Dataset Information

Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study.

ABSTRACT:

Background

Accurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of physical activity EE (PAEE) which is the most variable component of total EE (TEE).

Objective

To evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.

Design

23 women and 23 men (22-55 yrs, 48-104 kg, 8-46%body fat) underwent 45-min resting EE (REE) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. ACC and HR were monitored and TEE measured over 14 days using DLW. Diet-induced thermogenesis (DIT) was calculated from food-frequency questionnaire. PAEE (TEE ÷ REE ÷ DIT) and TEE were compared to estimates from ACC and HR using bias, root mean square error (RMSE), and correlation statistics.

Results

Mean(SD) measured PAEE and TEE were 66(25) kJ·day(-1)·kg(-1), and 12(2.6) MJ·day(-1), respectively. Estimated PAEE from ACC was 54(15) kJ·day(-1)·kg(-1) (p<0.001), with RMSE 24 kJ·day(-1)·kg(-1) and correlation r = 0.52. PAEE estimated from HR and ACC+HR with treadmill calibration were 67(42) and 69(25) kJ·day(-1)·kg(-1) (bias non-significant), with RMSE 34 and 20 kJ·day(-1)·kg(-1) and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise (RMSE: 37 and 24 kJ·day(-1)·kg(-1), r = 0.40 and r = 0.55). TEE models also had high validity, with biases <5%, and correlations r = 0.71 (ACC), r = 0.66-0.76 (HR), and r = 0.76-0.83 (ACC+HR).

Conclusions

Both accelerometry and heart rate may be used to estimate EE in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.

SUBMITTER: Brage S

PROVIDER: S-EPMC4562631 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study.

Brage Søren S Westgate Kate K Franks Paul W PW Stegle Oliver O Wright Antony A Ekelund Ulf U Wareham Nicholas J NJ

PloS one 20150908 9

<h4>Background</h4>Accurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of physical activity EE (PAEE) which is the most variable component of total EE (TEE).<h4>Objective</h4>To evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.<h4>Design</h4>23 women and 23 men (22-5 ...[more]

PMID: 26349056

Similar Datasets

Project description:BackgroundMany 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.MethodsMeasurements 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.ResultsMean 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).ConclusionsIn 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.

Project description:RationaleThe doubly labelled water (DLW) method is the reference method for the estimation of free-living total energy expenditure (TEE). In this method, where both 2 H and 18 O are employed, different approaches have been adopted to deal with the non-conformity observed regarding the distribution space for the labels being non-coincident with total body water. However, the method adopted can have a significant effect on the estimated TEE.MethodsWe proposed a Bayesian reasoning approach to modify an assumed prior distribution for the space ratio using experimental data to derive the TEE. A Bayesian hierarchical approach was also investigated. The dataset was obtained from 59 adults (37 women) who underwent a DLW experiment during which the 2 H and 18 O enrichments were measured using isotope ratio mass spectrometry (IRMS).ResultsTEE was estimated at 9925 (9106-11236) [median and interquartile range], 9646 (9167-10540), and 9,638 (9220-10340) kJ·day-1 for women and at 13961 (12851-15347), 13353 (12651-15088) and 13211 (12653-14238) kJ·day-1 for men, using normalized non-Bayesian, independent Bayesian and hierarchical Bayesian approaches, respectively. A comparison of hierarchical Bayesian with normalized non-Bayesian methods indicated a marked difference in behaviour between genders. The median difference was -287 kJ·day-1 for women, and -750 kJ·day-1 for men. In men there is an appreciable compression of the TEE distribution obtained from the hierarchical model compared with the normalized non-Bayesian methods (range of TEE 11234-15431 kJ·day-1 vs 10786-18221 kJ·day-1 ). An analogous, yet smaller, compression is seen in women (7081-12287 kJ·day-1 vs 6989-13775 kJ·day-1 ).ConclusionsThe Bayesian analysis is an appealing method to estimate TEE during DLW experiments. The principal advantages over those obtained using the classical least-squares method is the generation of potentially more useful estimates of TEE, and improved handling of outliers and missing data scenarios, particularly if a hierarchical model is used.

Project description:IntroductionWrist-mounted motion sensors can quantify the volume and intensity of physical activities, but little is known about their long-term validity. Our aim was to validate a wrist motion sensor in estimating daily energy expenditure, including any change induced by long-term participation in endurance and strength training. Supplemental heart rate monitoring during weekly exercise was also investigated.MethodsA 13-day doubly labeled water (DLW) measurement of total energy expenditure (TEE) was performed twice in healthy male subjects: during two last weeks of a 12-week Control period (n = 15) and during two last weeks of a 12-week combined strength and aerobic Training period (n = 13). Resting energy expenditure was estimated using two equations: one with body weight and age, and another one with fat-free mass. TEE and activity induced energy expenditure (AEE) were determined from motion sensor alone, and from motions sensor combined with heart rate monitor, the latter being worn during exercise only.ResultsWhen body weight and age were used in the calculation of resting energy expenditure, the motion sensor data alone explained 78% and 62% of the variation in TEE assessed by DLW at the end of Control and Training periods, respectively, with a bias of +1.75 (p <.001) and +1.19 MJ/day (p = .002). When exercise heart rate data was added to the model, the combined wearable device approach explained 85% and 70% of the variation in TEE assessed by DLW with a bias of +1.89 and +1.75 MJ/day (p <.001 for both). While significant increases in TEE and AEE were detected by all methods as a result of participation in regular training, motion sensor approach underestimated the change measured by DLW: +1.13±0.66 by DLW, +0.59±0.69 (p = .004) by motion sensor, and +0.98±0.70 MJ/day by combination of motion sensor and heart rate. Use of fat-free mass in the estimation of resting energy expenditure removed the biases between the wearable device estimations and the golden standard reference method of TEE and demonstrated a training-induced increase in resting energy expenditure by +0.18±0.13 MJ/day (p <.001).ConclusionsWrist motion sensor combined with a heart rate monitor during exercise sessions, showed high agreement with the golden standard measurement of daily TEE and its change induced by participation in a long-term training protocol. The positive findings concerning the validity, especially the ability to follow-up the change associated with a lifestyle modification, can be considered significant because they partially determine the feasibility of wearable devices as quantifiers of health-related behavior.

Project description:BackgroundPhysical activity or biomarker-calibrated energy intake (EI) alone is associated with mortality in older adults; the interaction relationship between the combined use of both factors and mortality has not been examined. We evaluated the relationship between mortality and calibrated EI and step counts in older adults.MethodsThis prospective study included 4,159 adults aged ≥65 years who participated in the Kyoto-Kameoka study in Japan and wore a triaxial accelerometer between 1 April and 15 November 2013. The calibrated EI was calculated based on a previously developed equation using EI biomarkers. The step count was obtained from the accelerometer ≥ 4 days. Participants were classified into the following four groups: low EI (LEI)/low step counts (LSC) group (EI: <2,400 kcal/day in men and <1,900 kcal/day in women; steps: <5,000 /day), n = 1,352; high EI (HEI)/LSC group (EI: ≥2,400 kcal/day in men and ≥1,900 kcal/day in women; steps: <5,000 /day), n = 1,586; LEI/high step counts (HSC) group (EI: <2,400 kcal/day in men and < 1,900 kcal/day in women; steps: ≥5,000 /day), n = 471; and HEI/HSC group (EI: ≥2,400 kcal/day in men and ≥1,900 kcal/day in women; steps: ≥5,000 /day), n = 750. Mortality-related data were collected until 30 November 2016. We performed a multivariable Cox proportional hazard analysis.ResultsThe median follow-up period was 3.38 years (14,046 person-years), and 111 mortalities were recorded. After adjusting for confounders, the HEI/HSC group had the lowest all-cause mortality rate compared to other groups (LEI/LSC: reference; HEI/LSC: hazard ratio [HR]: 0.71, 95% confidence interval [CI]: 0.41-1.23; LEI/HSC: HR: 0.59, 95% CI: 0.29-1.19; and HEI/HSC: HR: 0.10, 95% CI: 0.01-0.76). No significant interaction was observed between the calibrated EI and steps with mortality. The spline model showed that 35-42 kcal/100 steps/day of EI/100 steps was associated with the lowest mortality risk.ConclusionsHR mortality risk was lowest at 35-42 kcal/100 steps/day, suggesting that very high (≥56 kcal) or low (<28 kcal) EI/100 steps are not inversely associated with mortality. Adherence to optimal EI and adequate physical activity may provide sufficient energy balance to explain the inverse association with mortality among older Japanese adults.

Dataset Information

Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study.

Background

Objective

Design

Results

Conclusions

Publications

Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study.

Similar Datasets

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