Improving the study of brain-behavior relationships by revisiting basic assumptions.

Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Health behavior changes, such as prevention of suicidal thoughts and behaviors, smoking, drug use, and alcohol use; and the promotion of mental health, sleep, and physical activities, and decreases in sedentary behavior, are difficult to sustain. The ILHBN is a cooperative agreement network funded jointly by seven participating units within the National Institutes of Health to collaboratively study how factors that occur in individuals' everyday life and in their natural environment influence the success of positive health behavior changes. This article discusses how information collected using smartphones, wearables, and other devices can provide helpful active and passive reflections of the participants' extent of risk and resources at the moment for an extended period of time. However, successful engagement and retention of participants also require tailored adaptations of study designs, measurement tools, measurement intervals, study span, and device choices that create hurdles in integrating (harmonizing) data from multiple studies. We describe some of the challenges, opportunities, and solutions that emerged from harmonizing intensive longitudinal data under heterogeneous study and participant characteristics within the ILHBN, and share some tools and recommendations to facilitate future data harmonization efforts.

Blinding efficacy and adverse events following repeated transcranial alternating current, direct current, and random noise stimulation.

As transcranial electrical stimulation (tES) protocols advance, assumptions underlying the technique need to be retested to ensure they still hold. Whilst the safety of stimulation has been demonstrated mainly for a small number of sessions, and small sample size, adverse events (AEs) following multiple sessions remain largely untested. Similarly, whilst blinding procedures are typically assumed to be effective, the effect of multiple stimulation sessions on the efficacy of blinding procedures also remains under question. This is especially relevant in multisite projects where small unintentional variations in protocol could lead to inter-site difference. We report AE and blinding data from 1,019 participants who received up to 11 semi-consecutive sessions of active or sham transcranial alternating current stimulation (tACS), direct current stimulation (tDCS), and random noise stimulation (tRNS), at 4 sites in the UK and US. We found that AEs were often best predicted by factors other than tES, such as testing site or session number. Results from the blinding analysis suggested that blinding was less effective for tDCS and tACS than tRNS. The occurrence of AEs did not appear to be linked to tES despite the use of smaller electrodes or repeated delivery. However, blinding efficacy was impacted in tES conditions with higher cutaneous sensation, highlighting a need for alternative stimulation blinding protocols. This may be increasingly necessary in studies wishing to deliver stimulation with higher intensities.

Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol.

Background: Recent advances in mobile and wearable technologies have led to new forms of interventions, called "Just-in-Time Adaptive Interventions" (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology. Methods: The study involves a year-long deployment of HeartSteps, a JITAI for physical activity and sedentary behavior, with 96 sedentary, overweight, but otherwise healthy adults. The central purpose is twofold: (1) to support the development of modeling approaches for operationalizing dynamic, mathematically rigorous theories of health behavior; and (2) to serve as a testbed for the development of learning algorithms that JITAIs can use to individualize intervention provision in real time at multiple timescales. Discussion and Conclusions: We outline an innovative modeling paradigm to model and use ILD in real- or near-time to individually tailor JITIAs.

BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data.

In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.

Reducing Motion Artifacts of Pulse Intervals from Photoplethysmogram of a Commercial Wristband for Heart Rate Variability Analysis.

Heart rate monitoring based on photoplethysmography (PPG) is a noninvasive and inexpensive way of measuring many important cardiovascular metrics such as heart rate and heart rate variability, and has been used in many wearable devices. Unfortunately, the accuracy of the measurements is compromised by motion artifacts. We propose a theoretically sound method to reduce the motion artifacts of heart rate sensed by a commercial wristband. This method is based on outlier detection and singular spectrum analysis which enables us to reduce the movement-related noise in non-stationary signals. The results suggest that this method exhibits high correspondence to the simultaneously measured heart rate using ECG. Several metrics of heart rate variability computed from cleaned data also indicate high agreement with those obtained from ECG.

Investigating a classical neuropsychological test in a real world context.

This study was performed to investigate the validity of a real world version of the Trail Making Test (TMT) across age strata, compared to the current standard TMT which is delivered using a pen-paper protocol. We developed a real world version of the TMT, the Can-TMT, that involves the retrieval of food cans, with numeric or alphanumerical labels, from a shelf in ascending order. Eye tracking data was acquired during the Can-TMT to calculate task completion time and compared to that of the Paper-TMT. Results indicated a strong significant correlation between the real world and paper tasks for both TMTA and TMTB versions of the tasks, indicative of the validity of the real world task. Moreover, the two age groups exhibited significant differences on the TMTA and TMTB versions of both task modalities (paper and can), further supporting the validity of the real world task. This work will have a significant impact on our ability to infer skill or impairment with visual search, spatial reasoning, working memory, and motor proficiency during complex real-world tasks. Thus, we hope to fill a critical need for an exam with the resolution capable of determining deficits which subjective or reductionist assessments may otherwise miss.

Adaptive Health Coaching Technology for Tailored Interventions.

Recent advances in sensor and communications technology have enabled scalable methods for providing continuity of care to the home for patients with chronic conditions and older adults wanting to age in place. In this article we describe our framework for a health coaching platform with a dynamic user model that enables tailored health coaching messages. We have shown that this can improve coach efficiency without a loss of message quality. We also discovered many lessons for coaching technology, most demonstrating the need for more coach input on sample message content, perhaps even requiring that individual coaches be able to modify the message database directly. Overall, coaches felt that the structure of the automated message generation was useful in remembering what to say, easy to edit if necessary and especially helpful for training new health coaches.

Exploiting telerobotics for sensorimotor rehabilitation: a locomotor embodiment.

BACKGROUND: Manual treadmill training is used for rehabilitating locomotor impairments but can be physically demanding for trainers. This has been addressed by enlisting robots, but in doing so, the ability of trainers to use their experience and judgment to modulate locomotor assistance on the fly has been lost. This paper explores the feasibility of a telerobotics approach for locomotor training that allows patients to receive remote physical assistance from trainers. METHODS: In the approach, a trainer holds a small robotic manipulandum that shadows the motion of a large robotic arm magnetically attached to a locomoting patient's leg. When the trainer deflects the manipulandum, the robotic arm applies a proportional force to the patient. An initial evaluation of the telerobotic system's transparency (ability to follow the leg during unassisted locomotion) was performed with two unimpaired participants. Transparency was quantified by the magnitude of unwanted robot interaction forces. In a small six-session feasibility study, six individuals who had prior strokes telerobotically interacted with two trainers (separately), who assisted in altering a targeted gait feature: an increase in the affected leg's swing length. RESULTS: During unassisted walking, unwanted robot interaction forces averaged 3-4 N (swing-stance) for unimpaired individuals and 2-3 N for the patients who survived strokes. Transients averaging about 10 N were sometimes present at heel-strike/toe-off. For five of six patients, these forces increased with treadmill speed during stance (R2 = .99; p < 0.001) and increased with patient height during swing (R2 = .71; p = 0.073). During assisted walking, the trainers applied 3.0 ± 2.8 N (mean ± standard deviation across patients) and 14.1 ± 3.4 N of force anteriorly and upwards, respectively. The patients exhibited a 20 ± 21% increase in unassisted swing length between Days 1-6 (p = 0.058). CONCLUSIONS: The results support the feasibility of locomotor assistance with a telerobotics approach. Simultaneous measurement of trainer manipulative actions, patient motor responses, and the forces associated with these interactions may prove useful for testing sensorimotor rehabilitation hypotheses. Further research with clinicians as operators and randomized controlled trials are needed before conclusions regarding efficacy can be made.

FAST: A Novel, Executive Function-Based Approach to Cognitive Enhancement.

The present study introduces a novel cognitive intervention aimed at improving fluid intelligence (Gf), based on a framework we refer to as FAST: Flexible, Adaptive, Synergistic Training. FAST leverages a combination of novel game-based executive function (EF) training-designed specifically to enhance the likelihood of transfer-and transcranial electrical stimulation (tES), with aims to synergistically activate and strengthen mechanisms of cognitive control critical to Gf. To test our intervention, we collected three Gf measures from 113 participants [the advanced short Bochumer Matrizen-Test (BOMAT), Raven's Advanced Progressive Matrices (APM), and matrices similar to Raven's generated by Sandia labs], prior to and following one of three interventions: (1) the FAST + tRNS intervention, a combination of 30 min of daily training with our novel training game, Robot Factory, and 20 min of concurrent transcranial random noise stimulation applied to bilateral dorsolateral prefrontal cortex (DLPFC); (2) an adaptively difficult Active Control intervention comprised of visuospatial tasks that specifically do not target Gf; or (3) a no-contact control condition. Analyses of changes in a Gf factor from pre- to post-test found numerical increases for the FAST + tRNS group compared to the two control conditions, with a 0.3 SD increase relative to Active Control (p = 0.07), and a 0.19 SD increase relative to a No-contact control condition (p = 0.26). This increase was found to be largely driven by significant differences in pre- and post-test Gf as measured on the BOMAT test. Progression through the FAST training game (Robot Factory) was significantly correlated with changes in Gf. This is in contrast with progress in the Active Control condition, as well as with changes in individual EFs during FAST training, which did not significantly correlate with changes in Gf. Taken together, this research represents a useful step forward in providing new insights into, and new methods for studying, the nature of Gf and its malleability. Though our results await replication and extension, they provide preliminary evidence that the crucial characteristic of Gf may, in fact, be the ability to combine EFs rapidly and adaptively according to changing demand, and that Gf may be susceptible to targeted training.

Exploring Associations Between the Self-Reported Values, Well-Being, and Health Behaviors of Finnish Citizens: Cross-Sectional Analysis of More Than 100,000 Web-Survey Responses.

BACKGROUND: Understanding the relationship between personal values, well-being, and health-related behavior could facilitate the development of engaging, effective digital interventions for promoting well-being and the healthy lifestyles of citizens. Although the associations between well-being and values have been quite extensively studied, the knowledge about the relationship between health behaviors and values is less comprehensive. OBJECTIVE: The aim of this study was to assess retrospectively the associations between self-reported values and commitment to values combined with self-reported well-being and health behaviors from a large cross-sectional dataset. METHODS: We analyzed 101,130 anonymous responses (mean age 44.78 years [SD 13.82]; 78.88%, 79,770/101,130 women) to a Finnish Web survey, which were collected as part of a national health promotion campaign. The data regarding personal values were unstructured, and the self-reported value items were classified into value types based on the Schwartz value theory and by applying principal component analysis. Logistic and multiple linear regression were used to explore the associations of value types and commitment to values with well-being factors (happiness, communal social activity, work, and family-related distress) and health behaviors (exercise, eating, smoking, alcohol consumption, and sleep). RESULTS: Commitment to personal values was positively related to happiness (part r2=0.28), communal social activity (part r2=0.09), and regular exercise (part r2=0.06; P<.001 for all). Health, Power (social status and dominance), and Mental balance (self-acceptance) values had the most extensive associations with health behaviors. Regular exercise, healthy eating, and nonsmoking increased the odds of valuing Health by 71.7%, 26.8%, and 40.0%, respectively (P<.001 for all). Smoking, unhealthy eating, irregular exercise, and increased alcohol consumption increased the odds of reporting Power values by 27.80%, 27.78%, 24.66%, and 17.35%, respectively (P<.001 for all). Smoking, unhealthy eating, and irregular exercise increased the odds of reporting Mental balance values by 20.79%, 16.67%, and 15.37%, respectively (P<.001 for all). In addition, lower happiness levels increased the odds of reporting Mental balance and Power values by 24.12% and 20.69%, respectively (P<.001 for all). CONCLUSIONS: The findings suggest that commitment to values is positively associated with happiness and highlight various, also previously unexplored, associations between values and health behaviors.

Modulating fluid intelligence performance through combined cognitive training and brain stimulation.

It is debated whether cognitive training of specific executive functions leads to far transfer effects, such as improvements in fluid intelligence (Gf). Within this context, transcranial direct current stimulation and recently also novel protocols such as transcranial random noise and alternating current stimulation are being investigated with regards to their ability to enhance cognitive training outcomes. We compared the effects of four different transcranial electrical brain stimulation protocols in combination with nine daily computerized training sessions on Gf. 82 participants were randomly assigned to receive transcranial direct current stimulation (tDCS), random noise stimulation (tRNS), multifocal alternating current stimulation at 40 Hz (mftACS), or multifocal tDCS (mftDCS) in combination with an adaptive and synergistic executive function (EF) training, or to a no-contact control group. EF training consisted of gamified tasks drawing on isolated as well as integrated executive functions (working memory, inhibition, cognitive flexibility). Transfer was assessed with a combined measure of Gf including three established tests (Bochumer Matrizentest - BOMAT, Raven's Advanced Progressive Matrices - RAPM, and Sandia Matrices). We found significant improvements in Gf for the tDCS, mftDCS, and tRNS groups when compared with the no-contact group. In contrast, the mftACS group did not improve significantly and showed a similar pattern as the no-contact group. Mediation analyses indicated that the improvement in Gf was mediated through game progression in the mftDCS and tRNS group. Electrical brain stimulation in combination with sustained EF training can lead to transfer effects in Gf, which are mediated by training progression.

NUCare: Advancing research on technological integration for self-management in the aging population.

BACKGROUND: The Center for Technology in Support of Self-Management and Health (NUCare) is an exploratory research center funded by the National Institute of Nursing Research's P20 mechanism positioned to conduct rigorous research on the integration of technology in the self-management of the older adult population. PURPOSE: The purpose of this paper is to describe the development and application of an evaluation plan and preliminary evaluation results from the first year of implementation. METHODS: This evaluation plan is derived from and is consistent with Dorsey et al.'s (2014) logic model. Dorsey's model provided guidelines for evaluating sustainability, leveraging of resources, and interdisciplinary collaboration within the center. DISCUSSION: Preliminary results and strategies for addressing findings from the first year of evaluation are discussed. A secondary aim of this paper is to showcase the relevance of this center to the advancement and maintenance of health in the aging population.

Can accelerometry data improve estimates of heart rate variability from wrist pulse PPG sensors?

A key prerequisite for precision medicine is the ability to assess metrics of human behavior objectively, unobtrusively and continuously. This capability serves as a framework for the optimization of tailored, just-in-time precision health interventions. Mobile unobtrusive physiological sensors, an important prerequisite for realizing this vision, show promise in implementing this quality of physiological data collection. However, first we must trust the collected data. In this paper, we present a novel approach to improving heart rate estimates from wrist pulse photoplethysmography (PPG) sensors. We also discuss the impact of sensor movement on the veracity of collected heart rate data.

Development of a dynamic computational model of social cognitive theory.

Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems. SCT constructs, such as reciprocal determinism, are inherently dynamical in nature, but SCT has not been modeled as a dynamical system. This paper describes the development of a dynamical system model of SCT using fluid analogies and control systems principles drawn from engineering. Simulations of this model were performed to assess if the model performed as predicted based on theory and empirical studies of SCT. This initial model generates precise and testable quantitative predictions for future intensive longitudinal research. Dynamic modeling approaches provide a rigorous method for advancing health behavior theory development and refinement and for guiding the development of more potent and efficient interventions.

Advancing Models and Theories for Digital Behavior Change Interventions.

To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.

Design and Evaluation of an Interactive Exercise Coaching System for Older Adults: Lessons Learned.

Although the positive effects of exercise on the well-being and quality of independent living for older adults are well accepted, many elderly individuals lack access to exercise facilities, or the skills and motivation to perform exercise at home. To provide a more engaging environment that promotes physical activity, various fitness applications have been proposed. Many of the available products, however, are geared toward a younger population and are not appropriate or engaging for an older population. To address these issues, we developed an automated interactive exercise coaching system using the Microsoft Kinect. The coaching system guides users through a series of video exercises, tracks and measures their movements, provides real-time feedback, and records their performance over time. Our system consists of exercises to improve balance, flexibility, strength, and endurance, with the aim of reducing fall risk and improving performance of daily activities. In this paper, we report on the development of the exercise system, discuss the results of our recent field pilot study with six independently living elderly individuals, and highlight the lessons learned relating to the in-home system setup, user tracking, feedback, and exercise performance evaluation.

Behavioral informatics: Dynamical models for measuring and assessing behaviors for precision interventions.

Poor health-related behaviors represent a major challenge to healthcare due to their significant impact on chronic and acute diseases and their effect on the quality of life. Recent advances in technology have enabled an unprecedented opportunity to assess objectively, unobtrusively and continuously human behavior and have opened the possibility of optimizing individual-tailored, precision interventions within the framework of behavioral informatics. A key prerequisite for this optimization is the ability to assess and predict effects of interventions. This is potentially achievable with computational models of behavior and behavior change. In this paper we describe various approaches to computational modeling and describe a new hybrid model based on a dual process theoretical framework for behavior change. The model leverages cognitive learning theories and is shown to be consistent with mobile intervention data. We also illustrate how system-theoretic approaches can be used to assess the effect of coaching and participants' health behaviors.

Real-time measures of context to improve fall-detection models.

Real-time fall detection has been a challenging area of research and even more challenging as a viable commercial service, given the need for near perfect classification algorithms. True fall events are rare is monitored data sets, whereas confounding events for automated algorithms are quite frequent. In this paper we describe a decision theoretic approach to classification and alerting that incorporates context, such as location and activities, to improve probability and utility estimates for new classes, including near falls and known confounding events. We describe how to use monitored context to provide real-time assessment of true patient state to improve training data sets, as well as the use of context in improving classification, detection and alerting.

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care.

Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.