Table 1. Statistical management of data.

Analysis	Rationale or Approach
Standardized mean difference effect size	Standardized unitless measure of difference (each ES weighted by inverse of its sampling variance to give more precise ES more weight; Hedges & Olkin, 1985): between-group at outcome measure effect treatment vs. control within-group effect treatment within-group effect (correlations between pre- and post-intervention scores solicited from primary study authors) control within-group effect (correlations between pre- and post-intervention scores solicited from primary study authors) Treatment within-group and control within-group ESs complementary evidence
Random-effect model	ESs based on random-effects models with the between studies variance component ${\sigma }_{\delta }^2$ estimated by weighted method of moments Random-effects model appropriate when heterogeneity is expected because the model assumes both subject-level sampling error and study-level error
Outlier detection & management	ES estimates may contain values that do not represent the intended universe of effects Outlier management included excluding cases that depart substantially from others (Hedges & Olkin, 1985) Potential outliers identified by omitting each ES and checking for large externally standardized random-effects residuals or substantially reduced measures of heterogeneity Analyses without outliers emphasized in manuscript Sensitivity analyses conducted without excluding outliers
Heterogeneity	Q heterogeneity statistic to test homogeneity I2 index of heterogeneity beyond within-study sampling error Random-effect model for analyses because expected heterogeneity: model assumes both subject-level sampling error and study-level error Strategies to deal with heterogeneity (Higgins, Thompson, Deeks, & Altman, 2003): ○ Both location parameter and variability parameter reported ○ Reported findings of the random-effects model that assumes heterogeneity ○ Sources of heterogeneity explored by moderator analyses ○ Results interpreted in light of heterogeneity
Multiple treatment groups	Multiple treatment groups compared to same control group included by accounting for dependence resulting from shared control group (Gleser & Olkin, 2009)
ESs as original metric	Mean ES converted to original metric of body mass index and weight: Meta-analyzed means or SDs from samples that used the measure with the appropriate type of intervention to obtain the hypothetical reference SD and means used to express estimated mean effect sizes in a specific original metric To determine the mean BMI/weight for the treatment group, the product of the effect size and standard deviation was added to the control group mean
Potential publication bias	Potential publication bias explored using multiple approaches, including estimates of the number of omitted studies, tests of funnel-plot asymmetry, and selection function procedures (Gleser & Olkin, 1996; Rosenthal, 1979; Sterne & Egger, 2001; Sutton, 2009; Vevea & Hedges, 1995)
Exploratory moderator analyses	Moderator analyses conducted on study-level data (not individual subject level data) Continuous moderators: Conventional mixed-effects meta-regression procedure to estimate and test unstandardized regression coefficients for both linear and cubic forms of the moderator Polynomial regression method better detects relationships between ESs and moderators that may be more complex than linear analyses might suggest Dichotomous and categorical moderators: Meta-analytic analogue of ANOVA Between-groups heterogeneity statistic (QB) to test categorical moderators