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Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

We evaluated several recommender methods for accurate prediction, including K-nearest neighbors, probabilistic matrix factorization, collective matrix factorization, and the Bayesian probabilistic matrix factorization (BPMF) [33]. In evaluating rating prediction methods, we used a range of standard performance metrics including root mean squared error, Kendall tau-b, and normalized discounted cumulative gain. In all these tests, BPMF was identified as the best single model.

Ariana Kamberi, Benjamin Weitz, Julie Flahive, Julianna Eve, Reem Najjar, Tara Liaghat, Daniel Ford, Peter Lindenauer, Sharina Person, Thomas K Houston, Megan E Gauvey-Kern, Jackie Lobien, Rajani S Sadasivam

JMIR Res Protoc 2025;14:e63693

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