Screening measures such as the 15-item Geriatric Depression Scale (GDS-15) (Sheikh and Yesavage, 1986) and the Hospital Anxiety and Depression Scale (HADS) (Zigmond and Snaith, 1983) are important tools in the recognition of depressive symptoms in older people. While these measures are widely used, there is evidence of specific weaknesses in some cohorts and contexts, with the GDS-15 showing limitations in the context of cognitive impairment (Gilley and Wilson, 1997), and the depression subscale of the HADS (HADS-D) losing sensitivity in the context of older participants in hospital inpatient settings (Davies et al., 1993).

Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people.Trial registrationClinicaltrials.gov (NCT01038583)

As cameraphones become the dominant platform for consumer multimedia capture worldwide, multimedia researchers are faced both with the challenge of how to help users manage the billions of photographs they are collectively producing and the opportunity to leverage cameraphones' ability to automatically capture temporal, spatial, and social contextual metadata to help manage consumer multimedia content. In our Mobile Media Metadata 2 (MMM2) prototype, we apply collaborative filtering techniques to automatically gathered contextual metadata to infer the likely sharing recipients for photos captured on cameraphones. We show that while current cameraphone sharing interfaces are fraught with difficulty, it is possible to use a context-aware approach to make the sharing of cameraphone photos simpler and more satisfying for users. Based on our analysis of the relative contributions of different cameraphone sensors to predicting the likely recipients for photos, we discover for our user population that the temporal context of photo capture proved highly predictive of photo sharing behavior.

Daily temperature variations within seasons. These variations are the main determinant of the relative magnitude of demand during peak-and normal-demand days. Differences in climate and appliance saturations (e.g., air conditioning) can have significant impacts on demand patterns. The analysis presented. .. here considers demands in two regions corresponding to the North Central and South Central United States.** In general, climatic conditions in the I North Central Region are representat~ve of most northern-tier states; and, likewise, the South Central Region is representative Qf• the southern tier. Such ge.,peralizations do not apply to the West and Mid-Atlantic regions. The first step in this analysis was to review data sources and ;develop a base case, no-load-management, projection of electric demand patterns in *Integrated Analysis of Load Shapes and Energy Storage, March 1979, EPRI EA-970. **The North Central Region includes the East and West North Central census regions. The South Central Region includes the East and West South Central and South Atlantic. census regions.