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Home-Based Activity Changes Associated with MCI

Authors:
Jeffrey Kaye at Oregon Health & Science University
Jeffrey Kaye
Hiroko H. Dodge at MGH, Harvard Medical School
Hiroko H. Dodge
  • MGH, Harvard Medical School
Nora Mattek at Oregon Health & Science University
Nora Mattek
Daniel Austin at Oregon Health & Science University
Daniel Austin
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Abstract

In 113 non-demented ISAAC (Intelligent Systems for Assessing Aging Change) cohort (Kaye, 2008) seniors living independently (mean age 84; CDR ≤ 0.5) multiple daily walking episodes were unobtrusively recorded as subjects traversed a line of passive infra-red motion sensors placed strategically in their home (figure 1) for a mean of 319 ±127 days. Daily walking speeds (Hayes, 2008; Hagler, 2009) and the variance in these measures over time were calculated and compared to conventional single visit stop-watch derived speed recordings in subjects with and without MCI. Trajectory analysis using the coefficient of variation (COV) in weekly walking speeds was applied to assess differences in variability over time among subjects with and without MCI.
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Home-Based Activity Changes Associated with MCI
Jeffrey A. Kaye, Hiroko H. Dodge, Nora Mattek, Daniel Austin, Stuart Hagler,
Teresa Buracchio, Michael Pavel, Tamara Hayes
Oregon Health & Science University
Results
Objective
Background
Support: NIH AG08017; AG024059; AG024978; AG 023014; K01AG023014
Intel
References
1. Kaye JA, Hayes TL, Zitzelberger TA, et al. Deploying wide-scale in-home assessment technology.
Technology and Aging, A. Mihailidis, J. Boger, H. Kautz, and L. Normie, Eds., IOS Press, 2008, 21:19-
26
2. Hayes T, Abendroth F, Adami A, et al. Unobtrusive assessment of activity patterns associated with mild
cognitive impairment. Alzheimer's & Dementia 4(6): 395-405, 2008
3. Hagler S, Austin D, Hayes TL, Kaye J, Pavel M. Unobtrusive and Ubiquitous In-Home Monitoring: A
Methodology for Continuous Assessment of Gait Velocity in Elders. IEEE Transactions on Biomedical
Engineering, 2009..
To determine if variability in motor function assessed in the
home environment characterizes persons with MCI.
Figure 2: Continuous home acquired walking speed is significantly
different (p < .01) in naMCI (non-amnestic MCI) compared to intact
subjects. Single stop-watch measure does not distinguish groups.
Changes in motor function precede cognitive decline up to a
decade before symptoms appear, a conclusion primarily
derived from brief clinical measures of motor function such as
walking speed. We hypothesized that if these measures were
predictive of MCI, before they declined in absolute
magnitude, there would be a period where the measure
would first show increased variability.
Design/Methods
In 113 non-demented ISAAC (Intelligent Systems for
Assessing Aging Change) cohort (Kaye, 2008) seniors living
independently (mean age 84; CDR ≤ 0.5) multiple daily
walking episodes were unobtrusively recorded as subjects
traversed a line of passive infra-red motion sensors placed
strategically in their home (figure 1) for a mean of 319 ±
127 days. Daily walking speeds (Hayes, 2008; Hagler,
2009) and the variance in these measures over time were
calculated and compared to conventional single visit stop-
watch derived speed recordings in subjects with and without
MCI. Trajectory analysis using the coefficient of variation
(COV) in weekly walking speeds was applied to assess
differences in variability over time among subjects with and
without MCI.
Figure 1: [A] A home sensor line in place; [B] Schematic of a person
walking through a sensor line containing four sensors with their fields of
view shown. Sources: [A] Julie Keefe, the New Times, Nov 7,
2009; [B] Hagler et al. 2009.
Coefficient of
Variation (COV)
•Continuous unobtrusive home monitoring may
identify activity changes (walking speed and
variability) that are early markers of cognitive decline.
•Home-based continuous assessment metrics for
discerning subtle early change may provide new
measures of early change not currently accessible
through conventional methodologies
74.8
63.3
80.5
0
10
20
30
40
50
60
70
80
90
Intact
naMCI
aMCI
cm/s
Stopwatch Derived Walking Speed
65.1
51.4**
58.2
0
10
20
30
40
50
60
70
80
Intact
naMCI
aMCI
cm/s
Mean of Daily Median In-home Walking Speed
Days
Figure 3: Three trajectories best described walking speed COV. MCI
subjects were more likely to be in the high variability at baseline and
increasing over time group (Blue trajectory; OR = 1.41; p = 0.012).
Results
Conclusions/Relevance
References
[A] [B]
Disclosure: Drs. Kaye, Pavel and Hayes have received support from Intel for their
research; Dr. Hayes holds stock and/or stock options in Intel.
ResearchGate has not been able to resolve any citations for this publication.
Deploying wide-scale in-home assessment technology
Article
Full-text available
Cognitive or motor function decline are major causes of loss of independent living among the aged. Several methods employing ubiquitous or unobtrusive technologies have been proposed for application toward in-home assessment to identify clinically meaningful change. Most attempts at multi-dimensional home monitoring have been on a limited scale. This has been the result of both technical and clinical research challenges in applying and more importantly testing the efficacy of such methods on a community-wide scale. We designed and implemented a system for application to a community based clinical trial of the efficacy of a basic sensor net (motion and contact sensors, RF location systems, and personal home computer interaction) to be studied in 300 homes of independent seniors. In this manuscript we describe a protocol to ensure several key outcomes: facilitation of recruitment and enrollment, customized training of elders for in-home computer use, optimized sensor net installation, tracking of subject status and linkage to study management software to enable on-line, real-time testing and trouble-shooting with seniors. The methodology suggests that large-scale unobtrusive in-home assessment is feasible for research needed to establish the efficacy of such systems for detection of cognitive decline and related conditions of aging.
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Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders
Article
Full-text available
  • May 2010
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.
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Unobtrusive assessment of activity patterns associated with mild cognitive impairment
Article
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. 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. 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. 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.
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