Leveraging intensive longitudinal data to better understand health behaviors.

Behavioral scientists have historically relied on static modeling methodologies. The rise in mobile and wearable sensors has made intensive longitudinal data (ILD) -- behavioral data measured frequently over time -- increasingly available. Consequently, analytical frameworks are emerging that seek to reliably quantify dynamics reflected in these data.

A Hybrid Model Predictive Control Strategy for Optimizing a Smoking Cessation Intervention.

The chronic, relapsing nature of tobacco use represents a major challenge in smoking cessation treatment. Recently, novel intervention paradigms have emerged that seek to adjust treatments over time in order to meet a patient's changing needs. This article demonstrates that Hybrid Model Predictive Control (HMPC) offers an appealing framework for designing these optimized, time-varying smoking cessation interventions.

Continuous-Time System Identification of a Smoking Cessation Intervention.

Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behavior change. System identification problems that draw from two modeling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems.

Control Systems Engineering for Understanding and Optimizing Smoking Cessation Interventions.

Cigarette smoking remains a major public health issue. Despite a variety of treatment options, existing intervention protocols intended to support attempts to quit smoking have low success rates. An emerging treatment framework, referred to as adaptive interventions in behavioral health, addresses the chronic, relapsing nature of behavioral health disorders by tailoring the composition and dosage of intervention components to an individual's changing needs over time.

A dynamical systems approach to understanding self-regulation in smoking cessation behavior change.

Introduction: Self-regulation, a key component of the addiction process, has been challenging to model precisely in smoking cessation settings, largely due to the limitations of traditional methodological approaches in measuring behavior over time. However, increased availability of intensive longitudinal data (ILD) measured through ecological momentary assessment facilitates the novel use of an engineering modeling approach to better understand self-regulation.

Methods: Dynamical systems modeling is a mature engineering methodology that can represent smoking cessation as a self-regulation process.

Interprofessional Resource Centre: a knowledge translation strategy.

The Interprofessional Resource Centre (IRC) was based on an extensive literature search and a provincial consultative process that involved administrators, health care providers, educators, preceptors, and alternative and complementary health care providers from different disciplines. Information from the literature review was synthesized into a logic model that served as a preliminary outline for the IRC to be further developed during the stakeholder consultation. The findings from the literature were triangulated with the opinions of different groups of key stakeholders who participated in three different methods of data collection: 1) a large-scale deliberative survey, 2) an in-person dialogue, and 3) targeted questionnaires.