Using Directed Acyclic Graphs (DAGs) to Determine if the Total Causal Effect of an Individual Randomized Physical Activity-Promoting Intervention is Identifiable

ABSTRACT

Physical activity promotion is a best buy for public health because it has the potential to help individuals feel better, sleep better, and perform daily tasks more easily, in addition to providing disease prevention benefits. There is strong evidence that individual-level theory-based behavioral interventions are effective for increasing physical activity levels in adult populations but causal inference from these interventions often is unclearly articulated. A directed acyclic graph (DAG) can be, but rarely is, used to determine if the causal effect of an individual-level theory-based physical activity-promoting intervention is identifiable (e.g. stripped of any spurious association). The primary objective of the current study was to demonstrate how a DAG can be used to determine if the total causal effect of an individual randomized physical activity-promoting intervention is identifiable. The demonstration was based on the Well-Being and Physical Activity study (ClinicalTrials.gov, identifier: NCT03194854). Annotated files from DAGitty and Mplus are provided.

KEYWORDS:

• Potential outcome framework
• causal inference
• structural equation modeling
• self-efficacy theory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1091367X.2024.2318355