Cesar A. Martin

Agile science: creating useful products for behavior change in the real world

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

A Dynamical Systems Model of Social Cognitive Theory

Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of interventions for smoking cessation, weight management, and other related health outcomes. SCT and other related theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed to support new technology-driven interventions that can adapt over time for each person. This paper describes a dynamical system model of SCT using a fluid analogy scheme. A series of simulations were performed to explore and better understand SCT. The model incorporates a nonlinear feature called habituation, an important feature of behavioral response resulting from continuous stimulus. It also illustrates how control systems engineering principles provide a promising approach for advancing health behavior theory development, and for guiding the design of more potent and efficient effective interventions.

A system identification approach for improving behavioral interventions based on Social Cognitive Theory

Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering and system identification concepts in behavioral change settings. Social Cognitive Theory provides a recognized theoretical framework that can be applied to explain changes in behavior over time. Based on earlier work describing a dynamical model of this theory, a semi-physical system identification approach is developed in this paper for interventions associated with improving physical activity. An initial informative experiment that relies on prior knowledge from similar interventions is first designed to obtain basic insights regarding the dynamics of the system. Based on these results a second, optimized experiment is developed which solves a constrained optimization problem to find the intervention component profiles needed to mirror a desired behavioral pattern and to provide sufficient information that allows a more precise estimation of the parameters. A simulation study is presented to illustrate the design procedure.

The importance of behavior theory in control system modeling of physical activity sensor data

Among health behaviors, physical activity has the most extensive record of research using passive sensors. Control systems and other system dynamic approaches have long been considered applicable for understanding human behavior, but only recently has the technology provided the precise and intensive longitudinal data required for these analytic approaches. Although sensors provide intensive data on the patterns and variations of physical activity over time, the influences of these variations are often unmeasured. Health behavior theories provide an explanatory framework of the putative mediators of physical activity changes. Incorporating the intensive longitudinal measurement of these theoretical constructs is critical to improving the fit of control system model of physical activity and for advancing behavioral theory. Theory-based control models also provide guidance on the nature of the controllers which serve as the basis for just-in-time adaptive interventions based on these control system models.

A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control

Physical inactivity is a major contributor to morbidity and mortality worldwide. Many physical activity behavioral interventions at present have shown limited success addressing the problem from a long-term perspective that includes maintenance. This paper proposes the design of a decision algorithm for a mobile and wireless health (mHealth) adaptive intervention that is based on control engineering concepts. The design process relies on a behavioral dynamical model based on Social Cognitive Theory (SCT), with a controller formulation based on hybrid model predictive control (HMPC) being used to implement the decision scheme. The discrete and logical features of HMPC coincide naturally with the categorical nature of the intervention components and the logical decisions that are particular to an intervention for physical activity. The intervention incorporates an online controller reconfiguration mode that applies changes in the penalty weights to accomplish the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the controller using a hypothetical model for physical activity interventions, under realistic conditions.

An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation

This paper presents an identification test monitoring procedure for multivariable systems whose purpose is to define an experiment that is both sufficiently informative for identification purposes and of the shortest duration possible, given predefined levels of accuracy in the model. The procedure relies on uncertainty regions resulting from frequency-domain transfer function estimation that is performed during experimental execution. To obtain independent-in-frequency signals for estimation, input design relying on multi-sinusoidal signals with “zippered” power spectra is developed. Given the various approaches available for computing statistically-based uncertainty in the frequency domain, the method that offers the most general conditions with the least a priori information about the output noise structure is selected. Based on the computed uncertainties and user-defined bounds, a stopping criterion for the identification test is developed. Results are evaluated with a simulation study involving a representative process model. This includes a performance evaluation of the technique under various distinct noise models.

Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention

BACKGROUND Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. METHOD A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e.,

System identification of Just Walk: A behavioral mHealth intervention for promoting physical activity

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Control Systems Engineering for Optimizing Behavioral mHealth Interventions

1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. RESULTS Participants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. CONCLUSION The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

An enhanced identification test monitoring procedure for MIMO systems relying on uncertainty estimates

There is significant evidence to show that physical activity reduces the risk of many chronic diseases. With the rise of mobile health (mHealth) technologies, one promising approach is to design interventions that are responsive to an individuals changing needs. This is the overarching goal of Just Walk, an intensively adaptive physical activity intervention that has been designed on the basis of system identification and control engineering principles. Features of this intervention include the use of multisine signals as pseudo-random inputs for providing daily step goals and reward targets for participants, and an unconventional ARX estimation-validation procedure applied to judiciously-selected data segments that seeks to balance predictive ability over validation data segments with overall goodness of fit. Analysis of the estimated models provides important clues to individual participant characteristics that influence physical activity. The insights gained from black-box modeling are critical to building semi-physical models based on a dynamic extension of Social Cognitive Theory.