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.

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 Ϯ 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 ϭ 0.01) or moderate (p ϭ 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. It is of substantial importance to detect dementia at its earliest phases to sustain independence, to optimize treatment, to understand preclinical biology, and to ultimately develop prevention strategies. Past studies have found that slower walking speed and poorer motor function are associated with mild cognitive impairment (MCI) and are predictors of progression to frank dementia. [1][2][3][4] However, it is difficult to identify changes in these functions because changes evolve slowly over time and change measures have high test-to-test variability. Further, current methods for assessing this change rely largely on sparsely spaced or annual assessments that provide too few data points to discern subtle changes. An alternative to this approach is to deploy unobtrusive passive monitoring systems in people's homes, providing continuous assessment of daily activity and behaviors of interest. This kind of pervasive computing model has been established by the Intelligent Systems for Assessing Aging Change (ISAAC) study, 5,6 Conclusion:

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 to 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 usage (number of days with use, mean daily usage and coefficient of variation of use) measured by remotely monitoring computer session start and end times. Results Over 230,000 computer sessions from 113 computer users (mean age, 85; 38 with MCI) were acquired during a mean of 36 months. In mixed effects models there was no difference in computer usage 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=0.01), mean daily usage (~1% greater decrease/month; p=0.009) and an increase in day-to-day use variability (p=0.002). Conclusions Computer use change can be unobtrusively monitored and indicate individuals with MCI. With 79% of those 55–64 years old now online, this may be an ecologically valid and efficient approach to track subtle clinically meaningful change with aging.

Background Timely detection of early cognitive impairment is difficult. Measures taken in the clinic reflect a single snapshot of performance that may 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 neurological function in healthy and cognitively impaired elders. Methods Fourteen older adults 65 years and older living independently in the community were monitored in their homes using an unobtrusive sensor system. Measures of walking speed and amount of activity in the home were obtained. Wavelet analysis was used to examine variance in activity at multiple timescales. Results More than 108,000 person-hours of continuous activity data were collected over periods as long as 418 days (mean 315 ± 82 days). The coefficient of variation in the median walking speed was twice as high in the MCI group (0.147 ± 0.074) as compared to the healthy group (0.079 ± 0.027; t11 = 2.266, p<0.03). Furthermore, the 24-hour wavelet variance was greater in the MCI group (MCI: 4.07 ± 0.14, Healthy elderly: 3.79± 0.23; F = 7.58, p=<0.008), indicating that the day-to-day pattern of activity of subjects in the MCI group was more variable than that of the cognitively healthy controls. Conclusions The results not only demonstrate the feasibility of these methods, but also suggest clear potential advantages to this new methodology. This approach may provide an improved means of detecting the earliest transition to MCI compared to conventional episodic testing in a clinic environment.

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/sec. Characteristic fast walking speed was 96 cm/sec. 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.