intake (r = 0.47, p < 0.001) and also to protein intake (r = 0.33, p < 0.05) and to phase angle (r = 0.38, p < 0.01). Multiple regression analysis showed that energy intake and dietary protein intake were independently related to the intensity of physical activity. Our findings indicate that poor physical activity is highly prevalent in stable dialysis patients even when free from physical or neurological disabilities or severe comorbid conditions. The level and intensity of physical activity is positively related to body composition and to dietary nutrient intake. This confirms the strong interrelationship between exercise and nutrition, which in turn are associated with survival, rehabilitation and quality of life in dialysis patients.

Site selection and resource depletion in black‐tailed godwits Limosa l. limosa eating rice during northward migration

1. During migratory stopovers, animals are under strong time stress and need to maximize intake rates. We examine how foragers react to resource depletion by studying the foraging ecology and foraging site selection of black-tailed godwits Limosa l. limosa staging in rice fields during their northward migration stopover (January-March 2007). 2. We analysed godwit abundance and foraging behaviour, sampled the availability of rice in the fields and used the functional response model to predict the giving-up density (GUD) of rice kernels when godwits should give up a rice field. Sightings of individually colour-marked birds were used to verify whether individuals moving between rice fields confirmed the predicted GUD. 3. Black-tailed godwit intake rates at different rice densities fitted Holling's functional response curve. The predicted GUD of rice necessary to balance allometric estimates of daily energy expenditure (DEE) and measured time budgets were confirmed by GUD measured in the field. 4. Individually marked birds moved towards rice fields with higher rather than lower rice densities more often than randomly expected. These birds increased the measured intake rates after this move. 5. Godwit foraging caused a decrease in the rice density of individual fields during the stopover period. Despite this, overall intake rates remained constant as godwits reacted to resource depletion by moving to a new foraging site as soon as their intake rate falls below the required levels to achieve DEE.

Bioenergetics assessment of fish and crayfish consumption by river otter (Lontra canadensis): integrating prey availability, diet, and field metabolic rate

River otters ( Lontra canadensis ) are important predators in aquatic ecosystems, but few studies quantify their prey consumption. We trapped crayfish monthly as an index of availability and collected otter scat for diet analysis in the Ozark Mountains of northwestern Arkansas, USA. We measured otter daily energy expenditure (DEE) with the doubly labeled water method to develop a bioenergetics model for estimating monthly prey consumption. Meek’s crayfish ( Orconectes meeki ) catch-per-unit-effort was positively related to stream temperature, indicating that crayfish were more available during warmer months. The percentage frequency of occurrence for crayfish in scat samples peaked at 85.0% in summer and was lowest (42.3%) in winter. In contrast, the percentage occurrence of fish was 13.3% in summer and 57.7% in winter. Estimates of DEE averaged 4738 kJ·day–1 for an otter with a body mass of 7842 g. Total biomass consumption ranged from 35 079 to 52 653 g·month–1 (wet mass), corresponding to a high proportion of fish and crayfish in the diet, respectively. Otter consumption represents a large fraction of prey production, indicating potentially strong effects of otters on trophic dynamics in stream ecosystems.

A review of estimates of daily energy expenditure and food intake in cormorants ( Phalacrocorax spp.)

Daily energy expenditure (DEE) and daily food intake (DFI) are key parameters in estimating population level consumption by cormorants. A number of different methods are still employed in estimating these parameters along with different estimates for assimilation efficiency (if used) and prey energy density. The pellet/fish size reconstruction and percent adult body weight methods underestimate DFI for a number of reasons including an implied underestimation of DEE. In the absence of study-specific data, an assimilation efficiency of 0.80 and prey energy density of 5.42 kJ∙g − 1 are recommended. The bioenergetic model for field metabolic rate from Ellis and Gabrielsen (2002) is recommended for adults during the nesting season and their model for basal metabolic rate (BMR × 2.5) is recommended for adults or sub-adults outside the nesting season. Comparisons between empirical and bioenergetic models for chick DFI are also made with recommendations on estimating DFI.

Intensivmedizinische Patienten

Using indirect calorimetry (IC), required energy demand may be determined. The SenseWear (SW) armband uses skin temperature, galvanic skin response, heat flux, and a 2-axis accelerometer to estimate daily energy expenditure (EE). The aim of the present study was to evaluate accuracy of the SenseWear measurements in critically ill and ventilated patients. After approval of the local ethics committee and written informed consent, critically ill and ventilated patients were enrolled. During a 24-h study period EE was continuously measured using the SW armband (standardized position at right upper arm); an IC was performed additionally. Measurement bias (DeltaEE) was calculated as DeltaEE=EE(SW)-EE(IC). Daily energy expenditure of both techniques (IC vs. SW) was compared using regression analysis and the Bland Altman method. The t-Test for paired samples was used for statistical analysis, p<0.05 was considered statistically significant. In total, 23 critically ill and ventilated patients (17 male, 6 female; means: 59.9+/-17.3 years; body mass index 28.0+/-6.3 kg/m(-2)) were investigated. A mean bias of DeltaEE=-565.65+/-1,748.07 kJ (-135.0+/-417.2 kcal) [range: -4,709.56+/-2,224.89 kJ (-1,124 to +531 kcal); p=0.3547] was calculated. Bland-Altman analysis revealed that SW slightly overestimates IC energy expenditure for critically ill patients in the hypo- and normocaloric range [<7,123 kJ/24 h: DeltaEE=+644.42+/-1,038.70 kJ (<1,700 kcal/24 h: DeltaEE=+153.8+/-247.9 kcal); p=0.0838], but significantly underestimated IC values in the hypercaloric range [>10,056 kJ/24 h: DeltaEE=-2,679.09+/-1,698.63 kJ (>2,400 kcal/24 h: DeltaEE=-639.4+/-405.4 kcal); p=0.0098]. The SenseWear armband is non-invasive, convenient and easy to handle, but has a significant measurement bias in the hypercaloric range. Although IC is still best suited to determining metabolic need in intubated patients, measurements with the SenseWear armband provide significant advantages, e.g. in non-intubated patients, and give a fair estimation of daily energy expenditure when used alone.

Novel daily energy expenditure estimation by using objective activity type classification: where do we go from here?

ACCURATE ESTIMATION of daily energy expenditure (DEE) in population studies is challenged by factors such as feasibility, cost, and validity of the method under consideration. The use of wearable sensors, in particular accelerometers, is possibly the most promising currently available method to estimate DEE. Numerous accelerometer-based prediction models for estimating DEE and physical activity energy expenditure (PAEE) have been developed (5). Most of the prediction models published so far are based on linear regression analysis in experimental populations. To increase precision of these regression models, group-specific or even individual-specific regression models may be necessary. An alternative and potentially more effective and feasible approach may be to assess other individually specific characteristics that explain those yet unexplained differences between populations and between individuals. Assessing the major types of physical activity usually performed in daily life may improve the explained variation in DEE. In their article in the Journal of Applied Physiology, Bonomi and colleagues (3) present results from a study aimed to develop a model for estimating energy expenditure from accelerometer-based classification of types of physical activity in combination with published data on the energy cost [metabolic equivalent (MET) compendium values] of these activities (1, 3). The method proposed is novel because it does not rely on the use of regression analysis for predicting energy expenditure, nor does it rely on manufacturer-dependent information such as activity counts, the usual output from activity monitors based on accelerometry. Bonomi and colleagues compare their newly developed method with the more traditional method of using activity counts for DEE prediction. The high level of transparency of the proposed method is likely to inspire future research in this area. However, there are some additional issues that remain to be elucidated. The importance of classifying activity types when estimating energy expenditure is not yet fully understood. Previous studies have shown that the slope of the linear relation between energy expenditure and body acceleration varies across types of physical activity (6, 9). Theoretically, knowledge about the slope for each type of activity explains the variation in energy expenditure between types of activity within individuals that cannot be explained by acceleration (i.e., counts) alone. Between-individual differences in time spent in each type of physical activity may therefore be related to the average individuals’ slope across activities over a day and thus explain between-individual differences in DEE. In the model proposed by Bonomi and colleagues the types of physical activity identified by accelerometry and an accelerometer-based estimation of the speed of movement are used to estimate the MET intensity level defined by the compendium (1). In contrast to the assessment of counts by accelerometry, the compendium, at least in theory, takes into account differences in slope between types of activity because the activities included are based on measured or estimated energy expenditure rather than body acceleration. Therefore, the activity classification from accelerometry may have accounted for differences in slope between types of activity via the compendium. Future research will have to evaluate whether the main contribution of activity classification truly lies in the ability to account for differences in slope, which would indicate that activity classification needs to focus on explaining differences in slope rather than the activity type itself. Additionally, the y-intercept of the linear relation may also be an important parameter.

Energy Requirements in the Elderly*

The major components of daily energy expenditure may change during aging. A review of current estimates of energy requirements leads to the conclusion that direct estimates of daily energy expenditure are desirable to confirm the validity of the present recommendations. A more extensive examination of the metabolic and nutritional significance of the major food fuel sources is necessary to further define human energy needs.

Physical activity ratios for various commonly performed sedentary and physical activities in obese adolescents

The physical activity ratio (PAR) values are commonly used to convert subjects' physical activity recalls into estimates of daily energy expenditure (DEE). A PAR is defined as the ratio between energy expenditure corresponding to a sedentary or a physical activity (kJ/min) and basal metabolic rate [(BMR) kJ/min]. The objective of the present study was to determine the PAR for different sedentary and physical activities in obese adolescents. Thirty-three obese adolescents [mean body mass index: 35.1 kg/m2; 40.3 % fat mass] aged 11 to 17 yr participated in this study. BMR was assessed by indirect calorimetry after an overnight fast by means of an open-circuit, indirect computerised calorimetry with a rigid, transparent, ventilated canopy. Energy expenditures corresponding to various sedentary and physical activities in free-living conditions were determined using a portable metabolic unit, and body composition by bioelectrical impedance analysis (BIA). The PAR of each activity was obtained by dividing the assessed energy expenditure by BMR. In this study, the PAR of various sedentary and physical activities did not vary significantly with sex, age, and body mass. Thus, the data for boys and girls were combined and the PAR for 27 sedentary and physical activities were determined. The PAR values can be applied to determine the mean DEE (kJ) using the specific equation considering BMR (kJ/min), PAR, and duration (min) of each activity performed by the subject during the 24 h. This information will be useful to health-care professionals because estimation of DEE in obese adolescents is necessary in order to prescribe an adequate dietary therapy to induce a desired level of energy deficit.

Climate variables as predictors of basal metabolic rate: New equations

Estimation of basal metabolic rate (BMR) and daily energy expenditure (DEE) in living humans and in fossil hominins can be used to understand the way populations adapt to different environmental and nutritional circumstances. One variable that should be considered in such estimates is climate, which may influence between-population variation in BMR. Overall, populations living in warmer climates tend to have lower BMR than those living in colder climates, even after controlling for body size and composition. Current methods of estimating BMR ignore climate, or deal with its effects in an insufficient manner. This may affect studies that use the factorial method to estimate DEE from BMR, when BMR is not measured but predicted using an equation. The present meta-analysis of published BMR uses stepwise regression to investigate whether the inclusion of climate variables can produce a generally applicable model for human BMR. Regression results show that mean annual temperature and high heat index temperature have a significant effect on BMR, along with body size, age and sex. Based on the regression analysis, equations predicting BMR from body size and climate variables were derived and compared with existing equations. The new equations are generally more accurate and more consistent across climates than the older ones. Estimates of DEE in living and fossil humans using the new equations are compared with estimates using previously published equations, illustrating the utility of including climate variables in estimates of BMR. The new equations derived here may prove useful for future studies of human energy expenditure.