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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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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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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.

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Weimo Zhu and Miyoung Lee

Background:

The purpose of this study was to investigate the validity and reliability evidences of the Omron BI pedometer, which could count steps taken even when worn at different locations on the body.

Methods:

Forty (20 males and 20 females) adults were recruited to walk wearing 5 sets, 1 set at a time, of 10 BI pedometers during testing, 1 each at 10 different locations. For comparison, they also wore 2 Yamax Digi-Walker SW-200 pedometers and a Dynastream AMP 331 activity monitor. The subjects walked in 3 free-living conditions: a fat sidewalk, stairs, and mixed conditions.

Results:

Except for a slight decrease in accuracy in the pant pocket locations, Omron BI pedometers counted steps accurately across other locations when subjects walked on the fat sidewalk, and the performance was consistent across devices and trials. When the subjects climbed up stairs, however, the absolute error % of the pant pocket locations increased significantly (P < .05) and similar or higher error rates were found in the AMP 331 and SW-200s.

Conclusions:

The Omron BI pedometer can accurately count steps when worn at various locations on the body in free-living conditions except for front pant pocket locations, especially when climbing stairs.

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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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Bing Han, Deborah A. Cohen, Kathryn Pitkin Derose, Terence Marsh, Stephanie Williamson and Laura Raaen

Purpose:

This study aims to examine the reliability of a 12-button counter to simultaneously assess physical activity (PA) by age and gender subgroups in park settings.

Methods:

A total of 1,160 pairs of observations were conducted in 481 target areas of 19 neighborhood parks in the great Los Angeles, California, area between June 2013 and March 2014. Interrater reliability was assessed by Pearson’s correlation, intra-class correlation (ICC), and agreement probability in metabolic equivalents (METs). Cosine similarity was used to check the resemblance of distributions among age and gender categories. Pictures taken in a total of 112 target areas at the beginning of the observations were used as a second reliability check.

Results:

Interrater reliability was high for the total METs and METs in all age and gender categories (between 0.82 and 0.97), except for male seniors (correlations and ICC between 0.64 and 0.77, agreement probability 0.85 to 0.86). Reliability was higher for total METs than for METs spent in moderate-to-vigorous PA. Correlation and ICC between observers’ measurement and picture-based counts are also high (between 0.79 and 0.94).

Conclusion:

Trained observers can reliably use the 12-button counter to accurately assess PA distribution and disparities by age and gender.

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Rosemary A. Arthur, Nichola Callow, Ross Roberts and Freya Glendinning

framework and then validating two coaching of PS questionnaires. The third phase of the program was a quasi-experimental controlled trial to evaluate the effectiveness of the adjusted intervention informed by the pilot intervention and evaluated using the validated questionnaires. The pilot intervention and

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Berit Steenbock, Marvin N. Wright, Norman Wirsik and Mirko Brandes

relationships between accelerometer output and EE differ in preschoolers compared with older children, prediction equations require development and validation in this specific age group ( Butte et al., 2014 ). Considerable progress has been made in predicting EE for adults and older children ( Jimmy, Seiler