Caring for ageing migrants in nursing homes: 'doing diversity' while 'doing death'

The proposed paper will present first results of a research project investigating how nursing homes in Switzerland deal with migrant elders who are in intensive need of care. Focusing on the end-of-life in institutional care settings, the intention is to explore the dimensions of ‘doing death’ in Swiss nursing homes when the elderly involved are of migrant background. The focus is laid on the co-construction of end of life in interactions between residents of migrant background and professional carers involved (often of migrant background themselves), and will thereby focus on processes of ‘doing diversity’ while ‘doing death’. To do so, we chose an ethnographic approach focusing on the participant observation of everyday practices of ‘doing death’ and ‘death work’ and on interviewing staff, residents and their relatives. Caring for ageing migrants at the end of their lives is studied in different types of assisted living at the end of life: The field of research was entered by studying a group specific department for residents of so-called ‘Mediterranean’ background. It was contrasted by a department stressing the individuality of each resident but including a considerable number of residents with migrant background. We are interested in how (and if at all) specific forms of ‘doing community’ within different types of departments may also lead to specific ways of ‘doing death’, which aim at a stronger embeddedness of dying trajectories in social relations of reciprocity and exchange. Furthermore, migrant ‘doing death’ is expected to be particularly negotiable since the potential diversities of symbolic reference systems and daily practices are widened. If the respective resident is limited in his/her capacities to play an active part in negotiating about ‘good care’ and ‘good dying’ – either due to language competences, which would be migrant specific, or due to degenerative diseases, which is not migrant specific – the field of negotiations will be left up to the professionals within the organization (and to the relatives, which are, however, not constantly present). Strategies of stereotyping the ‘other’ as well as driving nurses, caring aides and other professionals of migrant background into roles of ‘cultural experts’ or ‘transcultural translators’ are expected to be common in such situations. However, the task of negotiating what would be a ‘good dying’ and what measures are appropriate is always at stake in contemporary heterogeneous societies. Therefore we would argue that studying dying processes involving migrant residents is looking at paradigmatic manifestations of doing death in recent contexts of reflexive modernity.

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.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Suicide assisted by right-to-die associations: a population based cohort study

Background: In Switzerland, assisted suicide is legal but there is concern that vulnerable or disadvantaged groups are more likely to die in this way than other people. We examined socio-economic factors associated with assisted suicide. Methods: We linked the suicides assisted by right-to-die associations during 2003–08 to a census-based longitudinal study of the Swiss population. We used Cox and logistic regression models to examine associations with gender, age, marital status, education, religion, type of household, urbanization, neighbourhood socio-economic position and other variables. Separate analyses were done for younger (25 to 64 years) and older (65 to 94 years) people. Results: Analyses were based on 5 004 403 Swiss residents and 1301 assisted suicides (439 in the younger and 862 in the older group). In 1093 (84.0%) assisted suicides, an underlying cause was recorded; cancer was the most common cause (508, 46.5%). In both age groups, assisted suicide was more likely in women than in men, those living alone compared with those living with others and in those with no religious affiliation compared with Protestants or Catholics. The rate was also higher in more educated people, in urban compared with rural areas and in neighbourhoods of higher socio-economic position. In older people, assisted suicide was more likely in the divorced compared with the married; in younger people, having children was associated with a lower rate. Conclusions: Assisted suicide in Switzerland was associated with female gender and situations that may indicate greater vulnerability such as living alone or being divorced, but also with higher education and higher socio-economic position.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Assisted and unassisted suicide in men and women: longitudinal study of the Swiss population.

BackgroundIn Switzerland assisted suicide is legal if no self-interest is involved.AimsTo compare the strength and direction of associations with sociodemographic factors between assisted and unassisted suicides.MethodWe calculated rates and used Cox and logistic regression models in a longitudinal study of the Swiss population.ResultsAnalyses were based on 5 004 403 people, 1301 assisted and 5708 unassisted suicides from 2003 to 2008. The rate of unassisted suicides was higher in men than in women, rates of assisted suicides were similar in men and women. Higher education was positively associated with assisted suicide, but negatively with unassisted. Living alone, having no children and no religious affiliation were associated with higher rates of both.ConclusionsSome situations that indicate greater vulnerability such as living alone were associated with both assisted and unassisted suicide. Among the terminally ill, women were more likely to choose assisted suicide, whereas men died more often by unassisted suicide.