Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions.

Background: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes.

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.

Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention.

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person's evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.

Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification.

Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM).