BACKGROUND: To let the early elderly live well, understanding how lifestyle and psychosocial factors related to a decline in competence in daily living is important. METHODS: We investigated the associations between lifestyle and psychosocial factors at age 64 years and a decline in the Tokyo Metropolitan Institute of Gerontology Index of Competence score of ≥ 2 points at age 70 years among the participants in comprehensive medical check-ups living in a city in Japan. Multivariable logistic regression analyses were performed separately for men and women. RESULTS: Of the 1113 eligible men and 1203 eligible women, 110 men and 80 women showed a deteriorated competence in daily living during the 6 years. In men, risk was increased with ≥ 2 nighttime awakenings (multivariable odds ratio [mOR] 2.14, 95% confidence interval [CI] 1.19-3.86) and living alone (mOR 4.68, 95% CI 1.22-18.0), whereas risk was significantly decreased with a medium or fast gait (mOR 0.37 and 0.21, 95% CI 0.21-0.67 and 0.08-0.58) and high academic achievement (mOR 0.32 and 0.43, 95% CI 0.19-0.53 and 0.25-0.72). In women, risk was decreased with high life satisfaction (mOR 0.39, 95% CI 0.16-0.91) and participation in community activities (mOR 0.50, 95% CI 0.29-0.86) but increased with depressive mood (mOR 1.86, 95% CI 1.09-3.18). CONCLUSION Living alone for men and low life satisfaction for women at age 64 years were markedly associated with the risk of a subsequent declining competence in daily living.
Activities of Daily Living ; psychology ; Aged ; Cohort Studies ; Community-Based Participatory Research ; Female ; Geriatric Assessment ; Healthy Aging ; Humans ; Independent Living ; psychology ; statistics & numerical data ; Japan ; Life Style ; Male ; Middle Aged ; Physical Functional Performance ; Protective Factors ; Risk Factors ; Surveys and Questionnaires

BACKGROUND: Predicting adverse health events and implementing preventative measures are a necessary challenge. It is important for healthcare planners and policymakers to allocate the limited resource to high-risk persons. Prediction is also important for older individuals, their family members, and clinicians to prepare mentally and financially. The aim of this study is to develop a prediction model for within 11-year dependent status requiring long-term nursing care or death in older adults for each sex. METHODS: We carried out age-specified cohort study of community dwellers in Nisshin City, Japan. The older adults aged 64 years who underwent medical check-up between 1996 and 2000 were included in the study. The primary outcome was the incidence of the psychophysically dependent status or death or by the end of the year of age 75 years. Univariable logistic regression analyses were performed to assess the associations between candidate predictors and the outcome. Using the variables with p-values less than 0.1, multivariable logistic regression analyses were then performed with backward stepwise elimination to determine the final predictors for the model. RESULTS: Of the 1525 female participants at baseline, 105 had an incidence of the study outcome. The final prediction model consisted of 15 variables, and the c-statistics for predicting the outcome was 0.763 (95% confidence interval [CI] 0.714-0.813). Of the 1548 male participants at baseline, 211 had incidence of the study outcome. The final prediction model consisted of 16 variables, and the c-statistics for predicting the outcome was 0.735 (95% CI 0.699-0.771). CONCLUSIONS We developed a prediction model for older adults to forecast 11-year incidence of dependent status requiring nursing care or death in each sex. The predictability was fair, but we could not evaluate the external validity of this model. It could be of some help for healthcare planners, policy makers, clinicians, older individuals, and their family members to weigh the priority of support.
Aged ; Cohort Studies ; Female ; Geriatric Assessment/statistics & numerical data* ; Humans ; Independent Living/statistics & numerical data* ; Japan ; Male ; Middle Aged ; Nursing Homes/statistics & numerical data* ; Risk Assessment ; Risk Factors