# Personalising Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge

arXiv: https://arxiv.org/abs/2304.03392 · HTML: https://mlsanity.com/paper/2304.03392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [What do you need to consider when designing mobile health intervention?](https://mlsanity.com/paper/2205.10679/) (2022)

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

- [Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions](https://mlsanity.com/paper/2604.11730/) (2026)

## Abstract (arXiv)

We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design which would enable evaluation of our system.