MATERIALS AND METHODS: We use the 2011/2012 Chinese Longitudinal Healthy Longevity Survey data (n = 6530) for this paper. Logistic regression is used to analyse the effects of socio-demographic, economic, health, instrumental activities of daily living, family and community factors on life satisfaction and depression among the oldest-old in China.
RESULTS: Our analysis confirms the significance of many factors affecting life satisfaction among the oldest-old in China. Factors that are correlated with life satisfaction include respondent's sex, education, place of residence, self-rated health status, cognitive ability (using mini mental state examination), regular physical examination, perceived relative economic status, access to social security provisions, commercialized insurances, living arrangements, and number of social services available in the community (p<0.05 for all these variables). Although life satisfaction is negatively associated with instrumental activities of daily living (β = -0.068, 95%CI = -.093-.043), and depression (β = -0.463, 95%CI = -.644-.282), the overall effect of self-rated health status is positive (p<0.001). This confirms the primacy of health as the determinant of well-being among the oldest-old.
CONCLUSIONS: Majority of the oldest-old in China rated their life satisfaction as good or very good. Our findings show that health and economic status are by far the most significant predictors of life satisfaction. Our finding on the primacy of health and relative income as determinants of well-being among the oldest-old, and the greater influence of self-rated health status over objective health measures is consistent with the findings of many past studies. Our results suggest that efforts should be directed at enhancing family support as well as health and social service provisions in the community to improve life satisfaction of older people.
STUDY DESIGN AND OUTCOME VARIABLES: This study used data from four waves of the Chinese Longitudinal Health and Longevity Survey (CLHLS) conducted in 2002, 2005, 2008 and 2011. The sample comprised 2137 older adults who were interviewed in 2002 and re-interviewed in the following waves. Cross-tabulations were run to show the rise in chronic disease and disability with age. Ordinal logistic regression was run to examine the debilitating effects of these diseases in terms of the ability of the oldest old to perform activities of daily living.
RESULTS: The prevalence of chronic diseases rose sharply with age. The prevalence rate of six major diseases increased between 38% (respiratory diseases) and 533% (neurological disorder) among respondents who were re-interviewed nine years later. Cardiovascular diseases were the most common. Neurological disorder and cancer were less common, but had the most debilitating effects on patients. Overall, 10.0%, 3.1% and 3.1% of the respondents were disabled by cardiovascular, musculoskeletal and sensorial diseases, respectively. Ordinal logistic regression showed that neurological disorder had the strongest debilitating effects, followed by musculoskeletal and cardiovascular diseases among 2137 older persons who had survived and were followed up from the base year (2002) through 2011.
CONCLUSION: The rapid rise in chronic diseases has resulted in an increased burden of disability among the oldest old in China. There is a need to improve health care systems for the prevention and management of chronic diseases.
MATERIALS AND METHODS: Data were used from the Well-being of the Singapore Elderly (WiSE) study, a nationally representative, cross-sectional survey among Singapore residents aged 60 years and above. Caregiver dependence was ascertained by asking the informant (the person who knows the older person best) a series of open-ended questions about the older person's care needs.
RESULTS: The older adult sample comprised 57.1% females and the majority were aged 60 to 74 years (74.8%), while 19.5% were 75 to 84 years, and 5.7% were 85 years and above. The prevalence of caregiver dependence was 17.2% among older adults. Significant sociodemographic risk factors of caregiver dependence included older age (75 to 84 years, and 85 years and above, P <0.001), Malay and Indian ethnicity (P <0.001), those who have never been married (P = 0.048) or have no education (P = 0.035), as well as being homemakers or retired (P <0.001). After adjusting for sociodemographic variables and all health conditions in multiple logistic regression analyses, dementia (P <0.001), depression (P = 0.011), stroke (P = 0.002), eyesight problems (P = 0.003), persistent cough (P = 0.016), paralysis (P <0.001), asthma (P = 0.016) and cancer (P = 0.026) were significantly associated with caregiver dependence.
CONCLUSION: Various sociodemographic and health-related conditions were significantly associated with caregiver dependence. Dependent older adults will put greater demands on health and social services, resulting in greater healthcare expenditures. Hence, effective planning, services and support are crucial to meet the needs of dependent older adults and their caregivers.
METHOD: The relationship between chronic disease and disability (independent and dependent variables) was examined using logistic regression. To demonstrate variability in activity performance with functional impairment, graphing was used. The relationship between functional impairment, activity performance, and social participation was examined graphically and using ANOVA. The impact of cognitive deficits was quantified through stratifying by dementia.
RESULTS: Disability is strongly related to chronic disease (Wald 25.5, p < .001), functional impairment with activity performance (F = 34.2, p < .001), and social participation (F= 43.6, p < .001). With good function, there is considerable variability in activity performance (inter-quartile range [IQR] = 2.00), but diminishes with high impairment (IQR = 0.00) especially with cognitive deficits.
DISCUSSION: Environment modification benefits those with moderate functional impairment, but not with higher grades of functional loss.
METHODS: A total of 110 hospitalized geriatric patients aged 60 years and older were selected using convenience sampling method in a cross-sectional study. Sociodemographic data and medical history were obtained from the medical records. Questionnaires were used during the in-person semistructured interviews, which were conducted in the wards. Linear regression analyses were used to determine the predictors of each domain of quality of life.
RESULTS: Multiple regression analysis showed that activities of daily living, depression, and appetite were the determinants of physical health domain of quality of life (R(2)=0.633, F(3, 67)=38.462; P<0.001), whereas depression and instrumental activities of daily living contributed to 55.8% of the variability in psychological domain (R(2)=0.558, F(2, 68)=42.953; P<0.001). Social support and cognitive status were the determinants of social relationship (R(2)=0.539, F(2, 68)=39.763; P<0.001) and also for the environmental domain of the quality of life (R(2)=0.496, F(2, 68)=33.403; P<0.001).
CONCLUSION: The findings indicated different predictors for each domain in the quality of life among hospitalized geriatric patients with diabetes mellitus. Nutritional, functional, and psychological aspects should be incorporated into rehabilitation support programs prior to discharge in order to improve patients' quality of life.
METHODS: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
RESULTS: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
CONCLUSIONS: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.