A risk-based model predictive control approach to adaptive interventions in behavioral health

Ascensión Zafra-Cabeza, Daniel E. Rivera, Linda M. Collins, Miguel A. Ridao, Eduardo F. Camacho

Research output: Contribution to journalArticlepeer-review


This brief examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based model predictive control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this brief can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm.

Original languageEnglish (US)
Article number5499451
Pages (from-to)891-901
Number of pages11
JournalIEEE Transactions on Control Systems Technology
Issue number4
StatePublished - Jul 2011


  • Adaptive interventions
  • behavioral health
  • predictive control
  • process control
  • risk analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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