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Francesco Campa, Catarina N. Matias, Elisabetta Marini, Steven B. Heymsfield, Stefania Toselli, Luís B. Sardinha and Analiza M. Silva

standardized for the subject’s stature to classify differences in total body water (TBW; negatively related to vector length) and cell mass (positively related to PA). Even if the accuracy of classic BIVA in assessing the percentage of fat mass (%FM) and hydration status (ie, detection of hyper- or hypo

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Francesco Campa, Hannes Gatterer, Henry Lukaski and Stefania Toselli

 = 1.3; P  = .276; η p 2 = .06  shower trial 37.9 (7.1) 35.4 (6.7)* 37.5 (6.8) 37.5 (6.7) 37.5 (6.9)      control trial 38.1 (6.9) 35.6 (7.0)* 37.0 (6.8)* 37.4 (6.9)* 37.3 (6.7)*     Vector length, Ω/m F  = 54.1; P  < .001; η p 2 = .75 F  = 4.5; P  = .003; η p 2 = .20  shower trial 277.3 (48

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Katia Ferrar, Carol Maher, John Petkov and Tim Olds

Background:

To date, most health-related time-use research has investigated behaviors in isolation; more recently, however, researchers have begun to conceptualize behaviors in the form of multidimensional patterns or clusters.

Methods:

The study employed 2 techniques: radar graphs and centroid vector length, angles and distance to quantify pairwise time-use cluster similarities among adolescents living in Australia (N = 1853) and in New Zealand (N = 679).

Results:

Based on radar graph shape, 2 pairs of clusters were similar for both boys and girls. Using vector angles (VA), vector length (VL) and centroid distances (CD), 1 pair for each sex was considered most similar (boys: VA = 63°, VL = 44 and 50 units, and CD = 48 units; girls: VA = 23°, VL = 65 and 85 units, and CD = 36 units). Both methods employed to determine similarity had strengths and weaknesses. Conclusions: The description and quantification of cluster similarity is an important step in the research process. An ability to track and compare clusters may provide greater understanding of complex multidimensional relationships, and in relation to health behavior clusters, present opportunities to monitor and to intervene.

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Masafumi Terada, Megan Beard, Sara Carey, Kate Pfile, Brian Pietrosimone, Elizabeth Rullestad, Heather Whitaker and Phillip Gribble

the mathematical algorithms previously described ( Richman & Moorman, 2000 ; Yentes et al., 2013 ). Briefly, SampEn takes the negative natural logarithm for conditional probability that a small window of data points (vector length, m ) would repeat itself at m  + 1. Data points in two windows ( m