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      The number of repeated observations needed to estimate the habitual physical activity of an individual to a given level of precision

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          Abstract

          Physical activity behavior varies naturally from day to day, from week to week and even across seasons. In order to assess the habitual level of physical activity of a person, the person must be monitored for long enough so that the level can be identified, taking into account this natural within-person variation. An important question, and one whose answer has implications for study- and survey design, epidemiological research and population surveillance, is, for how long does an individual need to be monitored before such a habitual level or pattern can be identified to a desired level of precision? The aim of this study was to estimate the number of repeated observations needed to identify the habitual physical activity behaviour of an individual to a given degree of precision. A convenience sample of 50 Swedish adults wore accelerometers during four consecutive weeks. The number of days needed to come within 5–50% of an individual's usual physical activity 95% of the time was calculated. To get an idea of the uncertainty of the estimates all statistical estimates were bootstrapped 2000 times. The mean number of days of measurement needed for the observation to, with 95% confidence, be within 20% of the habitual physical activity of an individual is highest for vigorous physical activity, for which 182 days are needed. For sedentary behaviour the equivalent number of days is 2.4. To capture 80% of the sample to within ±20% of their habitual level of physical activity, 3.4 days is needed if sedentary behavior is the outcome of interest, and 34.8 days for MVPA. The present study shows that for analyses requiring accurate data at the individual level a longer measurement collection period than the traditional 7-day protocol should be used. In addition, the amount of MVPA was negatively associated with the number of days required to identify the habitual physical activity level indicating that the least active are also those whose habitual physical activity level is the most difficult to identify. These results could have important implications for researchers whose aim is to analyse data on an individual level. Before recommendations regarding an appropriate monitoring protocol are updated, the present study should be replicated in different populations.

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          Most cited references24

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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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            Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables.

            Accelerometers are recognized as a valid and objective tool to assess free-living physical activity. Despite the widespread use of accelerometers, there is no standardized way to process and summarize data from them, which limits our ability to compare results across studies. This paper a) reviews decision rules researchers have used in the past, b) compares the impact of using different decision rules on a common data set, and c) identifies issues to consider for accelerometer data reduction. The methods sections of studies published in 2003 and 2004 were reviewed to determine what decision rules previous researchers have used to identify wearing period, minimal wear requirement for a valid day, spurious data, number of days used to calculate the outcome variables, and extract bouts of moderate to vigorous physical activity (MVPA). For this study, four data reduction algorithms that employ different decision rules were used to analyze the same data set. The review showed that among studies that reported their decision rules, much variability was observed. Overall, the analyses suggested that using different algorithms impacted several important outcome variables. The most stringent algorithm yielded significantly lower wearing time, the lowest activity counts per minute and counts per day, and fewer minutes of MVPA per day. An exploratory sensitivity analysis revealed that the most stringent inclusion criterion had an impact on sample size and wearing time, which in turn affected many outcome variables. These findings suggest that the decision rules employed to process accelerometer data have a significant impact on important outcome variables. Until guidelines are developed, it will remain difficult to compare findings across studies.
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              Actigraph GT3X: validation and determination of physical activity intensity cut points.

              The aims of this study were: to compare energy expenditure (EE) estimated from the existing GT3X accelerometer equations and EE measured with indirect calorimetry; to define new equations for EE estimation with the GT3X in youth, adults and older people; and to define GT3X vector magnitude (VM) cut points allowing to classify PA intensity in the aforementioned age-groups. The study comprised 31 youth, 31 adults and 35 older people. Participants wore the GT3X (setup: 1-s epoch) over their right hip during 6 conditions of 10-min duration each: resting, treadmill walking/running at 3, 5, 7, and 9 km · h⁻¹, and repeated sit-stands (30 times · min⁻¹). The GT3X proved to be a good tool to predict EE in youth and adults (able to discriminate between the aforementioned conditions), but not in the elderly. We defined the following equations: for all age-groups combined, EE (METs)=2.7406+0.00056 · VM activity counts (counts · min⁻¹)-0.008542 · age (years)-0.01380 ·  body mass (kg); for youth, METs=1.546618+0.000658 · VM activity counts (counts · min⁻¹); for adults, METs=2.8323+0.00054 · VM activity counts (counts · min⁻¹)-0.059123 · body mass (kg)+1.4410 · gender (women=1, men=2); and for the elderly, METs=2.5878+0.00047 · VM activity counts (counts · min⁻¹)-0.6453 · gender (women=1, men=2). Activity counts derived from the VM yielded a more accurate EE estimation than those derived from the Y-axis. The GT3X represents a step forward in triaxial technology estimating EE. However, age-specific equations must be used to ensure the correct use of this device. © Georg Thieme Verlag KG Stuttgart · New York.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 February 2018
                2018
                : 13
                : 2
                : e0192117
                Affiliations
                [001]Department of Sport Sciences, Linnaeus University, Kalmar, Sweden
                Pennington Biomedical Research Center, UNITED STATES
                Author notes

                Competing Interests: The author has declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-4934-8684
                Article
                PONE-D-17-11032
                10.1371/journal.pone.0192117
                5794157
                29390010
                f09e1d5d-49ee-4cf2-8ec6-3a99bc8ab7c7
                © 2018 Patrick Bergman

                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
                : 21 March 2017
                : 18 January 2018
                Page count
                Figures: 3, Tables: 2, Pages: 13
                Funding
                The author received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Engineering and Technology
                Electronics
                Accelerometers
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Research and Analysis Methods
                Research Assessment
                Research Monitoring
                Research and Analysis Methods
                Research Design
                Research and Analysis Methods
                Research Design
                Survey Research
                Medicine and Health Sciences
                Epidemiology
                Disease Surveillance
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Custom metadata
                The dataset supporting the conclusions of this article is available in the Open Science Framework repository, https://osf.io/4ttfq/.

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                Uncategorized

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