Energy expenditure in underweight free-living adults: impact of energy supplementation as determined by doubly labeled water and indirect calorimetry

1989 ◽  
Vol 49 (2) ◽  
pp. 239-246 ◽  
Author(s):  
J A Riumallo ◽  
D Schoeller ◽  
G Barrera ◽  
V Gattas ◽  
R Uauy
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Doubly Labeled Water ◽  
Free Living ◽  
Energy Supplementation

scholarly journals Indirect Calorimetry Protocol Development for Measuring Resting Metabolic Rate as a Component of Total Energy Expenditure in Free-Living Postmenopausal Women

2001 ◽  
Vol 131 (8) ◽  
pp. 2215-2218 ◽  
Author(s):  
Neilann K. Horner ◽  
Johanna W. Lampe ◽  
Ruth E. Patterson ◽  
Marian L. Neuhouser ◽  
Shirley A. Beresford ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Metabolic Rate ◽  
Total Energy ◽  
Postmenopausal Women ◽  
Indirect Calorimetry ◽  
Resting Metabolic Rate ◽  
Total Energy Expenditure ◽  
Free Living ◽  
Protocol Development

scholarly journals An evaluation of the IDEEA™ activity monitor for estimating energy expenditure

2012 ◽  
Vol 109 (1) ◽  
pp. 173-183 ◽  
Author(s):  
Stephen Whybrow ◽  
Patrick Ritz ◽  
Graham W. Horgan ◽  
R. James Stubbs
Keyword(s):  
Energy Balance ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Activity Patterns ◽  
Activity Monitor ◽  
Whole Body ◽  
Similar Estimate ◽  
Free Living ◽  
Doubly Labelled Water ◽  
D 12

Objective estimates of activity patterns and energy expenditure (EE) are important for the measurement of energy balance. The Intelligent Device for Energy Expenditure and Activity (IDEEA) can estimate EE from the thirty-five postures and activities it can identify and record. The present study evaluated the IDEEA system's estimation of EE using whole-body indirect calorimetry over 24 h, and in free-living subjects using doubly-labelled water (DLW) over 14 d. EE was calculated from the IDEEA data using calibration values for RMR and EE while sitting and standing, both as estimated by the IDEEA system (IDEEAest) and measured by indirect calorimetry (IDEEAmeas). Subjects were seven females and seven males, mean age 38·1 and 39·7 years, mean BMI 25·2 and 26·2 kg/m2, respectively. The IDEEAest method produced a similar estimate of EE to the calorimeter (10·8 and 10·8 MJ, NS), while the IDEEAmeas method underestimated EE (9·9 MJ, P < 0·001). After removing data from static cycling, which the IDEEA was unable to identify as an activity, both the IDEEAest and IDEEAmeas methods overestimated EE compared to the calorimeter (9·9 MJ, P < 0·001; 9·1 MJ, P < 0·05 and 8·6 MJ, respectively). Similarly, the IDEEA system overestimated EE compared to DLW over 14 d; 12·7 MJ/d (P < 0·01), 11·5 MJ/d (P < 0·01) and 9·5 MJ/d for the IDEEAest, IDEEAmeas and DLW, respectively. The IDEEA system overestimated EE both in the controlled laboratory and free-living environments. Using measured EE values for RMR, sitting and standing reduced, but did not eliminate, the error in estimated EE.

scholarly journals Predicting Free-Living Energy Expenditure Using a Miniaturized Ear-Worn Sensor: An Evaluation Against Doubly Labeled Water

2014 ◽  
Vol 61 (2) ◽  
pp. 566-575 ◽  
Author(s):  
Loubna Bouarfa ◽  
Louis Atallah ◽  
Richard Mark Kwasnicki ◽  
Claire Pettitt ◽  
Gary Frost ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Doubly Labeled Water ◽  
Free Living

scholarly journals The Energy Expenditure of Free-Living Physical Activities in Primary Schoolchildren

2016 ◽  
Vol 13 (s1) ◽  
pp. S57-S61 ◽  
Author(s):  
Alison L. Innerd ◽  
Liane B. Azevedo
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Indirect Calorimetry ◽  
Resting Metabolic Rate ◽  
Physical Activities ◽  
Free Living ◽  
Metabolic Equivalent ◽  
Primary Schoolchildren ◽  
Vigorous Intensity

Background:The aim of this study is to establish the energy expenditure (EE) of a range of child-relevant activities and to compare different methods of estimating activity MET.Methods:27 children (17 boys) aged 9 to 11 years participated. Participants were randomly assigned to 1 of 2 routines of 6 activities ranging from sedentary to vigorous intensity. Indirect calorimetry was used to estimate resting and physical activity EE. Activity metabolic equivalent (MET) was determined using individual resting metabolic rate (RMR), the Harrell-MET and the Schofield equation.Results:Activity EE ranges from 123.7± 35.7 J/min/Kg (playing cards) to 823.1 ± 177.8 J/min/kg (basketball). Individual RMR, the Harrell-MET and the Schofield equation MET prediction were relatively similar at light and moderate but not at vigorous intensity. Schofield equation provided a better comparison with the Compendium of Energy Expenditure for Youth.Conclusion:This information might be advantageous to support the development of a new Compendium of Energy Expenditure for Youth.

scholarly journals An Accelerometer-Based Earpiece to Monitor and Quantify Physical Activity

2009 ◽  
Vol 6 (6) ◽  
pp. 781-789 ◽  
Author(s):  
Chinmay Manohar ◽  
Shelly McCrady ◽  
Ioannis T. Pavlidis ◽  
James A. Levine
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Daily Activity ◽  
Free Living ◽  
Body Postures ◽  
Sensing Technology ◽  
Walking Velocity ◽  
Activity Sensing

Background:Physical activity is important in ill-health. Inexpensive, accurate and precise devices could help assess daily activity. We integrated novel activity-sensing technology into an earpiece used with portable music-players and phones; the physical-activity-sensing earpiece (PASE). Here we examined whether the PASE could accurately and precisely detect physical activity and measure its intensity and thence predict energy expenditure.Methods:Experiment 1: 18 subjects wore PASE with different body postures and during graded walking. Energy expenditure was measured using indirect calorimetry. Experiment 2: 8 subjects wore the earpiece and walked a known distance. Experiment 3: 8 subjects wore the earpiece and ‘jogged’ at 3.5mph.Results:The earpiece correctly distinguished lying from sitting/standing and distinguished standing still from walking (76/76 cases). PASE output showed excellent sequential increases with increased in walking velocity and energy expenditure (r2 > .9). The PASE prediction of free-living walking velocity was, 2.5 ± (SD) 0.18 mph c.f. actual velocity, 2.5 ± 0.16 mph. The earpiece successfully distinguished walking at 3.5 mph from ‘jogging’ at the same velocity (P < .001).Conclusions:The subjects tolerated the earpiece well and were comfortable wearing it. The PASE can therefore be used to reliably monitor free-living physical activity and its associated energy expenditure.

scholarly journals Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study

10.2196/13938 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e13938 ◽  
Author(s):  
Haruka Murakami ◽  
Ryoko Kawakami ◽  
Satoshi Nakae ◽  
Yosuke Yamada ◽  
Yoshio Nakata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Correlation Coefficients ◽  
Wearable Devices ◽  
Doubly Labeled Water ◽  
Free Living ◽  
Daily Lives ◽  
Self Monitoring ◽  
Metabolic Chamber ◽  
Positive Correlations

Background Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. Objective We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. Methods A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. Results On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. Conclusions Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

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