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

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 Net...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Chiam, Jodi, Lim, Aloysius, Nott, Cheryl, Nicholas, Mark, Teredesai, Ankur, Shinde, Sunil
Format: Article
Language:English
Subjects:
Algorithms
Customization
Exercise
Graph neural networks
Health behavior
Public health
Recommender systems
Wearable technology
Online Access:Get full text
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Summary: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.
ISSN:2331-8422