A randomized clinical trial to evaluate home-based assessment of people over 75 years old.

INTRODUCTION: There is an unmet need for effective methods for conducting dementia prevention trials. METHODS: Home-based assessment study compared feasibility and efficiency, ability to capture change over time using in-home instruments, and ability to predict cognitive conversion using predefined triggers in a randomized clinical trial in (1) mail-in questionnaire/live telephone interviews, (2) automated telephone/interactive voice recognition, and (3) internet-based computer Kiosk technologies. Primary endpoint was defined as cognitive conversion. RESULTS: Analysis followed a modified intent-to-treat principle. Dropout rates were low and similar across technologies but participants in Kiosk were more likely to dropout earlier. Staff resources needed were higher in Kiosk. In-home instruments distinguished conversion and stable groups. Cognitively stable group showed improvement in cognitive measures. Triggering was associated with higher likelihood of conversion but statistically significant only in mail-in questionnaire/live telephone interviews. DISCUSSION: Relatively low efficiency of internet-based assessment compared with testing by live-assessors has implications for internet-based recruitment and assessment efforts currently proposed for diverse populations.

The role of technology and engineering models in transforming healthcare.

The healthcare system is in crisis due to challenges including escalating costs, the inconsistent provision of care, an aging population, and high burden of chronic disease related to health behaviors. Mitigating this crisis will require a major transformation of healthcare to be proactive, preventive, patient-centered, and evidence-based with a focus on improving quality-of-life. Information technology, networking, and biomedical engineering are likely to be essential in making this transformation possible with the help of advances, such as sensor technology, mobile computing, machine learning, etc. This paper has three themes: 1) motivation for a transformation of healthcare; 2) description of how information technology and engineering can support this transformation with the help of computational models; and 3) a technical overview of several research areas that illustrate the need for mathematical modeling approaches, ranging from sparse sampling to behavioral phenotyping and early detection. A key tenet of this paper concerns complementing prior work on patient-specific modeling and simulation by modeling neuropsychological, behavioral, and social phenomena. The resulting models, in combination with frequent or continuous measurements, are likely to be key components of health interventions to enhance health and wellbeing and the provision of healthcare.

Developing dementia prevention trials: baseline report of the Home-Based Assessment study.

This report describes the baseline experience of the multicenter, Home-Based Assessment study, designed to develop methods for dementia prevention trials using novel technologies for test administration and data collection. Nondemented individuals of 75 years of age or more were recruited and evaluated in-person using established clinical trial outcomes of cognition and function, and randomized to one of 3 assessment methodologies: (1) mail-in questionnaire/live telephone interviews [mail-in/phone (MIP)]; (2) automated telephone with interactive voice recognition; and (3) internet-based computer Kiosk. Brief versions of cognitive and noncognitive outcomes were adapted to each methodology and administered at baseline and repeatedly over a 4-year period. "Efficiency" measures assessed the time from screening to baseline, and staff time required for each methodology. A total of 713 individuals signed consent and were screened; 640 met eligibility and were randomized to one of 3 assessment arms; and 581 completed baseline. Dropout, time from screening to baseline, and total staff time were highest among those assigned to internet-based computer Kiosk. However, efficiency measures were driven by nonrecurring start-up activities suggesting that differences may be mitigated over a long trial. Performance among Home-Based Assessment instruments collected through different technologies will be compared with established outcomes over this 4-year study.

Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging.

OBJECTIVES: To describe a longitudinal community cohort study, Intelligent Systems for Assessing Aging Changes, that has deployed an unobtrusive home-based assessment platform in many seniors homes in the existing community. METHODS: Several types of sensors have been installed in the homes of 265 elderly persons for an average of 33 months. Metrics assessed by the sensors include total daily activity, time out of home, and walking speed. Participants were given a computer as well as training, and computer usage was monitored. Participants are assessed annually with health and function questionnaires, physical examinations, and neuropsychological testing. RESULTS: Mean age was 83.3 years, mean years of education was 15.5, and 73% of cohort were women. During a 4-week snapshot, participants left their home twice a day on average for a total of 208 min per day. Mean in-home walking speed was 61.0 cm/s. Participants spent 43% of days on the computer averaging 76 min per day. DISCUSSION: 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.

Estimation of rest-activity patterns using motion sensors.

Disrupted sleep patterns are a significant problem in the elderly, leading to increased cognitive dysfunction and risk of nursing home placement. A cost-effective and unobtrusive way to remotely monitor changing sleep patterns over time would enable improved management of this important health problem. We have developed an algorithm to derive sleep parameters such as bed time, rise time, sleep latency, and nap time from passive infrared sensors distributed around the home. We evaluated this algorithm using 404 days of data collected in the homes of 8 elderly community-dwelling elders. Data from this algorithm were highly correlated to ground truth measures (bed mats) and were surprisingly robust to variability in sensor layout and sleep habits.

Pilot study to show the feasibility of a multicenter trial of home-based assessment of people over 75 years old.

UNLABELLED: This report describes a pilot study to evaluate feasibility of new home-based assessment technologies applicable to clinical trials for prevention of cognitive loss and Alzheimer disease. METHODS: Community-dwelling nondemented individuals >or=75 years old were recruited and randomized to 1 of 3 assessment methodologies: (1) mail-in questionnaire/ live telephone interviews (MIP); (2) automated telephone with interactive voice recognition (IVR); and (3) internet-based computer Kiosk (KIO). Brief versions of cognitive and noncognitive outcomes were adapted to the different methodologies and administered at baseline and 1-month. An Efficiency measure, consisting of direct staff-to-participant time required to complete assessments, was also compared across arms. RESULTS: Forty-eight out of 60 screened participants were randomized. The dropout rate across arms from randomization through 1-month was different: 33% for KIO, 25% for IVR, and 0% for MIP (Fisher Exact Test P=0.04). Nearly all participants who completed baseline also completed 1-month assessment (38 out of 39). The 1-way ANOVA across arms for total staff-to-participant direct contact time (ie, training, baseline, and 1-month) was significant: F (2,33)=4.588; P=0.017, with lowest overall direct time in minutes for IVR (Mn=44.4; SD=21.5), followed by MIP (Mn=74.9; SD=29.9), followed by KIO (Mn=129.4; SD=117.0). CONCLUSIONS: In this sample of older individuals, a higher dropout rate occurred in those assigned to the high-technology assessment techniques; however, once participants had completed baseline in all 3 arms, they continued participation through 1 month. High-technology home-based assessment methods, which do not require live testers, began to emerge as more time-efficient over the brief time of this pilot, despite initial time-intensive participant training.

Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders.

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.

Unobtrusive assessment of walking speed in the home using inexpensive PIR sensors.

Walking speed and activity are important measures of functional ability in the elderly. Our earlier studies have suggested that continuous monitoring may allow us to detect changes in walking speed that are also predictive of cognitive changes. We evaluated the use of passive infrared (PIR) sensors for measuring walking speed in the home on an ongoing basis. In comparisons with gait mat estimates (ground truth) and the results of a timed walk test (the clinical gold standard) in 18 subjects, we found that the clinical measure overestimated typical walking speed, and the PIR sensor estimations of walking speed were highly correlated to actual gait speed. Examination of in-home walking patterns from more than 100,000 walking speed samples for these subjects suggested that we can accurately assess walking speed in the home. We discuss the potential of this approach for continuous assessment.

Unobtrusive assessment of activity patterns associated with mild cognitive impairment.

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

Unobtrusive assessment of mobility.

This paper describes a model-based approach to the unobtrusive monitoring of elders in their home environment to assess their health, daily activities, and cognitive function. We present a semi-Markov model representation with automated learning to characterize individual elder's mobility in the home environment. The assessed mobility can be used to characterize the elder's speed of walking and can serve as one of the predictors of future cognitive functionality and the ability of elders to live independently in their home environment.