Assessment Of Energy Expenditure Research Articles

Modulation of plasma N-acylethanolamine levels and physiological parameters by dietary fatty acid composition in humans

N-Acylethanolamines (NAEs) are endogenous lipid-signaling molecules involved in satiety and energetics; however, how diet impacts circulating NAE concentrations and their downstream metabolic actions in humans remains unknown. Objectives were to examine effects of diets enriched with high-oleic canola oil (HOCO) or HOCO blended with flaxseed oil (FXCO), compared with a Western diet (WD), on plasma NAE levels and the association with energy expenditure and substrate oxidation. Using a randomized controlled crossover design, 36 hypercholesterolemic participants consumed three isoenergetic diets for 28 days, each containing 36% energy from fat, of which 70% was HOCO, FXCO, or WD. Ultra-performance liquid chromatography-MS/MS was used to measure plasma NAE levels and indirect calorimetry to assess energy expenditure and substrate oxidation. After 28 days, compared with WD, plasma oleoylethanolamide (OEA) and alpha-linolenoyl ethanolamide (ALEA) levels were significantly increased in response to HOCO and FXCO (P = 0.002, P < 0.001), respectively. Correlation analysis demonstrated an inverse association between plasma OEA levels and percent body fat (r = -0.21, P = 0.04), and a positive association was observed between the plasma arachidonoyl ethanolamide (AEA)/OEA ratio and android:gynoid fat (r = 0.23, P = 0.02), respectively. Results suggest that plasma NAE levels are upregulated via their dietary lipid substrates and may modulate regional and total fat mass through lipid-signaling mechanisms.

Activity-Related Energy Expenditure in Older Adults

The purposes of this article were to 1) provide an overview of the science of physical activity-related energy expenditure in older adults (≥65 yr), 2) offer suggestions for future research and guidelines for how scientists should be reporting their results in this area, and 3) present strategies for making these data more accessible to the layperson. This article was meant to serve as a preliminary blueprint for future empirical work in the area of energy expenditure in older adults and translational efforts to make these data useful and accurate for older adults. This document was based upon deliberations of experts involved in the Strategic Health Initiative on Aging Committee of the American College of Sports Medicine. The article was designed to reach a broad audience who might not be familiar with the complexities of assessing energy expenditure, especially in older adults.

Opportunities for meeting sustainability objectives

It is inevitable that theory and good practice in Human Factors/Ergonomics (HF/E) should comprise engineering for sustainability to safeguard ecology and maintaining the quality of life. There are immediate and longer-term opportunities for HF/E community to be a key contributor in solving sustainability issues. However, sustainability research in HF/E domain has only been partially explored. A further theoretical and practical contribution is needed. Digital Human Modeling (DHM) is a potential method to integrate human element into sustainability research. However, current DHM tools are limited in resolving these issues until sustainability objectives are explicitly considered. There are DHM tools available to evaluate human performance. However typically do not consider sustainability aspects of the work environment. This study introduces two conceptual digital ergonomics toolkits, Air Quality Index Assessment and Metabolic Energy Expenditure, to demonstrate the potential use of DHM in evaluating the health risks and worker's performance in work design. Incorporating human element into sustainability through DHM (digital ergonomics toolkits) reduces the need for in-situ human data collection and physical prototyping for work environments that are subject to poor air quality, toxic exposure and handling of hazardous materials. The DHM toolkits proposed in this study can bring attention towards building multidisciplinary collaboration that can enhance HF/E outreach goals in sustainability-related engineering design. Relevance to IndustryDHM can help industry to minimize the need of in-situ human data collection, reduce the need of physical prototyping, and optimize overall system performance for where subjects are exposed to poor air quality, toxic exposure, and handling of hazardous materials.

자전거 운동량 평가를 위한 전도성 직물 기반의 사용자 무구속 심전계 및 스마트폰 어플리케이션

As assessment of quantitative energy expenditure for effective exercise is becoming more important, many researches about the monitoring system for exercise status or result have been actively carried out. In this case, however, bicycle riders feel restrained and uncomfortable because the riders should wear a belt-type electrocardiograph or a watch-type accelerometer or GPS system during the assessment of bicycle exercise. In this study, therefore, an electrocardiograph based on textile electrode was developed for measuring ECG and calculating heart rate from the bicycle rider without feeling restraint, and smartphone application was also developed for monitoring the heart rate.

Energy requirements and the use of predictive equations versus indirect calorimetry in critically ill patients.

Nutrition support has been shown to have a positive impact on critically ill patients who meet their defined goals of nutrition therapy. However, inappropriate energy assessment can contribute to under- or overfeeding resulting in deleterious effects. Thus, assessment of energy expenditure in critically ill patients is crucial to prevent negative impacts from inappropriate feeding. Currently, the optimal energy requirement and appropriate energy assessment in these patients is controversial. Indirect calorimetry or predictive equations have been suggested to evaluate energy expenditure in critically ill patients. Indirect calorimetry is a gold standard for evaluating energy expenditure, but it is not always available and has some limitations. Many predictive equations, therefore, have been developed to predict energy expenditure in critically ill patients. However, these equations cannot be used generally in these patients since they were developed in a unique patient population. Many studies compared measured energy expenditure with predictive energy expenditure, but the data regarding accuracy is not robust. Therefore, clinicians should consider using these equations carefully based on the current supporting data. Indirect calorimetry is recommended for use in evaluating energy expenditure in critically ill patients if it is available.

Influence of energy-environmental factors on the competitiveness of hydrogen as a motor fuel (in transport energy installations)

It is described in the paper that for maintaining eco-safe motor transport systems evolution, the development of criteria, based on which it would be possible to evaluate the perfection and technical condition of automobiles is necessary. On the basis of experimental data handling, obtained while testing automobiles of a various make, based on the analysis and coordination of numerous research results, the authors have confirmed that benzopyrene is a reliable indicator of prioritized carcinogenic hydrocarbon emission level and can be used as an indicator of total carcinogenicity () of automobile exhaust gases. Formulas for the calculation are given. The changes in specific cost expenditures () when using hydrogen, as an additional energy carrier were calculated. An assessment of the total energy expenditure and atmospheric emissions when using different fuel technologies in motor transport was made. It is proved that adding hydrogen to the diluted air-fuel mixture in the ICE is economically sound. The expediency of wind-power potential at the self-supporting motorway filling stations was found. It is emphasized that with the full implementation of the mentioned technologies, an increase in socio-economical attractiveness of the hydrogen energetics transport component is expected.

Evaluation of the SenseWear Mini Armband to Assess Energy Expenditure During Pole Walking

The current study aimed to show the validity of a portable motion sensor, the SenseWear Armband (SWA), for the estimation of energy expenditure during pole walking. Twenty healthy adults (mean ± SD: age 30.1 ± 7.2 year, body mass 66.1 ± 10.6 kg, height 172.4 ± 8.0 cm, BMI 22.1 ± 2.4 kg · m(-2)) wore the armband during randomized pole walking activities at a constant speed (1.25 m · s(-1)) and at seven grades (0%, ± 5%, ± 15% and ± 25%). Estimates of total energy expenditure from the armband were compared with values derived from indirect calorimetry methodology (IC) using a 2-way mixed model ANOVA (Device × Slope), correlation analyses and Bland-Altman plots. Results revealed significant main effects for device, and slope (p < .025) as well as a significant interaction (p < .001). Significant differences between IC and SWA were observed for all conditions (p < .05). SWA generally underestimate the EE values during uphill PW by 0.04 kcal · kg(-1) · min(-1) (p < .05). Whereas, a significant overestimation has been detected during flat and downhill PW by 0.01 and 0.03 kcal · kg(-1) · min(-1) (p < .05), respectively. The Bland-Altman plots revealed bias of the armband compared with the indirect calorimetry at any condition examined. The present data suggest that the armband is not accurate to correctly detect and estimate the energy expenditure during pole walking activities. Therefore, the observed over- and under-estimations warrants more work to improve the ability of SWA to accurately measure EE for these activities.

Validity of heart rate indexes to assess wheeling efficiency in patients with spinal cord injuries.

Cross-sectional study. To validate heart rate (HR) indexes for the evaluation of energy expenditure during wheelchair propulsion in people with spinal cord injury (SCI). SARAH Rehabilitation Hospital Network-Brasilia, Brazil. Forty-eight men aged 29.0±7.8 years with a SCI between C6 and L3 causing complete motor impairment were enrolled in the study. The assessment consisted of 5 min of rest while seated in the wheelchair, followed by 5 min of wheelchair propulsion at a self-selected comfortable speed. The main outcomes measured were oxygen consumption, HR, physiological cost index (PCI), total heart beat index (THBI) and propulsion cardiac cost index (PCCI). Using oxygen uptake as gold standard, the highest correlations were found between this and PCCI (r=0.59, P<0.001, 95% confidence interval (CI) 0.36-0.75) and THBI (r=0.58, P<0.001, 95% CI 0.36-74.0), independently of the injury level. The PCI had the lowest correlation coefficient (r=0.38, P<0.01, 95% CI 0.11-0.60). Spearman correlation analysis showed that the indexes mostly associated with lesion level were PCCI (ρ=-0.531; P<0.001; 95% CI -0.708 to -0.291) and THBI (ρ=-0.524; P<0.001; 95% CI -0.704 to -0.282). Both THBI and PCCI seem to be valid measures for assessing energy expenditure. Both indexes showed a coherent correlation with the spinal injury level.

Hierarchical approaches to estimate energy expenditure using phone-based accelerometers.

Physical inactivity is linked with increase in risk of cancer, heart disease, stroke, and diabetes. Walking is an easily available activity to reduce sedentary time. Objective methods to accurately assess energy expenditure from walking that is normalized to an individual would allow tailored interventions. Current techniques rely on normalization by weight scaling or fitting a polynomial function of weight and speed. Using the example of steady-state treadmill walking, we present a set of algorithms that extend previous work to include an arbitrary number of anthropometric descriptors. We specifically focus on predicting energy expenditure using movement measured by mobile phone-based accelerometers. The models tested include nearest neighbor models, weight-scaled models, a set of hierarchical linear models, multivariate models, and speed-based approaches. These are compared for prediction accuracy as measured by normalized average root mean-squared error across all participants. Nearest neighbor models showed highest errors. Feature combinations corresponding to sedentary energy expenditure, sedentary heart rate, and sex alone resulted in errors that were higher than speed-based models and nearest-neighbor models. Size-based features such as BMI, weight, and height produced lower errors. Hierarchical models performed better than multivariate models when size-based features were used. We used the hierarchical linear model to determine the best individual feature to describe a person. Weight was the best individual descriptor followed by height. We also test models for their ability to predict energy expenditure with limited training data. Hierarchical models outperformed personal models when a low amount of training data were available. Speed-based models showed poor interpolation capability, whereas hierarchical models showed uniform interpolation capabilities across speeds.

Assessment of physical activity and energy expenditure: an overview of objective measures.

The ability to assess energy expenditure (EE) and estimate physical activity (PA) in free-living individuals is extremely important in the global context of non-communicable diseases including malnutrition, overnutrition (obesity), and diabetes. It is also important to appreciate that PA and EE are different constructs with PA defined as any bodily movement that results in EE and accordingly, energy is expended as a result of PA. However, total energy expenditure, best assessed using the criterion doubly labeled water (DLW) technique, includes components in addition to physical activity energy expenditure, namely resting energy expenditure and the thermic effect of food. Given the large number of assessment techniques currently used to estimate PA in humans, it is imperative to understand the relative merits of each. The goal of this review is to provide information on the utility and limitations of a range of objective measures of PA and their relationship with EE. The measures discussed include those based on EE or oxygen uptake including DLW, activity energy expenditure, physical activity level, and metabolic equivalent; those based on heart rate monitoring and motion sensors; and because of their widespread use, selected subjective measures.

Quantification of Energy Expenditure in Field Conditions: Comparison of Direct VO2 Versus VO2 Estimation from Heart Rate Measurements

IntroductionIn humans, oxygen consumption (VO2) and heart rate (HR) are commonly used to assess energy expenditure (EE) during exercise in field conditions. We hypothesized that the estimation of EE from HR measurements could be a relevant parameter to estimate EE during submaximal field exercise. This study aimed to compare the EE from VO2 and HR measurements in exercising trotters.MethodsSix harnessed French Trotters were submitted to a continuous field incremental test (FIT) at trot to establish their individual VO2/HR relationship. Then they undertook a submaximal exercise bout (EX) over 2375 m during 3.41 ± 0.47 min at 70% of maximal velocity. VO2 and HR were recorded continuously (equine K4b2, COSMED, Rome, Italy) throughout FIT and EX. EE of EX was calculated by multiplying the total VO2 (Liters), either directly measured or predicted from HR, with its caloric equivalent (4.86 kcal/liter). Means were compared using a Student's t test.ResultsEE of EX was estimated at 2422 ± 390 kcal and 2630 ± 706 kcal from direct VO2 and HR methods respectively (P = 0.54).ConclusionsWe found no significant difference between the two methods for estimating EE of submaximal exercise. This suggests that the prediction of EE from HR monitoring might be used to evaluate EE of moderate intensity workouts.Ethical Animal ResearchThe project was conducted under the ethics committee approval B1110. Sources of funding: INRA PHASE, the Burgundy Region and AgroSup Dijon. Competing interests: none.

Earbud-based sensor for the assessment of energy expenditure, HR, and VO2max.

The goal of this program was to determine the feasibility of a novel noninvasive, highly miniaturized optomechanical earbud sensor for accurately estimating total energy expenditure (TEE) and maximum oxygen consumption (VO2max). The optomechanical sensor module, small enough to fit inside commercial audio earbuds, was previously developed to provide a seamless way to measure blood flow information during daily life activities. The sensor module was configured to continuously measure physiological information via photoplethysmography and physical activity information via accelerometry. This information was digitized and sent to a microprocessor where digital signal-processing algorithms extract physiological metrics in real time. These metrics were streamed wirelessly from the earbud to a computer. In this study, 23 subjects of multiple physical habitus were divided into a training group of 14 subjects and a validation group of 9 subjects. Each subject underwent the same exercise measurement protocol consisting of treadmill-based cardiopulmonary exercise testing to reach VO2max. Benchmark sensors included a 12-lead ECG sensor for measuring HR, a calibrated treadmill for measuring distance and speed, and a gas-exchange analysis instrument for measuring TEE and VO2max. The earbud sensor was the device under test. Benchmark and device under test data collected from the 14-person training data set study were integrated into a preconceived statistical model for correlating benchmark data with earbud sensor data. Coefficients were optimized, and the optimized model was validated in the 9-person validation data set. It was observed that the earbud sensor estimated TEE and VO2max with mean ± SD percent estimation errors of -0.7 ± 7.4% and -3.2 ± 7.3%, respectively. The earbud sensor can accurately estimate TEE and VO2max during cardiopulmonary exercise testing.

Validation of the Fitbit wireless activity tracker for prediction of energy expenditure.

The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities. Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities. Mean bias for estimated energy expenditure for all activities was -4.5 ± 1.0 kcals/6 min (95% limits of agreement: -25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities. The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

Energy expenditure in individuals with spinal cord injury quantified by doubly labeled water and a multi-sensor armband.

The objective of this study was to determine whether doubly labeled water (DLW) and a multi-sensor armband (SWA) could detect the variation in energy expenditure incurred by a period of increased exercise (EXE) versus a period of high sedentary activity (SED), in individuals with spinal cord injury (SCI). Eight participants with SCI were submitted to 2 testing periods of energy expenditure assessment: 1) a 14-day phase during which sedentary living conditions were imposed and 2) a 14-day phase during which an exercise training intervention was employed. For each phase, total daily energy expenditure (TDEE) and physical activity energy expenditure (PAEE) were measured by DLW and SWA. Mean TDEE assessed by DLW, was significantly higher during EXE versus SED (11,605 ± 2151 kJ·day⁻¹ and 10,069 ± 2310 kJ·day⁻¹). PAEE predicted by DLW was also significantly higher during EXE versus SED (5422 ± 2240 kJ·day⁻¹ and 3855 ± 2496 kJ·day⁻¹). SWA-predicted PAEE significantly underestimated PAEE measured by the DLW during SED and EXE. DLW is sensitive to detect variation in within-individual energy expenditure during voluntary increase in physical activity in individuals with SCI. SWA failed to detect statistically significant variations in energy expenditure between periods of high versus low activity in SCI.

Accuracy of a combined heart rate and motion sensor for assessing energy expenditure in free-living adults during a double-blind crossover caffeine trial using doubly labeled water as the reference method.

A combined heart rate (HR) and motion sensor (Actiheart) has been proposed as an accurate method for assessing total energy expenditure (TEE) and physical activity energy expenditure (PAEE). However, the extent to which factors such as caffeine may affect the accuracy by which the estimated HR-related PAEE contribution will affect TEE and PAEE estimates is unknown. Therefore, we examined the validity of Actiheart in estimating TEE and PAEE in free-living adults under a caffeine trial compared with doubly labeled water (DLW) as reference criterion. Using a double-blind crossover trial (Clinicaltrials.gov ID: #NCT01477294) with two conditions (4-day each with a 3-day-washout period), randomly ordered as caffeine (5 mg/kg per day) and placebo (malt-dextrine) intake, TEE was measured by DLW in 17 physically active men (20-38 years) who were non-caffeine users. In each condition, resting energy expenditure (REE) was assessed by indirect calorimetry and PAEE was calculated as (TEE-(REE+0.1 TEE)). Simultaneously, PAEE and TEE were estimated by Actiheart using an individual calibration (ACC+HRstep). Under caffeine, ACC+HRstep explained 76 and 64% of TEE and PAEE from DLW, respectively; corresponding results for the placebo condition were 82 and 66%. No mean bias was found between ACC+HRstep and DLW for TEE (caffeine:-468 kJ per day; placebo:-407 kJ per day), although PAEE was slightly underestimated (caffeine:-856 kJ per day; placebo:-1147 kJ per day). Similar limits of agreement were observed in both conditions ranging from -2066 to 3002 and from -3488 to 1776 kJ per day for TEE and PAEE, respectively. Regardless of caffeine intake, the combined HR and motion sensor is valid for estimating free-living energy expenditure in a group of healthy men but is less accurate for an individual assessment.

Validity of the SenseWear Armband to Assess Energy Expenditure During Intermittent Exercise and Recovery in Rugby Union Players

Portable wearable devices that assess energy expenditure during intermittent exercise and recovery would be useful in team sports. Fourteen state-level male rugby union players (mean ± SD: age, 22 ± 4 years; body mass, 88.8 ± 11.2 kg; height, 1.81 ± 0.07 m, body fat, 18 ± 6%) participated in this study. Energy expenditure was measured by the SenseWear Armband (SWA) and validated against indirect calorimetry as the criterion measure during a 42-minute rugby-specific intermittent exercise test and an immediate postexercise 10-minute recovery period. Energy expenditure measurements from indirect calorimetry and the SWA were only moderately correlated during both the exercise test (r = 0.55, ±0.34; mean, ±90% confidence limits) and recovery period (r = 0.58, ±0.33). The SWA estimate of energy expenditure during exercise was unclear, with a mean bias of -1.9% (±5.3%), and during recovery energy expenditure was overestimated, with a mean bias of 17% (±12%) at the mean estimated energy expenditure. Typical error of SWA energy expenditure estimates expressed as a coefficient of variation (±90% confidence interval) was 10% (8-16%) during exercise and 19% (14-30%) during recovery. The SWA did not provide a valid measure of energy expenditure during rugby-specific intermittent exercise or 10-minute postexercise recovery. Further improvements are required in the performance of the SWA before it can be used routinely in intermittent sports and provide worthwhile information in relation to workloads of athletes for sport scientists and coaches.

Gait energy expenditure in children with Duchenne muscular dystrophy: case study

This case study aimed to verify the model of Rose et al.1 as a feasible to assess energy expenditure in gait of children with Duchenne muscular dystrophy (DMD). Three DMD patients aged 6, 7 and 8 years old participated of this study. It was obtained weight, height, leg length measurement (LLM), resting and gait heart rate (HR) held on as 55-meter oval circuit performed during a two-minute test at each speed. Energy expenditure was calculated using the HR. It was performed a descriptive analysis (average) and these were compared, individually, to normative data. The average gait speed of these three patients was similar to the normative data for slow speed and lower considering comfortable and fast speed. The energy expenditure to slow speed of the patients 2 and 3 was similar to the normality, and lowest for patient 1; at comfortable speed, the energy expenditure obtained for all patients was similar; at fast speed, the patients 1 and 2 presented similar to normal values, but the patient 3 presented higher energy expenditure. It was concluded that the energy expenditure evaluation using HR was easily executed in the clinical practice and it can help therapeutic choices. For patient 3, an aerobic training could be indicated and for the others, they could keep the routine assessments.

What does indirect calorimetry really tell us?

What does indirect calorimetry really tell us?

Resting metabolic rate and anthropometry in older people: a comparison of measured and calculated values

Accurate assessment of energy expenditure and anthropometry in older people is important for targeted nutritional support. The present study aimed to compare measured and calculated resting metabolic rate (m-RMR and c-RMR) and measured, calculated and estimated weight and height in older people aged ≥70years. Participants were healthy older people aged ≥70years. Indirect calorimetry using a ventilated hood calorimeter was performed for 30min on fasted participants, and was compared with c-RMR, as calculated using six commonly used equations. Measured, calculated and estimated height and weight were compared. Subjects comprised 14 males and 20 females and mean (SD) m-RMR was 5243(845)kJday(-1) [1253(202)kcalday(-1) ]. The Mifflin St-Jeor equation was the most consistently accurate, with the smallest mean difference between m-RMR and c-RMR of 58(553)kJday(-1) [14(132)kcalday(-1) ] and c-RMR was within 10% of m-RMR in the greatest number of participants (n=24; 70%). The Schofield equation was among the least accurate in this age group. In older males, self-reported height and weight were accurate, whereas, in females or those unable to self-report height, ulna length was the most accurate alternative to measured height. Current equations used to calculate RMR in older people have inaccuracies, although the Mifflin St-Jeor equation was most accurate. Future studies should investigate the validity, reliability, cost and practicality of using fat free mass as an item in novel equations to calculate RMR in this age group. Self-reported height and weight in males, and height calculated from ulna length in females, were the most accurate alternatives to measured values in the present study.

Evaluation of Actiheart and a 7 d activity diary for estimating free-living total and activity energy expenditure using criterion methods in 1·5- and 3-year-old children

Accurate and easy-to-use methods to assess free-living energy expenditure in response to physical activity in young children are scarce. In the present study, we evaluated the capacity of (1) 4d recordings obtained using the Actiheart (mean heart rate (mHR) and mean activity counts (mAC)) to provide assessments of total energy expenditure (TEE) and activity energy expenditure (AEE) and (2) a 7d activity diary to provide assessments of physical activity levels (PAL) using three sets of metabolic equivalent (MET) values (PALTorun, PALAdolph and PALAinsworth) in forty-four and thirty-one healthy Swedish children aged 1·5 and 3 years, respectively. Reference TEE, PALref and AEE were measured using criterion methods, i.e. the doubly labelled water method and indirect calorimetry. At 1·5 years of age, mHR explained 8% (P=0·006) of the variation in TEE above that explained by fat mass and fat-free mass. At 3 years of age, mHR and mAC explained 8 (P=0·004) and 6 (P=0·03)% of the variation in TEE and AEE, respectively, above that explained by fat mass and fat-free mass. At 1·5 and 3 years of age, average PALAinsworth values were 1·44 and 1·59, respectively, and not significantly different from PALref values (1·39 and 1·61, respectively). By contrast, average PALTorun (1·5 and 3 years) and PALAdolph (3 years) values were lower (P<0·05) than the corresponding PALref values. In conclusion, at both ages, Actiheart recordings explained a small but significant fraction of free-living energy expenditure above that explained by body composition variables, and our activity diary produced mean PAL values in agreement with reference values when using MET values published by Ainsworth.