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Florian Herbolsheimer, Stephanie Mosler, Richard Peter and the ActiFE Ulm Study Group

accelerometer. The maximum distance between a weather station and a participant’s residence was 40 km. Furthermore, sex and age (in years) were reported. Missing values Complete data were provided by 77.2% of all participants. An imputation procedure was applied for the depression scale if one of seven items

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Lisa Price, Katrina Wyatt, Jenny Lloyd, Charles Abraham, Siobhan Creanor, Sarah Dean and Melvyn Hillsdon

periods of nonwear in adults ( 23 ) with similar high compliance demonstrated in small samples of children ( 8 ). This, in turn, reduces the need for statistical imputation methods, assumptions regarding missing values ( 18 , 24 ), and the associated risks of selection bias and misclassification of PA. In

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James Annesi

participants who initiated treatment and deidentified prior to analyses. Multiple imputation 34 was used to replace missing cases within the longitudinal analyses. Because of consistencies in both the directionality of changes in self-regulation for physical activity, negative mood, exercise-related self

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Chris Knoester and Theo Randolph

’s reports of relationship closeness, and d) child’s reports of relationship closeness. In all models, multiple imputation with chained equations is used to account for missing data. This is a preferred method for addressing missing data ( Johnson & Young, 2011 ), yet the presented results are robust to the

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James J. Annesi

provided monthly to reinforce self-regulatory skills usage. All assessments were completed by study staff in a private area. Data Analyses After determining that the 11% of missing cases were missing at random, 15 the expectation–maximization algorithm 16 was applied for imputation. Based on an effect

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Jakob Tarp, Anna Bugge, Niels Christian Møller, Heidi Klakk, Christina Trifonov Rexen, Anders Grøntved and Niels Wedderkopp

missing data (n = 697). A flowchart of participants is available in Supplementary Figure S1 and multiple imputation analysis including all 1209 consenting participants at baseline is available in Supplementary Table S4 (available online). Results from imputed data yielded similar conclusions as

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Jay Johnson, Michelle D. Guerrero, Margery Holman, Jessica W. Chin and Mary Anne Signer-Kroeker

imputation procedures using the fully conditioned specification approach. A total of five imputed data sets were generated, with each data set comprising slightly different imputed values. The parameter estimates of the five imputations were pooled and yielded a single set of estimates. Given that little is

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Jocelyn Kernot, Lucy Lewis, Tim Olds and Carol Maher

the trial remained in the sample for analysis. 43 Multiple imputation (fully conditional specification—as testing showed that missing data were not random) was implemented to account for missing data. Data were combined using Rubin’s rule. 44 To determine the effectiveness of the MSIU program, random

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Jonathan M. Miller, Mark A. Pereira, Julian Wolfson, Melissa N. Laska, Toben F. Nelson and Dianne Neumark-Sztainer

Institute, Cary, NC) Proc MI and MIANALYZE. Twenty imputed data sets were created using a Markov Chain Monte Carlo algorithm and all correlates and demographics. Multiple imputation methods for EAT-2010 have been previously described in greater detail. 22 Statistical Analyses All statistical analyses were

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Yara Fidelix, Mara C. Lofrano-Prado, Leonardo S. Fortes, James O. Hill, Ann E. Caldwell, João P. Botero and Wagner L. do Prado

for gender and baseline BMI. The procedures were performed following an intention-to-treat principle without data imputation. In addition, the effect size (ES) for each intervention was calculated as proposed by Cohen ( d ). The significance level was set at P  < .05. Statistical procedures were