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      Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD)

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          Abstract

          Purpose

          Our recent study of three accelerometer brands in various ambulatory activities showed that the mean amplitude deviation (MAD) of the resultant acceleration signal performed best in separating different intensity levels and provided excellent agreement between the three devices. The objective of this study was to derive a regression model that estimates oxygen consumption (VO2) from MAD values and validate the MAD-based cut-points for light, moderate and vigorous locomotion against VO 2 within a wide range of speeds.

          Methods

          29 participants performed a pace-conducted non-stop test on a 200 m long indoor track. The initial speed was 0.6 m/s and it was increased by 0.4 m/s every 2.5 minutes until volitional exhaustion. The participants could freely decide whether they preferred to walk or run. During the test they carried a hip-mounted tri-axial accelerometer and mobile metabolic analyzer. The MAD was calculated from the raw acceleration data and compared to directly measured incident VO 2. Cut-point between light and moderate activity was set to 3.0 metabolic equivalent (MET, 1 MET = 3.5 ml · kg -1 · min -1) and between moderate and vigorous activity to 6.0 MET as per standard use.

          Results

          The MAD and VO 2 showed a very strong association. Within individuals, the range of r values was from 0.927 to 0.991 providing the mean r = 0.969. The optimal MAD cut-point for 3.0 MET was 91 mg (milligravity) and 414 mg for 6.0 MET.

          Conclusion

          The present study showed that the MAD is a valid method in terms of the VO 2 within a wide range of ambulatory activities from slow walking to fast running. Being a device-independent trait, the MAD facilitates directly comparable, accurate results on the intensity of physical activity with all accelerometers providing tri-axial raw data.

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          Most cited references22

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

          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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            Tri-Axial Dynamic Acceleration as a Proxy for Animal Energy Expenditure; Should We Be Summing Values or Calculating the Vector?

            Dynamic body acceleration (DBA) has been used as a proxy for energy expenditure in logger-equipped animals, with researchers summing the acceleration (overall dynamic body acceleration - ODBA) from the three orthogonal axes of devices. The vector of the dynamic body acceleration (VeDBA) may be a better proxy so this study compared ODBA and VeDBA as proxies for rate of oxygen consumption using humans and 6 other species. Twenty-one humans on a treadmill ran at different speeds while equipped with two loggers, one in a straight orientation and the other skewed, while rate of oxygen consumption ( ) was recorded. Similar data were obtained from animals but using only one (straight) logger. In humans, both ODBA and VeDBA were good proxies for with all r2 values exceeding 0.88, although ODBA accounted for slightly but significantly more of the variation in than did VeDBA (P<0.03). There were no significant differences between ODBA and VeDBA in terms of the change in estimated by the acceleration data in a simulated situation of the logger being mounted straight but then becoming skewed (P = 0.744). In the animal study, ODBA and VeDBA were again good proxies for with all r2 values exceeding 0.70 although, again, ODBA accounted for slightly, but significantly, more of the variation in than did VeDBA (P<0.03). The simultaneous contraction of muscles, inserted variously for limb stability, may produce muscle oxygen use that at least partially equates with summing components to derive DBA. Thus, a vectorial summation to derive DBA cannot be assumed to be the more ‘correct’ calculation. However, although within the limitations of our simple study, ODBA appears a marginally better proxy for . In the unusual situation where researchers are unable to guarantee at least reasonably consistent device orientation, they should use VeDBA as a proxy for .
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              Daily physical activity assessment with accelerometers: new insights and validation studies.

              The field of application of accelerometry is diverse and ever expanding. Because by definition all physical activities lead to energy expenditure, the doubly labelled water (DLW) method as gold standard to assess total energy expenditure over longer periods of time is the method of choice to validate accelerometers in their ability to assess daily physical activities. The aim of this paper was to provide a systematic overview of all recent (2007-2011) accelerometer validation studies using DLW as the reference. The PubMed Central database was searched using the following keywords: doubly or double labelled or labeled water in combination with accelerometer, accelerometry, motion sensor, or activity monitor. Limits were set to include articles from 2007 to 2011, as earlier publications were covered in a previous review. In total, 38 articles were identified, of which 25 were selected to contain sufficient new data. Eighteen different accelerometers were validated. There was a large variability in accelerometer output and their validity to assess daily physical activity. Activity type recognition has great potential to improve the assessment of physical activity-related health outcomes. So far, there is little evidence that adding other physiological measures such as heart rate significantly improves the estimation of energy expenditure. © 2013 The Authors. obesity reviews © 2013 International Association for the Study of Obesity.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                20 August 2015
                2015
                : 10
                : 8
                : e0134813
                Affiliations
                [1 ]The UKK Institute for Health Promotion Research, Tampere, Finland
                [2 ]Haaga-Helia University of Applied Sciences, Vierumäki, Finland
                University of St Andrews, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: HV-Y AM ToV TiV HS. Performed the experiments: AM TiV. Analyzed the data: HV-Y. Wrote the paper: HV-Y JS PH.

                ‡ These authors also contributed equally to this work.

                Article
                PONE-D-15-06379
                10.1371/journal.pone.0134813
                4546343
                26292225
                68188e2a-4e26-405c-8f76-9e77e337419d
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 19 February 2015
                : 15 July 2015
                Page count
                Figures: 7, Tables: 2, Pages: 13
                Funding
                This work was supported by a Tekes Grant 40247/12 ( http://www.tekes.fi/en/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                Ten minutes of accelerometer data is available as part of the Supporting Information. Additional accelerometer data is available upon request to the corresponding author due to the potentially identifying nature of this data through the use of biometric gait recognition.

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                Uncategorized

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