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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.

Open access

Alison L. Innerd and Liane B. Azevedo

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.

Open access

Jourdin Barkman, Karin Pfeiffer, Allie Diltz and Wei Peng

Background:

Replacing sedentary time with physical activity through new generation exergames (eg, XBOX Kinect) is a potential intervention strategy. The study’s purpose was to compare youth energy expenditure while playing different exergames in single- vs. multiplayer mode.

Methods:

Participants (26 male, 14 female) were 10 to 13 years old. They wore a portable metabolic analyzer while playing 4 XBOX Kinect games for 15 minutes each (2 single-, 2 multiplayer). Repeated-measures ANOVA (with Bonferroni correction) was used to examine player mode differences, controlling for age group, sex, weight status, and game.

Results:

There was a significant difference in energy expenditure between single player (mean = 15.4 ml/kg/min, SD = 4.5) and multiplayer mode (mean = 16.8 ml/kg/min, SD = 4.7). Overweight and obese participants (mean = 13.7 ml/kg/min, SD = 4.2) expended less energy than normal weight (mean = 17.8 ml/kg/min, SD = 4.5) during multiplayer mode (d = 0.93).

Conclusion:

Player mode, along with personal factors such as weight status, may be important to consider in energy expenditure during exergames.

Open access

Yong Gao, Haichun Sun, Jie Zhuang, Jian Zhang, Lynda Ransdell, Zheng Zhu and Siya Wang

Background:

This study determined the metabolic equivalents (METs) of several activities typically performed by Chinese youth.

Methods:

Thirty youth (12 years) performed 7 activities that reflected their daily activities while Energy Expenditure (EE) was measured in a metabolic chamber.

Results:

METs were calculated as activity EE divided by participant’s measured resting metabolic rate. A MET value ranging from 0.8 to 1.2 was obtained for sleeping, watching TV, playing computer games, reading and doing homework. Performing radio gymnastics had a MET value of 2.9. Jumping rope at low effort required 3.1 METs. Except for watching TV, METs for other activities in this study were lower than Youth Compendium values.

Conclusions:

The results provide empirical evidence for more accurately assessing EE of activities commonly performed by Chinese youth. This is the first study to determine METs for radio gymnastics and jump rope in Chinese youth.

Open access

Stephen D. Herrmann and Karin A. Pfeiffer

Open access

Christiana M.T. van Loo, Anthony D. Okely, Marijka Batterham, Tina Hinkley, Ulf Ekelund, Soren Brage, John J. Reilly, Gregory E. Peoples, Rachel Jones, Xanne Janssen and Dylan P. Cliff

Background:

To validate the activPAL3 algorithm for predicting metabolic equivalents (TAMETs) and classifying MVPA in 5- to 12-year-old children.

Methods:

Fifty-seven children (9.2 ± 2.3y, 49.1% boys) completed 14 activities including sedentary behaviors (SB), light (LPA) and moderate-to-vigorous physical activities (MVPA). Indirect calorimetry (IC) was used as the criterion measure. Analyses included equivalence testing, Bland-Altman procedures and area under the receiver operating curve (ROC-AUC).

Results:

At the group level, TAMETs were significantly equivalent to IC for handheld e-game, writing/coloring, and standing class activity (P < .05). Overall, TAMETs were overestimated for SB (7.9 ± 6.7%) and LPA (1.9 ± 20.2%) and underestimated for MVPA (27.7 ± 26.6%); however, classification accuracy of MVPA was good (ROC-AUC = 0.86). Limits of agreement were wide for all activities, indicating large individual error (SB: −27.6% to 44.7%; LPA: −47.1% to 51.0%; MVPA: −88.8% to 33.9%).

Conclusions:

TAMETs were accurate for some SB and standing, but were overestimated for overall SB and LPA, and underestimated for MVPA. Accuracy for classifying MVPA was, however, acceptable.

Open access

John M. Schuna Jr., Tiago V. Barreira, Daniel S. Hsia, William D. Johnson and Catrine Tudor-Locke

Background:

Energy expenditure (EE) estimates for a broad age range of youth performing a variety of activities are needed.

Methods:

106 participants (6–18 years) completed 6 free-living activities (seated rest, movie watching, coloring, stair climbing, basketball dribbling, jumping jacks) and up to 9 treadmill walking bouts (13.4 to 120.7 m/min; 13.4 m/min increments). Breath-by-breath oxygen uptake (VO2) was measured using the COSMED K4b2 and EE was quantified as youth metabolic equivalents (METy1:VO2/measured resting VO2, METy2:VO2/estimated resting VO2). Age trends were evaluated with ANOVA.

Results:

Seated movie watching produced the lowest mean METy1 (6- to 9-year-olds: 0.94 ± 0.13) and METy2 values (13- to 15-year-olds: 1.10 ± 0.19), and jumping jacks produced the highest mean METy1 (13- to 15-year-olds: 6.89 ± 1.47) and METy2 values (16- to 18-year-olds: 8.61 ± 2.03). Significant age-related variability in METy1 and METy2 were noted for 8 and 2 of the 15 evaluated activities, respectively.

Conclusions:

Descriptive EE data presented herein will augment the Youth Compendium of Physical Activities.

Full access

Kimberly Hannam, Kevin Deere, Sue Worrall, April Hartley and Jon H. Tobias

The purpose of this study was to establish the feasibility of using an aerobics class to produce potentially bone protective vertical impacts of ≥ 4g in older adults and to determine whether impacts can be predicted by physical function. Participants recruited from older adult exercise classes completed an SF-12 questionnaire, short physical performance battery, and an aerobics class with seven different components, performed at low and high intensity. Maximum g and jerk values were identified for each activity. Forty-one participants (mean 69 years) were included. Mean maximal values approached or exceeded the 4g threshold for four of the seven exercises. In multivariate analyses, age (−0.53; −0.77, −0.28) (standardized beta coefficient; 95% CI) and 4-m walk time (−0.39; −0.63, −0.16) were inversely related to maximum g. Aerobics classes can be used to produce relatively high vertical accelerations in older individuals, although the outcome is strongly dependent on age and physical function.

Full access

Kevin C. Deere, Kimberly Hannam, Jessica Coulson, Alex Ireland, Jamie S. McPhee, Charlotte Moss, Mark H. Edwards, Elaine Dennison, Cyrus Cooper, Adrian Sayers, Matthijs Lipperts, Bernd Grimm and Jon H. Tobias

Physical activity (PA) may need to produce high impacts to be osteogenic. The aim of this study was to identify threshold(s) for defining high impact PA for future analyses in the VIBE (Vertical Impact and Bone in the Elderly) study, based on home recordings with triaxial accelerometers. Recordings were obtained from 19 Master Athlete Cohort (MAC; mean 67.6 years) and 15 Hertfordshire Cohort Study (HCS; mean 77.7 years) participants. Data cleaning protocols were developed to exclude artifacts. Accelerations expressed in g units were categorized into three bands selected from the distribution of positive Y-axis peak accelerations. Data were available for 6.6 and 4.4 days from MAC and HCS participants respectively, with approximately 14 hr recording daily. Three-fold more 0.5−1.0g impacts were observed in MAC versus HCS, 20-fold more 1.0−1.5g impacts, and 140-fold more impacts ≥ 1.5g. Our analysis protocol successfully distinguishes PA levels in active and sedentary older individuals.

Full access

Felipe Fossati Reichert, Jonathan Charles Kingdom Wells, Ulf Ekelund, Ana Maria Baptista Menezes, Cesar Gomes Victora and Pedro C. Hallal

Background:

Physical activity may influence both fat and lean body mass. This study investigated the association between physical activity in children between the ages of 11 and 13 years and both fat and lean mass.

Methods:

A subsample of the 1993 Pelotas (Brazil) Birth Cohort was visited in 2004–2005 and 2006–2007. Physical activity was estimated through standardized questionnaires. Body composition (ie, fat and lean mass) was measured using deuterium dilution. Those with moderate-to-vigorous activity greater than 420 min/wk were classified as active, and physical activity trajectory was defined as being above or below the cutoff at each visit.

Results:

Four hundred eighty-eight adolescents (51.8% boys) were evaluated. The mean difference in fat mass in boys and girls who reported ≥ 420 min/wk of physical activity in both visits compared with those who were consistently inactive was –4.8 kg (P ≤ .001). There was an inverse association between physical activity and fat mass among boys in both crude and confounder-adjusted analyses, whereas for girls, the association was evident only in the crude analysis. There was no significant association between physical activity and lean mass.

Conclusion:

Physical activity may contribute to tackling the growing epidemic of adolescent obesity in low- and middle-income countries.