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.

翻译：自动塑造大规模人群健康行为的能力——跨越可穿戴设备类型和疾病状况——具有显著改善全球健康结果的巨大潜力。我们设计并实现了一个基于人工智能的数字算法助推平台，该平台由基于图神经网络（GNN）的推荐系统以及来自可穿戴健身设备的细粒度健康行为数据驱动。本文报告了该平台在新加坡为期12周内对84,764名个体实施个性化与情境化助推的效果结果。我们通过统计验证：与未接收任何助推的匹配对照组参与者相比，目标组中接收此类AI优化每日助推的参与者，其日常身体活动（如步数）增加了6.17%（p = 3.09×10^{-4}），每周中高强度身体活动（MVPA）分钟数增加了7.61%（p = 1.16×10^{-2}）。此外，这些助推的接受度极高：发送的助推中有13.1%被打开（打开率），其中11.7%被评价为有用，仅1.9%被评价为无用，从而证明了人群层面参与度指标的显著改善。