Health behavior change in advance care planning: An agent-based model

Significance: A practical and ethical challenge in public health research is controlling and intervening on human behavior. Additionally, observing dynamic changes in behavior proves difficult, though tracking these changes is important for intervention development. Agent-based modeling (ABM) allows researchers to integrate complex behavioral data into a controlled environment that is more easily alterable and observable. One such setting in which ABM is applicable is advance care planning decision making.

Methods: We developed an agent-based model depicting advance care planning as a decision making process. The agents were affected by becoming critically ill, experiencing the death of a loved one, and being influenced by other agents. Based on these experiences and social interactions, the agents moved through the Transtheoretical Model’s stages of change.

Findings: The agent-based model is the first of its kind to examine potential intervention points for behavior change in the context of advance care planning. We integrated barriers and facilitators found in the literature. In addition, we matched values for the distribution of the population across the stages of change, including half of agents reaching action-maintenance.

Implications: The agent-based modeling approach to advance care planning is a plausible method for demonstrating dynamic social and experiential influences on decision making. This model could be used in the future to test interventions theoretically before implementation in the field. Future studies can expand on this by gathering additional community data and integrating it into the ABM for a more comprehensive representation of decision-making patterns in a population.