Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions

Abstract

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or “just-in-time” behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

Short Nighttime Sleep Duration and High Number of Nighttime Awakenings Explain Increases in Gestational Weight Gain and Decreases in Physical Activity but Not Energy Intake among Pregnant Women with Overweight/Obesity

“…Regulating GWG has largely focused on moderating energy intake and increasing physical activity behaviors. As such, Symons Downs and colleagues expanded on a pregnancy energy balance model to also include planned and self-regulatory behaviors to predict GWG ( Figure 1 ) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. However, there is emerging interest in understanding the extent to which prenatal sleep behaviors relate to components of this energy balance model to explain GWG.…”

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

“…However, it is possible and in many instances desirable to incorporate theories from behavioral science in a dynamical systems framework relevant to interventions [40,41]. The interested reader is directed to work using the theory of planned behavior (TPB) to develop a dynamic model for a behavioral intervention for weight loss [20]; meanwhile, the combination of TPB and selfregulation to model the dynamics of an adaptive intervention for gestational weight gain is explored in [21][22][23][24]. The use of self-regulation for modeling smoking cessation dynamics is explored in [25][26][27][28], while a dynamical model for social cognitive theory (SCT) in the context of improving physical activity interventions is described in [41].…”

“…Published work that examines aspects of the control systems engineering approach described in this paper in other behavioral health settings includes work on prevention of conduct disorder [8,[16][17][18], promotion of moderate-to-vigorous physical activity [19], general weight change and body composition [20], gestational weight gain [21][22][23][24], and smoking cessation [25][26][27][28].…”

Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

“…The results presented in this paper can impact not only the treatment of fibromyalgia, but also the treatment of other chronic pain conditions and the development of adaptive behavioral interventions in general (Dong et al, 2014; Timms et al, 2014). While the focus of this paper was on black-box modeling, an increasing interest in using behavior change theories to inform “just-in-time” decisions in an intervention is receiving increasing activity (Rivera & Jimison, 2013; Martin et al, 2014).…”