A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

Street crossing behavior in younger and older pedestrians: an eye- and head-tracking study

BACKGROUND: Crossing a street can be a very difficult task for older pedestrians. With increased age and potential cognitive decline, older people take the decision to cross a street primarily based on vehicles' distance, and not on their speed. Furthermore, older pedestrians tend to overestimate their own walking speed, and could not adapt it according to the traffic conditions. Pedestrians' behavior is often tested using virtual reality. Virtual reality presents the advantage of being safe, cost-effective, and allows using standardized test conditions. METHODS: This paper describes an observational study with older and younger adults. Street crossing behavior was investigated in 18 healthy, younger and 18 older subjects by using a virtual reality setting. The aim of the study was to measure behavioral data (such as eye and head movements) and to assess how the two age groups differ in terms of number of safe street crossings, virtual crashes, and missed street crossing opportunities. Street crossing behavior, eye and head movements, in older and younger subjects, were compared with non-parametric tests. RESULTS: The results showed that younger pedestrians behaved in a more secure manner while crossing a street, as compared to older people. The eye and head movements analysis revealed that older people looked more at the ground and less at the other side of the street to cross. CONCLUSIONS: The less secure behavior in street crossing found in older pedestrians could be explained by their reduced cognitive and visual abilities, which, in turn, resulted in difficulties in the decision-making process, especially under time pressure. Decisions to cross a street are based on the distance of the oncoming cars, rather than their speed, for both groups. Older pedestrians look more at their feet, probably because of their need of more time to plan precise stepping movement and, in turn, pay less attention to the traffic. This might help to set up guidelines for improving senior pedestrians' safety, in terms of speed limits, road design, and mixed physical-cognitive trainings.

Age in antiretroviral therapy programmes in South Africa: a retrospective, multicentre, observational cohort study.

BACKGROUND As access to antiretroviral therapy (ART) expands, increasing numbers of older patients will start treatment and need specialised long-term care. However, the effect of age in ART programmes in resource-constrained settings is poorly understood. The HIV epidemic is ageing rapidly and South Africa has one of the highest HIV population prevalences worldwide. We explored the effect of age on mortality of patients on ART in South Africa and whether this effect is mediated by baseline immunological status. METHODS In this retrospective cohort analysis, we studied HIV-positive patients aged 16-80 years who started ART for the first time in six large South African cohorts of the International Epidemiologic Databases to Evaluate AIDS-Southern Africa collaboration, in KwaZulu-Natal, Gauteng, and Western Cape (two primary care clinics, three hospitals, and a large rural cohort). The primary outcome was mortality. We ascertained patients' vital status through linkage to the National Population Register. We used inverse probability weighting to correct mortality for loss to follow-up. We estimated mortality using Cox's proportional hazards and competing risks regression. We tested the interaction between baseline CD4 cell count and age. FINDINGS Between Jan 1, 2004, and Dec 31, 2013, 84,078 eligible adults started ART. Of these, we followed up 83,566 patients for 174,640 patient-years. 8% (1817 of 23,258) of patients aged 16-29 years died compared with 19% (93 of 492) of patients aged 65 years or older. The age adjusted mortality hazard ratio was 2·52 (95% CI 2·01-3·17) for people aged 65 years or older compared with those 16-29 years of age. In patients starting ART with a CD4 count of less than 50 cells per μL, the adjusted mortality hazard ratio was 2·52 (2·04-3·11) for people aged 50 years or older compared with those 16-39 years old. Mortality was highest in patients with CD4 counts of less than 50 cells per μL, and 15% (1103 of 7295) of all patients aged 50 years or older starting ART were in this group. The proportion of patients aged 50 years or older enrolling in ART increased with successive years, from 6% (290 of 4999) in 2004 to 10% (961 of 9657) in 2012-13, comprising 9% of total enrolment (7295 of 83 566). At the end of the study, 6304 (14%) of 44,909 patients still alive and in care were aged 50 years or older. INTERPRETATION Health services need reorientation towards HIV diagnosis and starting of ART in older individuals. Policies are needed for long-term care of older people with HIV. FUNDING National Institutes of Health (National Institute of Allergy and Infectious Diseases), US Agency for International Development, and South African Centre for Epidemiological Modelling and Analysis.