| Summary: | Continued advancement in the field of physical activity and health promotion relies heavily on the synthesis of rigorous scientific evidence. As such, systematic reviews and meta-analyses of randomized controlled trials have led to a better understanding of which intervention strategies are superior (i.e., produce the greatest effects) in physical activity-based health behavior change interventions. Indeed, standard meta-analytic approaches have allowed researchers in the field to synthesize relevant experimental evidence using pairwise procedures that produce reliable estimates of the homogeneity, magnitude, and potential biases in the observed effects. However, pairwise meta-analytic procedures are only capable to discerning differences in effects between a select intervention strategy and a select comparison or control condition. In order to maximize the impact of physical activity interventions on health-related outcomes, it is necessary to establish evidence concerning the comparative efficacy of all relevant physical activity intervention strategies. The development of network meta-analysis (NMA)—most commonly used in medical-based clinical trials—has allowed for the quantification of indirect comparisons, even in the absence of direct, head-to-head trials. Thus, it stands to reason that NMA can be applied in physical activity and health promotion research to identify the best intervention strategies. Given that this analysis technique is novel and largely unexplored in the field of physical activity and health promotion, care must be taken in its application to ensure reliable estimates and discernment of the effect sizes among interventions. Therefore, the purpose of this review is to comment on the potential application and importance of NMA in the field of physical activity and health promotion, describe how to properly and effectively apply this technique, and suggest important considerations for its appropriate application in this field. In this paper, overviews of the foundations of NMA and commonly used approaches for conducting NMA are provided, followed by assumptions related to NMA, opportunities and challenges in NMA, and a step-by-step example of developing and conducting an NMA.
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