In-Home Monitoring

Unobtrusive Continuous In-home Monitoring

This research consists of a number of projects in which we have deployed (or will be deploying) technology into homes of elders in the community. The first study focused on cross-sectional differences between healthy elders and those with Mild Cognitive Impairment. The next study will be a longitudinal study of 300 community-dwelling elders, to evaluate the efficacy of the in-home assessment system in predicting early cognitive changes in community-dwelling seniors.

Cross-sectional study | Longitudinal study

Cross-sectional study of community-dwelling elders

PI: Tamara Hayes
Funded By NIA (through the ADC pilot program, NIH/NIA P30AG08017)

Problem

Early stages of cognitive impairment in the elderly often go undetected and untreated due to a failure to assess the patient in a timely manner. Currently, cognitive change is assessed, if at all, during a clinic or medical office visit. Because of normal variability in how a person feels on the day of their clinic visit, and because the clinic visits are typically spaced many months or even years apart, it may take years to identify a clear trend in cognitive or motor measures that indicate the early stages of Alzheimer's disease.

Objective

To determine if continuous unobtrusive measures of in-home activity can differentiate healthy elders from those with mild cognitive impairment (MCI).

Methods

Continuous in-home monitoring with wireless motion sensors for up to 9 months.

Subjects

Functional measures for each group.

14 elderly subjects (mean age 89.2 ą 3.7 years) living independently and alone in their own homes
All subjects had normal motor function as measured by modified UPDRS scores (mean 1.86 ą 2.07)
Two groups:

7 MCI (Global CDR and/or CCDR = 0.5, and MMSE>24) , 3 men, 4 women
7 Healthy (Global CDR and CCDR = 0, and MMSE>24), 2 men, 5 women

Analysis

Activity data were analyzed using Detrended Fluctuation Analysis [1-2], which examines variability of a time series at all time scales. Daily walking speeds were calculated as the median walking speed over a 7-day moving window. Comparisons between groups were done using t-tests.

1) Peng CK, Hausdorff JM, Havlin S, Mietus JE, Stanley HE, Goldberger AL. 1998. Multiple-time scales analysis of physiological time series under neural control. Physica A 249:491-500.
2) Hausdorff JM, Ashkenazy Y, Peng CK, Ivanov PC, Stanley HE, Goldberger AL. 2001. When human walking becomes random walking: fractal analysis and modeling of gait rhythm fluctuations. Physica A 302:138-147.

Results

The variance in detrended activity levels was higher in the MCI group than in the healthy elderly group at all time scales. In addition, the DFA self-similarity parameter a was significantly greatly for the MCI group (MCI: 0.92 ą 0.075; Healthy: 0.78 ą 0.055, t11=3.93, p=0.001). In addition, there was less inter-subject variability in these measures among the MCI subjects
Although walking speed was not significantly different between the groups, the coefficient of variation of the walking speed was greater in the MCI group (MCI: 0.16 ą 0.089; Healthy: 0.070 ą 0.024, t7=2.26, p<0.03).

Left: 7-day moving average of daily activity levels for each subject. The X axis shows the number of days since start of monitoring; the Y axis shows activity levels, in 1000's of sensor firings. Below: Box plots of the Detrended variance in activity level across subjects, for 6 different interval lengths. Red boxes are MCI subjects; green boxes are Healthy subjects.

Conclusions + Next Steps

Continuous unobtrusive monitoring can capture differences in activity and walking speed between MCI and healthy elderly over a very short period of time (weeks).
We anticipate that this approach may also provide early markers of cognitive impairment. We have begun a longitudinal study to assess the predictive power of the approach.

Longitudinal study of aging changes

PI:Jeffrey Kaye
Funded By NIH/NIA (Bioengineering Research Partnership 1R01AG024059)

Background

Our rapidly aging population will result in an increasing number of people at risk for loss of independence through dementia, frailty and other syndromes of aging. Evolving sensor and other technologies now provide a means of early detection and intervention minimizing morbidity and cost. We hypothesize that integrated, continuous and unobtrusive home monitoring of activity (motor and cognitive) can detect transitional or early signal events important for maintaining cognitive and physical health.

Early stages of cognitive impairment in the elderly often go undetected and untreated due to a failure to assess the patient in a timely manner. Currently, cognitive change is assessed, if at all, during a medical office visit. Because of normal variability in how a person feels on the day of the visit, and because the visits are typically months or even years apart, it may take years to identify a clear trend in cognitive or motor measures that indicate the early stages of Alzheimer's disease.

Why motor measures

Recent research has shown that motor function in non-impaired individuals predicts later cognitive decline [1-3]. There is also a clear trend to greater variability in motor measures as individuals develop cognitive impairment.

1) Camicioli R, Howieson D, Oken B, Sexton G, Kaye J. 1998. Motor slowing precedes cognitive impairment in the oldest old. Neurology 50:1496-1498.
2) Verghese J, Lipton RB, Hall CB, Kuslansky G, Katz MJ, Buschke H. 2002. Abnormality of gait as a predictor of non-Alzheimer's dementia. N Engl J Med 347:1761-1768.
3) Marquis S, Moore MM, Howieson DB, Sexton G, Payami H, Kaye JA, Camicioli R. 2002. Independent predictors of cognitive decline in healthy elderly persons. Arch Neurol 59:601-606.

Objectives

Determine if continuous, unobtrusive monitoring of motor and cognitive activities detects incident cognitive decline in seniors living in typical community settings;
Develop novel algorithms and assessment techniques for detecting motor and cognitive change in these community settings and in the context of the ongoing BRP, to test evolving sensor technology; and
Identify the monitoring needs of, and optimal communication channels for lay individuals and health care professionals.

Methods

A 36-month longitudinal study will use unobtrusive sensing technologies to continuously monitor physical activity and computer use for up to 300 healthy seniors in their homes. In parallel, a standard clinical assessment protocol will be administered to detect incident cognitive decline. The longitudinal study will be informed by initial pilot work, as well as survey and focus group data collected to assess attitudes and beliefs of elders, caregivers and physicians around continuous assessment technologies.

Information flow enabled by the specific aims of this project.
Protocol for the longitudinal study.