A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

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

Estudio de la Enfermedad de la Fibromialgia como Sistema de Control

La fibromialgia -caracterizada por la hipersensibilidad y por un fuerte dolor musculoesquelético crónico-, es un desorden corporal sin cura universal cuyo tratamiento resulta arduo debido a las causas pseudo-desconocidas de la enfermedad. El objetivo principal de este material es facilitar el tratamiento del desorden de la fibromialgia mediante procedimientos propios de la ingeniería de control, buscando localizar el método que mejor se adapte a las necesidades del paciente y del equipo médico asociado en su lucha contra los significativos síntomas de esta enfermedad. Por otro lado, y como objetivo secundario, este trabajo presenta un desarrollo completo de varias metodologías de control con apoyo de la herramienta MATLAB®, sirviendo como caso ejemplo de aplicación de dichos procedimientos y facilitando así la adaptación de las tecnologías aplicadas a otros proyectos de índole similar, apoyando por tanto el uso de nuevas técnicas en el área de la salud y otros campos complejos que pueden beneficiarse de las ventajas asociadas a la ingeniería de control.Fibromyalgia -characterized by hypersensitivity and a strong chronic musculoskeletal pain- is a body disorder with no universal cure whose treatment is difficult due to the pseudo-unknown causes of the disease. The main objective of this material is to provide the treatment of fibromyalgia using control engineering procedures, trying to locate the best method that meet the needs of the patient and his medical staff in their fight against the symptoms of this disease. On the other hand, and as a secondary objective, this paper presents a complete development of several control methodologies with the support of the MATLAB® tool, serving as an example of the application of these procedures and facilitating the adaptation of technologies applied to other projects with a similar nature, supporting the use of new techniques in the health area and other complex fields that can benefit from the strenghts of the control engineering.Universidad de Sevilla. Grado en Ingeniería Electrónica, Robótica y Mecatrónic

A Novel Control Engineering Approach to Designing and Optimizing Adaptive Sequential Behavioral Interventions

abstract: Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.   A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.   Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework. The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.Dissertation/ThesisDoctoral Dissertation Chemical Engineering 201

PROCESS SYSTEMS ENGINEERING APPROACHES FOR INTERVENTION PLANNING DURING INFECTIOUS DISEASE OUTBREAKS

Ph.DDOCTOR OF PHILOSOPH