Assessing the wellbeing of family caregivers of multimorbid and homebound older adults-A scoping literature review.

BACKGROUND: The prevalence of homebound older adults in the United States more than doubled during the COVID-19 pandemic with greater burden on family caregivers. Higher caregiver burden, more specifically higher treatment burden, contributes to increased rates of nursing home placement. There exist a multitude of tools to measure caregiver well-being and they vary substantially in their focus. Our primary aim was to perform a scoping literature review to identify tools used to assess the facets of caregiver well-being experienced by caregivers of persons with multiple chronic conditions (MCC) with a special focus on those caregivers of homebound adult patients. METHODS: The search was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. After refining search terms, searches were performed of the peer-reviewed and gray literature. RESULTS: After removal of duplicate studies, a total of 5534 total articles were screened for relevance to our study. After all screening and review were completed, 377 total articles remained for full review which included 118 different quantitative tools and 20 different qualitative tools. We identified the 15 most commonly utilized tools in patients with MCC. The Zarit Burden Interview was the most commonly used tool across all of the studies. Of the 377 total studies, only eight of them focused on the homebound population and included 13 total tools. CONCLUSIONS: Building on prior categorization of well-being tools, our work has identified several tools that can be used to measure caregiver well-being with a specific focus on those caregivers providing support to older adults with MCC. Most importantly, we have identified tools that can be used to measure caregiver well-being of family caregivers providing support to homebound older adults, an ever-growing population who are high cost and high utilizers of health care services.

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning.

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.