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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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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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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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Meredith C. Peddie, Matthew Reeves, Millie K. Keown, Tracy L. Perry and C. Murray Skeaff

breaks of the appropriate intensity could be challenging in free-living studies as currently it would appear that the optimal intensity sits somewhere between the established cut-offs for light and moderate intensity activity identified for accelerometer data ( Freedson, Melanson, & Sirard, 1998

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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

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Kathryn J. DeShaw, Laura Ellingson, Yang Bai, Jeni Lansing, Maria Perez and Greg Welk

non-dominant wrist for the assessment phase while also concurrently wearing the ActiGraph and activPal two accelerometers to evaluate PA outcomes from the intervention. While the focus of the main study was on behavior change, the use of multiple monitors in this final week provides an ideal setting

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I-Min Lee, Eric J. Shiroma, Kelly R. Evenson, Masamitsu Kamada, Andrea Z. LaCroix and Julie E. Buring

available, rather than because the available evidence indicates no benefit of LPA on clinical outcomes. Since the 2008 guidelines, developments in technology have made it feasible to assess physical activity more precisely using accelerometers in large numbers of individuals, at reasonable cost ( Lee

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Catherine E. Tong, Joanie Sims Gould and Heather A. McKay

, accelerometer logs and instruction manuals), and we employed professional interpreters and multilingual research assistants for data collection. Data Collection and Analysis In total, 51 FBOAs volunteered to participate; 49 attended the measurement sessions and provided informed consent. This study received

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Alberto Flórez-Pregonero, Matthew S. Buman and Barbara E. Ainsworth

(e.g., watching TV); alternatively, stationary behaviors are any waking behavior done while lying, reclining, sitting, or standing, with no ambulation, irrespective of energy expenditure (e.g., standing) ( Tremblay et al., 2017 ). Accelerometer-based wearable monitors are used in sedentary behavior