View clinical trials related to Caregiver Stress Syndrome.
Filter by:This study aims to pilot test a culturally tailored behavioral intervention called "WECARE" to enhance caregiving mastery and improve psychosocial wellbeing of Chinese American family caregivers of persons with Alzheimer's Disease and related dementia.
The purpose of this study is to understand the impact of Family Connectors, a peer-to-peer support and education program for family members who have participated in OnTrackNY, a treatment program for adolescents and young adults.
The WeCareAdvisor is an online tool to help caregivers manage behavioral and psychological symptoms of people living with dementia. The trial will evaluate its efficacy to reduce caregiver distress, improve confidence managing behaviors, as well as reduce occurrences and severity of behavioral and psychological symptoms. Visit https://wecareadvisorstudy.com/ for more information.
The aim of the project is to test the efficacy of a systematic intervention for individual follow-up of caregivers at the intensive care unit during a 12 month randomized controlled trial.
The purpose of this project is to develop a monitoring, modeling, and interactive recommendation solution (for caregivers) for in-home dementia patient care that focuses on caregiver-patient relationships. This includes monitoring for mood and stress and analyzing the significance of monitoring those attributes to dementia patient care and subsequent behavior dynamics between the patient and caregiver. In addition, novel and adaptive behavioral suggestions at the right moments aims at helping improve familial interactions related to caregiving, which over time should ameliorate the stressful effects of the patient's illness and reduce strain on caregivers. The technical solution consists of a core set of statistical learning based techniques for automated generation of specialized modules required by in-home dementia patient care. There are three main technical components in the solution. The first obtains textual content and prosody from voice and uses advanced machine learning techniques to create classification models. This approach not only monitors patients' behavior, but also caregivers', and infers the underlying dynamics of their interactions, such as changes in mood and stress. The second is the automated creation of classifiers and inference modules tailored to the particular patients and dementia conditions (such as different stages of dementia). The third is an adaptive recommendation system that closes the loop of an in-home behavior monitoring system.