Activity Energy Expenditure Estimation Research Articles

Evaluation of mechanical energy for human dynamics using posture estimation method

The purpose of this study is to realize a tailor-made exercise evaluation technology that can simultaneously perform highly accurate estimation of activity energy expenditure (AEE) and posture measurement during exercise in order to provide tailor-made training content, exercise intensity, and competition time according to the individual. To this end, we developed a measurement system that integrates three technologies: marker-less motion capture, human body dimension measurement, and activity energy expenditure estimation (AEE estimation) based on mechanical energy. Using the measurement system, AEE estimation was performed for two exercises (Arm Swings and Standing Jumps). The results showed that the proposed method is more individualized than the conventional method for the exercise in which a major part of the body moves. In addition, the AEE estimation of the proposed system can be applied to arbitrary movements because the calculation is based on the 3D posture estimation by FrankMocap and the change in mechanical energy.

Comparison of total and activity energy expenditure estimates from physical activity questionnaires and doubly labelled water: a systematic review and meta-analysis.

Physical activity questionnaires (PAQ) could be suitable tools in free-living people for measures of physical activity, total and activity energy expenditure (TEE and AEE). This meta-analysis was performed to determine valid PAQ for estimating TEE and AEE using doubly labelled water (DLW). We identified data from relevant studies by searching Google Scholar, PubMed and Scopus databases. This revealed thirty-eight studies that had validated PAQ with DLW and reported the mean differences between PAQ and DLW measures of TEE (TEEDLW - TEEPAQ) and AEE (AEEDLW - AEEPAQ). We assessed seventy-eight PAQ consisting of fifty-nine PAQ that assessed TEE and thirty-five PAQ that examined AEE. There was no significant difference between TEEPAQ and TEEDLW with a weighted mean difference of -243·3 and a range of -841·4 to 354·6 kJ/d, and a significant weighted mean difference of AEEDLW - AEE PAQ 414·6 and a range of 78·7-750·5. To determine whether any PAQ was a valid tool for estimating TEE and AEE, we carried out a subgroup analysis by type of PAQ. Only Active-Q, administered in two seasons, and 3-d PA diaries were correlated with TEE by DLW at the population level; however, these two PAQ did not demonstrate an acceptable limit of agreement at individual level. For AEE, no PAQ was correlated with DLW either at the population or at the individual levels. Active-Q and 3-d PA diaries were identified as the only valid PAQ for TEE estimation. Further well-designed studies are needed to verify this result and identify additional valid PAQ.

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

Background:Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.Methods:Measurements for 193 UK adults (105 men, 88 women, aged 40–66 years, BMI 20.4–36.6 kg m−2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9–14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation.Results:Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day−1 and 49.8 (16.3) kJ day−1 kg−1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day−1 kg−1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day−1 kg−1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day−1, r ~ 0.90).Conclusions:In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Simultaneous Validation of Seven Physical Activity Questionnaires Used in Japanese Cohorts for Estimating Energy Expenditure: A Doubly Labeled Water Study.

BackgroundPhysical activity questionnaires (PAQs) used in large-scale Japanese cohorts have rarely been simultaneously validated against the gold standard doubly labeled water (DLW) method. This study examined the validity of seven PAQs used in Japan for estimating energy expenditure against the DLW method.MethodsTwenty healthy Japanese adults (9 men; mean age, 32.4 [standard deviation {SD}, 9.4] years, mainly researchers and students) participated in this study. Fifteen-day daily total energy expenditure (TEE) and basal metabolic rate (BMR) were measured using the DLW method and a metabolic chamber, respectively. Activity energy expenditure (AEE) was calculated as TEE − BMR − 0.1 × TEE. Seven PAQs were self-administered to estimate TEE and AEE.ResultsThe mean measured values of TEE and AEE were 2,294 (SD, 318) kcal/day and 721 (SD, 161) kcal/day, respectively. All of the PAQs indicated moderate-to-strong correlations with the DLW method in TEE (rho = 0.57–0.84). Two PAQs (Japan Public Health Center Study [JPHC]-PAQ Short and JPHC-PAQ Long) showed significant equivalence in TEE and moderate intra-class correlation coefficients (ICC). None of the PAQs showed significantly equivalent AEE estimates, with differences ranging from −547 to 77 kcal/day. Correlations and ICCs in AEE were mostly weak or fair (rho = 0.02–0.54, and ICC = 0.00–0.44). Only JPHC-PAQ Short provided significant and fair agreement with the DLW method.ConclusionsTEE estimated by the PAQs showed moderate or strong correlations with the results of DLW. Two PAQs showed equivalent TEE and moderate agreement. None of the PAQs showed equivalent AEE estimation to the gold standard, with weak-to-fair correlations and agreements. Further studies with larger sample sizes are needed to confirm these findings.

Understanding the Nature of Measurement Error When Estimating Energy Expenditure and Physical Activity via Physical Activity Recall

The National Health and Nutrition Examination Survey physical activity questionnaire (PAQ) is used to estimate activity energy expenditure (AEE) and moderate to vigorous physical activity (MVPA). Bias and variance in estimates of AEE and MVPA from the PAQ have not been described, nor the impact of measurement error when utilizing the PAQ to predict biomarkers and categorize individuals. The PAQ was administered to 385 adults to estimate AEE (AEE:PAQ) and MVPA (MVPA:PAQ), while simultaneously measuring AEE with doubly labeled water (DLW; AEE:DLW) and MVPA with an accelerometer (MVPA:A). Although AEE:PAQ [3.4 (2.2) MJ·d-1] was not significantly different from AEE:DLW [3.6 (1.6) MJ·d-1; P > .14], MVPA:PAQ [36.2 (24.4) min·d-1] was significantly higher than MVPA:A [8.0 (10.4) min·d-1; P < .0001]. AEE:PAQ regressed on AEE:DLW and MVPA:PAQ regressed on MVPA:A yielded not only significant positive relationships but also large residual variances. The relationships between AEE and MVPA, and 10 of the 12 biomarkers were underestimated by the PAQ. When compared with accelerometers, the PAQ overestimated the number of participants who met the Physical Activity Guidelines for Americans. Group-level bias in AEE:PAQ was small, but large for MVPA:PAQ. Poor within-participant estimates of AEE:PAQ and MVPA:PAQ lead to attenuated relationships with biomarkers and misclassifications of participants who met or who did not meet the Physical Activity Guidelines for Americans.

Towards valid estimates of activity energy expenditure using an accelerometer: searching for a proper analytical strategy and big data

estimating energy expenditure and the metabolic response to physical activity are complex areas of research, manifested by the increasingly sophisticated instruments and methodologies being proposed in the literature. Wearable motion sensors, such as accelerometers, are the most widely studied tools

Agreement in activity energy expenditure assessed by accelerometer and self-report in adolescents: Variation by sex, age, and weight status

The agreement between self-reported and objective estimates of activity energy expenditure was evaluated in adolescents by age, sex, and weight status. Altogether, 403 participants (217 females, 186 males) aged 13–16 years completed a 3-day physical activity diary and wore a GT1M accelerometer on the same days. Partial correlations (controlling for body mass) were used to determine associations between estimated activity energy expenditure (kcal · min−1) from the diary and accelerometry. Differences in the magnitude of the correlations were examined using Fisher's r to z transformations. Bland–Altman procedures were used to determine concordance between the self-reported and objective estimates. Partial correlations between assessments of activity energy expenditure (kcal · min−1) did not differ significantly by age (13–14 years: r = 0.41; 15–16 years: r = 0.42) or weight status (normal weight: r = 0.42; overweight: r = 0.39). The magnitude of the association was significantly affected by sex (Δr = 0.11; P < 0.05). The agreement was significantly higher in males than in females. The relationship between activity energy expenditure assessed by the objective method and the 3-day diary was moderate (controlling for weight, correlations ranged between 0.33 and 0.44). However, the 3-day diary revealed less agreement in specific group analyses; it markedly underestimated activity energy expenditure in overweight/obese and older adolescents. The assessment of activity energy expenditure is complex and may require a combination of methods.

The IDEFICS validation study on field methods for assessing physical activity and body composition in children: design and data collection

To describe the design, measurements and fieldwork of the IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) physical activity and body composition validation study, and to determine the potential and limitations of the data obtained. Multicentre validation study. A total of 98 children from four different European countries (age: 4-10 years). An 8-day measurement protocol was carried out in all children using a collaborative protocol. Reference methods were the doubly labelled water method for physical activity, and a three- and a four-compartment model for body composition. Investigated field methods were accelerometers, a physical activity questionnaire and various anthropometric measurements. For the validation of physical activity field methods, it was possible to gather data from 83 to 89 children, laying the basis for age- and sex-specific results. The validation of body composition field methods is possible in 64-80 children and allows sex-specific analyses but has only limited statistical power in the youngest age group (<6 years). The amount of activity energy expenditure (AEE) varied between centres, sexes and age groups, with boys and older children having higher estimates of AEE. After normalisation of AEE by body weight, most group-specific differences diminished, except for country-specific differences. The IDEFICS validation study will allow age- and sex-specific investigation of questions pertaining to the validity of several field methods of body composition and physical activity, using established reference methods in four different European countries. From the participant analyses it can be concluded that the compliance for the investigated field methods was higher than that for the reference methods used in this validation study.

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.

Accelerometer Prediction of Energy Expenditure

It is suggested that triaxial accelerometers (RT3) are superior to single-plane accelerometers for predicting energy expenditure (EE). To compare the RT3 uniaxial and triaxial prediction of activity EE (AEE) during treadmill activities (TM) and activities of daily living (ADL). Two hundred and twelve subjects (aged 20-60 yr) completed TM speeds of 1.34, 1.56, and 2.23 m x s(-1) at 0% and 3% grades, stair ascent/descent, moving a box, and two randomly assigned ADL. Subjects wore a portable indirect calorimeter to measure EE to calculate AEE by subtracting resting metabolic rate. Acceleration counts in the vertical (V), medial-lateral, and anterior-posterior planes were collected in a single RT3 secured to the hip. Predicted AEE (RT3AEE) was estimated from vector magnitude (VM) counts using a proprietary algorithm. A paired t-test compared RT3AEE versus AEE. The relationship among V and VM counts and AEE was examined using linear regression analyses. RT3 overestimated AEE for all activities combined, overestimated for TM (9.0%), and underestimated for ADL (34.3%; P < 0.001). The R2 values between RT3AEE and AEE for TM and ADL were R2 = 0.78 and R2 = 0.15, respectively. The RT3 underestimated activity with greater upper body movements by 24.4%-64.5% (P < 0.001). V and VM counts were similarly related to AEE (R2 = 0.35) and RT3AEE (R2 = 0.83-0.89). Although the RT3 did not accurately predict AEE from accelerometer counts, stronger relationships existed between predicted and measured AEE for TM compared with ADL. Compared with V counts, using VM counts to predict AEE did not significantly improve the relationship between counts and AEE. Analytic techniques beyond linear regression with VM as a covariate or with counts from each axis entering the model separately may improve estimates of AEE from triaxial accelerometers.

Erratum for ‘Estimation of activity energy expenditure based on activity classification using multi-site triaxial accelerometry’

Erratum for ‘Estimation of activity energy expenditure based on activity classification using multi-site triaxial accelerometry’

Validation of an Accelerometer Estimate of Activity Energy Expenditure

Several studies have evaluated the validity of accelerometers in estimating activity energy expenditure (AEE) using linear regression modeling. A new approach has been proposed for the omnidirectional accelerometers where either a single (1R) or double (2R) regression is used to predict AEE depending on whether activity is performed using lower (locomotion) (1R) or upper (activities of daily living, ADLs) (2R) body movement (Heil, 2006). PURPOSE: We evaluated the validity of the 1R and 2R models in predicting AEE for locomotion and ADLs. METHODS: One hundred and two subjects (64 female and 37 male; age = 34.9 ± 12.4 y) completed 6 treadmill (TM) activities at 1.34, 1.56 and 2.23 m·sec−1 at 0 and 3% grades. ADLs consisted of vacuuming, dusting, mopping, sweeping, gardening and other household activities. All activities were performed for 7 minutes with subjects wearing a portable metabolic measurement system. Resting Metabolic Rate (RMR) was measured prior to completion of the activity protocol. A repeated measures analysis of variance was used to examine the difference between measured activity energy expenditure (AEEmeas) and monitor estimated AEE (AEEest). Least squares means (standard error) are reported. RESULTS: For all activities, there was a significant difference between AEEmeas and AEEest, with AEE underestimated by 0.012 (± 0.001) kcal·kg·min−1 and 0.011 (± 0.001) kcal·kg·min−1 for the 1R and 2R, respectively. The AEEest obtained from both the 1R and 2R methods was accurate for TM activities, while each method significantly underestimated AEE R1, 0.024 (± 0.002 kcal·kg·min−1); R2, 0.023 (± 0.002 kcal·kg·min−1) for the ADLs. CONCLUSIONS: Although both the 1R and 2R provided accurate estimates of AEE for TM activities, neither model was accurate for all activities or for the ADLs. The use of this monitor in a free-living environment, where individuals perform a variety of activities, may result in a significant underestimation of activity energy expenditure.

Estimation of activity energy expenditure based on activity classification using multi-site triaxial accelerometry

A wireless networked multi-site triaxial accelerometry system has been devised to estimate activity energy expenditure during daily life. A notable feature of the devised system is the utilisation of activity classification based on multi-site acceleration signals. During performance evaluation tests on a single subject, the proposed method showed higher correlation (adjusted R 2 = 0.952) and smaller estimation deviation (0.76 kcal/min) than two methods without activity classification (multi-site case: adjusted R 2 = 0.924, estimation deviation = 0.892 kcal/min and single-site case: adjusted R 2 = 0.855, estimation deviation =1.18 kcal/min).

Estimating activity energy expenditure: how valid are physical activity questionnaires?

Activity energy expenditure (AEE) is the modifiable component of total energy expenditure (TEE) derived from all activities, both volitional and nonvolitional. Because AEE may affect health, there is interest in its estimation in free-living people. Physical activity questionnaires (PAQs) could be a feasible approach to AEE estimation in large populations, but it is unclear whether or not any PAQ is valid for this purpose. Our aim was to explore the validity of existing PAQs for estimating usual AEE in adults, using doubly labeled water (DLW) as a criterion measure. We reviewed 20 publications that described PAQ-to-DLW comparisons, summarized study design factors, and appraised criterion validity using mean differences (AEEPAQ − AEEDLW, or TEEPAQ − TEEDLW), 95% limits of agreement, and correlation coefficients (AEEPAQ versus AEEDLW or TEEPAQ versus TEEDLW). Only 2 of 23 PAQs assessed most types of activity over the past year and indicated acceptable criterion validity, with mean differences (TEEPAQ − TEEDLW) of 10% and 2% and correlation coefficients of 0.62 and 0.63, respectively. At the group level, neither overreporting nor underreporting was more prevalent across studies. We speculate that, aside from reporting error, discrepancies between PAQ and DLW estimates may be partly attributable to 1) PAQs not including key activities related to AEE, 2) PAQs and DLW ascertaining different time periods, or 3) inaccurate assignment of metabolic equivalents to self-reported activities. Small sample sizes, use of correlation coefficients, and limited information on individual validity were problematic. Future research should address these issues to clarify the true validity of PAQs for estimating AEE.

Accuracy of the Actiheart for the assessment of energy expenditure in adults

The Actiheart (Mini Mitter, Sunriver, OR, USA) uses heart rate (HR) and activity data to predict activity energy expenditure (AEE). Currently, the Actiheart has only been tested during laboratory conditions. Therefore, the objective of this study was to validate the Actiheart prediction method against indirect calorimetry during a wide range of activities in a field setting. Forty-eight participants (age: 35+/-11.4 years) were recruited for the study. Eighteen activities were split into three routines of six activities and each routine was performed by 20 participants. During each routine, the participants wore an Actiheart and simultaneously, AEE was measured with a Cosmed K4b(2) portable metabolic system. The manufacturer's HR algorithm, activity algorithm, and combined activity and HR algorithm were used to estimate AEE. The mean error (and 95% prediction intervals) for the combined activity and HR algorithm, HR algorithm, and activity algorithm versus the Cosmed K4b(2) were 0.02 kJ kg(-1) min(-1) (-0.17, 0.22 kJ kg(-1) min(-1)), -0.03 kJ kg(-1) min(-1) (-0.24, 0.18 kJ kg(-1) min(-1)), and 0.14 kJ kg(-1) min(-1) (-0.12, 0.40 kJ kg(-1) min(-1)), respectively. The Actiheart combined activity and HR algorithm and HR algorithm provide similar estimates of AEE on both a group and individual basis.

Concurrent Validation of the Bouchard Diary with an Accelerometry-Based Monitor

This study was conducted to examine the relationships between the Bouchard activity diary and a temporally matched objective measure: the Tritrac-R3D (Professional Products, Reining International, Madison, WI) accelerometer. A secondary purpose was to examine the intraindividual variability in correlation coefficients between the two temporally matched measures. A total of 70 participants (ages 18-23 yr) wore a Tritrac monitor and completed the Bouchard activity diary during a 24-h period. Estimates of activity energy expenditure (AEE) and total energy expenditure (TEE) were compared using correlations, t-tests, and Bland-Altman graphical procedures. A strong linear relationship was found between the Tritrac and the Bouchard measure for both AEE (r = 0.72) and TEE (r = 0.86). Large and significant differences were seen in the estimates of AEE (576 +/- 381 kcal) and TEE (645 +/- 494) from the two instruments. The Bouchard diary yielded higher estimates of TEE than the Tritrac for nearly all participants (97%) and the Bland-Altman plots revealed a systematic bias in the relationships (smaller bias for lower energy expenditure levels and higher bias for higher energy expenditure estimates). Intraindividual correlations between estimated TEE from the Tritrac and Bouchard diary ranged from 0.34 to 0.93 (mean r = 0.74). The overall response indicates that individuals are able to provide reasonable self-reported estimates of their activity levels across a day.