TY - JOUR
T1 - Understanding Between-Person Interventions With Time-Intensive Longitudinal Outcome Data
T2 - Longitudinal Mediation Analyses
AU - Berli, Corina
AU - Inauen, Jennifer
AU - Stadler, Gertraud
AU - Scholz, Urte
AU - Shrout, Patrick E
N1 - Acknowledgments
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.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - 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.
AB - 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.
KW - Longitudinal mediation
KW - Multilevel mediation
KW - Temporal dynamics
KW - Health behavior change interventions
KW - Between-person intervention
KW - Intensive longitudinal data
U2 - 10.1093/abm/kaaa066
DO - 10.1093/abm/kaaa066
M3 - Article
VL - 55
SP - 476
EP - 488
JO - Annals of Behavioral Medicine
JF - Annals of Behavioral Medicine
SN - 0883-6612
IS - 5
ER -