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

Journal: JMIR research protocols
PMID: 40239194

Abstract

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.

Authors

  • Ariana Kamberi
    Division of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States.
  • Benjamin Weitz
    Division of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States.
  • Julie Flahive
    Division of Biostatistics and Health Services Research, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States.
  • Julianna Eve
    Department of Healthcare Delivery and Population Sciences, University of Massachusetts Chan Medical School-Baystate, Springfield, MA, United States.
  • Reem Najjar
    Division of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States.
  • Tara Liaghat
    Institute for Clinical and Translational Research, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Daniel Ford
    Institute for Clinical and Translational Research, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Peter Lindenauer
    Department of Healthcare Delivery and Population Sciences, University of Massachusetts Chan Medical School-Baystate, Springfield, MA, United States.
  • Sharina Person
    Division of Biostatistics and Health Services Research, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States.
  • Thomas K Houston
    Division of Health Informatics and Implementation Science, Quantitative Health Sciences, University of Massachusetts Medical Scool, Worcester, MA, United States.
  • Megan E Gauvey-Kern
    Institute for Clinical and Translational Research, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Jackie Lobien
    Institute for Clinical and Translational Research, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Rajani S Sadasivam
    Division of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States.

Keywords

External Resources

Popular Topics
Recent Journals