Objective: Remote telepresence provided by tele-operated robotics represents a new means for obtaining important health information, improving older adults' social and daily functioning and providing peace of mind to family members and caregivers who live remotely. In this study we tested the feasibility of use and acceptance of a remotely controlled robot with video-communication capability in independently living, cognitively intact older adults. Materials and Methods: A mobile remotely controlled robot with video-communication ability was placed in the homes of eight seniors. The attitudes and preferences of these volunteers and those of family or friends who communicated with them remotely via the device were assessed through survey instruments. Results: Overall experiences were consistently positive, with the exception of one user who subsequently progressed to a diagnosis of mild cognitive impairment. Responses from our participants indicated that in general they appreciated the potential of this technology to enhance their physical health and well-being, social connectedness, and ability to live independently at home. Remote users, who were friends or adult children of the participants, were more likely to test the mobility features and had several suggestions for additional useful applications. Conclusions: Results from the present study showed that a small sample of independently living, cognitively intact older adults and their remote collaterals responded positively to a remote controlled robot with video-communication capabilities. Research is needed to further explore the feasibility and acceptance of this type of technology with a variety of patients and their care contacts.

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...

A s treatments for life-threatening illnesses improve, life expectancy increases along with the proportion of healthcare dollars supporting chronic care. Combine this with the growing number of aging baby boomers (who are most at risk for these chronic diseases), and we see a greater demand for healthcare alternatives. Issues at stake include rising costs and the importance of quality of life to the chronically ill and the elderly.Most people approach healthcare by reacting to triggered problems: when we get sick, we typically wait until the symptoms start interfering with our daily life, and then we visit a clinic. At this point, particularly for populations at risk such as the elderly and the chronically ill, the treatment can often be riskier and much more expansive than if the problem had been dealt with earlier. Conversely, a proactive approach to healthcare would in many cases result in more effective and much less expensive treatments, by predicting or detecting conditions earlier.Both early detection and health maintenance require a health-delivery system that can monitor health status. Early detection of physical and mental changes requires sensitive and frequent measurement of physiological and behavioral data. Physiological monitoring of an individual's physical condition usually involves checking changes in heart rate, blood pressure, blood glucose levels, and day-to-day weight. Increasing evidence shows that we can also use daily behaviors, such as sleep patterns, walking speed, and movements in and outside the home, to monitor physical health and even mental condition. However, gathering behavioral data requires an intensity of monitoring that's difficult and expensive to achieve in a clinic environment. As we show in our work, which permits unobtrusive continuous behavioral monitoring of individuals in their home, pervasive computing technology offers a practical and economically feasible way to make frequent assessments. Behavioral monitoring in the homeOur research's basic premise is that we can observe and assess many informative behaviors in a person's normal life just as a skilled clinician can assess a patient's state from his posture, gait, and demeanor.For example, a condition such as arthritis causes physical discomfort, which can be reflected in an individual's overt behaviors. In addition to the obvious physical connection, increasing evidence shows that we can relate observable everyday behaviors to neurological states. Studies show that variability in mobility measures, such as Employing pervasive computing technologies can help enable continuous patient monitoring and assessment in various settings outside of hospitals, lowering healthcare costs and allowing earlier detection of problems.

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With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders’ behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.