Is social support related to better mental health, treatment continuation and success rates among individuals undergoing in-vitro fertilization? Systematic review and meta-analysis protocol

Infertility and its treatment via in-vitro fertilization (IVF) represent a global health area of increasing importance. However, the physical and psychological burden of IVF can negatively impact psychological wellbeing, as well as treatment retention and success. Social support has been found to have positive health effects among populations facing health-related stressors worldwide, and its potential protective role for IVF patients merits further attention. We present a protocol for a systematic review of peer-reviewed published studies quantitatively investigating associations between social support and i) mental health; ii) the decision to (dis)continue with IVF treatment cycles and; iii) IVF success (pregnancy and birth rates); among individuals who are undertaking or have undertaken IVF cycles. Studies will be included if they work with human subjects, provide correlation coefficients between measures of social support and at least one of the outcomes of interest, and are in the English language. Social support may derive from both naturally occurring networks and more formalized sources or interventions. The protocol for this systematic review was developed according to the PRISMA-P guidelines. Ten health-, psychology- and sociology-related databases will be searched using composite search terms that include keywords for ‘IVF’ and ‘social support’. To assess methodological quality, the authors will use a modified version of the Newcastle-Ottawa Scale. Should three or more moderate or good quality studies be identified for any one outcome of interest, correlation meta-analyses, using the Hedges-Olkin method, will be conducted to pool effect sizes and heterogeneity will be assessed. Should the number, quality and characteristics of eligible studies not allow for reliable quantitative synthesis, the authors will limit the analysis to qualitative synthesis, with a focus on implications of findings for future research and programming.