[Active and healthy living in old age--results from a representative survey of community-dwelling senior citizens in Hamburg]

BACKGROUND: The majority of community-dwelling people 60 years and older are independent and live actively. However, there is little information about elderly persons' views on aging, health and health promotion. METHODS: Therefore, an anonymous, written questionnaire survey was performed in a representative sample of inhabitants from a section of the city of Hamburg, 60 years and older; 5 year intervals, 14 subsamples according to 7 age groups of females and males. RESULTS: Questionnaires from 950 participants (29% response) could be evaluated: mean age 71.5 years, 58% women, 34% living alone, 5% with professional healthcare needs as indicated by status according to German nursing care insurance. Senior citizens' positive attitudes towards aging and health were predominant: 69% of respondents felt young, 85% worried about loss of autonomy in old age. CONCLUSIONS: The results provide evidence indicating potential for improving health-promoting lifestyles in parts of the older population by evaluating and strengthening older persons' competencies and by considering their concerns seriously. These results provide valuable information for future plans in the public-health sector in the city of Hamburg where particular health-promoting actions for elderly persons will be considered.

Marital status, living arrangement and mortality: does the association vary by gender?

Men appear to benefit more from being married than women with respect to mortality in middle age. However, there is some uncertainty about gender differences in mortality risks in older individuals, widowed, divorced and single individuals and about the impact of living arrangements.

Health risk appraisal in older people 1: are older people living alone an "at-risk" group?

BACKGROUND: In the UK, population screening for unmet need has failed to improve the health of older people. Attention is turning to interventions targeted at 'at-risk' groups. Living alone in later life is seen as a potential health risk, and older people living alone are thought to be an at-risk group worthy of further intervention. AIM: To explore the clinical significance of living alone and the epidemiology of lone status as an at-risk category, by investigating associations between lone status and health behaviours, health status, and service use, in non-disabled older people. Design of study: Secondary analysis of baseline data from a randomised controlled trial of health risk appraisal in older people. SETTING: Four group practices in suburban London. METHOD: Sixty per cent of 2641 community-dwelling non-disabled people aged 65 years and over registered at a practice agreed to participate in the study; 84% of these returned completed questionnaires. A third of this group, (n = 860, 33.1%) lived alone and two-thirds (n = 1741, 66.9%) lived with someone else. RESULTS: Those living alone were more likely to report fair or poor health, poor vision, difficulties in instrumental and basic activities of daily living, worse memory and mood, lower physical activity, poorer diet, worsening function, risk of social isolation, hazardous alcohol use, having no emergency carer, and multiple falls in the previous 12 months. After adjustment for age, sex, income, and educational attainment, living alone remained associated with multiple falls, functional impairment, poor diet, smoking status, risk of social isolation, and three self-reported chronic conditions: arthritis and/or rheumatism, glaucoma, and cataracts. CONCLUSION: Clinicians working with independently-living older people living alone should anticipate higher levels of disease and disability in these patients, and higher health and social risks, much of which will be due to older age, lower educational status, and female sex. Living alone itself appears to be associated with higher risks of falling, and constellations of pathologies, including visual loss and joint disorders. Targeted population screening using lone status may be useful in identifying older individuals at high risk of falling.

Preclinical disability as a risk factor for falls in community-dwelling older adults

BACKGROUND: Falls are common and serious problems in older adults. The goal of this study was to examine whether preclinical disability predicts incident falls in a European population of community-dwelling older adults. METHODS: Secondary data analysis was performed on a population-based longitudinal study of 1644 community-dwelling older adults living in London, U.K.; Hamburg, Germany; Solothurn, Switzerland. Data were collected at baseline and 1-year follow-up using a self-administered multidimensional health risk appraisal questionnaire, including validated questions on falls, mobility disability status (high function, preclinical disability, task difficulty), and demographic and health-related characteristics. Associations were evaluated using bivariate and multivariate logistic regression analyses. RESULTS: Overall incidence of falls was 24%, and increased by worsening mobility disability status: high function (17%), preclinical disability (32%), task difficulty (40%), test-of-trend p <.003. In multivariate analysis adjusting for other fall risk factors, preclinical disability (odds ratio [OR] = 1.7, 95% confidence interval [CI], 1.1-2.5), task difficulty (OR = 1.7, 95% CI, 1.1-2.6) and history of falls (OR = 4.7, 95% CI, 3.5-6.3) were the strongest significant predictors of falls. In stratified multivariate analyses, preclinical disability equally predicted falls in participants with (OR = 1.7, 95% CI, 1.0-3.0) and without history of falls (OR = 1.8, 95% CI, 1.1-3.0). CONCLUSIONS: This study provides longitudinal evidence that self-reported preclinical disability predicts incident falls at 1-year follow-up independent of other self-reported fall risk factors. Multidimensional geriatric assessment that includes preclinical disability may provide a unique early warning system as well as potential targets for intervention.

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.