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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Oregon Health & Science University

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.

Estimation of rest-activity patterns using motion sensors.

Ubiquitous and unobtrusive in-home monitoring has the potential to detect physical and mental decline earlier and with higher precision than current clinical methods. However, given that this field is in its infancy, the specific metrics through which these changes are detected are not well defined. The work presented here offers room-transitions, the act of physically moving from one area of a home to another, as a quantifiable measure for total daily activity that can be inferred from a network of passive infrared sensors. We describe a method to calculate this value from raw sensor data and validate this method on an acute health event: an 18-day quarantine at a retirement community that was initiated in the midst of a norovirus outbreak. The results from this case study show that room-transition values increased significantly as subjects remained in their homes during the quarantine, demonstrating a mean increase of 12 transitions per day. Furthermore, a time-adjusted measure of room-transitions is examined that did not significantly change across the group. Finally, the healthy subjects and those that fell ill were analyzed separately, and significant differences were found between them for both the raw and time-adjusted metrics. As detection algorithms improve, these types of measures may be useful in the early detection of a change in health status.

Measuring changes in activity patterns during a norovirus epidemic at a retirement community.

Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patient's own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the "gold-standard" sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.

Unobtrusive classification of sleep and wakefulness using load cells under the bed.

We explored the relationship between sleep disturbances and mild cognitive impairment (MCI) in community-dwelling seniors. Recent evidence suggests that sleep habits are differentially compromised in different subtypes of MCI, but the relationship between sleep disruption and MCI remains poorly understood. We gathered daily objective measures of sleep disturbance from 45 seniors, including 16 with MCI (mean age, 86.9±4.3 y), over a 6-month period. We also collected self-report measures of sleep disturbance. Although there were no differences between groups in any of our self-report measures, we found that amnestic MCI (aMCI) volunteers had less disturbed sleep than both nonamnestic MCI (naMCI) and cognitively intact volunteers, as measured objectively by movement in bed at night (F2,1078=4.30, P=0.05), wake after sleep onset (F2,1078=41.6, P<0.001), and number of times up at night (F2,1078=26.7, P<0.001). The groups did not differ in total sleep time. In addition, the aMCI group had less day-to-day variability in these measures than the intact and naMCI volunteers. In general, the naMCI volunteers showed a level of disturbed sleep that was intermediate to that of aMCI and intact volunteers. These differences in sleep disruption between aMCI and naMCI may be related to differences in the pathology underlying these MCI subtypes. 

Sleep habits in mild cognitive impairment.

The relationship between recent episodes of poor sleep and cognitive testing performance in healthy cognitively intact older adults is not well understood. In this exploratory study we examined the impact of recent sleep disturbance, sleep duration, and sleep variability on cognitive performance in 63 cognitively intact older adults using a novel unobtrusive in-home sensor-based sleep assessment methodology. Specifically, we examined the impact of sleep the night prior, the week prior, and the month prior to a neuropsychological evaluation on cognitive performance. Results showed that mildly disturbed sleep the week prior and month prior to cognitive testing was associated with reduced working memory on cognitive evaluation. One night of mild sleep disturbance was not associated with decreased cognitive performance the next day. Sleep duration was unrelated to cognition. In-home, unobtrusive, sensor monitoring technologies provide a novel method for objective, long-term, and continuous assessment of sleep behavior and other everyday activities that might contribute to decreased or variable cognitive performance in healthy older adults. 

The impact of sleep on neuropsychological performance in cognitively intact older adults using a novel in-home sensor-based sleep assessment approach.

Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals' health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients' and caregivers' ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies. 

Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy.

[This corrects the article on p. 102 in vol. 7, PMID: 26113819.]. 

Corrigendum: Pervasive computing technologies to continuously assess Alzheimer's disease progression and intervention efficacy.

Sleep apnea is a breathing disorder that affects many individuals and has been associated with serious health conditions such as cardiovascular disease. Clinical diagnosis of sleep apnea requires that a patient spend the night in a sleep clinic while being wired up to numerous obtrusive sensors. We are developing a system that utilizes respiration rate and breathing amplitude inferred from non-contact bed sensors (i.e. load cells placed under bed supports) to detect sleep apnea. Multi-harmonic artifacts generated either biologically or as a result of the impulse response of the bed have made it challenging to track respiration rate and amplitude with high resolution in time. In this paper, we present an algorithm that can accurately track respiration on a second-by-second basis while removing noise harmonics. The algorithm is tested using data collected from 5 patients during overnight sleep studies. Respiration rate is compared with polysomnography estimations of respiration rate estimated by a technician following clinical standards. Results indicate that certain subjects exhibit a large harmonic component of their breathing signal that can be removed by our algorithm. When compared with technician transcribed respiration rates using polysomnography signals, we demonstrate improved accuracy of respiration rate tracking using harmonic artifact rejection (mean error: 0.18 breaths/minute) over tracking not using harmonic artifact rejection (mean error: -2.74 breaths/minute).

A time-frequency respiration tracking system using non-contact bed sensors with harmonic artifact rejection.

Loneliness is a common condition in older adults and is associated with increased morbidity and mortality, decreased sleep quality, and increased risk of cognitive decline. Assessing loneliness in older adults is challenging due to the negative desirability biases associated with being lonely. Thus, it is necessary to develop more objective techniques to assess loneliness in older adults. In this paper, we describe a system to measure loneliness by assessing in-home behavior using wireless motion and contact sensors, phone monitors, and computer software as well as algorithms developed to assess key behaviors of interest. We then present results showing the accuracy of the system in detecting loneliness in a longitudinal study of 16 older adults who agreed to have the sensor platform installed in their own homes for up to 8 months. We show that loneliness is significantly associated with both time out-of-home ([Formula: see text] and [Formula: see text]) and number of computer sessions ([Formula: see text] and [Formula: see text]). [Formula: see text] for the model was 0.35. We also show the model's ability to predict out-of-sample loneliness, demonstrating that the correlation between true loneliness and predicted out-of-sample loneliness is 0.48. When compared with the University of California at Los Angeles loneliness score, the normalized mean absolute error of the predicted loneliness scores was 0.81 and the normalized root mean squared error was 0.91. These results represent first steps toward an unobtrusive, objective method for the prediction of loneliness among older adults, and mark the first time multiple objective behavioral measures that have been related to this key health outcome. 

A Smart-Home System to Unobtrusively and Continuously Assess Loneliness in Older Adults.

Subtle changes in instrumental activities of daily living often accompany the onset of mild cognitive impairment (MCI) but are difficult to measure using conventional tests.

Weekly observations of online survey metadata obtained through home computer use allow for detection of changes in everyday cognition before transition to mild cognitive impairment.

Multimodal interventions are increasingly targeting multiple cognitive decline risk factors. However, technology remains mostly adjunctive, largely prioritizes age relevancy over cultural relevancy, and often targets individual health without lasting, community-wide deliverables. Meanwhile, African Americans remain overburdened by cognitive risk factors yet underrepresented in cognitive health and technology studies. The Sharing History through Active Reminiscence and Photo-imagery (SHARP) program increases physical, social, and cognitive activity within a culturally meaningful context that produces community deliverables-an oral history archive and cognitive health education.

Thomas Riley

Department of Neurology,
ORCATECH - Oregon Center for Aging & Technology,
Department of Neurology, ORCATECH - Oregon Center for Aging & Technology

Oregon Health & Science University

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Thomas Riley

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Neurology,ORCATECH - Oregon Center for Aging & Technology,Department of Neurology, ORCATECH - Oregon Center for Aging & Technology

Oregon Health & Science University

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Department of Neurology,
ORCATECH - Oregon Center for Aging & Technology,
Department of Neurology, ORCATECH - Oregon Center for Aging & Technology