Self Efficacy clinical trials at UCSF
2 in progress, 1 open to eligible people
open to eligible people ages 18 years and up
These caregivers are a vulnerable group due to their physical isolation and well-documented rural disparities in health care access and quality. Many rural dementia caregivers experience serious health consequences due to caregiving responsibilities that can limit their ability to maintain their caregiving role. Thus, there is a pressing need for effective, scalable, and accessible programs to support rural dementia caregivers. Online programs offer a convenient and readily translatable option for program delivery because they can be accessed by caregivers in the home and at the convenience of the user. Building Better Caregivers is an online 6-week, interactive, small-group self-management, social support, and skills-building workshop developed for caregivers of individuals with Alzheimer's disease or related dementia. The investigators will conduct a hybrid effectiveness-implementation randomized controlled trial that will enroll and randomize 640 rural dementia caregivers into two groups: the intervention (workshop) group and the attention control group. Caregivers will be recruited throughout the United States. Primary outcomes will be caregiver stress and depression symptoms. The investigators hypothesize that stress scores and depression symptoms will be significantly improved at 12 months in the intervention group versus control group. The investigators will also identify key strengths (facilitators) and weaknesses (barriers) of workshop implementation. The investigators will use the RE-AIM implementation framework and a mixed methods approach to identify implementation characteristics pertinent to both caregivers and rural community organizations. If the Building Better Caregivers workshop is proven to be effective, this research has the potential to open new research horizons, particularly on how to reach and effectively support isolated dementia caregivers in rural areas with an intervention that is scalable, even in low-resourced settings. If the workshop can achieve its goals with rural dementia caregivers, some of those most isolated, it would also be expected to be scalable in other low-resourced settings (e.g., in urban or suburban environments).
San Francisco, California
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As most adolescents visit a healthcare provider once a year, health behavior change interventions linked to clinic-based health information technologies hold significant promise for improving healthcare quality and subsequent behavioral health outcomes for adolescents (Baird, 2014, Harris, 2017). Recognizing the potential to leverage recent advances in machine learning and interactive narrative environments, the investigators are now well positioned to design health behavior change systems that extend the reach of clinicians to realize significant impacts on behavior change for adolescent preventive health. The proposed project centers on the design, development, and evaluation of a clinically-integrated health behavior change system for adolescents. CHANGEGRADIENTS will introduce an innovative reinforcement learning-based feedback loop in which adolescent patients interact with personalized behavior change interactive narratives that are dynamically personalized and realized in a rich narrative-centered virtual environment. CHANGEGRADIENTS will iteratively improve its behavior change models using policy gradient methods for Reinforcement Learning (RL) designed to optimize adolescents' achieved behavior change outcomes. This in turn will enable CHANGEGRADIENTS to generate more effective behavior change narratives, which will then lead to further improved behavior change outcomes. With a focus on risky behaviors and an emphasis on alcohol use, adolescents will interact with CHANGEGRADIENTS to develop an experiential understanding of the dynamics and consequences of their alcohol use decisions. The proposed project holds significant transformative potential for (1) producing theoretical and practical advances in how to realize significant impacts on adolescent health behavior change through novel interactive narrative technologies integrated with policy-based reinforcement learning, (2) devising sample-efficient policy gradient methods for RL that produce personalized behavior change experiences by integrating theoretically based models of health behavior change with data-driven models of interactive narrative generation, and (3) promoting new models for integrating personalized health behavior change technologies into clinical care that extend the effective reach of clinicians.
San Francisco, California