Co-Pilot for Health: Personalized Algorithmic AI Nudging to Improve Health Outcomes

TL;DR

This study presents an AI-driven platform using a Graph-Neural Network to personalize health nudges, significantly improving physical activity among 84,764 individuals over 12 weeks.

Contribution

It introduces a novel AI platform for personalized digital nudging using GNNs, validated at scale with positive health behavior outcomes.

Findings

Participants increased daily step count by 6.17%.

Weekly MVPA increased by 7.61%.

Nudge engagement was high with 13.1% open rate.

Abstract

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.