Statistical and Dynamical Systems Modeling of Real-Time Adaptive M-Intervention for Pain

• Activity Number: 528
• Type: Topic Contributed
• Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Chaeryon Kang* and Daniel M. Abrams and Jingyi (Jessica) Li and Qi Long and Nirmish R. Shah
• Companies: University of Pittsburgh and Northwestern University and University of California at Los Angeles and Emory University and Duke University
• Keywords: mHealth ; Pain management ; Personalized treatment ; Differential equation ; SAMSI

Abstract

With the growing popularity of mobile phone technology, new opportunities have arisenfor real-time adaptive medical intervention. The simultaneous growth of multiple "big data" sources allows for the development of personalized recommendations. In this project, we develop a new mathematical model for changes in subjective pain over time in patients with chronic conditions. The proposed model consists of a dynamical systems approach using differential equations to forecast future pain levels, as well as a statistical approach tying system parameters to patient data (including reported pain levels, medication history, personal characteristics and other health records). The model is combined with statistical techniques to ultimately obtain optimized, continuously-updated treatment plans balancing competing demands of pain reduction and medication minimization. Application of the resulting personalized treatment plans to a currently active pilot study on mobile intervention in patients living with chronic pain due to sickle cell disease (SCD) will be presented.