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John R. Sirard, Ann Forsyth, J. Michael Oakes and Kathryn H. Schmitz

Background:

The purpose of this study was to determine 1) the test-retest reliability of adult accelerometer-measured physical activity, and 2) how data processing decisions affect physical activity levels and test-retest reliability.

Methods:

143 people wore the ActiGraph accelerometer for 2 7-day periods, 1 to 4 weeks apart. Five algorithms, varying nonwear criteria (20 vs. 60 min of 0 counts) and minimum wear requirements (6 vs. 10 hrs/day for ≥ 4 days) and a separate algorithm requiring ≥ 3 counts per min and ≥ 2 hours per day, were used to process the accelerometer data.

Results:

Processing the accelerometer data with different algorithms resulted in different levels of counts per day, sedentary, and moderate-to-vigorous physical activity. Reliability correlations were very good to excellent (ICC = 0.70−0.90) for almost all algorithms and there were no significant differences between physical activity measures at Time 1 and Time 2.

Conclusions:

This paper presents the first assessment of test-retest reliability of the Actigraph over separate administrations in free-living subjects. The ActiGraph was highly reliable in measuring activity over a 7-day period in natural settings but data were sensitive to the algorithms used to process them.

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Dinesh John, Qu Tang, Fahd Albinali and Stephen Intille

cross-study uniformity in accelerometer data processing, and, thus, limit the interpretation of findings across studies. Raw accelerometer data processing to detect specific activity type, intensity, and duration is an active area of research ( Mannini, Rosenberger, Haskell, Sabatini, & Intille, 2017

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Chuhe Chen, Gerald J. Jerome, Daniel LaFerriere, Deborah Rohm Young and William M. Vollmer

Background:

Accelerometers measure intensity, frequency, and duration of physical activity. However, the scarcity of reports on data reduction makes comparing accelerometer results across studies difficult.

Methods:

Participants were asked to wear a triaxial accelerometer (RT3) for ≥10 hours for at least 4 days, including one weekend day. We summarize our data-cleaning procedures and assess the impact of defining a usable day of measurements as at least 6, 8, or 10 hours of wear time, and of standardizing data to a 12-hour day.

Results:

Eighty-two percent of participants met wear time requirements; 93% met requirements when we defined a day as 8-or-more hours of wear time. Normalization of data to a 12-hour day had little impact on estimates of daily moderate-to-vigorous physical activity (MVPA; 16.9 vs. 17.1 minutes); restricting MVPA to activities occurring in bouts of 10 minutes or longer had greater impact (16.9 vs. 6.3 minutes per day).

Conclusion:

Our account of accelerometry quality-control and data-cleaning procedures documents the small impact of variations in daily wear time requirements on MVPA estimates, and the larger impact of evaluating total MVPA vs. MVPA occurring in extended bouts. This paper should allow other researchers to duplicate or revise our methods as needed.

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Claire L. Cleland, Sara Ferguson, Paul McCrorie, Jasper Schipperijn, Geraint Ellis and Ruth F. Hunter

adults. Challenges can occur during two main phases: (a) data collection and (b) data processing ( Toftager et al., 2013 ; Ward, Evenson, Vaughn, Rodgers, & Troiano, 2005 ). As part of the (a) data collection phase, researchers make decisions regarding device selection (cost, memory, and battery life

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Anna Pulakka, Eric J. Shiroma, Tamara B. Harris, Jaana Pentti, Jussi Vahtera and Sari Stenholm

new challenges to accelerometer data processing ( McVeigh et al., 2016 ; Meredith-Jones, Williams, Galland, Kennedy, & Taylor, 2016 ; Tracy et al., 2014 ; van der Berg et al., 2016 ). Before being able to analyze either sleep or physical activity, one needs to separate non-wear, wake and sleep time

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Patty Freedson

identify continued challenges facing the field, including a lack of consensus on methods for data collection (e.g., sensor, body location, determination of a valid day) or data processing method (e.g., algorithm or cut-point) ( Lee et al., 2018 ). The measurement community must find ways to work

Open access

Patty Freedson

-based physical activity. This is a new approach for determining which days of activity monitoring with an accelerometer are used to quantify physical activity. We typically use rules (e.g., 10 hrs/day of ‘good data’) to determine which days of multi-day monitoring are included in device-based data processing of

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Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson and John R. Sirard

processing methods used in the pilot study were the same methods used in the main study (see Phase III, Data Processing). Briefly, intra-rater agreement was acceptable (>80%) for all variables except MET value (69 ± 27%). Inter-rater agreement ( n  = 6 coders compared with expert coder) ranged from 50

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Laura D. Ellingson, Paul R. Hibbing, Gregory J. Welk, Dana Dailey, Barbara A. Rakel, Leslie J. Crofford, Kathleen A. Sluka and Laura A. Frey-Law

Questionnaire (IPAQ-SF) ( Craig et al., 2003 ) to assess self-reported PA over the same week. Data Processing The raw accelerometry signals were exported from the ActiLife software as comma separated value files and processed using four different methods in R (R Foundation for Statistical Computing, Vienna

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Patty Freedson

sensor, sampling rate) and firmware (i.e., onboard data pre-processing such as signal filtering) specifications, battery life, data storage and compression, and the volume of onboard data processing (i.e., proprietary methods that translate motion signals into behavior attributes). Variability in few or