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 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.

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