TY - JOUR
T1 - A risk-based model predictive control approach to adaptive interventions in behavioral health
AU - Zafra-Cabeza, Ascensión
AU - Rivera, Daniel E.
AU - Collins, Linda M.
AU - Ridao, Miguel A.
AU - Camacho, Eduardo F.
N1 - Funding Information:
Manuscript received October 20, 2009; revised March 23, 2010; accepted May 23, 2010. Manuscript received in final form June 01, 2010. Date of publication July 01, 2010; date of current version June 17, 2011. Recommended by Associate Editor J. H. Lee. This work was supported by the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health and the National Institute on Drug Abuse (NIDA) under Grant R21 DA024266, Grant K25 DA021173, and Grant P50 DA010075. The work of A. Zafra-Cabeza, M. A. Ridao, and E. F. Camacho was supported by the Spanish MCYT under Grant DPI2008-05818 and Grant P07-TEP-02720.
PY - 2011/7
Y1 - 2011/7
N2 - 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.
AB - 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.
KW - Adaptive interventions
KW - behavioral health
KW - predictive control
KW - process control
KW - risk analysis
UR - http://www.scopus.com/inward/record.url?scp=79959609283&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959609283&partnerID=8YFLogxK
U2 - 10.1109/TCST.2010.2052256
DO - 10.1109/TCST.2010.2052256
M3 - Article
AN - SCOPUS:79959609283
SN - 1063-6536
VL - 19
SP - 891
EP - 901
JO - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
IS - 4
M1 - 5499451
ER -