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Cecilia Hoi Sze Chan, Amy Sau Ching Ha and Johan Yau Yin Ng

. Intra-class correlation coefficient (ICC) for this adapted perceived movement skill competence questionnaire was 0.84, indicating a high (>0.8) reliability ( Landis & Koch, 1977 ). Actual FMS Competence Test examiners assessed children’s actual FMS performance using The Test of Gross Motor Development

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Cheryl A. Howe, Kimberly A. Clevenger, Danielle McElhiney, Camille Mihalic and Moira A. Ragan

’s games in real time. Asking children to score each game immediately after participation, they found no correlations between state enjoyment and PA intensity ( r  = −.07). Using Rasch Rating Scale Modeling (RRSM) analyses, the 9-item FAS was found to have too many options to accurately assess perceived

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Lene Levy-Storms, Lin Chen and Anastasia Loukaitou-Sideris

, and results/outcomes in the reviewed articles. We did not undertake any formal assessment of risk of bias because all but one of the 48 studies were descriptive and/or correlational. In other words, the results from this synthesis do not highlight parameter estimates in outcomes, with the exception of

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Zachary C. Pope, Nan Zeng, Xianxiong Li, Wenfeng Liu and Zan Gao

were estimating EE. Statistical Analysis Data were analyzed in late 2017 and were first screened for physiological implausible values. Next, Pearson correlation coefficients were calculated to observe the association between smartwatch EE estimates and indirect calorimetry EE measurements at rest and

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Craig Donnachie, Kate Hunt, Nanette Mutrie, Jason M.R. Gill and Paul Kelly

heterogeneity regarding the number of PA metrics being reported, limiting comparability between studies ( Silfee et al., 2018 ). Distinct forms of PA measurement can provide confusing or even contradictory findings ( Thompson et al., 2009 ). Numerous studies have shown that correlations between device-based and

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Robert Weinberg, Deanna Morrison, Megan Loftin, Thelma Horn, Elizabeth Goodwin, Emily Wright and Carly Block

normality. Univariate correlational analyses were conducted to examine the degree of correlation between all study variables. To determine whether the four experimental groups differed in regard to relevant demographics (e.g., age, year in school, basketball involvement, rating of basketball ability), a one

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Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage

Equation 1 was combined with one of three variations on Bridge Equation 2. In addition, we examine the performance of meta-analyzing multiple indirect routes. If available, we used published bridge equations. If relevant equations were unavailable but correlation coefficients and basic (mean and SD

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Christina M. Patch, Caterina G. Roman, Terry L. Conway, Ralph B. Taylor, Kavita A. Gavand, Brian E. Saelens, Marc A. Adams, Kelli L. Cain, Jessa K. Engelberg, Lauren Mayes, Scott C. Roesch and James F. Sallis

measuring objective crime and physical activity 3 found that most studies of adults (6/10) reported no significant correlation between crime-related safety and physical activity. In contrast, 6 of 11 studies of adolescents reported that higher crime rates are correlated with lower physical activity. 3 Sex

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Ray M. Merrill

) across the counties. Sex-specific county-level LTPI by the predictor variables were first assessed using simple regression. Associations were then evaluated using univariate and multivariate 2-level organizational models (or mixed models). The intraclass correlation coefficient (ICC) was computed to

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Nathan F. Meier, Yang Bai, Chong Wang and Duck-chul Lee

(%). Paired t tests and Pearson’s correlation were used to compare differences in the measures of body composition for whole and segmental variables between the two methods (BIA vs. DXA). The mean percentage error and mean absolute percentage error examined the accuracy of the BIA compared with the DXA