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Marco Giurgiu, Johannes B.J. Bussmann, Holger Hill, Bastian Anedda, Marcel Kronenwett, Elena D. Koch, Ulrich W. Ebner-Priemer, and Markus Reichert

behavior.” According to previous validation studies, only a few activity monitors such as the thigh-worn ActivPAL (PAL Technologies Ltd., Glasgow, United Kingdom) have proven to be an accurate sensor for device-based measuring of sedentary behavior ( Grant, Ryan, Tigbe, & Granat, 2006 ; Kim, Barry, & Kang

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Mary Emily Littrell, Young-Hui Chang, and Brian P. Selgrade

difficult for less-experienced clinicians and often unreliable, 7 , 8 would be complemented by quantitative gait analysis. As medical devices and rehabilitation become more expensive, 9 it becomes even more important to quantify efficacy of these interventions to justify their cost. Here, we validate a

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Kayla J. Nuss, Nicholas A. Hulett, Alden Erickson, Eric Burton, Kyle Carr, Lauren Mooney, Jacob Anderson, Ashley Comstock, Ethan J. Schlemer, Lucas J. Archambault, and Kaigang Li

(Euclidean Norm Minus One or ENMO), and more complex methods, like the Staudenmayer linear method (SLM) and Staudenmayer’s random forest (SRF) method ( Staudenmayer, He, Hickey, Sasaki, & Freedson, 2015 ). However, no evidence has been found to support the validation of those methods. Rather, Ellingson et

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Leilani A. Madrigal, Vincenzo Roma, Todd Caze, Arthur Maerlender, and Debra Hope

exploratory factor analysis was necessary to explore the emergence of sports-related anxiety factors. Next, using a split-sample procedure, a confirmatory factor analysis was used to validate the factor structure from exploratory factor analysis to determine how well the model fit the data. In further

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Vincent Shieh, Ashwini Sansare, Minal Jain, Thomas Bulea, Martina Mancini, and Cris Zampieri

clinically feasible manner. However, access to this technology for clinicians and researchers is limited by its cost, space requirements, and portability; therefore, there has been an effort to validate other instruments that provide the same objective measurements. Recent findings have shown that wearable

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Tori M. Stone, Jonathan E. Wingo, Brett S. Nickerson, and Michael R. Esco

aforementioned studies comparing BMC from DXA and BIA used small samples and correlational analyses, so more definitive conclusions about the efficacy of using BIA to estimate BMC, and the extent of bias between BMC values from DXA and BIA, remain unknown. Accordingly, the purpose of this study was to validate

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Kyoungyoun Park-Braswell, Sandra J. Shultz, and Randy J. Schmitz

validate an MRI-compatible anterior knee joint loading device that translates the tibia relative to the femur in the same manner as commercial arthrometers designed to assess anterior knee joint laxity. Figure 1 MRI-compatible anterior joint loading device. (1) Air bladder, (2) patellar stabilizer, (3

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Jessica Gorzelitz, Chloe Farber, Ronald Gangnon, and Lisa Cadmus-Bertram

physical activity ( Coughlin & Stewart, 2016 ; O’Driscoll et al., 2018 ). Publications on the validity of wearable trackers for physical activity assessment have used many different strategies, devices, and criteria for validation. To date, no systematic review has addressed minutes of activity (e

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Chunxiao Li, Lijuan Wang, Martin E. Block, Raymond K.W. Sum, and Yandan Wu

-efficacy toward teaching students with ASD, it is important to validate a self-efficacy scale that can be used in China to proliferate the research in terms of including students with ASD. Self-Efficacy Scale for Including ASD The Physical Educators’ Self-Efficacy Toward Including Students with Disabilities

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Euna Han, Lisa Powell, Sandy Slater, and Christopher Quinn

Background:

Secondary data are often necessary to assess the availability of commercial physical activity (PA) facilities and examine its association with individual behaviors and outcomes, yet the validity of such sources has been explored only in a limited number of studies.

Methods:

Field data were collected on the presence and attributes of commercial PA facilities in a random sample of 30 urban, 15 suburban, and 15 rural Census tracts in the Chicago metropolitan statistical area and surrounding area.

Results:

Approximately 40% of PA establishments in the field data were listed for both urban and nonurban tracts in both lists except for nonurban tracts in D&B (35%), which was significantly improved in the combined list of D&B and InfoUSA. Approximately one-quarter of the PA facilities listed in D&B were found on the ground, whereas 40% to 50% of PA facilities listed in InfoUSA were found on the ground. PA establishments that offered instruction programs or lessons or that had a court or pool were less likely to be listed, particularly in the nonurban tracts.

Conclusions:

Secondary commercial business lists on PA facilities should be used with caution in assessing the built environment.