Tamara L. Hayes

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.

Medication Adherence in Healthy Elders: Small Cognitive Changes Make a Big Difference

Objective: This was a cross-sectional study of the ability of independently living healthy elders to follow a medication regimen. Participants were divided into a group with High Cognitive Function (HCF) or Low Cognitive Function (LCF) based on their scores on the ADAS-Cog. Method: Thirty-eight participants aged 65 or older and living independently in the community followed a twice-daily vitamin C regimen for 5 weeks. Adherence was measured using an electronic 7-day pillbox. Results: The LCF group had significantly poorer total adherence than the HCF group (LCF: 63.9 ± 11.2%, HCF: 86.8 ± 4.3%, t 36 = 2.57, p = .007), and there was a 4.1 relative risk of non-adherence in the LCF group as compared to the HCF group. Discussion: This study has important implications for the conduct of clinical drug trials, as it provides strong evidence that even very mild cognitive impairment in healthy elderly has a detrimental impact on medication adherence.

Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders

Gait velocity has been shown to quantitatively estimate risk of future hospitalization, a predictor of disability, and has been shown to slow prior to cognitive decline. In this paper, we describe a system for continuous and unobtrusive in-home assessment of gait velocity, a critical metric of function. This system is based on estimating walking speed from noisy time and location data collected by a ¿sensor line¿ of restricted view passive infrared motion detectors. We demonstrate the validity of our system by comparing with measurements from the commercially available GAITRite walkway system gait mat. We present the data from 882 walks from 27 subjects walking at three different subject-paced speeds (encouraged to walk slowly, normal speed, or fast) in two directions through a sensor line. The experimental results show that the uncalibrated system accuracy (average error) of estimated velocity was 7.1 cm/s (SD = 11.3 cm/s), which improved to 1.1 cm/s (SD = 9.1 cm/s) after a simple calibration procedure. Based on the average measured walking speed of 102 cm/s, our system had an average error of less than 7% without calibration and 1.1% with calibration.

Unobtrusive assessment of activity patterns associated with mild cognitive impairment

Timely detection of early cognitive impairment is difficult. Measures taken in the clinic reflect a single snapshot of performance that might be confounded by the increased variability typical in aging and disease. We evaluated the use of continuous, long‐term, and unobtrusive in‐home monitoring to assess neurologic function in healthy and cognitively impaired elders.

An Electronic Pillbox for Continuous Monitoring of Medication Adherence

We have developed an instrumented pillbox, called a MedTracker, which allows monitoring of medication adherence on a continuous basis. This device improves on existing systems by providing mobility, frequent and automatic data collection, more detailed information about non-adherence and medication errors, and the familiar interface of a 7-day drug store pillbox. We report on the design of the MedTracker, and on the results of a field trial in 39 homes to evaluate the device

In-home walking speeds and variability trajectories associated with mild cognitive impairment

Objective: To determine whether unobtrusive long-term in-home assessment of walking speed and its variability can distinguish those with mild cognitive impairment (MCI) from those with intact cognition. Methods: Walking speed was assessed using passive infrared sensors fixed in series on the ceiling of the homes of elderly individuals participating in the Intelligent Systems for Assessing Aging Change (ISAAC) cohort study. Latent trajectory models were used to analyze weekly mean speed and walking speed variability (coefficient of variation [COV]). Results: ISAAC participants living alone included 54 participants with intact cognition, 31 participants with nonamnestic MCI (naMCI), and 8 participants with amnestic MCI at baseline, with a mean follow-up of 2.6 ± 1.0 years. Trajectory models identified 3 distinct trajectories (fast, moderate, and slow) of mean weekly walking speed. Participants with naMCI were more likely to be in the slow speed group than in the fast (p = 0.01) or moderate (p = 0.04) speed groups. For COV, 4 distinct trajectories were identified: group 1, the highest baseline and increasing COV followed by a sharply declining COV; groups 2 and 3, relatively stable COV; and group 4, the lowest baseline and decreasing COV. Participants with naMCI were more likely to be members of either highest or lowest baseline COV groups (groups 1 or 4), possibly representing the trajectory of walking speed variability for early- and late-stage MCI, respectively. Conclusion: Walking speed and its daily variability may be an early marker of the development of MCI. These and other real-time measures of function may offer novel ways of detecting transition phases leading to dementia.

Detection of Movement in Bed Using Unobtrusive Load Cell Sensors

Quality of sleep is an important attribute of an individuals health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of motor activities during sleep can be a disease marker, or can reflect various abnormal physiological and neurological conditions. Presently, there are no convenient, unobtrusive ways to assess quality of sleep outside of a clinic. This paper describes a system for unobtrusive detection of movement in bed that uses load cells installed at the corners of a bed. The system focuses on identifying when a movement occurs based on the forces sensed by the load cells. The movement detection approach estimates the energy in each load cell signal over short segments to capture the variations caused by movement. The accuracy of the detector is evaluated using data collected in the laboratory. The detector is capable of detecting voluntary movements in bed while the subjects were awake, with an average equal error rate of 3.22% (±0.54). Its performance is invariant with respect to the individuals characteristics, e.g., weight, as well as those of the bed. The simplicity of the resulting algorithms and their relative insensitivity to the weight and height of the monitored individual make the approach practical and easily deployable in residential and clinical settings.

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 persons typical ability. Using a system that unobtrusively and continuously measures walking activity in a persons 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.

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

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.

Distributed Healthcare: Simultaneous Assessment of Multiple Individuals

Employing pervasive computing technologies can help enable continuous patient monitoring and assessment in various settings outside of hospitals, lowering healthcare costs and allowing earlier detection of problems