Prevention of Eating Disorders at Universities: A Systematic Review and Meta-Analysis

Abstract

Background. Eating problems are highly prevalent among young adults. Universities could be an optimal setting to prevent eating disorders through psychological intervention. As part of the World Mental Health-International College Student initiative, this systematic review and meta-analysis synthesizes data on the efficacy of eating disorder prevention programs targeting university students.Method. A systematic literature search of bibliographical databases (CENTRAL, MEDLINE, PsycINFO) for randomized trials comparing psychological preventive interventions for eating disorders in university students to psychoeducation or inactive controls was performed through October 8th, 2018.Results. Twenty-two studies were included. Eight (36.4%) were rated to have a low risk of bias. The relative risk of developing a subthreshold or full-blown eating disorder was IRR=0.62 (95%CI: 0.44-0.87, n=8; standardized clinical interviews only), indicating a 38% decrease in incidence in the intervention groups compared to controls. Small to moderate between- group effects at post-test were found on self-reported global eating disorder symptoms (g=0.36, 95%CI: 0.25-0.47, n=20), dieting (g=0.47, 95%CI: 0.30- 0.64, n=18), body dissatisfaction (g=0.50, 95%CI: 0.33-0.67, n=14), drive for thinness (g=0.43, 95%CI: 0.27-0.59, n=12), weight concerns (g=0.33, 95%CI: 0.10-0.57, n=13) and affective symptoms (g=0.28, 95%CI: 0.16-0.40, n=14). Effects on bulimia were not significant. Heterogeneity was low to moderate across comparisons.Discussion. Eating disorder prevention on campus can have significant, small-to-moderate effects on eating disorder symptoms and risk factors. Results also suggest that the prevention of subthreshold and full-syndrome eating disorders is feasible using such interventions. More research is needed to identify effects on academic functioning, as well as ways to motivate students to use preventive eating disorder interventions.

Measuring effects of trainee professional development on research productivity: A cross-institutional meta-analysis

“…Power calculations suggest that with the average sample sizes and number of participating institutions' data available for this analysis (N1=72, N2=115, alpha=0.05, k=9, d=0. 20), and an observed moderate heterogeneity (I 2 = 39.36% -40.14% < 50% cutoff for moderate heterogeneity; τ 2 =0.01, SEτ=0.01; Q=12.50 -12.83, p=0.12-0.13) in a random effects model we have 87% power to detect a small effect size or larger (35,36).…”

“…Power calculations suggest that with the average sample sizes and number of participating institutions' data available for this study (N1=78, N2=95; alpha=0.05, k=10, d=0. 20), and an observed low heterogeneity (I 2 = 24.72% < 25% cutoff for low heterogeneity; τ 2 =0.002, SEτ=0.004; Q=12.91, p=0.17) in a random effects model (35,36), we had nearly 90% power to detect a small effect size (89%). Given that our study was cross-institutional and well exceeded the acceptable rate of 80% power (with an alpha of 0.05; 33, 34), we can confidently say that we had the ability to detect an effect size of this magnitude or greater.…”

“…Post-hoc power analyses determined that >80% power was achieved for each meta-analysis, indicating that a sufficient number of subjects and studies were included. Meta-analytic power was calculated in accordance with recommendations by Harrer, Cuijpers, Furukawa, & Ebert (35). Meta analysis results and power calculations are grouped with other relevant analyses.…”

Weight loss, insulin resistance, and study design confound results in a meta-analysis of animal models of fatty liver

“…Statistical analysis was performed using R 4.0.2 for Mac ( Harrer et al, 2019 ; R Core Development team, 2019 ) with packages dmetar ( Harrer et al, 2019 ), meta (RRID: SCR_019055 , [ Schwarzer G, 2007 ]), and metafor ( Viechtbauer, 2010 ). Graphs were also generated using GraphPad Prism (RRID: SCR_002798 , v8.0 for Mac, GraphPad Software, La Jolla California, USA).…”

Optimal treatment strategy of fremanezumab in migraine prevention: a systematic review with network meta-analysis of randomized clinical trials

“…There are some debate concerning the selection of model of network meta-analysis. In our study,a contrast-based network meta-analysis (frequentist model) was used because relevant studies have indicated that pooled estimates in frequentist models are similar to those in a Bayesian model 43 , 44 , and the frequentist approach is easily understood and commonly applied in the statistical field 45 . In contrast with the Bayesian model, the frequentist model does not require any prior for carrying out network meta-analysis.…”