# User Engagement in Mobile Health Applications

arXiv: https://arxiv.org/abs/2206.08178 · HTML: https://mlsanity.com/paper/2206.08178/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles](https://mlsanity.com/paper/1710.02264/) (2017)
- [Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs](https://mlsanity.com/paper/2107.02480/) (2021)
- [Survival trees for left-truncated and right-censored data, with application to time-varying covariate data](https://mlsanity.com/paper/1606.03033/) (2016)
- [A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries](https://mlsanity.com/paper/2111.01786/) (2021)
- [A Time Series Approach To Player Churn and Conversion in Videogames](https://mlsanity.com/paper/2003.10287/) (2020)
- [Churn prediction in online gambling](https://mlsanity.com/paper/2201.02463/) (2022)
- [Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning](https://mlsanity.com/paper/1907.03947/) (2019)
- [Unpacking Adherence and Engagement in Pervasive Health Games](https://mlsanity.com/paper/2106.13747/) (2021)
- [Investigating behavior change indicators and cognitive measures in persuasive health games](https://mlsanity.com/paper/2106.13753/) (2021)

## Built on by (most cited first, 2 of 2)

- [Actionable Recourse via GANs for Mobile Health](https://mlsanity.com/paper/2211.06525/) (2022)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)

## Abstract (arXiv)

Mobile health apps are revolutionizing the healthcare ecosystem by improving communication, efficiency, and quality of service. In low- and middle-income countries, they also play a unique role as a source of information about health outcomes and behaviors of patients and healthcare workers, while providing a suitable channel to deliver both personalized and collective policy interventions. We propose a framework to study user engagement with mobile health, focusing on healthcare workers and digital health apps designed to support them in resource-poor settings. The behavioral logs produced by these apps can be transformed into daily time series characterizing each user's activity. We use probabilistic and survival analysis to build multiple personalized measures of meaningful engagement, which could serve to tailor content and digital interventions suiting each health worker's specific needs. Special attention is given to the problem of detecting churn, understood as a marker of complete disengagement. We discuss the application of our methods to the Indian and Ethiopian users of the Safe Delivery App, a capacity-building tool for skilled birth attendants. This work represents an important step towards a full characterization of user engagement in mobile health applications, which can significantly enhance the abilities of health workers and, ultimately, save lives.