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.

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.

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.

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.

A framework for harmonizing sensor data to support embedded health assessment.

The use of in-home and mobile sensing is likely to be a key component of future care and has recently been studied by many research groups world-wide. Researchers have shown that embedded sensors can be used for health assessment such as early illness detection and the management of chronic health conditions. However, research collaboration and data sharing have been hampered by disparate sets of sensors and data collection methods. To date, there have been no studies to investigate common measures that can be used across multiple sites with different types of sensors, which would facilitate large scale studies and reuse of existing datasets. In this paper, we propose a framework for harmonizing heterogeneous sensor data through an intermediate layer, the Conceptual Sensor, which maps physical measures to clinical space. Examples are included for sleep quality and ambulatory physical function.

Comparative assessment of sleep quality estimates using home monitoring technology.

Poor sleep quality is associated with chronic diseases, weight increase and cognitive dysfunction. Home monitoring solutions offer the possibility of offering tailored sleep coaching interventions. There are several new commercially available devices for tracking sleep, and although they have been tested in sleep laboratories, little is known about the errors associated with the use in the home. To address this issue we performed a study in which we compared the sleep monitoring data from two commercially available systems: Fitbit One and Beddit Pro. We studied 23 subjects using both systems over a week each and analyzed the degree of agreement for different aspects of sleep. The results suggest the need for individual-tailoring of the estimation process. Not only do these models address improved accuracy of sleep quality estimates, but they also provide a framework for the representation and harmonization for monitoring data across studies.

Quantification of human movement for assessment in automated exercise coaching.

Quantification of human movement is a challenge in many areas, ranging from physical therapy to robotics. We quantify of human movement for the purpose of providing automated exercise coaching in the home. We developed a model-based assessment and inference process that combines biomechanical constraints with movement assessment based on the Microsoft Kinect camera. To illustrate the approach, we quantify the performance of a simple squatting exercise using two model-based metrics that are related to strength and endurance, and provide an estimate of the strength and energy-expenditure of each exercise session. We look at data for 5 subjects, and show that for some subjects the metrics indicate a trend consistent with improved exercise performance.

Mobile health technology evaluation: the mHealth evidence workshop.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

The role of technology and engineering models in transforming healthcare.

The healthcare system is in crisis due to challenges including escalating costs, the inconsistent provision of care, an aging population, and high burden of chronic disease related to health behaviors. Mitigating this crisis will require a major transformation of healthcare to be proactive, preventive, patient-centered, and evidence-based with a focus on improving quality-of-life. Information technology, networking, and biomedical engineering are likely to be essential in making this transformation possible with the help of advances, such as sensor technology, mobile computing, machine learning, etc. This paper has three themes: 1) motivation for a transformation of healthcare; 2) description of how information technology and engineering can support this transformation with the help of computational models; and 3) a technical overview of several research areas that illustrate the need for mathematical modeling approaches, ranging from sparse sampling to behavioral phenotyping and early detection. A key tenet of this paper concerns complementing prior work on patient-specific modeling and simulation by modeling neuropsychological, behavioral, and social phenomena. The resulting models, in combination with frequent or continuous measurements, are likely to be key components of health interventions to enhance health and wellbeing and the provision of healthcare.

Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging.

OBJECTIVES: To describe a longitudinal community cohort study, Intelligent Systems for Assessing Aging Changes, that has deployed an unobtrusive home-based assessment platform in many seniors homes in the existing community. METHODS: Several types of sensors have been installed in the homes of 265 elderly persons for an average of 33 months. Metrics assessed by the sensors include total daily activity, time out of home, and walking speed. Participants were given a computer as well as training, and computer usage was monitored. Participants are assessed annually with health and function questionnaires, physical examinations, and neuropsychological testing. RESULTS: Mean age was 83.3 years, mean years of education was 15.5, and 73% of cohort were women. During a 4-week snapshot, participants left their home twice a day on average for a total of 208 min per day. Mean in-home walking speed was 61.0 cm/s. Participants spent 43% of days on the computer averaging 76 min per day. DISCUSSION: These results demonstrate for the first time the feasibility of engaging seniors in a large-scale deployment of in-home activity assessment technology and the successful collection of these activity metrics. We plan to use this platform to determine if continuous unobtrusive monitoring may detect incident cognitive decline.

Unobtrusive in-home assessment by means of everyday computer mouse usage.

An important component of future proactive healthcare is the detection of changes in the individual's physical or cognitive performance, especially for aging and for those with neurodegenerative diseases. For a variety of reasons, the current techniques for neuropsychological assessment are not suitable for continuous monitoring and assessment. This paper proposes a technique for continuous monitoring of behaviors that could potentially be used to complement the traditional assessment techniques. In particular we monitor the movements of a computer pointing device (mouse) to assess cognitive and sensory-motor functionality of human users unobtrusively. The focus of this paper is on an approach that can be used to identify moves so that they can later be used as the basis for constructing sensory-motor measures. Due to the nature of the data the distinction between moves and pauses between moves is not immediately apparent. The segmentation of the data into moves is done by constructing an estimated distribution of the mouse cursor velocity for the entire computer session and locating a particular minimum which indicates a likely point of division between active moves and inter-move intervals. We analyzed computer usage data for 113 elderly participants over a period of two years, and the technique applied to that data was able to divide data from a session of computer usage into a series of mouse moves in 98% of observed computer sessions with a physically sensible value for the cutoff dividing moves from stops.

Estimation of rest-activity patterns using motion sensors.

Disrupted sleep patterns are a significant problem in the elderly, leading to increased cognitive dysfunction and risk of nursing home placement. A cost-effective and unobtrusive way to remotely monitor changing sleep patterns over time would enable improved management of this important health problem. We have developed an algorithm to derive sleep parameters such as bed time, rise time, sleep latency, and nap time from passive infrared sensors distributed around the home. We evaluated this algorithm using 404 days of data collected in the homes of 8 elderly community-dwelling elders. Data from this algorithm were highly correlated to ground truth measures (bed mats) and were surprisingly robust to variability in sensor layout and sleep habits.

Unobtrusive monitoring of divided attention in a cognitive health coaching intervention for the elderly.

Assessment of cognitive functionality is an important aspect of care for elders. Unfortunately, few tools exist to measure divided attention, the ability to allocate attention to different aspects of tasks. An accurate determination of divided attention would allow inference of generalized cognitive decline, as well as providing a quantifiable indicator of an important component of driving skill. We propose a new method for determining relative divided attention ability through unobtrusive monitoring of computer use. Specifically, we measure performance on a dual-task cognitive computer exercise as part of a health coaching intervention. This metric indicates whether the user has the ability to pay attention to both tasks at once, or is primarily attending to one task at a time (sacrificing optimal performance). The monitoring of divided attention in a home environment is a key component of both the early detection of cognitive problems and for assessing the efficacy of coaching interventions.

Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders.

Gait velocity has been shown to quantitatively estimate risk of future hospitalization, a predictor of disability, and has been shown to slow prior to cognitive decline. In this paper, we describe a system for continuous and unobtrusive in-home assessment of gait velocity, a critical metric of function. This system is based on estimating walking speed from noisy time and location data collected by a "sensor line" of restricted view passive infrared motion detectors. We demonstrate the validity of our system by comparing with measurements from the commercially available GAITRite walkway system gait mat. We present the data from 882 walks from 27 subjects walking at three different subject-paced speeds (encouraged to walk slowly, normal speed, or fast) in two directions through a sensor line. The experimental results show that the uncalibrated system accuracy (average error) of estimated velocity was 7.1 cm/s (SD = 11.3 cm/s), which improved to 1.1 cm/s (SD = 9.1 cm/s) after a simple calibration procedure. Based on the average measured walking speed of 102 cm/s, our system had an average error of less than 7% without calibration and 1.1% with calibration.

Divided attention in computer game play: analysis utilizing unobtrusive health monitoring.

Divided attention is a vital cognitive ability used in important daily activities (e.g., driving), which tends to deteriorate with age. As with Alzheimer's and other neural degenerative conditions, treatment for divided attention problems is likely to be more effective the earlier it is detected. Thus, it is important that a method be found to detect changes in divided attention early on in the process, for both safety and health care reasons. We present here a new method for detecting divided attention unobtrusively, using performance on a computer game designed to force players to attend to different dimensions simultaneously in order to succeed. Should this model prove to predict scores on a standard test for divided attention, it could help to detect cognitive decline earlier in our increasingly computer-involved aging population, providing treatment efficacy benefits to those who will experience cognitive decline.

Unobtrusive assessment of walking speed in the home using inexpensive PIR sensors.

Walking speed and activity are important measures of functional ability in the elderly. Our earlier studies have suggested that continuous monitoring may allow us to detect changes in walking speed that are also predictive of cognitive changes. We evaluated the use of passive infrared (PIR) sensors for measuring walking speed in the home on an ongoing basis. In comparisons with gait mat estimates (ground truth) and the results of a timed walk test (the clinical gold standard) in 18 subjects, we found that the clinical measure overestimated typical walking speed, and the PIR sensor estimations of walking speed were highly correlated to actual gait speed. Examination of in-home walking patterns from more than 100,000 walking speed samples for these subjects suggested that we can accurately assess walking speed in the home. We discuss the potential of this approach for continuous assessment.

Unobtrusive assessment of activity patterns associated with mild cognitive impairment.

BACKGROUND: Timely detection of early cognitive impairment is difficult. Measures taken in the clinic reflect a single snapshot of performance that might be confounded by the increased variability typical in aging and disease. We evaluated the use of continuous, long-term, and unobtrusive in-home monitoring to assess neurologic function in healthy and cognitively impaired elders. METHODS: Fourteen older adults 65 years and older living independently in the community were monitored in their homes by using an unobtrusive sensor system. Measures of walking speed and amount of activity in the home were obtained. Wavelet analysis was used to examine variance in activity at multiple time scales. RESULTS: More than 108,000 person-hours of continuous activity data were collected during periods as long as 418 days (mean, 315 +/- 82 days). The coefficient of variation in the median walking speed was twice as high in the mild cognitive impairment (MCI) group (0.147 +/- 0.074) as compared with the healthy group (0.079 +/- 0.027; t(11) = 2.266, P < .03). Furthermore, the 24-hour wavelet variance was greater in the MCI group (MCI, 4.07 +/- 0.14; healthy elderly, 3.79 +/- 0.23; F = 7.58, P

Gesture recognition for interactive exercise programs.

This paper describes a gesture recognition system which can recognize seated exercises that will be incorporated into an in-home automated interactive exercise program. Hidden Markov Models (HMMs) are used as a motion classifier, with motion features extracted from the grayscale images and the location of the subject's head estimated at initialization. An overall recognition rate of 94.1% is achieved.

Towards fish-eye camera based in-home activity assessment.

Indoors localization, activity classification, and behavioral modeling are increasingly important for surveillance applications including independent living and remote health monitoring. In this paper, we study the suitability of fish-eye cameras (high-resolution CCD sensors with very-wide-angle lenses) for the purpose of monitoring people in indoors environments. The results indicate that these sensors are very useful for automatic activity monitoring and people tracking. We identify practical and mathematical problems related to information extraction from these video sequences and identify future directions to solve these issues.

Unobtrusive assessment of mobility.

This paper describes a model-based approach to the unobtrusive monitoring of elders in their home environment to assess their health, daily activities, and cognitive function. We present a semi-Markov model representation with automated learning to characterize individual elder's mobility in the home environment. The assessed mobility can be used to characterize the elder's speed of walking and can serve as one of the predictors of future cognitive functionality and the ability of elders to live independently in their home environment.