Unobtrusive assessment of activity patterns associated with mild cognitive impairment

Abstract

Background: 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.

Methods: Fourteen older adults 65 years and older living independently in the community were monitored in their homes by 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 time scales.

Results: More than 108,000 person-hours of continuous activity data were collected during 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 mild cognitive impairment (MCI) group (0.147 +/- 0.074) as compared with the healthy group (0.079 +/- 0.027; t(11) = 2.266, P < .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 </= .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 might provide an improved means of detecting the earliest transition to MCI compared with conventional episodic testing in a clinic environment.

Figure 1

Problem with infrequent measurements. In this figure, the left panel depicts test scores taken during a standard clinic visit, taken at 6-month intervals, for 2 patients. The right panel depicts how continuous assessment could reveal a very different picture.

Figure 2

Walking times in the morning and evening, averaged over 6 months, for each subject. Numbers indicate the subject ID’s. Across all subjects, the mean walking times were longer in the evening than in the morning, with nine of the twelve subjects showing some slowing later in the day. Comparisons across all walking times in individuals showed that this slowing (reflected in longer walking times) was significantly greater in 7 subjects. Asterisks indicate those subjects whose walking time was significantly increased in the evening.

Figure 3

Daily activity levels for each subject, calculated using a 7-day moving average. The abscissa shows the number of days since start of monitoring; the ordinate shows activity levels, in 1000's of sensor firings. The measure is the number of sensor firings per day. The horizontal green lines indicate one standard deviation above and below the mean. Not all subjects were monitored for the same period of time. Asterisks indicate subjects for whom the Y axis is scaled differently due to a greater variance in the daily activity levels. Missing data indicate absences from home (e.g. subjects S7 and s12); sharp peaks typically correspond to periods in which the subject had an overnight guest in the home (e.g. S1, S2).

Figure 4

Figure 4A. Box plots of the log variance in the wavelet representation of activity levels across subjects for six time scales. Dashed boxes are MCI subjects; solid boxes are Healthy subjects. 4B. Plot of the mean of the 24-hour wavelet variance across MCI and healthy subjects, for 6 consecutive months. Solid diamonds are MCI subjects, open boxes are healthy subjects.