Abstract Background Mediation analysis is an important tool for understanding the processes through which interventions affect health outcomes over time. Typically the temporal intervals between X, M, and Y are fixed by design, and little focus is given to the temporal dynamics of the processes. Purpose In this article, we aim to highlight the importance of considering the timing of the causal effects of a between-person intervention X, on M and Y, resulting in a deeper understanding of mediation. Methods We provide a framework for examining the impact of a between-person intervention X on M and Y over time when M and Y are measured repeatedly. Five conceptual and analytic steps involve visualizing the effects of the intervention on Y, M, the relationship of M and Y, and the mediating process over time and selecting an appropriate analytic model. Results We demonstrate how these steps can be applied to two empirical examples of health behavior change interventions. We show that the patterns of longitudinal mediation can be fit with versions of longitudinal multilevel structural equation models that represent how the magnitude of direct and indirect effects vary over time. Conclusions We urge researchers and methodologists to pay more attention to temporal dynamics in the causal analysis of interventions.
The physical activity trial (Example 2) was funded by the Swiss National Science Foundation awarded to U.S. (PP00P1_133632/1). J.I. (P2ZHP1_155103) and C.B. (P2BEP1_158975) were supported by fellowships of the Swiss National Science Foundation. The authors thank Melanie Amrein, Pamela Rackow, and involved students for their contributions to the data collection in the eating behavior trial (Example 1). We also thank Niall Bolger for valuable discussions on this topic, and the New York University Couples Lab for helpful feedback on an earlier version of this manuscript.
- Longitudinal mediation
- Multilevel mediation
- Temporal dynamics
- Health behavior change interventions
- Between-person intervention
- Intensive longitudinal data