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      Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

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          Abstract

          <p class="first" id="P1">Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus. </p><div class="section"> <a class="named-anchor" id="S1"> <!-- named anchor --> </a> <h5 class="section-title" id="d2649598e198">Objectives</h5> <p id="P2">The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified. </p> </div><div class="section"> <a class="named-anchor" id="S2"> <!-- named anchor --> </a> <h5 class="section-title" id="d2649598e203">Methods</h5> <p id="P3">Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015. </p> </div><div class="section"> <a class="named-anchor" id="S3"> <!-- named anchor --> </a> <h5 class="section-title" id="d2649598e208">Results</h5> <p id="P4">The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific. </p> </div><div class="section"> <a class="named-anchor" id="S4"> <!-- named anchor --> </a> <h5 class="section-title" id="d2649598e213">Conclusion</h5> <p id="P5">This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data. </p> </div><div class="section"> <a class="named-anchor" id="S5"> <!-- named anchor --> </a> <h5 class="section-title" id="d2649598e218">PROSPERO registration number</h5> <p id="P6">CRD42016039991.</p> </div>

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          Most cited references74

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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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            The nuts and bolts of PROSPERO: an international prospective register of systematic reviews

            Background Following publication of the PRISMA statement, the UK Centre for Reviews and Dissemination (CRD) at the University of York in England began to develop an international prospective register of systematic reviews with health-related outcomes. The objectives were to reduce unplanned duplication of reviews and provide transparency in the review process, with the aim of minimizing reporting bias. Methods An international advisory group was formed and a consultation undertaken to establish the key items necessary for inclusion in the register and to gather views on various aspects of functionality. This article describes the development of the register, now called PROSPERO, and the process of registration. Results PROSPERO offers free registration and free public access to a unique prospective register of systematic reviews across all areas of health from all around the world. The dedicated web-based interface is electronically searchable and available to all prospective registrants. At the moment, inclusion in PROSPERO is restricted to systematic reviews of the effects of interventions and strategies to prevent, diagnose, treat, and monitor health conditions, for which there is a health-related outcome. Ideally, registration should take place before the researchers have started formal screening against inclusion criteria but reviews are eligible as long as they have not progressed beyond the point of completing data extraction. The required dataset captures the key attributes of review design as well as the administrative details necessary for registration. Submitted registration forms are checked against the scope for inclusion in PROSPERO and for clarity of content before being made publicly available on the register, rejected, or returned to the applicant for clarification. The public records include an audit trail of major changes to planned methods, details of when the review has been completed, and links to resulting publications when provided by the authors. Conclusions There has been international support and an enthusiastic response to the principle of prospective registration of protocols for systematic reviews and to the development of PROSPERO. In October 2011, PROSPERO contained 200 records of systematic reviews being undertaken in 26 countries around the world on a diverse range of interventions.
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              Automatic sleep/wake identification from wrist activity.

              The purpose of this study was to develop and validate automatic scoring methods to distinguish sleep from wakefulness based on wrist activity. Forty-one subjects (18 normals and 23 with sleep or psychiatric disorders) wore a wrist actigraph during overnight polysomnography. In a randomly selected subsample of 20 subjects, candidate sleep/wake prediction algorithms were iteratively optimized against standard sleep/wake scores. The optimal algorithms obtained for various data collection epoch lengths were then prospectively tested on the remaining 21 subjects. The final algorithms correctly distinguished sleep from wakefulness approximately 88% of the time. Actigraphic sleep percentage and sleep latency estimates correlated 0.82 and 0.90, respectively, with corresponding parameters scored from the polysomnogram (p < 0.0001). Automatic scoring of wrist activity provides valuable information about sleep and wakefulness that could be useful in both clinical and research applications.
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                Author and article information

                Journal
                Sports Medicine
                Sports Med
                Springer Nature
                0112-1642
                1179-2035
                September 2017
                March 2017
                : 47
                : 9
                : 1821-1845
                Article
                10.1007/s40279-017-0716-0
                6231536
                28303543
                8bf2af69-4d74-4874-87f8-2516b9800dc9
                © 2017
                History

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