Co-Pilot for Health: Personalized Algorithmic AI Nudging to Improve Health Outcomes
This addresses the challenge of enhancing global health outcomes through automated, scalable nudging for large populations, representing an incremental application of existing methods to a new domain.
The study tackled the problem of improving health behaviors at scale by implementing an AI-driven platform for personalized algorithmic nudging, resulting in a 6.17% increase in daily step count and a 7.61% increase in weekly moderate to vigorous physical activity among participants.
The ability to shape health behaviors of large populations automatically, across wearable types and disease conditions at scale has tremendous potential to improve global health outcomes. We designed and implemented an AI driven platform for digital algorithmic nudging, enabled by a Graph-Neural Network (GNN) based Recommendation System, and granular health behavior data from wearable fitness devices. Here we describe the efficacy results of this platform with its capabilities of personalized and contextual nudging to $n=84,764$ individuals over a 12-week period in Singapore. We statistically validated that participants in the target group who received such AI optimized daily nudges increased daily physical activity like step count by 6.17% ($p = 3.09\times10^{-4}$) and weekly minutes of Moderate to Vigorous Physical Activity (MVPA) by 7.61% ($p = 1.16\times10^{-2}$), compared to matched participants in control group who did not receive any nudges. Further, such nudges were very well received, with a 13.1% of nudges sent being opened (open rate), and 11.7% of the opened nudges rated useful compared to 1.9% rated as not useful thereby demonstrating significant improvement in population level engagement metrics.