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      From Sedentary Time to Sedentary Patterns: Accelerometer Data Reduction Decisions in Youth

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          Abstract

          Aim

          This study aims to establish evidence-based accelerometer data reduction criteria to accurately assess total sedentary time and sedentary patterns in children.

          Methods

          Participants (n = 1057 European children; 9–13 yrs) were invited to wear an accelerometer for at least 6 consecutive days. We explored 1) non-wear time criteria; 2) minimum daily valid wear time; 3) differences between weekday and weekend day; and 4) minimum number of days of accelerometer wear by comparing the effects of commonly used data reduction decisions on total sedentary time, and duration and number of prolonged sedentary bouts.

          Results

          More than 60 consecutive minutes of zero counts was the optimal criterion for non-wear time. Increasing the definition of a valid day from 8 to 10 hours wear time hardly influenced the sedentary outcomes, while the sample size of children with more than 4 valid days increased from 69 to 81%. On weekdays, children had on average 1 hour more wear time, 50 minutes more total sedentary time, 26 minutes more sedentary time accumulated in bouts, and 1 more sedentary bout. At least 6 days of accelerometer data were needed to accurately represent weekly sedentary time and patterns.

          Conclusions

          Based on our results we recommend 1) a minimum of 60 minutes of consecutive zeros as the most realistic criterion for non-wear time; and 2) including at least six days with minimum eight valid hours to characterize children's usual total sedentary time and patterns, preferably including one weekend day.

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          Most cited references18

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          Conducting accelerometer-based activity assessments in field-based research.

          The purpose of this review is to address important methodological issues related to conducting accelerometer-based assessments of physical activity in free-living individuals. We review the extant scientific literature for empirical information related to the following issues: product selection, number of accelerometers needed, placement of accelerometers, epoch length, and days of monitoring required to estimate habitual physical activity. We also discuss the various options related to distributing and collecting monitors and strategies to enhance compliance with the monitoring protocol. No definitive evidence exists currently to indicate that one make and model of accelerometer is more valid and reliable than another. Selection of accelerometer therefore remains primarily an issue of practicality, technical support, and comparability with other studies. Studies employing multiple accelerometers to estimate energy expenditure report only marginal improvements in explanatory power. Accelerometers are best placed on hip or the lower back. Although the issue of epoch length has not been studied in adults, the use of count cut points based on 1-min time intervals maybe inappropriate in children and may result in underestimation of physical activity. Among adults, 3-5 d of monitoring is required to reliably estimate habitual physical activity. Among children and adolescents, the number of monitoring days required ranges from 4 to 9 d, making it difficult to draw a definitive conclusion for this population. Face-to-face distribution and collection of accelerometers is probably the best option in field-based research, but delivery and return by express carrier or registered mail is a viable option. Accelerometer-based activity assessments requires careful planning and the use of appropriate strategies to increase compliance.
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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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              Breaking prolonged sitting reduces postprandial glycemia in healthy, normal-weight adults: a randomized crossover trial.

              Sedentary behavior is a risk factor for cardiometabolic disease. Regularly interrupting sedentary behavior with activity breaks may lower this risk. We compared the effects of prolonged sitting, continuous physical activity combined with prolonged sitting, and regular activity breaks on postprandial metabolism. Seventy adults participated in a randomized crossover study. The prolonged sitting intervention involved sitting for 9 h, the physical activity intervention involved walking for 30 min and then sitting, and the regular-activity-break intervention involved walking for 1 min 40 s every 30 min. Participants consumed a meal-replacement beverage at 60, 240, and 420 min. The plasma incremental area under the curve (iAUC) for insulin differed between interventions (overall P < 0.001). Regular activity breaks lowered values by 866.7 IU · L(-1) · 9 h(-1) (95% CI: 506.0, 1227.5 IU · L(-1) · 9 h(-1); P < 0.001) when compared with prolonged sitting and by 542.0 IU · L(-1) · 9 h(-1) (95% CI: 179.9, 904.2 IU · L(-1) · 9 h(-1); P = 0.003) when compared with physical activity. Plasma glucose iAUC also differed between interventions (overall P < 0.001). Regular activity breaks lowered values by 18.9 mmol · L(-1) · 9 h(-1) (95% CI: 10.0, 28.0 mmol · L(-1) · 9 h(-1); P < 0.001) when compared with prolonged sitting and by 17.4 mmol · L(-1) · 9 h(-1) (95% CI: 8.4, 26.3 mmol · L(-1) · 9 h(-1); P < 0.001) when compared with physical activity. Plasma triglyceride iAUC differed between interventions (overall P = 0.023). Physical activity lowered values by 6.3 mmol · L(-1) · 9 h(-1) (95% CI: 1.8, 10.7 mmol · L(-1) · 9 h(-1); P = 0.006) when compared with regular activity breaks. Regular activity breaks were more effective than continuous physical activity at decreasing postprandial glycemia and insulinemia in healthy, normal-weight adults. This trial was registered with the Australian New Zealand Clinical Trials registry as ACTRN12610000953033.
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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, USA )
                1932-6203
                2014
                4 November 2014
                : 9
                : 11
                : e111205
                Affiliations
                [1 ]Department of Public and Occupational Health and the EM GO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
                [2 ]Science Support, Utrecht, The Netherlands
                [3 ]Department of Movement and Sport Science, Ghent University, Ghent, Belgium
                [4 ]Department of Epidemiology and Biostatistics and EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
                University of Geneva, Switzerland
                Author notes

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

                Analyzed the data: MC. Contributed reagents/materials/analysis tools: MdN TA. Wrote the paper: MC. Designed the study, performed the statistical analysis and wrote the initial draft of the manuscript: MC. Developed the matlab program: MC TA MdN. Commented on drafts, and read and approved the final manuscript: MC MdN MV IDB JB TA.

                Article
                PONE-D-14-09982
                10.1371/journal.pone.0111205
                4219709
                25369021
                2ba364a8-7eca-42e6-801a-93db2fd97b2d
                Copyright @ 2014

                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
                : 10 March 2014
                : 29 September 2014
                Page count
                Pages: 6
                Funding
                The ENERGY-project is funded by the Seventh Framework Programme (CORDIS FP7, http://cordis.europa.eu/fp7/home_en.html) of the European Commission, HEALTH (FP7-HEALTH-2007-8). The contributions of MC and TA were funded by the Netherlands Organization for Health Research and Development (ZonMw projectnr 91211057). The Swiss part of the ENERGY-project was funded by the Swiss Federal Office for Sport (BASPO) and the Swiss Federal Office of Public Health (BAG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Epidemiology
                Epidemiological Methods and Statistics
                Epidemiological Statistics
                Pediatric Epidemiology
                Public and Occupational Health
                Sports and Exercise Medicine

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                Uncategorized

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