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Nicole C.A. Strock, Kristen J. Koltun, Emily A. Southmayd, Nancy I. Williams and Mary Jane De Souza

Harris–Benedict, Cunningham (1980 and 1991), and DXA prediction methods against indirect calorimetry-measured RMR; (b) determine how each ratio relates to known physiological consequences of energy deficiency; and (c) confirm the use of a 0.90 ratio or propose alternative threshold cutoff values for use

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Sarah Staal, Anders Sjödin, Ida Fahrenholtz, Karen Bonnesen and Anna Katarina Melin

, 45 , 257 – 268 . PubMed ID: 25281333 doi:10.1007/s10862-010-9207-4 10.1007/s40279-014-0268-5 Carlsohn , A. , Scharhag-Rosenberger , F. , Cassel , M. , & Mayer , F. ( 2011 ). Resting metabolic rate in elite rowers and canoeists: Difference between indirect calorimetry and prediction

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Ben J. Lee and Charles Douglas Thake

8 to 9 of each level of BWS. A CBS was obtained at the end of each exercise bout. BWS indicates body weight support; HR, heart rate; SpO 2 , hemoglobin oxygen saturation; DB, Douglas bag measurements; RPE, rating of perceived exertion; CBS, capillary blood sample. Indirect calorimetry was used to

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Mhairi J. MacDonald, Samantha G. Fawkner, Ailsa G. Niven and David Rowe

energy cost of treadmill and overground walking with regard to step rate (steps·min −1 ) and step rate-associated intensity in youth. The energy cost of walking was assessed using indirect calorimetry (METS derived from oxygen uptake) during both the treadmill and overground walking trials. Therefore

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Jorge Cañete García-Prieto, Vicente Martinez-Vizcaino, Antonio García-Hermoso, Mairena Sánchez-López, Natalia Arias-Palencia, Juan Fernando Ortega Fonseca and Ricardo Mora-Rodriguez

The aim of this study was to examine the energy expenditure (EE) measured using indirect calorimetry (IC) during playground games and to assess the validity of heart rate (HR) and accelerometry counts as indirect indicators of EE in children´s physical activity games. 32 primary school children (9.9 ± 0.6 years old, 19.8 ± 4.9 kg · m-2 BMI and 37.6 ± 7.2 ml · kg-1 · min-1 VO2max). Indirect calorimetry (IC), accelerometry and HR data were simultaneously collected for each child during a 90 min session of 30 playground games. Thirty-eight sessions were recorded in 32 different children. Each game was recorded at least in three occasions in other three children. The intersubject coefficient of variation within a game was 27% for IC, 37% for accelerometry and 13% for HR. The overall mean EE in the games was 4.2 ± 1.4 kcals · min-1 per game, totaling to 375 ± 122 kcals/per 90 min/session. The correlation coefficient between indirect calorimetry and accelerometer counts was 0.48 (p = .026) for endurance games and 0.21 (p = .574) for strength games. The correlation coefficient between indirect calorimetry and HR was 0.71 (p = .032) for endurance games and 0.48 (p = .026) for strength games. Our data indicate that both accelerometer and HR monitors are useful devices for estimating EE during endurance games, but only HR monitors estimates are accurate for endurance games.

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Charles F. Morgan, Allison R. Tsuchida, Michael William Beets, Ronald K. Hetzler and Christopher D. Stickley

Background:

Physical activity guidelines for youth and adults include recommendations for moderate intensity activity to attain health benefits. Indirect calorimetry studies have consistently reported a 100 ste·min−1 threshold for moderate intensity walking in adults. No indirect calorimetry studies have investigated step-rate thresholds in children and therefore the primary purpose of the study was to determine preliminary step-rate thresholds for moderate physical activity walking in children.

Methods:

Oxygen consumption was measured at rest and used to determine 3 and 4 age-adjusted metabolic equivalents (A-AMETs) for 4 treadmill trials (self-selected, 2.5, 3.0, and 3.5 MPH). Two trained observers simultaneously counted children’s steps during each walking trial. Step-rate thresholds associated with moderate-intensity activity, defined as 3 and 4 A-AMETs, were determined using hierarchical linear modeling.

Results:

Regression analysis determined an overall step rate of 112 and 134 step·min-1 for 3 and 4 A-AMETs respectively. Body mass index (BMI) weight status and age were positively related to A-AMETs.

Conclusions:

We suggest age and BMI weight status specific recommendations that range from a low of 100 step·min-1 threshold (3 A-AMETs) for overweight/obese 11- to 12-year-olds to a high of 140 step·min-1 threshold (4 A-AMETs) for healthy weight 9- to 10-year-old children.

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Maurice R. Puyau, Anne L. Adolph, Yan Liu, Theresa A. Wilson, Issa F. Zakeri and Nancy F. Butte

Background:

The absolute energy cost of activities in children increases with age due to greater muscle mass and physical capability associated with growth and developmental maturation; however, there is a paucity of data in preschool-aged children. Study aims were 1) to describe absolute and relative energy cost of common activities of preschool-aged children in terms of VO2, energy expenditure (kilocalories per minute) and child-specific metabolic equivalents (METs) measured by room calorimetry for use in the Youth Compendium of Physical Activity, and 2) to predict METs from age, sex and heart rate (HR).

Methods:

Energy expenditure (EE), oxygen consumption (VO2), HR, and child-METs of 13 structured activities were measured by room respiration calorimetry in 119 healthy children, ages 3 to 5 years.

Results:

EE, VO2, HR, and child-METs are presented for 13 structured activities ranging from sleeping, sedentary, low-, moderate- to high-active. A significant curvilinear relationship was observed between child-METs and HR (r 2 = .85; P = .001).

Conclusion:

Age-specific child METs for 13 structured activities in preschool-aged children will be useful to extend the Youth Compendium of Physical Activity for research purposes and practical applications. HR may serve as an objective measure of MET intensity in preschool-aged children.

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Alicia Ann Thorp, Bronwyn A. Kingwell, Coralie English, Louise Hammond, Parneet Sethi, Neville Owen and David W. Dunstan

Background:

To determine whether alternating bouts of sitting and standing at work influences daily workplace energy expenditure (EE).

Methods:

Twenty-three overweight/obese office workers (mean ± SD; age: 48.2 ± 7.9 y, body mass index: 29.6 ± 4.0 kg/m2) undertook two 5-day experimental conditions in an equal, randomized order. Participants wore a “metabolic armband” (SenseWear Armband Mini) to estimate daily workplace EE (KJ/8 h) while working (1) in a seated work posture (SIT condition) or (2) alternating between a standing and seated work posture every 30 minutes using a sit-stand workstation (STAND-SIT condition). To assess the validity of the metabolic armband, a criterion measure of acute EE (KJ/min; indirect calorimetry) was performed on day 4 of each condition.

Results:

Standing to work acutely increased EE by 0.7 [95% CI 0.3–1.0] KJ/min (13%), relative to sitting (P = .002). Compared with indirect calorimetry, the metabolic armband provided a valid estimate of EE while standing to work (mean bias: 0.1 [–0.3 to 0.4] KJ/min) but modestly overestimated EE while sitting (P = .005). Daily workplace EE was greatest during the STAND-SIT condition (mean condition difference [95% CI]: 76 [8–144] KJ/8-h workday, P = .03).

Conclusions:

Intermittent standing at work can modestly increase daily workplace EE compared with seated work in overweight/obese office workers.

Open access

Wonwoo Byun, Allison Barry and Jung-Min Lee

Background:

There has been a call for updating the Youth Compendium of Energy Expenditure (YCEE) by including energy expenditure (EE) data of young children (ie, < 6-year-old children). Therefore, this study examined the activity EE in 3 to 6 year old children using indirect calorimetry.

Methods:

Using Oxycon Mobile portable indirect calorimetry, both the oxygen consumption (VO2) and the EE of 28 children (Girls: 46%, Age: 4.8 ± 1.0, BMI: 16.4 ± 1.6) were measured while they performed various daily living activities (eg, watching TV, playing with toys, shooting baskets, soccer).

Results:

Across physical activities, averages of VO2 (ml·kg·min-1), VO2 (L·min-1), and EE ranged from 8.9 ± 1.5 to 33.3 ± 4.8 ml·kg·min-1, from 0.17 ± 0.04 to 0.64 ± 0.16 L·min-1, and from 0.8 ± 0.2 to 3.2 ± 0.7 kcal·min-1, respectively.

Conclusions:

These findings will contribute to the upcoming YCEE update.

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Scott E. Crouter, Diane M. DellaValle, Jere D. Haas, Edward A. Frongillo and David R. Bassett

Background:

The purpose of this study was to compare the 2006 and 2010 Crouter algorithms for the ActiGraph accelerometer and the NHANES and Matthews cut-points, to indirect calorimetry during a 6-hr free-living measurement period.

Methods:

Twenty-nine participants (mean ± SD; age, 38 ± 11.7 yrs; BMI, 25.0 ± 4.6 kg·m-2) were monitored for 6 hours while at work or during their leisure time. Physical activity (PA) data were collected using an ActiGraph GT1M and energy expenditure (METs) was measured using a Cosmed K4b2. ActiGraph prediction equations were compared with the Cosmed for METs and time spent in sedentary behaviors, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA).

Results:

The 2010 Crouter algorithm overestimated time spent in LPA, MPA, and VPA by 9.0%−44.5% and underestimated sedentary time by 20.8%. The NHANES cut-points overestimated sedentary time and LPA by 8.3%−9.9% and underestimated MPA and VPA by 50.4%−56.7%. The Matthews cut-points overestimated sedentary time (9.9%) and MPA (33.4%) and underestimated LPA (25.7%) and VPA (50.1%). The 2006 Crouter algorithm was within 1.8% of measured sedentary time; however, mean errors ranged from 34.4%−163.1% for LPA, MPA, and VPA.

Conclusion:

Of the ActiGraph prediction methods examined, none of them was clearly superior for estimating free-living PA compared with indirect calorimetry.