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Luke J. Boyd, Kevin Ball, and Robert J. Aughey

Purpose:

To assess the reliability of triaxial accelerometers as a measure of physical activity in team sports.

Methods:

Eight accelerometers (MinimaxX 2.0, Catapult, Australia) were attached to a hydraulic universal testing machine (Instron 8501) and oscillated over two protocols (0.5 g and 3.0 g) to assess within- and between-device reliability. A static assessment was also conducted. Secondly, 10 players were instrumented with two accelerometers during Australian football matches. The vector magnitude was calculated, expressed as Player load and assessed for reliability using typical error (TE) ± 90% confidence intervals (CI), and expressed as a coefficient of variation (CV%). The smallest worthwhile difference (SWD) in Player load was calculated to determine if the device was capable of detecting differences in physical activity.

Results:

Laboratory: Within- (Dynamic: CV 0.91 to 1.05%; Static: CV 1.01%) and between-device (Dynamic: CV 1.02 to 1.04%; Static: CV 1.10%) reliability was acceptable across each test. Field: The between-device reliability of accelerometers during Australian football matches was also acceptable (CV 1.9%). The SWD was 5.88%.

Conclusions:

The reliability of the MinimaxX accelerometer is acceptable both within and between devices under controlled laboratory conditions, and between devices during field testing. MinimaxX accelerometers can be confidently utilized as a reliable tool to measure physical activity in team sports across multiple players and repeated bouts of activity. The noise (CV%) of Player load was lower than the signal (SWD), suggesting that accelerometers can detect changes or differences in physical activity during Australian football.

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Jonghoon Park, Kazuko Ishikawa-Takata, Sachiko Tanaka, Kyoko Bessyo, Shigeho Tanaka, and Toshihide Kimura

The purpose of this study was to examine the accuracy of uni- and triaxial accelerometers in monitoring step counts and gait intensity in older people who did or did not use an assistive device. Forty-nine healthy and frail older adults wore uniaxial (Lifecorder, Suzuken Co. Ltd.) and triaxial accelerometers (Activity Monitor, Matsushita Electronic Works, Ltd., and Active Style Pro, Omron Healthcare Co., Ltd.) during three trials at different gait speeds. All accelerometers gave relatively accurate step counts for healthy older participants compared with direct observation; however, the error was greater for frail older people with assistive devices. Gait intensity detection error was unaffected by gait speed. Among frail older people with assistive devices, the gait intensity error was smaller than for step count error. To accurately assess the steps walked or the gait intensity among frail older people using assistive devices, more study is needed on these groups of participants.

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Liezel Hurter, Anna M. Cooper-Ryan, Zoe R. Knowles, Lorna A. Porcellato, Stuart J. Fairclough, and Lynne M. Boddy

school prior to the start of data collection, which was used for anthropometric measurements, explanation, and fitting of accelerometers and familiarization with the DCDC application on the tablet. Anthropometrics Body mass was measured in light clothing without shoes, to the nearest 0.1 kg using an

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Rodrigo Torres-Castro, Luis Vasconcello-Castillo, Roberto Acosta-Dighero, Nicolás Sepúlveda-Cáceres, Marisol Barros-Poblete, Homero Puppo, Roberto Vera-Uribe, Jordi Vilaró, and Mario Herrera-Romero

, and duration 7 and it can be measured by objective methods, such as energy expenditure or movement, using instruments, such as a pedometer or an accelerometer, or by subjective methods, such as questionnaires or diaries. 3 Questionnaires are the most common type of subjective method; they are

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Rajni Rai, Michelle I. Jongenelis, Ben Jackson, Robert U. Newton, and Simone Pettigrew

preferences of the participants. Participants who completed the questionnaire were invited to attend an on-campus appointment, during which their height, weight, and waist girth were measured and accelerometers were distributed. Participants were instructed on how to use the accelerometers; this included

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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

A widely used tool to assess physical activity (PA) and sedentary behavior (SB) is the wearable accelerometer. Accelerometers are often used in free-living settings for surveillance and intervention studies. To quantify the amount and intensity of body movement, prediction models are applied to

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Kavita A. Gavand, Kelli L. Cain, Terry L. Conway, Brian E. Saelens, Lawrence D. Frank, Jacqueline Kerr, Karen Glanz, and James F. Sallis

from 2 metropolitan areas in the United States. The present study fills gaps in understanding by including both reported and objective measures of PA, and measures of use of various recreation facilities. Objective accelerometer measures provided more precise estimates of total PA, and self

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Kelsie M. Full, Eileen Johnson, Michelle Takemoto, Sheri J. Hartman, Jacqueline Kerr, Loki Natarajan, Ruth E. Patterson, and Dorothy D. Sears

activity behavior substitution. LIPA indicates light-intensity physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behavior. The primary aim of this study is to use accelerometer data to construct isotemporal substitution models to assess the associations to cancer recurrence

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Genevieve F. Dunton, Daniel Chu, Christine H. Naya, Britni R. Belcher, and Tyler B. Mason

where they completed a paper questionnaire separately (ie, sitting at different tables) to assess perceived stress, had anthropometric assessments taken, and received an accelerometer that they were asked to wear for the next 7 days (excluding during any water-based activities and sleep). Children and

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Alexander H.K. Montoye, John Vusich, John Mitrzyk, and Matt Wiersma

the CPAM industry ( Lamkin, 2016 ), it is vital to understand how, and how well, CPAMs measure PA variables. Early CPAM models functioned similar to pedometers, counting steps through an accelerometer and incorporating user-entered data (e.g., height, weight, age) and steps accumulated to predict