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. Asian Americans are a rapidly growing segment of the older adult population, who are less likely to meet these guidelines, and are frequently under-represented in clinical trials. This quasi-experimental pilot study evaluated the feasibility, acceptability, and preliminary effectiveness of a culturally tailored walking program to improve PA and social engagement for older Chinese Americans in Boston, MA. Participants at two community organizations were assigned to an enhanced walking or walking only condition for 12 weeks. Mixed effect repeated measures analysis addressed the study aims. The enhanced walking group (intervention) had fewer steps at baseline and less of a reduction in steps by 12 weeks as compared with the walking only (control) condition. Mean social engagement scores were significantly higher at 12 weeks (p = .03) for the intervention group. A culturally tailored walking intervention was feasible and acceptable for older Chinese Americans, improving social engagement and PA scores.

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.

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.

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.

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.

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.

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. HRV is affected by multiple factors of which some are unmodifiable (such as age and gender) but many are related to daily lifestyle choices (e.g. alcohol consumption, physical activity, sleeping times). The purpose of this study was to investigate the association of these aforementioned factors on HRV-based recovery during sleep on a large sample. Variable importance measures yielded by random forest were used for identifying the most relevant predictors of sleep-time recovery. The results emphasize the disturbing effects of alcohol consumption on sleep-time recovery. Good physical fitness is associated to good recovery, but acute physical activity seems to challenge or delay the recovery process for the next night. Longer sleeping time enables more recovery minutes, but the proportion of recovery (i.e. recovery efficiency) seems to peak around 7.0-7.25 hours of sleep.

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.

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. We used an insight-based methodology to assess the effectiveness of the visualizations. The results show that adequate visualization of holistic health data helps users without medical background to better understand the overall health situation and possible health risks related to lifestyles. Furthermore, we found that the application of insight-based methodology in the health and wellbeing domain remains unexplored and additional research and methodology development are needed.

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. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

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. This lack of understanding may make clinical inferences from test results about health or disease state less accurate because important aspects of the task such as variability or fatigue are unmeasured. To overcome these limitations, we enhanced the tapper with a sensor that enables us to more fully characterize all the aspects of tapping. This modification enabled us to decompose the tapping performance into six component phases and represent each phase with a set of parameters having clear functional interpretation. This results in a set of 29 total parameters for each trial, including change in tapping over time, and trial-to-trial and tap-to-tap variability. These parameters can be used to more precisely link different aspects of cognition or motor function to tapping behavior. We demonstrate the benefits of this new instrument with a simple hypothesis-driven trial comparing single and dual-task tapping.

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. An empirical evaluation of the model suggests that it is possible to use the game data to estimate the parameters of the underlying cognitive processes and using the values of the parameters to estimate the TMT performance. Cognitive measures and trends in these measures can be used to identify individuals for further assessment, to provide a mechanism for improving the early detection of neurological problems, and to provide feedback and monitoring for cognitive interventions in the home.

Unobtrusive measurement of daily computer use to detect mild cognitive impairment.

BACKGROUND: Mild disturbances of higher order activities of daily living are present in people diagnosed with mild cognitive impairment (MCI). These deficits may be difficult to detect among those still living independently. Unobtrusive continuous assessment of a complex activity such as home computer use may detect mild functional changes and identify MCI. We sought to determine whether long-term changes in remotely monitored computer use differ in persons with MCI in comparison with cognitively intact volunteers. METHODS: Participants enrolled in a longitudinal cohort study of unobtrusive in-home technologies to detect cognitive and motor decline in independently living seniors were assessed for computer use (number of days with use, mean daily use, and coefficient of variation of use) measured by remotely monitoring computer session start and end times. RESULTS: More than 230,000 computer sessions from 113 computer users (mean age, 85 years; 38 with MCI) were acquired during a mean of 36 months. In mixed-effects models, there was no difference in computer use at baseline between MCI and intact participants controlling for age, sex, education, race, and computer experience. However, over time, between MCI and intact participants, there was a significant decrease in number of days with use (P = .01), mean daily use (∼1% greater decrease/month; P = .009), and an increase in day-to-day use variability (P = .002). CONCLUSIONS: Computer use change can be monitored unobtrusively and indicates individuals with MCI. With 79% of those 55 to 64 years old now online, this may be an ecologically valid and efficient approach to track subtle, clinically meaningful change with aging.