Activity Energy Expenditure Prediction Research Articles

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.

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

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.

Predicting physical activity energy expenditure in manual wheelchair users.

This study aimed to assess the influence of anatomical placement of an accelerometer on physical activity energy expenditure prediction in manual wheelchair users. Ten accelerometer units (ActiGraph GT3X+) were attached to a multiaxis shaker table and subjected to a sinusoidal oscillation procedure to assess mechanical validity and reliability. Fifteen manual wheelchair users (mean ± SD: age, 36 ± 11 yr; body mass, 70 ± 12 kg) then completed five activities, including desk work and wheelchair propulsion (2, 4, 6, and 8 km·h). Expired gases were collected throughout. GT3X+ accelerometers were worn on the right wrist, upper arm, and waist. The relations between physical activity counts and metabolic rate were subsequently assessed, and bias ± 95% limits of agreement was calculated. During mechanical testing, coefficients of variation ranged from 0.2% to 4.7% (intraunit) and 0.9% to 5.2% (interunit) in all axes. During human exercise testing, physical activity counts at each anatomical location was significantly (P < 0.01) correlated with metabolic rate (wrist, r = 0.93; upper arm, r = 0.87; waist, r = 0.73). The SEE for each correlation were 3.34, 4.38, and 6.07 kJ·min for the wrist, upper arm, and waist, respectively. The absolute bias ± 95% limits of agreement values were 0.0 ± 6.5 kJ·min, 0.0 ± 8.5 kJ·min, and 0.0 ± 11.8 kJ·min for the wrist, upper arm, and waist, respectively. The ActiGraph GT3X+ is a reliable tool for determining mechanical movements within the physiological range of human movement. Of the three anatomical locations considered, a wrist-mounted accelerometer explains more of the variance and results in the lowest random error when predicting physical activity energy expenditure in manual wheelchair users.

Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction During WheelChair

An understanding of the impact of physical activity on the health of manual wheelchair users (MWUs) could be improved with the development of an accurate and unobtrusive monitoring tool. PURPOSE: (i) to identify the most appropriate anatomical location and accelerometer-based device to predict physical activity energy expenditure (PAEE) in MWUs and (ii) to develop and test associated regression equations to predict PAEE. METHODS: Seventeen manual wheelchair users (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 GENEA accelerometers were worn on the right wrist and upper arm. Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the developed regression equations for devices at the wrist using a leave-one-out cross-validation analysis. RESULTS: Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (Upper arm, GT3X+; r = 0.68 and GENEA r = 0.87. Wrist, GT3X+; r = 0.82 and GENEA; r = 0.88). Average PAEE estimation error for all activities using new regression equations at the wrist was 13.8 ± 49.6% and 3.8 ± 25.7% for GT3X+ and GENEA, respectively. Absolute PAEE estimation errors for devices worn on the wrist varied from 17.1 to 122.0% for the GT3X+ and from 16.5 to 32.1% for the GENEA. The larger overall error associated with the GT3X+ worn on the wrist was mostly accounted for by a 1.2 Kcal.min-1 over-prediction of PAEE during the folding clothes activity. CONCLUSION: Of the two accelerometers and anatomical locations considered in this study, the results indicate that the GENEA worn on the wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is thought to be associated with inherent differences in internal components and on-board filtering processes of each device. The GT3X+ when worn on the wrist appears to lack sensitivity to accurately quantify the PAEE during low intensity activities, with low frequency components, which could lead to erroneous PAEE measurement during free-living monitoring.

신체활동 비교를 통한 개인 맞춤형 신체활동 에너지 소비량 예측 알고리즘

The purpose of this study suggests a personalized algorithm of physical activity energy expenditure prediction through comparison and analysis of individual physical activity. The research for a 3-axial accelerometer sensor has increased the role of physical activity in promoting health and preventing chronic disease has long been established. Estimating algorithm of physical activity energy expenditure was implemented by using a tri-axial accelerometer motion detector of the SVM(Signal Vector Magnitude) of 3-axis(x, y, z). A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activities protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks.

Review of Prediction Models to Estimate Activity-Related Energy Expenditure in Children and Adolescents

Purpose. To critically review the validity of accelerometry-based prediction models to estimate activity energy expenditure (AEE) in children and adolescents. Methods. The CINAHL, EMBASE, PsycINFO, and PubMed/MEDLINE databases were searched. Inclusion criteria were development or validation of an accelerometer-based prediction model for the estimation of AEE in healthy children or adolescents (6–18 years), criterion measure: indirect calorimetry, or doubly labelled water, and language: Dutch, English or German. Results. Nine studies were included. Median methodological quality was 5.5 ± 2.0 IR (out of a maximum 10 points). Prediction models combining heart rate and counts explained 86–91% of the variance in measured AEE. A prediction model based on a triaxial accelerometer explained 90%. Models derived during free-living explained up to 45%. Conclusions. Accelerometry-based prediction models may provide an accurate estimate of AEE in children on a group level. Best results are retrieved when the model combines accelerometer counts with heart rate or when a triaxial accelerometer is used. Future development of AEE prediction models applicable to free-living scenarios is needed.

A NOVEL ENERGY EXPENDITURE EQUATION FOR INTERMITTENT ACTIVITY, BASED ON HEART RATE (HR), RECOVERY HR AND MOTION COUNTS

On the basis of the linear relationship between heart rate (HR) and oxygen consumption (VO2), it is possible to predict activity energy expenditure (EE) from HR, alone or in combination with accelerometry. To our knowledge, the impact of intermittent activity, and HR from previous workload or recovery periods, has not yet been studied in the estimation of EE. PURPOSE: This study was designed i) to characterise relative contributions of current and preceding HR and motion counts in the prediction of activity EE; and ii) to model these and other determinants using linear regression analysis. METHODS: 75 subjects (52 men, 23 women, 35 ± 9 yrs, BMI, 23.7 ± 2.5 kg/m2) were tested. On visit 1, body composition and maximum VO2 (using multi-stage treadmill test) were measured. On visit 2, subjects completed 12 four-minute workloads, separated by 4 one-minute rest periods. HR, triaxial motion counts and VO2 were continously monitored. RESULTS: The following parameters contributed significantly to the estimation of EE during intermittent activity: age, gender, VO2 max max HR, HR, motion counts (P < 0.0001), and preceding HR (P < 0.02). The final regression equation yielded an adjusted R2 of 0.84, with a standard error of the estimate of 6.4 kJ/min. Preceding HR contributed > than 3% to total variance, and attenuated the slope of the relationship between HR and EE. CONCLUSION: HR and motion counts may be used to successfully predict EE during intermittent activity. Including maximal HR data and HR during the preceding workload or recovery periods may provide additional accuracy for EE prediction. Supported by Winning Wellness BODYiQ