Measuring the energy costs of household, family, and eldercare physical activities (PAs) requires knowing the accurate intensities of PAs performed. The 2011 Compendium of Physical Activities 1 (Compendium) provides energy cost values for these PAs as metabolic equivalents (METs) in the Home
Search Results
Yiyan Li, Jiajia Liu, Minghui Quan, Jie Zhuang, Zhen-Bo Cao, Zheng Zhu, Yongming Li, Stephen D. Herrmann, and Barbara E. Ainsworth
Astrid C.J. Balemans, Han Houdijk, Gilbert R. Koelewijn, Marjolein Piek, Frank Tubbing, Anne Visser-Meily, and Olaf Verschuren
.5 metabolic equivalents (METs), while in a sitting, reclining or lying posture.” 4 An underlying assumption in the current general definition is a lack of muscle activity in the large muscle groups that contribute to the weight-bearing of the body during a sitting or reclining posture. 5 , 6 A lack of
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.
Herbert W. Marsh
Physical activity measures for a large, nationally representative sample of Australian boys and girls aged 9, 12, and 15 were related to multiple dimensions of physical fitness. Physical activity during a one-week period was only modestly related to physical fitness. However, relations tended to be higher for length of time multiplied by METs (METs - minday1) than for time alone, time multiplied by perceived effort, or METs - min day−1 multiplied by effort, whereas time multiplied by effort did no better than time alone. Relations tended to be nonlinear in that progressively higher levels of activity had less positive associations with physical fitness. The pattern and size of the relations were consistent across scores for boys and girls aged 9 to 15. Self-report measures of typical and recent (within one week) physical activity both contributed to the prediction of physical fitness, indicating that both aspects of physical activity are important.
Makoto Ayabe, Takuya Yahiro, Myumi Yoshioka, Hiroyuki Higuchi, Yasuki Higaki, and Hiroaki Tanaka
Background:
The purpose of the present investigation was to examine the relationship between age and the intensity of the daily physical activity in men and women, aged 18 to 69 years.
Methods:
A total 507 volunteers continuously wore a pedometer with a uni-ax-ial accelerometer (Lifecorder, Kenz, Japan) for 7 days, to determine the number of steps (steps/day) as well as the time spent in physical activity (minutes/day) at light (below 3 METs), moderate (3 to 6 METs), and vigorous (above 6 METs) intensities, respectively. All procedures carried out in the present investigation were conducted from 1999 to 2000 in Japan.
Results:
The time spent in moderate to vigorous intensity physical activity significantly decreased with aging (P < 0.01). In contrast, the middle- to older-aged individuals spent a longer time in light intensity physical activity in comparison with the younger individuals (P < 0.05). Furthermore, these age-associated differences of physical activity were also significant, even though the number of steps did not differ significantly.
Conclusions:
These results indicate that the intensity of daily physical activity decreases with increasing age regardless of the amount of daily physical activity.
Jill Dawson, Melvyn Hillsdon, Irene Boller, and Charlie Foster
The authors investigated whether low levels of walking among older adults in the UK were associated with demographic and health characteristics, as well as perceived environmental attributes. Survey data were obtained from self-administered standard questionnaires given to 680 people age 50+ (mean age 64.4 yr) attending nationally led walking schemes. Items concerned with demographic characteristics and perceived barriers to neighborhood walking were analyzed using multiple logistic regression. Citing more than 1 environmental barrier to walking, versus not, was associated with significantly reduced levels of (leisure) walking (MET/hr) in the preceding week (Z = –2.35, p = .019), but physical activity levels overall did not differ significantly (Z = –0.71, p = .48). Citing a health-related barrier to walking significantly adversely affected overall physical activity levels (Z = –2.72, p = .006). The authors concluded that, among older people who favor walking, health problems might more seriously affect overall physical activity levels than perceived environmental barriers.
Emma L. J. Eyre, Jason Tallis, Susie Wilson, Lee Wilde, Liam Akhurst, Rildo Wanderleys, and Michael J. Duncan
epochs for various reasons. Firstly, the physical activity guidelines are recommended as minutes spent and thus in doing so we sought to be comparable/translatable to these guidelines. Secondly, data derived from oxygen and determined as MET is calculated in the same time frames. Thirdly, research has
Gráinne Hayes, Kieran Dowd, Ciaran MacDonncha, and Alan Donnely
challenge is to determine how counts can be converted into more meaningful units. This challenge is usually addressed in calibration studies where the accelerometer counts are related to either energy expenditure, oxygen consumption, or metabolic equivalents (METs) to give a more interpretable measure of
Nora E. Miller, Scott J. Strath, Ann M. Swartz, and Susan E. Cashin
This study examined the predictive validity of accelerometers (ACC) to estimate physical activity intensity (PAI) across age and differences in intensity predictions when expressed in relative and absolute PAI terms. Ninety adults categorized into 3 age groups (20–29, 40–49, and 60–69 yr) completed a treadmill calibration study with simultaneous ACC (7164 Actigraph) and oxygen-consumption assessment. Results revealed strong linear relations between ACC output and measured PAI (R 2 = .62–.89) across age and similar ACC cut-point ranges across age delineating absolute PAI ranges compared with previous findings. Comparing measured metabolic equivalents (METs) with estimated METs derived from previously published regression equations revealed that age did not affect predictive validity of ACC estimates of absolute PAI. Comparing ACC output expressed in relative vs. absolute terms across age revealed substantial differences in PAI ACC count ranges. Further work is warranted to increase the applicability of ACC use relative to PAI differences associated with physiological changes with age.
Sarah Kozey, Kate Lyden, John Staudenmayer, and Patty Freedson
Purpose:
To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml·kg−1·min−1 (standMET) and corrected METs [corrMET = mean standMET × (3.5 ÷ Harris-Benedict RMR)] in subgroups.
Methods:
RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.
Results:
The (mean ± SD) measMET was 6.1 ± 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (−0.02 (−0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.
Conclusion:
Using 3.5 ml·kg−1·min−1 to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.
