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Ecological momentary interventions (EMI) are digital mobile health (mHealth) interventions that are administered in an individual's daily life with the intent to improve mental health outcomes by tailoring intervention components to person, moment, and context. Questions regarding which intervention is most effective in a given individual, when it is best delivered, and what mechanisms of change underlie observed effects therefore naturally arise in this setting. To achieve this, EMI are typically informed by the collection of multivariate, intensive longitudinal data of various target constructs - designed to assess an individual’s psychological state - using ecological momentary assessments (EMA). However, the dynamic and interconnected nature of such multivariate time series data poses several challenges when analyzing and interpreting findings. This may be illustrated when understanding psychological variables as part of an interconnected network of dynamic variables, and the delivery of EMI as time-specific perturbations to these variables. Network control theory (NCT) is a branch of dynamical systems theory that precisely deals with the formal analysis of such network perturbations and provides solutions of how to perturb a network to reach a desired state in an optimal manner. In doing so, NCT may help to formally quantify and evaluate proximal intervention effects, as well as to identify optimal intervention approaches given a set of reasonable (temporal or energetic) constraints. In this proof-of-concept study, we leverage concepts from NCT to analyze the data of 10 individuals undergoing joint EMA and EMI for several weeks. We show how simple metrics derived from NCT can provide insightful information on putative mechanisms of change in the inferred EMA networks and contribute to identifying optimal leveraging points. We also outline what additional considerations might play a role in the design of effective intervention strategies in the future from the perspective of NCT. |
