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. We conducted a total of 14 semi-structured in-depth open-ended dyadic interviews with OSA patients and their partners. Phase 1 of the development was to inform key functions of an engaging tool. Phase 2 focused on developing functions to engage positive conversation in the dyad and obtained feedback for this initial prototype. RESULTS: The OurSleepKit prototype was developed and demonstrated high acceptability and engagement. Three key functions included periodic assessments based on developmental stages of PAP treatment, a Coaching Board which provides customized and dynamically updated support content - primarily brief story-telling videos featuring real-life couples' experiences - and timely tailored prompts (for action, learning, and conversation) through push notifications in the evening to facilitate positive conversation in the dyad and offer in-the-moment support for PAP use. CONCLUSIONS: Going beyond the traditional and prevailing view of PAP use as an individual phenomenon, OurSleepKit is a novel mHealth intervention engaging both the patient and partner holding great promise to promote PAP adherence.

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.

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.

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.

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. These principles are applied in the described system for assisting older adults in meeting their physical exercise goals with a tailored interactive video system with just-in-time feedback and encouragement.

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. Consequently, a needs assessment was performed by using video conferencing software to conduct semi-structured interviews with 10 LDCs. Interviews were enriched through the use of stimulus materials that included the demonstration of a prototype LDC health management web/mobile app. Responses were recorded, transcribed and then analyzed. Subjects indicated that information regarding medication regimens and adherence, calendaring, and cognitive health were most needed. Participants also described needs for video calling, activity data regarding sleep and physical exercise, asynchronous communication, photo sharing, journaling, access to online health resources, real-time monitoring, an overall summary of health, and feedback/suggestions to help them improve as caregivers. In addition, all respondents estimated their usage of a LDC health management website would be at least once per week, with half indicating a desire to access the website from a smartphone. These findings are being used to inform the design of a LDC health management website to promote the meaningful involvement of distant family members in the care of older adults.

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.

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.

A socialization intervention in remote health coaching for older adults in the home.

Previous studies have shown that social ties enhance both physical and mental health, and that social isolation has been linked to increased cognitive decline. As part of our cognitive training platform, we created a socialization intervention to address these issues. The intervention is designed to improve social contact time of older adults with remote family members and friends using a variety of technologies, including Web cameras, Skype software, email and phone. We used usability testing, surveys, interviews and system usage monitoring to develop design guidance for socialization protocols that were appropriate for older adults living independently in their homes. Our early results with this intervention show increased number of social contacts, total communication time (we measure email, phone, and Skype usage) and significant participant satisfaction with the intervention.

Computer-related self-efficacy and anxiety in older adults with and without mild cognitive impairment.

BACKGROUND: This study examines differences in computer-related self-efficacy and anxiety in subgroups of older adults, and changes in those measures after exposure to a systematic training program and subsequent computer use. METHODS: Participants were volunteers in the Intelligent Systems for Assessment of Aging Changes study (ISAAC) carried out by the Oregon Center for Aging and Technology. Participants were administered two questionnaires before training and again 1 year later, which were related to computer self-efficacy and anxiety. Continuous recording of computer use was also assessed for a subset of participants. RESULTS: Baseline comparisons by sex, age, education, living arrangement, and computer proficiency, but not cognitive status, yielded significant differences in confidence and anxiety related to specific aspects of computer use. At 1-year follow-up, participants reported less anxiety and greater confidence. However, the benefits of training and exposure varied by group and task. Comparisons based on cognitive status showed that the cognitively intact participants benefited more from training and/or experience with computers than did participants with mild cognitive impairment (MCI), who after 1 year continued to report less confidence and more anxiety regarding certain aspects of computer use. CONCLUSION: After 1 year of consistent computer use, cognitively intact participants in this study reported reduced levels of anxiety and increased self-confidence in their ability to perform specific computer tasks. Participants with MCI at baseline were less likely to demonstrate increased efficacy or confidence than their cognitively intact counterparts.

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.

Subjective risk vs. objective risk can lead to different post-cesarean birth decisions based on multiattribute modeling.

OBJECTIVE: To compare birth recommendations for pregnant women with a prior cesarean produced from a decision model using absolute risks vs. one using subjective interpretation of the same risks: (1) a multiattribute decision model based on patient prioritization of risks (subjective risk) and (2) a hybrid model that used absolute risks (objective risk). STUDY DESIGN AND SETTING: The subjective risk multiattribute model used the Analytic Hierarchy Process to elicit priorities for maternal risks, neonatal risks, and the delivery experience from 96 postnatal women with a prior cesarean. The hybrid model combined the priorities for delivery experience obtained in the first model with the unadjusted absolute risk values. RESULTS: The multiattribute model generated more recommendations for repeat cesarean delivery than the hybrid model: 73% vs. 18%, (P-value <0.001). The multiattribute model favored repeat cesarean because women heavily prioritized avoiding any risk (even rare risk) to the infant. The hybrid model favored the trial of labor because of lower probabilities of risk to the mother and its high success rate of vaginal birth after cesarean. CONCLUSION: This study highlights the importance of patients and clinicians discussing the patient's priorities regarding the risks and other nonclinical considerations that may be important to her in the birthing decision.

Models of cognitive performance based on home monitoring data.

Modeling cognitive performance using home monitoring data is a new approach to managing neurologic conditions and for monitoring the effects of cognitive exercise interventions. The data consists of activity monitoring from motion sensors and specific cognitive metrics embedded within our adaptive computer games. The frequency and continuity of data collection allows us to analyze within subject trends of cognitive performance and to assess day to day variability. This approach provides a framework for clinicians and care managers to have the potential of detecting patients' cognitive problems early and to have timely feedback on treatment interventions.

The exploration & forensic analysis of computer usage data in the elderly.

Unobtrusive in-home computer monitoring could one day be used to deliver cost-effective diagnostic information about the cognitive abilities of the elderly. This could allow for early detection of cognitive impairment and would additionally be coupled with the cost advantages that are associated with a semi-automated system. Before using the computer usage data to draw conclusions about the participants, we first needed to investigate the nature of the data that was collected. This paper represents a forensics style analysis of the computer usage data that is being collected as part of a larger study of cognitive decline, and focuses on the isolation and removal of non user-generated activities that were recorded by our computer monitoring software (CMS).

Usability testing finds problems for novice users of pediatric portals.

OBJECTIVE: Patient portals may improve pediatric chronic disease outcomes, but few have been rigorously evaluated for usability by parents. Using scenario-based testing with think-aloud protocols, we evaluated the usability of portals for parents of children with cystic fibrosis, diabetes or arthritis. DESIGN Sixteen parents used a prototype and test data to complete 14 tasks followed by a validated satisfaction questionnaire. Three iterations of the prototype were used. MEASUREMENTS: During the usability testing, we measured the time it took participants to complete or give up on each task. Sessions were videotaped and content-analyzed for common themes. Following testing, participants completed the Computer Usability Satisfaction Questionnaire which measured their opinions on the efficiency of the system, its ease of use, and the likeability of the system interface. A 7-point Likert scale was used, with seven indicating the highest possible satisfaction. RESULTS: Mean task completion times ranged from 73 (+/- 61) seconds to locate a document to 431 (+/- 286) seconds to graph laboratory results. Tasks such as graphing, location of data, requesting access, and data interpretation were challenging. Satisfaction was greatest for interface pleasantness (5.9 +/- 0.7) and likeability (5.8 +/- 0.6) and lowest for error messages (2.3 +/- 1.2) and clarity of information (4.2 +/- 1.4). Overall mean satisfaction scores improved between iteration one and three. CONCLUSIONS: Despite parental involvement and prior heuristic testing, scenario-based testing demonstrated difficulties in navigation, medical language complexity, error recovery, and provider-based organizational schema. While such usability testing can be expensive, the current study demonstrates that it can assist in making healthcare system interfaces for laypersons more user-friendly and potentially more functional for patients and their families.

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.

A framework for cognitive monitoring using computer game interactions.

Many countries are faced with a rapidly increasing economic and social challenge of caring for their elderly population. Cognitive issues are at the forefront of the list of concerns. People over the age of 75 are at risk for medically related cognitive decline and confusion, and the early detection of cognitive problems would allow for more effective clinical intervention. However, standard cognitive assessments are not diagnostically sensitive and are performed infrequently. To address these issues, we have developed a set of adaptive computer games to monitor cognitive performance in a home environment. Assessment algorithms for various aspects of cognition are embedded in the games. The monitoring of these metrics allows us to detect within subject trends over time, providing a method for the early detection of cognitive decline. In addition, the real-time information on cognitive state is used to adapt the user interface to the needs of the individual user. In this paper we describe the software architecture and methodology for monitoring cognitive performance using data from natural computer interactions in a home setting.

In-home monitoring of persons with dementia: ethical guidelines for technology research and development.

Innovative technologies are rapidly emerging that offer caregivers the support and means to assist older adults with cognitive impairment to continue living "at home." Technology research and development efforts applied to older adults with dementia invoke special grant review and institutional review board concerns, to ensure not only safe but also ethically appropriate interventions. Evidence is emerging, however, that tensions are growing between innovators and reviewers. Reviewers with antitechnology biases are in a position to stifle needed innovation. Technology developers who fail to understand the clinical and caregiving aspects of dementia may design applications that are not in alignment with users' capabilities. To bridge this divide, we offer an analysis of the ethical issues surrounding home monitoring, a model framework, and ethical guidelines for technology research and development for persons with Alzheimer's disease and their caregivers.