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Weimo Zhu

To make scores from tests designed for special populations exchangeable, the tests must first be equated on the same scale. This study examined the utility of a Rasch model in equating motor function tasks. Using an existing gross motor function data set and a semisimulation design, an artificial equating and cross-validation sample, as well as two artificial tests, were created. Based on these samples and tests, the accuracy and stability of Rasch equating was empirically determined using a standardized difference statistic. It was found that Rasch equating could accurately equate tests and was generalizable when applied to a cross-validation sample. After equating, tests can be compared on the same scale, and interpretation of cross-test scores becomes possible. In addition, with the conversion table and graph generated from Rasch equating, the application of test equating was demonstrated as simple and practical.

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Ang Chen and Weimo Zhu

Background:

A physically active or inactive lifestyle begins with intuitive interest at a very young age. This study examined the impact of selected personal, school, and home variables on young children’s intuitive interests in physical and sedentary activities.

Methods:

National data from the Early Childhood Longitudinal Study (US Department of Education) were examined using Cohen’s d, hierarchical log-linear analyses, and logistic regression.

Results:

Children’s interest in physical activity is accounted for fractionally by personal variables, but substantially by school and home variables including number of physical education classes per week, teacher experiences of teaching PE, and neighborhood safety.

Conclusion:

School and home environment variables have stronger impact than personal variables on children’s intuitive interest in physical activity. Future interventions should focus on strengthening school physical education and providing a safe home environment to help nurture young children’s intuitive interest in physical activity.

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Weimo Zhu and Ang Chen

This paper provides an overview of the long and vigorous efforts made in the development, applications, and contributions of the Value Orientation Inventory (VOI) by Dr. Catherine D. Ennis, her students, and her colleagues. After a brief review of the development, validation, and cross-validation of the VOI and corresponding applications, the authors describe the contributions the VOI made in pedagogy research and the impact of teachers’ value orientations on their teaching behaviors. They also discuss how a measurement tool should be developed and present Ennis’s work as a model of how a research line should be established. Finally, they reflect on the limitations in measurement tool development in kinesiology and outline future directions for VOI revision and application.

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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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Weiyun Chen, Kristin Hendricks and Weimo Zhu

The purpose of this study was to design and validate the Basketball Offensive Game Performance Instrument (BOGPI) that assesses an individual player’s offensive game performance competency in basketball. Twelve physical education teacher education (PETE) students playing two 10-minute, 3 vs. 3 basketball games were videotaped at end of a basketball unit in one physical education teaching methods course. Two investigators independently coded each player’s offensive game behaviors with BOGPI. The interrater reliability of the BOGPI was 99% and the alpha reliability coefficient for the total scale of the BOGPI was .95. The content validity evidence of the BOGPI was established by six experienced experts’ judgment. The results of this study indicate that the BOGPI is a theoretically sound and psychometrically supported measure that can be used by researchers and teacher educators to assess the preservice teachers’ offensive game performance ability in basketball.

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Bradley J. Cardinal, Hermann-J. Engels and Weimo Zhu

The Transtheoreticai Model of behavior change was applied to a sample of 669 preadolescents (M age = 8.2) to determine whether stages of exercise could be observed. Associations between stage of exercise classification and demographic, fitness, and cognitive variables were examined. Stage of exercise classifications, on the basis of the Children’s Stage of Exercise Algorithm, were as follows: maintenance (50.8%), action (36.5%), preparation (3.1%), contemplation (4.9%), and precontemplation (4.6%). Stage of exercise was significantly related to gender, age, and grade level. Controlling for these differences, the relationship between exercise beliefs and stage of exercise was significant.

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Chunmei Cao, Yu Liu, Weimo Zhu and Jiangjun Ma

Background:

Recently developed active workstation could become a potential means for worksite physical activity and wellness promotion. The aim of this review was to quantitatively examine the effectiveness of active workstation in energy expenditure and job performance.

Methods:

The literature search was conducted in 6 databases (PubMed, SPORTDiscuss, Web of Science, ProQuest, ScienceDirect, and Scopuse) for articles published up to February 2014, from which a systematic review and meta-analysis was conducted.

Results:

The cumulative analysis for EE showed there was significant increase in EE using active workstation [mean effect size (MES): 1.47; 95% confidence interval (CI): 1.22 to 1.72, P < .0001]. Results from job performance indicated 2 findings: (1) active workstation did not affect selective attention, processing speed, speech quality, reading comprehension, interpretation and accuracy of transcription; and (2) it could decrease the efficiency of typing speed (MES: –0.55; CI: –0.88 to –0.21, P < .001) and mouse clicking (MES: –1.10; CI: –1.29 to –0.92, P < .001).

Conclusion:

Active workstation could significantly increase daily PA and be potentially useful in reducing workplace sedentariness. Although some parts of job performance were significantly lower, others were not. As a result there was little effect on real-life work productivity if we made a good arrangement of job tasks.

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Hongjun Yu, Xiaoping Chen, Weimo Zhu and Chunmei Cao

Purpose:

To examine the effectiveness of threshold and polarized models in the training organization of Chinese top-level sprint speed skaters using a 2-y quasi-experimental design.

Methods:

Two years (2004–05 and 2005–06 seasons) of the Chinese national speed-skating team’s daily training load (N = 9; 5 men, 23.6 ± 1.7 y, weight 76.6 ± 4.1 kg, competitive experience 5.0 ± 0.8 y, 500-m time 35.45 ± 0.72 s, 1000-m time 71.18 ± 2.28 s; 4 women, 25.3 ± 6.8 y, 73.0 ± 8.5 kg, 6.3 ± 3.5 y, 37.81 ± 0.46 s, 75.70 ± 0.81 s) were collected and analyzed. Each season’s training load included overall duration (calculated in min and km), frequency (calculated by overall sessions), and training intensity (measured by ear blood lactate or estimated by heart rate), Their performances at national, World Cup, and Olympic competitions during the 2 seasons (2004–06), as well as lactate data measured 15 and 30 min after these competitions, were also collected and analyzed. Based on the lactate data (<2, 2–4, >4 mmol/L), training zones were classified as low, moderate, and high intensity.

Results:

The total durations and frequencies of the training load were similar across the seasons, but a threshold-training model distribution was used in 2004–05, and a polarized-training-load organization in 2005–06. Under the polarized-training model, or load organization, all speed skaters’ performance improved and their lactate after competition decreased considerably.

Conclusion:

Training-intensity distribution based on a polarized-training model led to the success in top Chinese sprint speed skaters in the 2005–06 season.

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Robert W. Motl, Weimo Zhu, Youngsik Park, Edward McAuley, Jennifer A. Scott and Erin M. Snook

We examined the reliability of scores from physical activity monitors in a sample of 193 individuals with multiple sclerosis (MS) who wore a pedometer and an accelerometer for a 7-day period. There were no significant differences among days for the pedometer (p = .12) or the accelerometer (p = .15) indicating that week and weekend days can be analyzed in a single intra-class correlation (ICC) analytic model. The 7 days of monitoring yielded ICC estimates of .93 for both the pedometer and accelerometer, and a minimum of 3 days yielded a reliability of .80 for both the pedometer and accelerometer. Results indicated that physical activity monitor scores are reliable measures of physical activity for individuals with MS.

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Barbara E. Ainsworth, Carl J. Caspersen, Charles E. Matthews, Louise C. Mâsse, Tom Baranowski and Weimo Zhu

Context:

Assessment of physical activity using self-report has the potential for measurement error that can lead to incorrect inferences about physical activity behaviors and bias study results.

Objective:

To provide recommendations to improve the accuracy of physical activity derived from self report.

Process:

We provide an overview of presentations and a compilation of perspectives shared by the authors of this paper and workgroup members.

Findings:

We identified a conceptual framework for reducing errors using physical activity self-report questionnaires. The framework identifies 6 steps to reduce error: 1) identifying the need to measure physical activity, 2) selecting an instrument, 3) collecting data, 4) analyzing data, 5) developing a summary score, and 6) interpreting data. Underlying the first 4 steps are behavioral parameters of type, intensity, frequency, and duration of physical activities performed, activity domains, and the location where activities are performed. We identified ways to reduce measurement error at each step and made recommendations for practitioners, researchers, and organizational units to reduce error in questionnaire assessment of physical activity.

Conclusions:

Self-report measures of physical activity have a prominent role in research and practice settings. Measurement error may be reduced by applying the framework discussed in this paper.