Predictors of Sleep Among Spousal Care Dyads Living With Chronic Conditions

Abstract

Systematic screening improves delirium detection among hospitalized older adults. This poster describes the development and pilot testing of an iOS-based app that incorporates the Ultra-Brief Confusion Assessment Method (UB-CAM), a two-step, delirium detection protocol that combines the UB-2 (2-item screener) and 3D-CAM. Previous work tested a RedCAP-based UB-CAM app in 527 patients with 399 physicians, nurses, and certified nursing assistants (CNAs) showing it can be successfully completed by all three disciplines in 97% of eligible patients in 80 seconds on average with over 85% accuracy relative to a gold standard. To improve accessibility to the clinical setting, our research team now collaborated with a computer scientist to develop and refine an iOS-based UB-CAM app for the iPhone and iPad through iterative “laboratory” testing. The app was piloted by non-clinician, research testers in hospitalized older adults (age x̄ =83, SD= 8.0) with dementia (Clinical Dementia Rating Scale x̄ =1.1, SD= .30); 64% were assessed to be delirium positive. The app demonstrated preliminary efficiency (90 seconds on average), high acceptability (100% satisfaction of users), and reliability (100% inter-rater). This project underscores the need for close collaboration between researchers, clinicians, and computer scientists with iterative testing of bedside-facing apps prior to testing with patients. Next steps include testing effectiveness in a pragmatic trial with clinician users (physicians, nurses, CNAs), integrating the UB-CAM app into the routine hospital care of all older patients. Having rapid, accurate bedside delirium detection has the potential to transform care.