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Abstract

The ability to assess energy expenditure (EE) and estimate physical activity (PA) in free-living individuals is extremely important in the global context of non-communicable diseases including malnutrition, overnutrition (obesity), and diabetes. It is also important to appreciate that PA and EE are different constructs with PA defined as any bodily movement that results in EE and accordingly, energy is expended as a result of PA. However, total energy expenditure, best assessed using the criterion doubly labeled water (DLW) technique, includes components in addition to physical activity energy expenditure, namely resting energy expenditure and the thermic effect of food. Given the large number of assessment techniques currently used to estimate PA in humans, it is imperative to understand the relative merits of each. The goal of this review is to provide information on the utility and limitations of a range of objective measures of PA and their relationship with EE. The measures discussed include those based on EE or oxygen uptake including DLW, activity energy expenditure, physical activity level, and metabolic equivalent; those based on heart rate monitoring and motion sensors; and because of their widespread use, selected subjective measures.

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Most cited references 174

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Statistical methods for assessing agreement between two methods of clinical measurement.

J M Bland, D G Altman (1986)

In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.

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Compendium of physical activities: an update of activity codes and MET intensities.

Melody Swartz, W Haskell, Jennifer D. Bassett … (2000)

We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.

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Calibration of the Computer Science and Applications, Inc. accelerometer.

P S Freedson, E. Melanson, J. R. Sirard (1998)

We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.

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DOI:: 10.3389/fnut.2014.00005

PMC ID:: 4428382

PubMed ID:: 25988109

License:

http://creativecommons.org/licenses/by/3.0/

Keywords: physical activity assessment,human energy expenditure,objective measurement techniques,stable isotopes,accelerometry

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Keywords: physical activity assessment, human energy expenditure, objective measurement techniques, stable isotopes, accelerometry

Assessment of Physical Activity and Energy Expenditure: An Overview of Objective Measures

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Most cited references 174

Statistical methods for assessing agreement between two methods of clinical measurement.

Compendium of physical activities: an update of activity codes and MET intensities.

Calibration of the Computer Science and Applications, Inc. accelerometer.

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