  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.
