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New Data for an Updated Youth Energy Expenditure Compendium: An Introduction
Stephen D. Herrmann and Karin A. Pfeiffer
How Many Hours Are Enough? Accelerometer Wear Time May Provide Bias in Daily Activity Estimates
Stephen D. Herrmann, Tiago V. Barreira, Minsoo Kang, and Barbara E. Ainsworth
Background:
There is little consensus on how many hours of accelerometer wear time is needed to reflect a usual day. This study identifies the bias in daily physical activity (PA) estimates caused by accelerometer wear time.
Methods:
124 adults (age = 41 ± 11 years; BMI = 27 ± 7 kg·m-2) contributed approximately 1,200 days accelerometer wear time. Five 40 day samples were randomly selected with 10, 11, 12, 13, and 14 h·d-1 of wear time. Four semisimulation data sets (10, 11, 12, 13 h·d-1) were created from the reference 14 h·d-1 data set to assess Absolute Percent Error (APE). Repeated-measures ANOVAs compared min·d-1 between 10, 11, 12, 13 h·d-1 and the reference 14 h·d-1 for inactivity (<100 cts·min-1), light (100−1951 cts·min-1), moderate (1952−5724 cts·min-1), and vigorous (≥5725 cts·min-1) PA.
Results:
APE ranged from 5.6%−41.6% (10 h·d-1 = 28.2%−41.6%; 11 h·d-1 = 20.3%−36.0%; 12 h·d-1 = 13.5%−14.3%; 13 h·d-1 = 5.6%−7.8%). Min·d-1 differences were observed for inactivity, light, and moderate PA between 10, 11, 12, and 13 h·d-1 and the reference (P < .05).
Conclusions:
This suggests a minimum accelerometer wear time of 13 h·d-1 is needed to provide a valid measure of daily PA when 14 h·d-1 is used as a reference.
Energy Expenditure and Intensity of Classroom Physical Activity in Elementary School Children
Jeffery J. Honas, Erik A. Willis, Stephen D. Herrmann, Jerry L. Greene, Richard A. Washburn, and Joseph E. Donnelly
Background:
There is limited data regarding objectively measured energy cost and intensity of classroom instruction. Therefore, the purpose of current study was to objectively measure energy cost and subsequently calculate MET values using a portable indirect calorimeter (IC) for both normal classroom instruction (NCI) and active classroom instruction (ACI).
Methods:
We assessed energy expenditure (EE) and intensity levels (METs) in elementary school children (17 boys and 15 girls) using an IC (COSMED K4b2). Independent t-tests were used to evaluate potential sex and grade level differences for age, BMI, VO2, EE, and METs.
Results:
The average EE for NCI and ACI were 1.8 ± 0.4 and 3.9 ± 1.0, respectively. The average intensity level for NCI and ACI were 1.9 ± 0.4 and 4.2 ± 0.9 METs, respectively.
Conclusions:
PA delivered through ACI can elicit EE at a moderate intensity level. These results provide evidence for ACI as a convenient/feasible avenue for increasing PA in youth without decreasing instruction time.
Energy Costs of Household and Eldercare Activities in Young to Middle-Aged Chinese Adults
Yiyan Li, Jiajia Liu, Minghui Quan, Jie Zhuang, Zhen-Bo Cao, Zheng Zhu, Yongming Li, Stephen D. Herrmann, and Barbara E. Ainsworth
Background: The 2011 Compendium of Physical Activities provides metabolic equivalent (MET) values for household and eldercare activities (physical activities [PAs]). METs are from published studies, estimated if values are not published, or combined with other PAs with different METs in a single entry. Some PAs are missing from the Compendium. This study measures the energy costs for 15 household and eldercare PAs with estimated METs, PAs in combined entries, and new PAs. Methods: Participants were 30 adults (14 males and 16 females), ages 22–58 years (33.7 [11.2] y). PAs were measured in a laboratory for 8 minutes with a 4-minute rest between PAs. A portable indirect calorimeter measured oxygen uptake (in milliliters per kilogram per minute). Standard METs were computed as activity VO2/3.5 mL·kg−1·min−1. Results: Cooking, meal tasks, laundry, light cleaning, and watering plants ranged from 1.8 to 2.3 METs. Sweeping, walking, and carrying groceries and boxes on the ground and stairs ranged from 3.0 to 5.5 METs. Eldercare ranged from 1.8 to 3.0 METs. Measured METs differed from estimated values by ±0.3 to 2.2 METs. Most measured METs were lower than estimated METs. Conclusion: Updating estimated METs with measured values and separating PAs from combined entries increases the accuracy of household and eldercare PAs presented in the Compendium.
Energy Cost Expression for a Youth Compendium of Physical Activities: Rationale for Using Age Groups
Karin A. Pfeiffer, Kathleen B. Watson, Robert G. McMurray, David R. Bassett, Nancy F. Butte, Scott E. Crouter, Stephen D. Herrmann, Stewart G. Trost, Barbara E. Ainsworth, Janet E. Fulton, David Berrigan, and For the CDC/NCI/NCCOR Research Group*
Purpose: This study compared the accuracy of physical activity energy expenditure (PAEE) prediction using 2 methods of accounting for age dependency versus 1 standard (single) value across all ages. Methods: PAEE estimates were derived by pooling data from 5 studies. Participants, 6–18 years (n = 929), engaged in 14 activities while in a room calorimeter or wearing a portable metabolic analyzer. Linear regression was used to estimate the measurement error in PAEE (expressed as youth metabolic equivalent) associated with using age groups (6–9, 10–12, 13–15, and 16–18 y) and age-in-years [each year of chronological age (eg, 12 = 12.0–12.99 y)] versus the standard (a single value across all ages). Results: Age groups and age-in-years showed similar error, and both showed less error than the standard method for cycling, skilled, and moderate- to vigorous-intensity activities. For sedentary and light activities, the standard had similar error to the other 2 methods. Mean values for root mean square error ranged from 0.2 to 1.7 youth metabolic equivalent across all activities. Error reduction ranged from −0.2% to 21.7% for age groups and −0.23% to 18.2% for age-in-years compared with the standard. Conclusions: Accounting for age showed lower errors than a standard (single) value; using an age-dependent model in the Youth Compendium is recommended.