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      Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review

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          Abstract

          Background

          The location in which physical activity and sedentary behavior are performed can provide valuable behavioral information, both in isolation and synergistically with other areas of physical activity and sedentary behavior research. Global positioning systems (GPS) have been used in physical activity research to identify outdoor location; however, while GPS can receive signals in certain indoor environments, it is not able to provide room- or subroom-level location. On average, adults spend a high proportion of their time indoors. A measure of indoor location would, therefore, provide valuable behavioral information.

          Objective

          This systematic review sought to identify and critique technology which has been or could be used to assess the location of physical activity and sedentary behavior.

          Methods

          To identify published research papers, four electronic databases were searched using key terms built around behavior, technology, and location. To be eligible for inclusion, papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed up to February 4, 2015. This was supplemented by backward and forward reference searching. In an attempt to include novel devices which may not yet have made their way into the published research, searches were also performed using three Internet search engines. Specialized software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms.

          Results

          A total of 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras, and radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems, and 21 wearable cameras. Real-time locating systems determine the indoor location of a wearable tag via the known location of reference nodes. Although the type of reference node and location determination method varies between manufacturers, Wi-Fi appears to be the most popular method.

          Conclusions

          The addition of location information to existing measures of physical activity and sedentary behavior will provide important behavioral information.

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          Most cited references260

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          LANDMARC: Indoor Location Sensing Using Active RFID

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            Activity space environment and dietary and physical activity behaviors: a pilot study.

            This study examined relationships among individual demographics, environmental features (e.g., fast food outlet density, park land use) of residential neighborhoods and activity spaces, and weight-related behaviors (diet, physical activity). Participants' movement was tracked for 7 days using global positioning systems (GPS). Two activity space measures (one standard deviation ellipse, daily path area) were derived from the GPS data. Activity spaces were generally larger than residential neighborhoods; environmental features of residential neighborhoods and activity spaces were weakly associated; and some activity space environmental features were related to dietary behaviors. Activity spaces may provide new insights into environmental influences on obesity-related behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Activity identification using body-mounted sensors--a review of classification techniques.

              With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                August 2015
                05 August 2015
                : 17
                : 8
                : e192
                Affiliations
                [1] 1National Centre for Sport and Exercise Medicine School of Sport, Exercise and Health Sciences Loughborough University LoughboroughUnited Kingdom
                [2] 2National Institute for Health Research (NIHR) Leicester-Loughborough Diet, Lifestyle and Physical Activity Bio-medical Research Unit LeicesterUnited Kingdom
                Author notes
                Corresponding Author: Adam Loveday a.loveday@ 123456lboro.ac.uk
                Author information
                http://orcid.org/0000-0002-5440-4305
                http://orcid.org/0000-0002-9942-5433
                http://orcid.org/0000-0003-2103-0631
                http://orcid.org/0000-0003-3530-4189
                http://orcid.org/0000-0002-1236-6419
                Article
                v17i8e192
                10.2196/jmir.4761
                4705371
                26245157
                8614d492-4f68-443b-a900-af0d5134e636
                ©Adam Loveday, Lauren B Sherar, James P Sanders, Paul W Sanderson, Dale W Esliger. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.08.2015.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 28 May 2015
                : 23 June 2015
                : 30 June 2015
                : 3 July 2015
                Categories
                Review
                Review

                Medicine
                wearable camera,global positioning system,real-time locating system,sitting,context
                Medicine
                wearable camera, global positioning system, real-time locating system, sitting, context

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