Data-Driven Causal Discovery: Insights from a Longitudinal Study with Wearable Data

Radoslava Švihrová, Davide Marzorati, Alvise Dei Rossi, Tiziano Gerosa, Francesca Dalia Faraci

Abstract: Wearable devices offer unprecedented opportunities for continuous health monitoring and data collection in the general or clinical populations. In this study, we explore the feasibility of using data from consumer-grade wearable devices, combined with daily questionnaires, for long-term telemonitoring. To validate the plausibility of collected data at the level of relationships among derived variables, the causal structure is estimated in a data-driven way, and evaluated against existing literature to ensure alignment with established findings. Identification of the causal relationships is done with Structural Expectation-Maximization (SEM) algorithm, to address the challenge of missing data, and further strengthened by providing an initial structure estimated by the bootstrapped Temporal Peter-Clark (TPC) algorithm. Obtained results demonstrate plausibility of the discovered relationships, evaluated both against existing literature and in a data-driven way by fitting a Bayesian network. This supports the utility of consumer-grade wearables for continuous monitoring, and enabling new directions for designing of targeted interventions.

External IDs:doi:10.1007/978-3-031-95841-0_75