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Matthew C. Varley, Arne Jaspers, Werner F. Helsen and James J. Malone


Sprints and accelerations are popular performance indicators in applied sport. The methods used to define these efforts using athlete-tracking technology could affect the number of efforts reported. This study aimed to determine the influence of different techniques and settings for detecting high-intensity efforts using global positioning system (GPS) data.


Velocity and acceleration data from a professional soccer match were recorded via 10-Hz GPS. Velocity data were filtered using either a median or an exponential filter. Acceleration data were derived from velocity data over a 0.2-s time interval (with and without an exponential filter applied) and a 0.3-second time interval. High-speed-running (≥4.17 m/s2), sprint (≥7.00 m/s2), and acceleration (≥2.78 m/s2) efforts were then identified using minimum-effort durations (0.1–0.9 s) to assess differences in the total number of efforts reported.


Different velocity-filtering methods resulted in small to moderate differences (effect size [ES] 0.28–1.09) in the number of high-speed-running and sprint efforts detected when minimum duration was <0.5 s and small to very large differences (ES –5.69 to 0.26) in the number of accelerations when minimum duration was <0.7 s. There was an exponential decline in the number of all efforts as minimum duration increased, regardless of filtering method, with the largest declines in acceleration efforts.


Filtering techniques and minimum durations substantially affect the number of high-speed-running, sprint, and acceleration efforts detected with GPS. Changes to how high-intensity efforts are defined affect reported data. Therefore, consistency in data processing is advised.

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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

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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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Timothy C. Sell, Mita T. Lovalekar, Takashi Nagai, Michael D. Wirt, John P. Abt and Scott M. Lephart

, Marlboro, MA) to a personal computer for additional signal and data processing. Procedures Participants reported to the Human Performance Research Laboratory for a single-test session. Dynamic and static postural stability were both assessed due to the lack of correlation observed in performance of these

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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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Antoine Falisse, Sam Van Rossom, Johannes Gijsbers, Frans Steenbrink, Ben J.H. van Basten, Ilse Jonkers, Antonie J. van den Bogert and Friedl De Groote

results obtained with different software systems, the added challenge is that discrepancies might result from differences between data processing workflows besides differences between models. To our knowledge, no studies have assessed differences in joint kinematics, kinetics, and muscle forces induced by

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James W. Roberts

is adopted for the sake of brevity and without conflating the principle objective of this study. There were 20 movement trials in the present study condition. Data Processing and Analysis Position data were filtered using a second-order, dual-pass Butterworth filter with a 10-Hz low-pass cutoff

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Kelli L. Cain, Edith Bonilla, Terry L. Conway, Jasper Schipperijn, Carrie M. Geremia, Alexandra Mignano, Jacqueline Kerr and James F. Sallis

important to get an accurate assessment of sedentary time. Accelerometers are commonly used to objectively measure sedentary behavior, despite the limitation that accelerometers cannot distinguish sitting from standing ( 26 , 38 ). The definition of nonwear time used in accelerometer data processing

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Damien Moore, Tania Pizzari, Jodie McClelland and Adam I. Semciw

actions) were performed for data normalization ( Supplementary Table S1 [available online]). Statistical Analysis The R statistical software package (version 3.4.1; ) was used for analysis. The EMG data processing has been described in detail previously. 6 Muscle activity