Finding the spiritual in the secular: a meta-analysis of changes in spirituality following secular mindfulness-based programs

Objectives: Spirituality has historically been a neglected aspect of people’s lives within a healthcare context. Previous meta-analyses of the effect of mindfulness-based programs (MBPs) on spirituality have been limited by the small number of includable studies that were available at the time, by not comparing MBPs to active controls, and by not investigating whether effects continue to be observed at follow-up. Therefore, the current systematic review and meta-analysis aimed to more comprehensively examine whether, and to what extent, secular MBPs increase spirituality, and to identify moderators of any observed effects. Methods: Random effects meta-analyses were conducted on 13 controlled trials of MBPs measuring spirituality that were identified by a systematic search of PsycInfo and Medline. Results: At post-intervention, MBPs increased spirituality compared to both passive and active controls (passive: g = 0.52, 95% C.I.: 0.35 to 0.68; active: g = 0.34, 95% C.I.: 0.14 to 0.54), and effects continued to be observed at follow-up (passive: g = 0.32, 95% C.I.: 0.09 to 0.55; active: g = 0.44, 95% C.I.: 0.18 to 0.71). For passive controls at post-intervention, cancer samples showed a significantly larger pooled effect than the non-cancer ones (cancer: g = 0.75, 95% C.I.: 0.52 to 0.98; non-cancer: g = 0.38, 95% C.I.: 0.20 to 0.56; χ2(1) = 6.14, p = .01), but moderation analysis was not possible at follow-up or for active controls. Study quality was not significantly associated with effect size. Conclusions: Secular MBPs appear to increase spirituality, these effects endure beyond the end of the MBP and they cannot wholly be attributed to non-specific therapeutic factors. Limitations are discussed

Sample Size Considerations in Prevention Research Applications of Multilevel Modeling and Structural Equation Modeling

When the goal of prevention research is to capture in statistical models some measure of the dynamic complexity in structures and processes implicated in problem behavior and its prevention, approaches such as multilevel modeling (MLM) and structural equation modeling (SEM) are indicated. Yet the assumptions that must be satisfied if these approaches are to be used responsibly raise concerns regarding their use in prevention research involving smaller samples. In this manuscript we discuss in nontechnical terms the role of sample size in MLM and SEM and present findings from the latest simulation work on the performance of each approach at sample sizes typical of prevention research. For each statistical approach, we draw from extant simulation studies to establish lower bounds for sample size (e.g., MLM can be applied with as few as 10 groups comprising 10 members with normally distributed data, restricted maximum likelihood estimation, and a focus on fixed effects; sample sizes as small as N = 50 can produce reliable SEM results with normally distributed data and at least three reliable indicators per factor) and suggest strategies for making the best use of the modeling approach when N is near the lower bound