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In this article, we consider whether adjustments to study designs, particularly of clinical trials, might provide important information to help explain how interventions for patients with low back pain (LBP) work. Back pain clinical trials have typically shown small effects associated with various treatments as compared with no treatment or current usual care, which could mean that these treatments are not very effective,1,2 or that the treatments are only effective for specific clinical subgroups and interventions therefore need better targeting.3 However, researchers also need to consider new methods to optimize treatment outcomes by investigating the processes which explain how or why specific treatments work.4 Such studies have been referred to as mediation analyses,5 and this article seeks to highlight why and how future studies should be designed to incorporate mediation analyses.The primary importance of mediation analysis is that it can help us understand how to improve treatments for patients with back pain. For example, psychological factors such as fear avoidance and self-efficacy are known to be predictive of patient outcomes such as disability and return to work, but it is less clear whether targeting an intervention specifically at reducing fear avoidance or increasing self-efficacy is able to further improve such outcomes. By analyzing the causal pathway between treatment, potential mediators and outcomes, we can identify the key factors to focus on during treatment and also identify which factors do not mediate outcome, to further streamline and improve intervention efficacy and efficiency.At the Twelfth International Low Back Pain Primary Care Research Forum in Odense, Denmark 2012, a dedicated workshop was held in which various aspects of mediation study design were explored and methodological considerations discussed. The workshop brought together experienced and internationally recognized LBP researchers and clinicians to provide an overview of the importance of mediation analyses and explore its usefulness in testing our assumptions about how our interventions may be influencing outcomes. We provide a summary of the workshop findings and discuss the implications for embedding mediation analysis methodology within future interventional research. We also offer definitions for some commonly used and misused terms, provide examples of mediation research using different study designs, and incorporate information on the methodology of mediation analysis from the wider literature, to formulate recommendations for future mediation research in the field of LBP.It is important to acknowledge that while the focus of this article is on mediators of specific treatment effects, the identification of mediator variables often comes from a program of work, which may involve different study designs. This article therefore includes a discussion of research using observational designs to explore mediating processes, although the limitations of evidence from such studies is noted and ultimately randomized controlled trials (RCTs) are required to confirm which factors mediate specific treatment effects.When longitudinal data from an observational study (single groups of individuals observed over time) or an intervention study (2 or more groups of individuals, exposed to different treatment conditions) are analyzed, some factors may be consistently predictive of outcome. However, although such factors may show a statistical association with outcome following treatment, the relationship can take a number of different forms such as follows: a prognostic factor, a treatment effect moderator, or a treatment effect mediator.Prognostic factors are baseline characteristics that are associated with outcome regardless of treatment, such as high baseline pain and high baseline activity limitation, which are generally known to be associated with poor outcome in patients with spinal pain across a variety of different treatment interventions.