Let's Walk: A Quasi-Experimental Multi-Component Intervention to Improve Physical Activity and Social Engagement for Older Chinese American Adults.

Physical activity (PA) is critical for healthy aging, yet < 16% of U.S. older adults meet federal recommendations for moderate to vigorous PA.

Unobtrusive, Continuous LIDAR-Based Measurement of Gait Characteristics at Home.

This paper describes a novel approach to the unobtrusive assessment of a subset of gait characteristics using a light detection and ranging (LIDAR) device. The developed device is poised to enable unobtrusive, nearly continuous monitoring and inference of patients' gait characteristics to assess physical and cognitive states. The device provides a rapidly sampled signal representing the distance of a participant's body from the LIDAR device.

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.

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.

Developing OurSleepKit: A Couple-focused mHealth Tool to Support Adherence to Positive Airway Pressure Treatment.

Background And Aim: Existing interventions promoting positive airway pressure (PAP) adherence focus only on the diagnosed individual, despite the fact that partners are often the most impacted by obstructive sleep apnea (OSA), and are delivered mostly by health professionals, with limited success. The goal of this work is to develop a prototype of OurSleepKit, a couple-focused mobile health (mHealth) tool to coach mutual engagement and promote adherence to PAP treatment.

Methods: We used an iterative participatory approach working with future end users of OurSleepKit to support the development of this prototype.

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.

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.

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.

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.

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.

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.

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 shape of health: A comparison of five alternative ways of visualizing personal health and wellbeing.

The combination of clinical and personal health and wellbeing data can tell us much about our behaviors, risks and overall status. The way this data is visualized may affect our understanding of our own health. To study this effect, we conducted a small experiment with 30 participants in which we presented a holistic overview of the health and wellbeing of two modeled individuals, one of them with metabolic syndrome.

Remote health coaching for interactive exercise with older adults in a home environment.

Optimal health coaching interventions are tailored to individuals' needs, preferences, motivations, barriers, timing, and readiness to change. Technology approaches are useful in both monitoring a user's adherence to their behavior change goals and also in providing just-in-time feedback and coaching messages. User models that incorporate dynamically varying behavior change variables with algorithms that trigger tailored messages provide a framework for making health interventions more effective.

Exploratory analysis of associations between individual lifestyles and heart rate variability -based recovery during sleep.

Sleep is the most important period for recovering from daily stress and load. Assessment of the stress recovery during sleep is therefore, an important metric for care and quality of life. Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system (ANS) activity, and HRV-based methods can be used to assess physiological recovery, characterized by parasympathetic domination of the ANS.

Time-series modeling of long-term weight self-monitoring data.

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes.

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.

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.

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.

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.

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.

A statistical characterization of the finger tapping test: modeling, estimation, and applications.

Sensory-motor performance is indicative of both cognitive and physical function. The Halstead-Reitan finger tapping test is a measure of sensory-motor speed commonly used to assess function as part of a neuropsychological evaluation. Despite the widespread use of this test, the underlying motor and cognitive processes driving tapping behavior during the test are not well characterized or understood.

Assessing executive function using a computer game: computational modeling of cognitive processes.

Early and reliable detection of cognitive decline is one of the most important challenges of current healthcare. In this project, we developed an approach whereby a frequently played computer game can be used to assess a variety of cognitive processes and estimate the results of the pen-and-paper trail making test (TMT)--known to measure executive function, as well as visual pattern recognition, speed of processing, working memory, and set-switching ability. We developed a computational model of the TMT based on a decomposition of the test into several independent processes, each characterized by a set of parameters that can be estimated from play of a computer game designed to resemble the TMT.

A mobile/web app for long distance caregivers of older adults: functional requirements and design implications from a user centered design process.

Recent trends of population aging and globalization have required an increasing number of individuals to act as long distance caregivers (LDCs) to aging family members. Information technology solutions may ease the burden placed on LDCs by providing remote monitoring, easier access to information and enhanced communication. While some technology tools have been introduced, the information and technology needs of LDCs in particular are not well understood.

Systems modeling of behavior change: two illustrations from optimized interventions for improved health outcomes.

Systems science techniques are becoming increasingly important as tools for modeling behavior change and as enablers for delivering more effective tailored interventions [1], [2]. Systems approaches offer a fresh perspective on the understanding of behavior change, providing a means for better capturing complexity, exposing gaps in the existing body of knowledge, enhancing the predictive capability of models, and ultimately enabling optimal decision making in behavioral intervention settings.