An Active Inference Approach to Dissecting Reasons for Nonadherence to Antidepressants

Antidepressant medication adherence is among the most important problems in health care worldwide. Interventions designed to increase adherence have largely failed, pointing toward a critical need to better understand the underlying decision-making processes that contribute to adherence. A computational decision-making model that integrates empirical data with a fundamental action selection principle could be pragmatically useful in 1) making individual-level predictions about adherence and 2) providing an explanatory framework that improves our understanding of nonadherence.

Here we formulated a partially observable Markov decision process model based on the active inference framework that can simulate several processes that plausibly influence adherence decisions.

Using model simulations of the day-to-day decisions to take a prescribed selective serotonin reuptake inhibitor, we show that several distinct parameters in the model can influence adherence decisions in predictable ways. These parameters include differences in policy depth (i.e., how far into the future one considers when deciding), decision uncertainty, beliefs about the predictability (stochasticity) of symptoms, beliefs about the magnitude and time course of symptom reductions and side effects, and strength of medication-taking habits that one has acquired.

Clarifying these influential factors will be an important first step toward empirically determining which factors are contributing to nonadherence to antidepressants in individual patients. The model can also be seamlessly extended to simulate adherence to other medications (by incorporating the known symptom reduction and side effect trajectories of those medications), with the potential promise of identifying which treatments may be best suited for different patients.