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

arXiv: https://arxiv.org/abs/2401.10816 · HTML: https://mlsanity.com/paper/2401.10816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation](https://mlsanity.com/paper/1908.04032/) (2019)

## Built on by (most cited first, 1 of 1)

- [ECLIPTICA -- A Framework for Switchable LLM Alignment via CITA - Contrastive Instruction-Tuned Alignment](https://mlsanity.com/paper/2601.06157/) (2026)

## Abstract (arXiv)

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 = 3.09×10-4) and weekly minutes of Moderate to Vigorous Physical Activity (MVPA) by 7.61% (p = 1.16×10-2), 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.