Predicting early user churn in a public digital weight loss intervention
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Author / Producer
Date
2024-05
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Digital health interventions (DHIs) offer promising solutions to the rising global challenges of noncommunicable diseases by promoting behavior change, improving health outcomes, and reducing healthcare costs. However, high churn rates are a concern with DHIs, with many users disengaging before achieving desired outcomes. Churn prediction can help DHI providers identify and retain at-risk users, enhancing the efficacy of DHIs. We analyzed churn prediction models for a weight loss app using various machine learning algorithms on data from 1,283 users and 310,845 event logs. The best-performing model, a random forest model that only used daily login counts, achieved an F1 score of 0.87 on day 7 and identified an average of 93% of churned users during the week-long trial. Notably, higher-dimensional models performed better at low false positive rate thresholds. Our findings suggest that user churn can be forecasted using engagement data, aiding in timely personalized strategies and better health results.
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Publication status
published
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Book title
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems
Journal / series
Volume
Pages / Article No.
994
Publisher
Association for Computing Machinery
Event
ACM Conference on Human Factors in Computing Systems (CHI 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Machine learning; Churn; Dropout; Attrition; mHealth; Digital health
Organisational unit
03681 - Fleisch, Elgar / Fleisch, Elgar
02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.
03995 - von Wangenheim, Florian / von Wangenheim, Florian