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Understanding Between-Person Interventions With Time-Intensive Longitudinal Outcome Data: Longitudinal Mediation Analyses


Berli, Corina; Inauen, Jennifer; Stadler, Gertraud; Scholz, Urte; Shrout, Patrick E (2020). Understanding Between-Person Interventions With Time-Intensive Longitudinal Outcome Data: Longitudinal Mediation Analyses. Annals of Behavioral Medicine:Epub ahead of print.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
08 Research Priority Programs > Dynamics of Healthy Aging
Dewey Decimal Classification:150 Psychology
Language:English
Date:5 September 2020
Deposited On:24 Sep 2020 11:08
Last Modified:24 Sep 2020 11:08
Publisher:Springer
ISSN:0883-6612
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1093/abm/kaaa066
PubMed ID:32890399
Project Information:
  • : FunderSNSF
  • : Grant IDPP00P1_133632
  • : Project TitleInter- and intraindividual dynamics in health behavior change
  • : FunderSNSF
  • : Grant IDP2ZHP1_155103
  • : Project TitleIntention-behavior relations: An intraindividual perspective
  • : FunderSNSF
  • : Grant IDP2BEP1_158975
  • : Project TitleSelf-regulation interventions in health behavior change: Examining mediating mechanisms in everyday life

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