# Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation

arXiv: https://arxiv.org/abs/2311.07139 · HTML: https://mlsanity.com/paper/2311.07139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 13 Nov 2023.

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

- [Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data](https://mlsanity.com/paper/1902.01506/) (2019)
- [Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes](https://mlsanity.com/paper/2103.09052/) (2021)

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

- [Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health](https://mlsanity.com/paper/2507.16356/) (2025)
- [Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program](https://mlsanity.com/paper/2410.21405/) (2024)

## Abstract (arXiv)

Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the world's largest mobile health programs which delivers time sensitive audio-messages to pregnant women and new mothers. We have been collaborating with ARMMAN, a non-profit in India which operates the Kilkari program, to identify bottlenecks to improve the efficiency of the program. In particular, we provide an initial analysis of the trajectories of beneficiaries' interaction with the mHealth program and examine elements of the program that can be potentially enhanced to boost its success. We cluster the cohort into different buckets based on listenership so as to analyze listenership patterns for each group that could help boost program success. We also demonstrate preliminary results on using historical data in a time-series prediction to identify beneficiary dropouts and enable NGOs in devising timely interventions to strengthen beneficiary retention.