Machine learning-driven recommender systems to improve engagement with health content in a low-resource setting: Poster

Abstract

Digital technological tools offer the opportunity to design and disseminate targeted information to influence health behaviors and outcomes. However, extended engagement with health information is vital to promote sustained behavior change in health consumers. Our content-led mobile application for children's health, Saathealth, deployed in a low-resource setting, was used to develop and run machine learning algorithms to build health recommender systems using content and collaborative filtering techniques. We aimed to explore changes in engagement associated with the recommendation systems on the Saathealth app by assessing various aspects of user engagement: videos watched on the app, time spent on the app, sessions on the app, and correct quiz responses. We conducted two A/B experiments to compare the effect of (i) content filtering with no recommendations, and (ii) content filtering with collaborative filtering, on content consumption on the Saathealth app. In experiment 1, the content filtering recommender system was associated with a 25.00% higher median number of videos watched per user compared to when no recommendations were provided after 45 days (5.00 [interquartile range (IQR), 2.00–12.00] vs. 4.00 [IQR, 2.00–10.50], respectively). Content filtering also led to 53.80% more complete video watches and 13.96% higher proportions of correct quiz responses. When the content filtering recommender system was compared with the collaborative filtering one in experiment 2, users in the collaborative filtering arm watched 66.67% more videos, both at 45 days and 90 days. At 90 days, the median number of videos watched per user was 5.00 (IQR, 2.00–9.25) in the collaborative filtering arm and 3.00 (IQR, 2.00–6.00) in the content filtering arm. Collaborative filtering also led to 15.01% more time spent on the app and 59.05% higher complete video watches. We found that machine learning-driven health recommender systems may be effective tools to sustain user engagement with health content. These tools have the potential to address various global health challenges by improving health awareness and behaviors in low-resource settings.