Expenditure Prediction Research Articles

Impact of anatomical placement of an accelerometer on prediction of physical activity energy expenditure in lower-limb amputees.

PurposeTo assess the influence of the anatomical placement of a tri-axial accelerometer on the prediction of physical activity energy expenditure (PAEE) in traumatic lower-limb amputees during walking and to develop valid population-specific prediction algorithms.MethodsThirty participants, consisting of unilateral (n = 10), and bilateral (n = 10) amputees, and non-injured controls (n = 10) volunteered to complete eight activities; resting in a supine position, walking on a flat (0.48, 0.67, 0.89, 1.12, 1.34 m.s-1) and an inclined (3 and 5% gradient at 0.89 m.s-1) treadmill. During each task, expired gases were collected and an Actigraph GT3X+ accelerometer was worn on the right hip, left hip and lumbar spine. Linear regression analyses were conducted between outputs from each accelerometer site and criterion PAEE (indirect calorimetry). Mean bias ± 95% limits of agreement were calculated. Additional covariates were incorporated to assess whether they improved the prediction accuracy of regression models. Subsequent mean absolute error statistics were calculated for the derived models at all sites using a leave-one out cross-validation analysis.ResultsPredicted PAEE at each anatomical location was significantly (P< 0.01) correlated with criterion PAEE (P<0.01). Wearing the GT3X+ on the shortest residual limb demonstrates the strongest correlation (unilateral; r = 0.86, bilateral; r = 0.94), smallest ±95% limits of agreement (unilateral; ±2.15, bilateral ±1.99 kcal·min-1) and least absolute percentage error (unilateral; 22±17%, bilateral 17±14%) to criterion PAEE.ConclusionsWe have developed accurate PAEE population specific prediction models in lower-limb amputees using an ActiGraph GT3X+ accelerometer. Of the 3 anatomical locations considered, wearing the accelerometer on the side of the shortest residual limb provides the most accurate prediction of PAEE with the least error in unilateral and bilateral traumatic lower-limb amputees.

レシートログと消費行動に関する未来予測(ライフログ,人工現実感)

レシートログと消費行動に関する未来予測(ライフログ,人工現実感)

Influence of Accelerometer Placement and/or Heart Rate on Energy Expenditure Prediction during Uphill Exercise

ABSTRACTStudies using a triaxial accelerometer and heart rate (HR) simultaneously for estimating energy expenditure (EE) during uphill exercise are rare. Exploring the optimal location for placing the accelerometer for predicting EE during uphill exercise is essential. Sixteen healthy male participants (M ± SEM; age 25.00 ± 0.61 years; body weight 74.13 ± 2.51 kg; body height 1.74 ± 0.01 m; body mass index 24.30 ± 0.63 kg/m2) exercised on a treadmill under 12 conditions (4 speeds and 3 gradients) on 3 days. Triaxial accelerometers, an HR recorder, and a metabolic measurement system were simultaneously used. Accelerometer outputs from various anatomical locations (upper arm, chest, lower back, waist, thigh, and instep) showed significant positive correlations with EE (0.819, 0.846, 0.816, 0.820, 0.672, and 0.669, respectively; p < .05). The linear regression equation for changes in HR showed the highest coefficient of determination (r2) of .837 with 87.9% reliability. When the HR signal was included, the r2 value (> .842) and reliability (87.9%) between the accelerometer outputs and EE improved. Accelerometer outputs from the waist position alone provide highly accurate EE values. Using both accelerometer outputs and HR for EE estimation during uphill exercise is feasible and improves the accuracy of EE prediction.

Prediction of energy expenditure during walking in adults with down syndrome.

When developing walking programmes for improving health in adults with Down syndrome (DS), physical activity professionals are in need of an equation for predicting energy expenditure. We therefore developed and cross-validated an equation for predicting the rate of oxygen uptake (VO2 ; an index of energy expenditure) for adults with and without DS. A total of 469 VO2 observations during walking across different speeds were available from 54 adults with DS and 61 adults without DS. Significant predictors of VO2 were speed, speed square, group and group-by-speed interaction. Separate models for each group showed that speed and its square significantly predicted VO2 . Absolute per cent error was small and did not differ between groups. Adults with DS have different VO2 response to walking speed from persons without DS. VO2 is predicted from speed with acceptable accuracy for persons with DS.

Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings

ABSTRACTThis study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square error (RMSE) for EE prediction (1.04–1.23 vs. 1.10–1.34 METs for V1/V2, V2/V2), and the ankle-worn accelerometer had the lowest RMSE of all accelerometers (1.04–1.18 vs. 1.17–1.34 METs for other placements). The ankle-worn accelerometer and associated EE prediction models developed using data from both structured and simulated free-living settings should be considered for optimal EE prediction accuracy.

Validation of energy intake from a web-based food recall for children and adolescents.

The purpose of this study was to validate estimated energy intake from a web-based food recall, designed for children and adolescents. We directly compared energy intake to estimates of total energy expenditure, calculated from accelerometer outputs, combined with data on weight and sex or resting energy expenditure prediction equations. Children (8–9 years) and adolescents (12–14 years) were recruited through schools in Norway in 2013 (N = 253). Results showed that more than one third (36–37%) were identified as under-reporters of energy. In contrast, only 2–4% were defined as over-reporters of energy. The mean energy intake was under-reported with -1.83 MJ/day for the entire study sample. Increased underestimation was observed for overweight and obese participants, the oldest age group (12–14 years), boys, those with parents/legal guardians with low educational level and those living in non-traditional families. In conclusion, energy intake from the web-based food recall is significantly underestimated compared with total energy expenditure, and should be used with caution in young people.

A Novel Approach to Predict 24-Hour Energy Expenditure Based on Hematologic Volumes: Development and Validation of Models Comparable to Mifflin-St Jeor and Body Composition Models

BackgroundAccurate prediction of 24-hour energy expenditure (24EE) relies on knowing body composition, in particular fat-free mass (FFM), the largest determinant of 24EE. FFM is closely correlated with hematologic volumes: blood volume (BV), red cell mass (RCM), and plasma volume (PV). However, it is unknown whether predicted hematologic volumes, based on easily collected variables, can improve 24EE prediction. ObjectiveThe aim was to develop and validate equations to predict 24EE based on predicted BV, RCM, and PV and to compare the accuracy and agreement with models developed from FFM and with the Mifflin-St Jeor equation, which is recommended for clinical use by the Academy of Nutrition and Dietetics. DesignParticipants had body composition measured by underwater weighing and 24EE by respiratory chamber. BV, RCM, and PV were calculated from five published equations. Participants/settingNative American and white men and women were studied (n=351). Participants were healthy adults aged 18 to 49 years from the Phoenix, AZ, metropolitan area. Main outcome measureAccuracy to within ±10% of measured 24EE and agreement by Bland-Altman analysis. Statistical analysisRegression models to predict 24EE from hematologic and body composition variables were developed in half the dataset and validated in the other half. ResultsHematologic volumes were all strongly correlated with FFM in both men and women (r≥0.94). Whereas the accuracy of FFM alone was 69%, four hematologic volumes were individually more accurate (75% to 78%) in predicting 24EE. Equations based on hematologic volumes plus demographics had mean prediction errors comparable to those based on body composition plus demographics; although the Mifflin-St Jeor had modestly better mean prediction error, body composition, hematologic, and Mifflin-St Jeor models all had similar accuracy (approximately 80%). ConclusionsPrediction equations based on hematologic volumes were developed, validated, and found to be comparable to Mifflin-St Jeor and body composition models in this population of healthy adults.

Validation Of Accelerometer-based Energy Expenditure Prediction Models In Structured And Semi-structured Settings

Validation Of Accelerometer-based Energy Expenditure Prediction Models In Structured And Semi-structured Settings

Accuracy of Accelerometer-based Activity Energy Expenditure Prediction Equations for Children ages 3 to 6 years

Accuracy of Accelerometer-based Activity Energy Expenditure Prediction Equations for Children ages 3 to 6 years

Estimation of Energy Expenditure Based on Divided Manual Material Handling Task: Waste Collection Example

The objectives of this study were: i) to present an approach for potential areas of ergonomic analysis software usage and to get familiar with the usage of this kind of problem assessment by applying the software to a practical example in a problem area with significant ergonomic problems to be analyzed. ii) to estimate energy expenditure rates for waste collection task using the University of Michigan Energy Expenditure Prediction Program (EEPP) to comfort worker safety and health. iii) to compare with regression equations in literature to illustrate the performance of the EEPP software. The assessment will make use of a University of Michigan EEPP to predict energy expenditure of materials handling tasks to comfort worker safety and health. A manual waste collection task was selected as a job analysis and the results will be compared with NIOSH guidelines and regression equations in literature to illustrate the performance of the EEPP software. The results show that EEPP software and prediction equation estimated quite close average task energy rate (kcal/min). This predicted information can be useful for waste collection job design instead of using oxygen consumption measurement which takes long time and costly. Furthermore, these results can be used for recommendations to improve ergonomic factors of the waste collection tasks in form of a guideline.

Classification of Physical Activity Cut-Points and the Estimation of Energy Expenditure During Walking Using the GT3X+ Accelerometer in Overweight and Obese Adults

ABSTRACTThis study establishes tri-axial activity count (AC) cut-points for the GT3X+ accelerometer to classify physical activity intensity in overweight and obese adults. Further, we examined the accuracy of established and novel energy expenditure (EE) prediction equations based on AC and other metrics. Part 1: Twenty overweight or obese adults completed a 30 minute incremental treadmill walking protocol. Heart rate (HR), EE, and AC were measured using the GT3X+ accelerometer. Part 2: Ten overweight and obese adults conducted a self-paced external walk during which EE, AC, and HR were measured. Established equations (Freedson et al., 1998; Sasaki et al., 2011) overestimated EE by 40% and 31%, respectively (p < .01). Novel gender-specific prediction equations provided good estimates of EE during treadmill and outdoor walking (standard error of the estimate = .91 and .65, respectively). We propose new cut-points and prediction equations to estimate EE using the GT3X+ tri-axial accelerometer in overweight and obese adults.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r = 0.71–0.88, RMSE: 1.11–1.61 METs; p > 0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN—correlations: r = 0.89, RMSE: 1.07–1.08 METs. Linear models—correlations: r = 0.88, RMSE: 1.10–1.11 METs; p > 0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN—correlation: r = 0.88, RMSE: 1.12 METs. Linear models—correlations: r = 0.86, RMSE: 1.18–1.19 METs; p < 0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN—correlations: r = 0.82–0.84, RMSE: 1.26–1.32 METs. Linear models—correlations: r = 0.71–0.73, RMSE: 1.55–1.61 METs; p < 0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Accuracy of Accelerometer for the Prediction of Energy Expenditure and Activity Intensity in Athletic Elementary School Children During Selected Activities

Accuracy of Accelerometer for the Prediction of Energy Expenditure and Activity Intensity in Athletic Elementary School Children During Selected Activities

Accuracy of dietary reference intake predictive equation for estimated energy requirements in female tennis athletes and non-athlete college students: comparison with the doubly labeled water method

BACKGROUND/OBJECTIVESThe purpose of this study was to assess the accuracy of a dietary reference intake (DRI) predictive equation for estimated energy requirements (EER) in female college tennis athletes and non-athlete students using doubly labeled water (DLW) as a reference method.MATERIALS/METHODSFifteen female college students, including eight tennis athletes and seven non-athlete subjects (aged between 19 to 24 years), were involved in the study. Subjects' total energy expenditure (TEE) was measured by the DLW method, and EER were calculated using the DRI predictive equation. The accuracy of this equation was assessed by comparing the EER calculated using the DRI predictive equation (EERDRI) and TEE measured by the DLW method (TEEDLW) based on calculation of percentage difference mean and percentage of accurate prediction. The agreement between the two methods was assessed by the Bland-Altman method.RESULTSThe percentage difference mean between the methods was -1.1% in athletes and 1.8% in non-athlete subjects, whereas the percentage of accurate prediction was 37.5% and 85.7%, respectively. In the case of athletic subjects, the DRI predictive equation showed a clear bias negatively proportional to the subjects' TEE.CONCLUSIONSThe results from this study suggest that the DRI predictive equation could be used to obtain EER in non-athlete female college students at a group level. However, this equation would be difficult to use in the case of athletes at the group and individual levels. The development of a new and more appropriate equation for the prediction of energy expenditure in athletes is proposed.

Accelerometers can measure total and activity‐specific energy expenditures in free‐ranging marine mammals only if linked to time‐activity budgets

Summary Energy expenditure is an important component of foraging ecology, but is extremely difficult to estimate in free‐ranging animals and depends on how animals partition their time between different activities during foraging. Acceleration data have emerged as a new way to determine energy expenditure at a fine scale but this needs to be tested and validated in wild animals. This study investigated whether vectorial dynamic body acceleration (VeDBA) could accurately predict the energy expended by marine predators during a full foraging trip. We also aimed to determine whether the accuracy of predictions of energy expenditure derived from acceleration increased when partitioned by different types of at‐sea activities (i.e. diving, transiting, resting and surface activities). To do so, we equipped 20 lactating northern (Callorhinus ursinus) and 20 lactating Antarctic fur seals (Arctocephalus gazella) with GPS, time‐depth recorders and tri‐axial accelerometers and obtained estimates of field metabolic rates using the doubly labelled water (DLW) method. VeDBA was derived from tri‐axial acceleration, and at‐sea activities (diving, transiting, resting and surface activities) were determined using dive depth, tri‐axial acceleration and travelling speed. We found that VeDBA did not accurately predict the total energy expended by fur seals during their full foraging trips (R2 = 0·36). However, the accuracy of VeDBA as a predictor of total energy expenditure increased significantly when foraging trips were partitioned by activity and when activity‐specific VeDBA was paired with time‐activity budgets (R2 = 0·70). Activity‐specific VeDBA also accurately predicted the energy expenditures of each activity independent of each other (R2 &gt; 0·85). Our study confirms that acceleration is a promising way to estimate energy expenditures of free‐ranging marine mammals at a fine scale never attained before. However, it shows that it needs to be based on the time‐activity budgets that make up foraging trips rather than being derived as a single measure of VeDBA applied to entire foraging trips. Our activity‐based method provides a cost‐effective means to accurately calculate energy expenditures of fur seals using acceleration and time‐activity budgets, that can be transfered to studies on other species.

Energy expenditure estimation in beta-blocker-medicated cardiac patients by combining heart rate and body movement data.

Accurate assessment of energy expenditure provides an opportunity to monitor physical activity during cardiac rehabilitation. However, the available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker-medicated cardiac rehabilitation patients. Sixteen male cardiac rehabilitation patients (age: 55.8 ± 7.3 years, weight: 93.1 ± 11.8 kg) underwent a physical activity protocol with 11 low- to moderate-intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom-limited exercise test and resting metabolic rate assessment. Energy expenditure estimation models were developed using multivariate regression analyses based on HR and body movement data and/or patient characteristics. In addition, a HR-flex model was developed. The model combining HR and body movement data and patient characteristics showed the highest correlation and lowest error (r2 = 0.84, root mean squared error = 0.834 kcal/minute) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accuracy (i2 = 0.83, root mean squared error = 0.992 kcal/minute). Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods that have been developed for healthy subjects. The proposed methodology does not require individual calibration and is based on the data that are available in clinical practice.

Wrist-independent energy expenditure prediction models from raw accelerometer data

Purposes: (1) Develop artificial neural network (ANN) models for wrist accelerometer data which can predict energy expenditure (EE) using data collected from either wrist. (2) Develop ANNs for detecting the wrist on which the accelerometer was worn.Forty-four adults wore GENEActiv accelerometers on the left and right wrists and a portable metabolic analyzer while participating in a 90 min semi-structured activity protocol. Participants performed 14 sedentary, lifestyle, exercise, and ambulatory activities and were allowed to choose activity order, duration, and intensity. ANNs were created to predict EE and wrist detection using a leave-one-out cross-validation. In total, 12 combinations of feature sets (mean and variance of raw, vector magnitude, and absolute value data), training methods (left- and right- wrist), and testing methods (left- and right-wrist data) were used to develop EE prediction ANNs. Accuracy of the ANNs was evaluated using correlations, root mean square error (RMSE), and bias, using metabolic analyzer data as the criterion for EE.ANNs using raw data from the same wrist (e.g. EE predicted from right wrist ANNs using accelerometer data from right wrist) had the highest accuracy for EE prediction (r = 0.84, RMSE = 1.25–1.26 METs); conversely, opposite-wrist prediction accuracy (e.g. EE predicted from right wrist ANNs using accelerometer data from left wrist) was lower (r = 0.60–0.64, RMSE = 1.93–2.01 METs). Preprocessing into absolute values prior to ANN development allowed for, high EE prediction accuracy, with no difference in accuracy for same- versus opposite-wrist prediction (r = 0.80–0.83, RMSE = 1.30–1.49 METs). Wrist detection ANNs correctly determined wrist placement 100% of the time.Highly accurate, wrist-independent EE prediction ANNs were developed by computing absolute values of raw acceleration data prior to ANN development. This method provides a potential approach for advancing predictive accuracy of wrist-worn accelerometers.

Investigating optimal accelerometer placement for energy expenditure prediction in children using a machine learning approach

Accurate measurement of energy expenditure (EE) is imperative for identifying and targeting health-associated implications. Whilst numerous accelerometer-based regression equations to predict EE have been developed, there remains little consensus regarding optimal accelerometer placement. Therefore, the purpose of the present study was to validate and compare artificial neural networks (ANNs) developed from accelerometers worn on various anatomical positions, and combinations thereof, to predict EE.Twenty-seven children (15 boys; 10.8 ± 1.1 years) participated in an incremental treadmill test and 30 min exergaming session wearing a portable gas analyser and nine ActiGraph GT3X+ accelerometers (chest and left and right wrists, hips, knees, and ankles). Age and sex-specific resting EE equations (Schofield) were used to estimate METs from the oxygen uptake measures. Using all the data from both exergames, incremental treadmill test and the transition period in between, ANNs were created and tested separately for each accelerometer and for combinations of two or more using a leave-one-out approach to predict EE compared to measured EE. Six features (mean and variance of the three accelerometer axes) were extracted within each 15 s window as inputs in the ANN. Correlations and root mean square error (RMSE) were calculated to evaluate prediction accuracy of each ANN, and repeated measures ANOVA was used to statistically compare accuracy of the ANNs.All single-accelerometer ANNs and combinations of two-, three-, and four-accelerometers performed equally (r = 0.77–0.82), demonstrating higher correlations than the 9-accelerometer ANN (r = 0.69) or the Freedson linear regression equation (r = 0.75). RMSE did not differ between single-accelerometer ANNs or combinations of two, three, or four accelerometers (1.21–1.31 METs), demonstrating lower RMSEs than the 9-accelerometer ANN (1.46 METs) or Freedson equation (1.74 METs).These findings provide preliminary evidence that ANNs developed from single accelerometers mounted on various anatomical positions demonstrate equivalency in the accuracy to predict EE in a semi-structured setting, supporting the use of ANNs in improving EE prediction accuracy compared with linear regression.

Thigh Accelerometer Placement Accuracy for Physical Activity and Energy Expenditure Prediction in a Semi-Structured Setting

An inclinometer-based accelerometer (PAL) worn on the thigh, coupled with proprietary software, may provide accurate estimates of sedentary time. However, its accuracy for estimating other physical activity (PA) intensities and energy expenditure (EE) is unknown. PURPOSE: To evaluate accuracy of the PAL software for prediction of time spent in PA intensities [sedentary, light, and moderate-to-vigorous (MVPA)] and EE and compare its accuracy to that of a machine learning model (ANN) developed from raw PAL data. METHODS: Participants (n=39 [19 male]; age=22.1±4.3) completed a 90-min, semi-structured protocol in which they performed 13 activities for 3-10 min each, choosing activity order, duration, and intensity. Participants wore a PAL accelerometer on the right thigh and a portable metabolic analyzer (OXY). Time spent in sedentary, light, and MVPA was determined using MET values of ≤1.5, 1.6-2.9, and ≥3.0, respectively, calculated from OXY. Estimated times in each PA intensity from the PAL software and ANN were compared with OXY using difference scores and 95% confidence intervals; non-overlap of confidence intervals with zero indicated significant differences from OXY. Window-by-window EE prediction was assessed using correlations and root mean square error (RMSE). RESULTS: PAL software-predicted sedentary time was not different from OXY (-1.6 min; 95% CI: -3.6 - 0.4 min), but light PA was over-predicted (6.2 min; 95% CI: 4.1 - 8.3 min) and MVPA was under-predicted (-4.6 min; 95% CI: -6.5 - -2.7 min). ANN-predicted sedentary time and light PA were not different from OXY, (-0.7 min, 95% CI: -2.3 - 0.8 min; -0.9 min; 95% CI: -2.8 - 1.1 min, respectively), but MVPA was over-predicted (1.6 min; 95% CI: 0.1 - 3.2 min). For EE prediction, the PAL software had lower correlations (r=0.76 vs. r =0.89) and higher RMSE (1.74 vs. 1.07 METs) than the ANN. Under-prediction by the PAL software was more pronounced at intensities >5.0 METs. CONCLUSIONS: The PAL software distinguished between sedentary and non-sedentary activities but had high error for EE prediction, especially for higher-intensity PA. The ANN had high accuracy for prediction of sedentary and light PA and EE prediction, indicating strong potential for raw data from a thigh-worn PAL to be used in PA assessment. Supported by Blue Cross Blue Shield of Michigan Foundation.

Validation of a wireless accelerometer network for energy expenditure measurement

ABSTRACTThe purpose of this study was to validate a wireless network of accelerometers and compare it to a hip-mounted accelerometer for predicting energy expenditure in a semi-structured environment. Adults (n = 25) aged 18–30 engaged in 14 sedentary, ambulatory, exercise, and lifestyle activities over a 60-min protocol while wearing a portable metabolic analyser, hip-mounted accelerometer, and wireless network of three accelerometers worn on the right wrist, thigh, and ankle. Participants chose the order and duration of activities. Artificial neural networks were created separately for the wireless network and hip accelerometer for energy expenditure prediction. The wireless network had higher correlations (r = 0.79 vs. r = 0.72, P < 0.01) but similar root mean square error (2.16 vs. 2.09 METs, P > 0.05) to the hip accelerometer. Measured (from metabolic analyser) and predicted energy expenditure from the hip accelerometer were significantly different for the 3 of the 14 activities (lying down, sweeping, and cycle fast); conversely, measured and predicted energy expenditure from the wireless network were not significantly different for any activity. In conclusion, the wireless network yielded a small improvement over the hip accelerometer, providing evidence that the wireless network can produce accurate estimates of energy expenditure in adults participating in a range of activities.