Summary: MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. Availability and implementation: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation: MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/. Contact: [email protected] . Supplementary information: Supplementary data are available at Bioinformatics online.

OBJECTIVE: Our aim was to evaluate the use and effectiveness of interventions using social networking sites (SNSs) to change health behaviors. MATERIALS AND METHODS: Five databases were scanned using a predefined search strategy. Studies were included if they focused on patients/consumers, involved an SNS intervention, had an outcome related to health behavior change, and were prospective. Studies were screened by independent investigators, and assessed using Cochrane's 'risk of bias' tool. Randomized controlled trials were pooled in a meta-analysis. RESULTS: The database search retrieved 4656 citations; 12 studies (7411 participants) met the inclusion criteria. Facebook was the most utilized SNS, followed by health-specific SNSs, and Twitter. Eight randomized controlled trials were combined in a meta-analysis. A positive effect of SNS interventions on health behavior outcomes was found (Hedges' g 0.24; 95% CI 0.04 to 0.43). There was considerable heterogeneity (I(2) = 84.0%; T(2) = 0.058) and no evidence of publication bias. DISCUSSION: To the best of our knowledge, this is the first meta-analysis evaluating the effectiveness of SNS interventions in changing health-related behaviors. Most studies evaluated multi-component interventions, posing problems in isolating the specific effect of the SNS. Health behavior change theories were seldom mentioned in the included articles, but two particularly innovative studies used 'network alteration', showing a positive effect. Overall, SNS interventions appeared to be effective in promoting changes in health-related behaviors, and further research regarding the application of these promising tools is warranted. CONCLUSIONS: Our study showed a positive effect of SNS interventions on health behavior-related outcomes, but there was considerable heterogeneity. Protocol registration The protocol for this systematic review is registered at http://www.crd.york.ac.uk/PROSPERO with the number CRD42013004140.