This is a study of an app intended to support recent sexual assault survivors, called THRIVE. In a prior grant, we pilot tested THRIVE and found that it reduced risk for alcohol misuse and posttraumatic stress. In the first year of this grant, we aim to revise the app to increase usability and inclusivity. In the second through fifth years of the grant, we will conduct a larger randomized controlled trial of multiple versions of the app among college students who have experienced sexual assault in the past 12 weeks. This will allow us to identify the most effective and low-burden version of THRIVE. If we are successful in identifying a highly effective and efficient version of THRIVE, this intervention would represent a highly-scalable strategy to reduce the substantial burden of posttraumatic stress and alcohol misuse on student survivors and campus service systems.
Practice Type: Online/remote/apps/social media
Needs Assessment for Supporting Technology use and Harm Reduction (STaHR Study)
The proposed study entails a needs assessment to develop a program for Supporting Technology use and Harm Reduction (STaHR) among HF residents with lived experience of homelessness and substance use. This study will qualitatively explore HF residents’ technology literacy as well as their perspectives on barriers and facilitators to the use of technology, broadly, and for harm-reduction service provision. Then, with a community advisory board (CAB) made up of HF residents, staff, and management, we will inform and provide recommendations to HF management and leadership ways to improve HF resident technology use and engagement with online harm-reduction services.
Developing a digital training resource for clinicians learning CBT for psychosis (CBTpro)
The Cognitive Behavioral Therapy Training Study will rigorously test CBTpro — a novel tool that uses spoken language technologies and conversational Artificial Intelligence to train behavioral health practitioners in Cognitive Behavioral Therapy. We conducted a 2-week field trial, followed by a Randomized Clinical Trail in community mental health agencies to evaluate both learner and client outcomes. The study aims to expand global access to CBT training to students and practitioners, support quality psychological treatments for clients with a range of behavioral health disorders (including Serious Mental Illness), and support ongoing clinical quality assurance in routine care settings.
Evaluation of an asynchronous remote communities approach to behavioral activation for depressed adolescents
In an effort to address the significant challenges in access to and engagement with evidence-based psychosocial interventions for adolescent depression, the proposed research is piloting the use of Asynchronous Remote Communities (ARC) supported behavioral activation (BA) to treat adolescent depression. We aim to 1) build and conduct usability testing on a functional and robust ActivaTeen platform that will satisfy the needs of mental health clinicians and adolescent patients and 2) test the feasibility, usability, and change in proposed target mechanisms (therapist alliance, timeliness of intervention, social belongingness, and engagement) and outcomes of BA+ActivaTeen compared to BA treatment only within a moderately-sized randomized control trial conducted within Seattle Children’s Hospital outpatient psychiatry clinic.
Reducing barriers to accessing mental health care using a web-based program for young adults
Most young adults with mental health (e.g., depression, anxiety) or substance use disorders do not receive treatment. Untreated mental health and substance use can be associated with impairments in social relationships, overall functioning and suicide. National data indicate that almost half of young adults with symptoms of a mental health disorder reported they needed mental health care in the past year but did not access those services. Barriers to accessing mental health care include stigma, not knowing where to go, lack of transportation and cost.
This project aims to develop a personalized web-based program for young adults to reduce self-reported barriers and increase motivation to access mental health and substance use services. Investigators will work with clinicians and young adults to develop strategies and solutions to address the identified barriers. The team will work with a community advisory board to develop program content that will be further refined through focus groups and individual interviews with young adults and clinicians. From this, the team will develop the web-based program which will serve as the first step needed to establish a larger program of research focused on reducing barriers and increasing access to mental health care to improve young adult well-being.
Using neurocomputational modeling to track memory decline
The most salient and debilitating aspect of dementia is memory loss. Unfortunately, memory loss is also the most difficult to quantify because it relies on doctor-administered tests that cannot be repeated very often. Without frequent and accurate measurements, it is difficult for clinicians to make reliable diagnoses, for patients and their caretakers to prepare in advance and for researchers to better understand the relationship between brain changes and cognitive decline.
This project will recruit 100 patients who are just beginning to experience memory loss as well as 100 healthy controls. Their memory function will be measured weekly through a brief, online test that can be accessed through any device and performed in less than 10 minutes. Data from the test will be fed to a computer model that simulates how fast memories fade in each patient’s brain, and the parameter that represents each patient’s speed of forgetting will be tracked over time. While the model simulates the patient, it also adapts the difficulty of the weekly task, ensuring it remains engaging but doable as memory declines.
The weekly estimates will provide the first, detailed trajectories of how fast memory declines over time in healthy aging and in different forms of dementia. The trajectory of the rate of forgetting will be used to analyze MRI data, producing precise associations between different types of memory loss and different types of brain damage.
Leveraging artificial intelligence to improve digital mental health interventions
Cognitive therapies help patients by providing ways to modify habitual but unproductive thought patterns, known as maladaptive thinking styles. Cognitive therapies are effective in treating depression, amongst other conditions, and are increasingly delivered remotely as text-based interventions. This trend toward digital delivery has accelerated on account of physical isolation and psychological stressors during the global pandemic. While this means cognitive therapy can potentially reach more patients, the effectiveness of this therapy depends on the ability of a skilled practitioner to recognize types of maladaptive thinking, and there is a critical shortage of mental health practitioners with this expertise.
In radiology, computer-aided diagnosis systems driven by artificial intelligence are used to help physicians detect signs of illness they may otherwise miss. In this project, we will develop a computer-aided detection system to support text-based cognitive therapy. To do so, we will identify indicators of maladaptive thinking styles within a set of text messages exchanged between clients and their therapists, and train neural networks to detect these indicators automatically. The resulting tools will provide a basis for an artificial intelligence-based decision support system to help clinicians recognize and manage maladaptive thinking styles that will enhance the quality and effectiveness of text-based cognitive therapy.
Improving patient-focused, population-informed care in clinical neurosciences
UW Medicine has amassed detailed patient treatment and business data in its electronic medical record (EMR). This information is a treasure trove that is not used to its full potential for two reasons: 1) For each clinical encounter, only a fraction of the information in the EMR is relevant, and virtually all of the information a clinician engages remains in a format that obscures patterns and trends; and 2) In groups of patients with the same illness, data from the EMR could be used to discern larger trends in the course of the disease or evaluate the effect of practice patterns on patient outcomes. The EMR currently does not provide a way to access this information in an agile way.
We have developed innovative software, “Leaf,” that allows medical providers to access population-based EMR data in real time. Leaf is now used at several academic medical centers nationally. In this project, we will collaborate with the UW Memory and Brain Wellness Center to design and evaluate “dashboards” that visualize how a patient’s history and trajectory compare to other, similar patients. For instance, daily function and cognitive testing data for a person with Alzheimer’s disease, already gathered over the course of several years, could be graphed and compared to the same information from all UW patients with Alzheimer’s disease. We will pilot these dashboards in Leaf and collect patient and provider feedback. We intend to publish our results and make code available as part of the open Leaf platform for rapid dissemination.
Empowering caregivers of persons with Lewy Body Dementias using a virtual peer-to-peer intervention
Lewy body dementias (LBD), a term referring to both dementia with Lewy bodies and Parkinson’s disease dementia, are the second most common type of degenerative dementia in older adults. These are complex disorders in which patients may exhibit disruptive behaviors that make caregiving challenging. Compared to other types of dementias, caregivers of people with LBD report higher stress and more severe depressive symptoms. The ongoing COVID-19 pandemic has multiplied the challenges that caregivers of persons with dementia face in providing care for their loved ones. As such, support interventions for caregivers of persons with LBD are urgently needed.
In this study, we will adapt our online intervention for older adults with frailty to target the unique needs of caregivers of people with LBD. We will conduct participatory design sessions with potential users to determine their needs and priorities specific to LBD and deploy the re-designed intervention in a pilot study focused on usability and efficacy. Through this newly tailored support system, we aim to bolster the health of caregivers as well as their ability to assist care partners living with LBD.
This intervention could potentially be used in conjunction with usual care and/or as a stand-alone module in emergent circumstances, such as the current pandemic, when routine professional interventions may not be readily available. By fostering the development of a community-driven online support system, this project will begin to lay the groundwork for promoting resilience within families affected by the behavioral challenges of dementia.
Discovery of conversational best practices in online mental health support
Millions of people lack access to mental health treatment due to barriers such as limited therapist availability, long wait times, high cost, and stigma. The COVID-19 pandemic has problematically increased demand for treatment while decreasing access. Because the internet is widely available, many people first turn to the internet for mental health support, giving rise to massive online psychotherapy, counseling and peer-to-peer support platforms such as Ginger and Talklife. However, not all conversations lead to improvement and may miss opportunities to help or even make things worse as platforms struggle to keep up with the increasing demands and lack methods for evaluating and promoting high-quality conversations.
This project seeks to improve the quality and scalability of online mental health support through real-time, evidence-based conversation feedback. We will leverage and analyze datasets of support interactions and associated outcomes across millions of individuals that use the partnering online mental health platforms at Ginger and Talklife. Our goal is to develop and pilot-test artificial intelligence methods that provide supporters on these platforms with practical just-in-time feedback and training. If successful, at least three benefits will follow our work. First, millions of help seekers using partnering mental health platforms Ginger and Talklife will receive higher quality responses through, for example, an expression of higher empathy. Second, those providing help will gain expertise faster and with less distress. Third, platforms and researchers will discover conversational best practices which can then be used to improve helper training and quality evaluation.
