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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We have developed an instrumented pillbox, called a MedTracker, which allows monitoring of medication adherence on a continuous basis. This device improves on existing systems by providing mobility, frequent and automatic data collection, more detailed information about non-adherence and medication errors, and the familiar interface of a 7-day drug store pillbox. We report on the design of the MedTracker, and on the results of a field trial in 39 homes to evaluate the device.

Objectives-This was a cross-sectional study of the ability of independently living healthy elders to follow a medication regimen. Participants were divided into a group with High Cognitive Function (HCF) or Low Cognitive Function (LCF) based on their scores on the ADAS-Cog.Methods-Thirty-eight participants aged 65 or older and living independently in the community followed a twice-daily vitamin C regimen for five weeks. Adherence was measured using an electronic 7-day pill box.Results-The LCF group had significantly poorer total adherence than the HCF group (LCF: 63.9 ± 11.2%, HCF: 86.8 ± 4.3%, t 36 =2.57, p=0.007), and there was a 4.1 relative risk of nonadherence in the LCF group as compared to the HCF group.Discussion-This study provides strong evidence that even very mild cognitive impairment in healthy elderly living independently in the community has a detrimental and significant impact on adherence to a medication regimen. This study has important implications for the conduct of clinical drug trials in this population. Keywords cognitive impairment; medication; adherenceCorrect medication use is an important part of healthy aging (Monane, Monane, & Semla, 1997). More than 75% of people aged 65 and older take prescription medication, and on average they take 3 or more medications a day (Helling et al., 1987;Ostrom, Hammarlund, Christensen, Plein, & Kethley, 1985). Unfortunately, more than 50% of these individuals are non-adherent to their medication regimen (Botelho & Dudrak, 1992;Kendrick & Bayne, 1982), which can have tremendous impact on their health. The financial cost of this medication mismanagement is also significant, since it leads to increased hospitalization and drug side-effects (Col, Fanale, & Kronholm, 1990). The importance of proper medication adherence is underscored by the fact that ability to manage medication is considered an (IADL), that is, a skill that is essential to maintaining independence in the elderly (Fillenbaum & Smyer, 1981).There are many reasons why the elderly may be non-adherent (Fitten, Coleman, Siembieda, Yu, & Ganzell, 1995;Paes, Bakker, & Soe-Agnie, 1997;Salzman, 1995), including the high number of medications (and complexity of regimen) used by this population (Cramer, Mattson, Prevey, Scheyer, & Ouellette, 1989;Helling et al., 1987;Paes et al., 1997), increased sensitivity to side effects, the high cost of medications, and forgetting or confusion about dosage schedule. There is evidence that memory deficits can lead to a decrease in medication management abilities, reflecting the important role that memory may play in medication adherence. While most studies have looked at the correlation between medication adherence and MMSE scores, which is not a highly sensitive measure of small memory changes (Edelberg, Shallenberger, & Wei, 1999;Fitten et al., 1995;Morrell, Park, Kidder, & Martin, 1997;Patrick & Howell, 1998), Insel and colleagues found that a composite of memory and executive function scores was predictive of poor medication adherence in community-dwe...

Most current state-of-the-art automatic speaker recognition systems extract speaker-dependent features by looking at shortterm spectral information. This approach ignores long-term information that can convey supra-segmental information, such as prosodics and speaking style. We propose two approaches that use the fundamental frequency and energy trajectories to capture long-term information. The first approach uses bigram models to model the dynamics of the fundamental frequency and energy trajectories for each speaker. The second approach uses the fundamental frequency trajectories of a pre-defined set of words as the speaker templates and then, using dynamic time warping, computes the distance between the templates and the words from the test message. The results presented in this work are on Switchboard I using the NIST Extended Data evaluation design. We show that these approaches can achieve an equal error rate of 3.7%, which is a 77% relative improvement over a system based on short-term pitch and energy features alone.