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., $0.20-$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 ±â€¯6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ±â€¯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.

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 = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant's median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = -247, t = -5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

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. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions. OBJECTIVE: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions. OVERVIEW: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step. IMPLICATIONS: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

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). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison of HRM curves of the rDNA internal transcribed spacer (ITS) region from 51 strains of 18 fungal species using DTW distances allowed accurate classification and clustering of all 51 strains. The utility of DTW distances for species identification was demonstrated by matching HRM curves from 243 previously identified clinical isolates against a database of curves from standard reference strains. The results revealed a number of prior misclassifications, discriminated species that are not resolved by routine phenotypic tests, and accurately identified all 243 test strains. In addition to DTW, several other distance functions, Edit Distance on Real sequence (EDR) and Shape-based Distance (SBD), showed promise. It is concluded that DTW-based distances provide a useful metric for the automated identification of fungi based on HRM curves of the ITS region and that this provides the foundation for a robust and automatable method applicable to the clinical setting.