A randomized clinical trial to evaluate home-based assessment of people over 75 years old.

INTRODUCTION: There is an unmet need for effective methods for conducting dementia prevention trials. METHODS: Home-based assessment study compared feasibility and efficiency, ability to capture change over time using in-home instruments, and ability to predict cognitive conversion using predefined triggers in a randomized clinical trial in (1) mail-in questionnaire/live telephone interviews, (2) automated telephone/interactive voice recognition, and (3) internet-based computer Kiosk technologies. Primary endpoint was defined as cognitive conversion. RESULTS: Analysis followed a modified intent-to-treat principle. Dropout rates were low and similar across technologies but participants in Kiosk were more likely to dropout earlier. Staff resources needed were higher in Kiosk. In-home instruments distinguished conversion and stable groups. Cognitively stable group showed improvement in cognitive measures. Triggering was associated with higher likelihood of conversion but statistically significant only in mail-in questionnaire/live telephone interviews. DISCUSSION: Relatively low efficiency of internet-based assessment compared with testing by live-assessors has implications for internet-based recruitment and assessment efforts currently proposed for diverse populations.

Day-to-Day Variability of Postural Sway and Its Association With Cognitive Function in Older Adults: A Pilot Study.

Introduction: Increased variability in motor function has been observed during the initial stages of cognitive decline. However, the natural variability of postural control, as well as its association with cognitive status and decline, remains unknown. The objective of this pilot study was to characterize the day-to-day variability in postural sway in non-demented older adults. We hypothesized that older adults with a lower cognitive status would have higher day-to-day variability in postural sway. Materials and Methods: A Nintendo Wii balance board (WBB) was used to quantify postural sway in the home twice daily for 30 days in 20 non-demented, community-dwelling older adults: once under a single-task condition and once under a dual-task condition (using a daily word search task administered via a Nook tablet). Mean sway distance, velocity, area, centroidal frequency and frequency dispersion were derived from the center of pressure data acquired from the WBB. Results: Linear relationships were observed between the day-to-day variability in postural sway and cognitive status (indexed by cognitive global z-scores). More variability in time-domain postural sway (sway distance and area) and less variability in frequency-domain postural sway (centroidal sway frequency) were associated with a lower cognitive status under both the single- and dual-task conditions. Additionally, lower cognitive performance rates on the daily word search task were related to a lower cognitive status. Discussion: This small pilot study conducted on a short time scale motivates large-scale implementations over more extended time periods. Tracking longitudinal changes in postural sway may further our understanding of early-stage postural decline and its association with cognitive decline and, in turn, may aid in the early detection of dementia during preclinical stages when the utility of disease-modifying therapies would be greatest.

Validating and calibrating the Nintendo Wii balance board to derive reliable center of pressure measures.

The Nintendo Wii balance board (WBB) has generated significant interest in its application as a postural control measurement device in both the clinical and (basic, clinical, and rehabilitation) research domains. Although the WBB has been proposed as an alternative to the "gold standard" laboratory-grade force plate, additional research is necessary before the WBB can be considered a valid and reliable center of pressure (CoP) measurement device. In this study, we used the WBB and a laboratory-grade AMTI force plate (AFP) to simultaneously measure the CoP displacement of a controlled dynamic load, which has not been done before. A one-dimensional inverted pendulum was displaced at several different displacement angles and load heights to simulate a variety of postural sway amplitudes and frequencies (<1 Hz). Twelve WBBs were tested to address the issue of inter-device variability. There was a significant effect of sway amplitude, frequency, and direction on the WBB's CoP measurement error, with an increase in error as both sway amplitude and frequency increased and a significantly greater error in the mediolateral (ML) (compared to the anteroposterior (AP)) sway direction. There was no difference in error across the 12 WBB's, supporting low inter-device variability. A linear calibration procedure was then implemented to correct the WBB's CoP signals and reduce measurement error. There was a significant effect of calibration on the WBB's CoP signal accuracy, with a significant reduction in CoP measurement error (quantified by root-mean-squared error) from 2-6 mm (before calibration) to 0.5-2 mm (after calibration). WBB-based CoP signal calibration also significantly reduced the percent error in derived (time-domain) CoP sway measures, from -10.5% (before calibration) to -0.05% (after calibration) (percent errors averaged across all sway measures and in both sway directions). In this study, we characterized the WBB's CoP measurement error under controlled, dynamic conditions and implemented a linear calibration procedure for WBB CoP signals that is recommended to reduce CoP measurement error and provide more reliable estimates of time-domain CoP measures. Despite our promising results, additional work is necessary to understand how our findings translate to the clinical and rehabilitation research domains. Once the WBB's CoP measurement error is fully characterized in human postural sway (which differs from our simulated postural sway in both amplitude and frequency content), it may be used to measure CoP displacement in situations where lower accuracy and precision is acceptable.

Unobtrusive in-home detection of time spent out-of-home with applications to loneliness and physical activity.

Loneliness is a common condition in elderly associated with severe health consequences including increased mortality, decreased cognitive function, and poor quality of life. Identifying and assisting lonely individuals is therefore increasingly important-especially in the home setting-as the very nature of loneliness often makes it difficult to detect by traditional methods. One critical component in assessing loneliness unobtrusively is to measure time spent out-of-home, as loneliness often presents with decreased physical activity, decreased motor functioning, and a decline in activities of daily living, all of which may cause decrease in the amount of time spent outside the home. Using passive and unobtrusive in-home sensing technologies, we have developed a methodology for detecting time spent out-of-home based on logistic regression. Our approach was both sensitive (0.939) and specific (0.975) in detecting time out-of-home across over 41,000 epochs of data collected from four subjects monitored for at least 30 days each in their own homes. In addition to linking time spent out-of-home to loneliness, (r = -0.44, p = 0.011) as measured by the UCLA Loneliness Index, we demonstrate its usefulness in other applications such as uncovering general behavioral patterns of elderly and exploring the link between time spent out-of-home and physical activity ( r = 0.415, p = 0.031), as measured by the Berkman Social Disengagement Index.

Regularity and predictability of human mobility in personal space.

Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity - most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.

Associations between observed in-home behaviors and self-reported low mood in community-dwelling older adults.

OBJECTIVES: To ascertain the association between self-report of low mood and unobtrusively measured behaviors (walking speed, time out of residence, frequency of room transitions, and computer use) in community-dwelling older adults using novel monitoring technologies. DESIGN: Longitudinal cohort study of older adults whose homes were outfitted with activity sensors. Participants completed Internet-based weekly health questionnaires with questions about mood. SETTING: Apartments and homes of older adults living in the Portland, Oregon, metropolitan area. PARTICIPANTS: Adults, average age 84, followed for an average of 3.7 years (n = 157). MEASUREMENTS: Mood was assessed according to self-report each week. Walking speed, time spent out of residence, and room transitions were estimated using data from sensors; computer use was measured by timing actual use. The association between global or weekly low mood and the four behavior measures was ascertained, adjusting for baseline characteristics. RESULTS: Eighteen thousand nine hundred sixty weekly observations of mood were analyzed; 2.6% involved low mood. Individuals who reported low mood more often had no average differences in any behavior parameters from those who reported low mood less often. During weeks when they reported low mood, participants spent significantly less time out of residence and on the computer but showed no change in walking speed or room transitions. CONCLUSION: Low mood in these community-dwelling older adults involved going out of the house less and using the computer less but no consistent changes in movements. Technologies to monitor in-home behavior may have potential for research and clinical care.

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.

Sleep habits in mild cognitive impairment.

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.

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.

Accurate scoring of the apnea-hypopnea index using a simple non-contact breathing sensor.

Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patient's sleep during the overnight testing. We investigated the use of breathing signals derived from non-contact force sensors (i.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral-nasal thermistor, chest belt and abdominal belt). The apnea-hypopnea index estimated using the load cells was not different than that estimated using standard polysomnography leads (t44  = 0.37, P = 0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra-class correlation coefficient of 0.97 for the apnea-hypopnea index and an intra-class correlation coefficient of 0.85 for the respiratory disturbance index when compared with scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the apnea-hypopnea index.

Actigraphy-based scratch detection using logistic regression.

Incessant scratching as a result of diseases such as atopic dermatitis causes skin break down, poor sleep quality, and reduced quality of life for affected individuals. In order to develop more effective therapies, there is a need for objective measures to detect scratching. Wrist actigraphy, which detects wrist movements over time using micro-accelerometers, has shown great promise in detecting scratch because it is lightweight, usable in the home environment, can record longitudinally, and does not require any wires. However, current actigraphy-based scratch-detection methods are limited in their ability to discriminate scratch from other nighttime activities. Our previous work demonstrated the separability of scratch from both walking and restless sleep using a clustering technique which employed four features derived from the actigraphic data: number of accelerations above 0.01 gs, epoch variance, peak frequency, and autocorrelation value at one lag. In this paper, we extended these results by employing these same features as independent variables in a logistic regression model. This allows us to directly estimate the conditional probability of scratching for each epoch. Our approach outperforms competing actigraphy-based approaches and has both high sensitivity (0.96) and specificity (0.92) for identifying scratch as validated on experimental data collected from 12 healthy subjects. The model must still be fully validated on clinical data, but shows promise for applications to clinical trials and longitudinal studies of scratch.

Developing dementia prevention trials: baseline report of the Home-Based Assessment study.

This report describes the baseline experience of the multicenter, Home-Based Assessment study, designed to develop methods for dementia prevention trials using novel technologies for test administration and data collection. Nondemented individuals of 75 years of age or more were recruited and evaluated in-person using established clinical trial outcomes of cognition and function, and randomized to one of 3 assessment methodologies: (1) mail-in questionnaire/live telephone interviews [mail-in/phone (MIP)]; (2) automated telephone with interactive voice recognition; and (3) internet-based computer Kiosk. Brief versions of cognitive and noncognitive outcomes were adapted to each methodology and administered at baseline and repeatedly over a 4-year period. "Efficiency" measures assessed the time from screening to baseline, and staff time required for each methodology. A total of 713 individuals signed consent and were screened; 640 met eligibility and were randomized to one of 3 assessment arms; and 581 completed baseline. Dropout, time from screening to baseline, and total staff time were highest among those assigned to internet-based computer Kiosk. However, efficiency measures were driven by nonrecurring start-up activities suggesting that differences may be mitigated over a long trial. Performance among Home-Based Assessment instruments collected through different technologies will be compared with established outcomes over this 4-year study.

A Gaussian model for movement detection during sleep.

Quality of sleep is an important attribute of an individual's health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of mobility in bed during sleep can be a disease marker or can reflect various abnormal physiological and neurological conditions. This paper describes a method for detection of movement in bed that is evaluated on data collected from patients admitted for regular polysomnography. The system is based on load cells installed at the supports of a bed. Since the load cell signal varies the most during movement, the approach uses a weighted combination of the short-term mean-square differences of each load cell signal to capture the variations in the signal caused by movement. We use a single univariate Gaussian model to represent each class: movement versus non-movement. We assess the performance of the method against manual annotation performed by a sleep clinic technician from seventeen patients. The proposed detection method achieved an overall sensitivity of 97.9% and specificity of 98.7%.

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

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.

SVM to detect the presence of visitors in a smart home environment.

With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.

One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults.

Physical performance measures predict health and function in older populations. Walking speed in particular has consistently predicted morbidity and mortality. However, single brief walking measures may not reflect a person's typical ability. Using a system that unobtrusively and continuously measures walking activity in a person's home we examined walking speed metrics and their relation to function. In 76 persons living independently (mean age, 86) we measured every instance of walking past a line of passive infra-red motion sensors placed strategically in their home during a four-week period surrounding their annual clinical evaluation. Walking speeds and the variance in these measures were calculated and compared to conventional measures of gait, motor function and cognition. Median number of walks per day was 18±15. Overall mean walking speed was 61±17 cm/s. Characteristic fast walking speed was 96 cm/s. Men walked as frequently and fast as women. Those using a walking aid walked significantly slower and with greater variability. Morning speeds were significantly faster than afternoon/evening speeds. In-home walking speeds were significantly associated with several neuropsychological tests as well as tests of motor performance. Unobtrusive home walking assessments are ecologically valid measures of walking function. They provide previously unattainable metrics (periodicity, variability, range of minimum and maximum speeds) of everyday motor function.

Executive function predicts risk of falls in older adults without balance impairment.

BACKGROUND: Executive dysfunction has previously been found to be a risk factor for falls. The aim of this study is to investigate the association between executive dysfunction and risk of falling and to determine if this association is independent of balance. METHODS: Participants were 188 community-dwelling individuals aged 65 and older. All participants underwent baseline and annual evaluations with review of health history, standardized neurologic examination, neuropsychological testing, and qualitative and quantitative assessment of motor function. Falls were recorded prospectively using weekly online health forms. RESULTS: During 13 months of follow-up, there were 65 of 188 participants (34.6%) who reported at least one fall. Univariate analysis showed that fallers were more likely to have lower baseline scores in executive function than non-fallers (p = 0.03). Among participants without balance impairment we found that higher executive function z-scores were associated with lower fall counts (p = 0.03) after adjustment for age, sex, health status and prior history of falls using negative binomial regression models. This relationship was not present among participants with poor balance. CONCLUSIONS: Lower scores on executive function tests are a risk factor for falls in participants with minimal balance impairment. However, this effect is attenuated in individuals with poor balance where physical or more direct motor systems factors may play a greater role in fall risk.

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.

On the Disambiguation of Passively Measured In-home Gait Velocities from Multi-person Smart Homes.

In-home monitoring of gait velocity with passive PIR sensors in a smart home has been shown to be an effective method of continuously and unobtrusively measuring this important predictor of cognitive function and mobility. However, passive measurements of velocity are nonspecific with regard to who generated each measurement or walking event. As a result, this method is not suitable for multi-person homes without additional information to aid in the disambiguation of gait velocities. In this paper we propose a method based on Gaussian mixture models (GMMs) combined with infrequent clinical assessments of gait velocity to model in-home walking speeds of two or more residents. Modeling the gait parameters directly allows us to avoid the more difficult problem of assigning each measured velocity individually to the correct resident. We show that if the clinically measured gait velocities of residents are separated by at least 15 cm/s a GMM can be accurately fit to the in-home gait velocity data. We demonstrate the accuracy of this method by showing that the correlation between the means of the GMMs and the clinically measured gait velocities is 0.877 (p value < 0.0001) with bootstrapped 95% confidence intervals of (0.79, 0.94) for 54 measurements of 20 subjects living in multi-person homes. Example applications of using this method to track in-home mean velocities over time are also given.

Measuring motor speed through typing: a surrogate for the finger tapping test.

Motor speed is an important indicator and predictor of both cognitive and physical function. One common assessment of motor speed is the finger-tapping test (FTT), which is typically administered as part of a neurological or neuropsychological assessment. However, the FTT suffers from several limitations, including infrequent in-person administration, the need for a trained assessor and dedicated equipment, and potential short-term sensory-motor fatigue. In this article, we propose an alternative method of measuring motor speed, with face validity to the FTT, that addresses these limitations by measuring the interkeystroke intervals (IKI) of familiar and repeated login data collected in the home during a subject's regular computer use. We show significant correlations between the mean tapping speeds from the FTT and the median IKIs of the nondominant (r = .77) and dominant (r = .70) hands, respectively, in an elderly cohort of subjects living independently. Finally, we discuss how the proposed method for measuring motor speed fits well into the framework of unobtrusive and continuous in-home assessment.