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

Background: Individuals who are socioeconomically disadvantaged have high smoking rates and face barriers to participating in smoking cessation interventions. Computer-tailored health communication, which is focused on finding the most relevant messages for an individual, has been shown to promote behavior change. We developed a machine learning approach (the Adapt2Quit recommender system), and our pilot work demonstrated the potential to increase message relevance and smoking cessation effectiveness among individuals who are socioeconomically disadvantaged.

Strategies to Improve Enrollment and Participation in Pulmonary Rehabilitation Following a Hospitalization for COPD: RESULTS OF A NATIONAL SURVEY.

Purpose: Pulmonary rehabilitation (PR) improves outcomes for patients with chronic obstructive pulmonary disease (COPD); however, very few patients attend. We sought to describe strategies used to promote participation in PR after a hospitalization for COPD.

Methods: A random sample of 323 United States based PR programs was surveyed.