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.
Practice Type: Online/remote/apps/social media
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.
Understanding the support needs of gender expansive youth
Approximately 35% of youth who identified as transgender report having attempted suicide in the past 12 months. Despite this high risk, few preventive interventions have been developed specifically to address the unique needs of this group who experience high rates of marginalization, victimization and social isolation based on their gender identities. This study will use human centered design principles to adapt the Caring Contacts intervention for suicide prevention for transgender and gender expansive (TGE) youth and user test this intervention with suicidal ideation who are identified via a Zero Suicide-based screening program in the Seattle Children’s Gender Clinic.
Developing a resource toolkit for clinician survivors of suicide loss
This project will develop a resource toolkit for clinician survivors of suicide loss. For clinicians, the death of a patient by suicide is a dreaded event and can be more distressing than death and dying encountered in other clinical situations. In response to patient suicide, some clinician survivors experience emotional and psychological distress that may reach clinical levels and negative and sometimes persisting effects on professional practice. Building on existing reference materials, we will develop a toolkit of resources to guide and support faculty, clinical staff and trainee clinician-survivors affiliated with the department hospitals. These resources would address educational, emotional, administrative and spiritual needs of clinician-survivors.
Project Better
The co-occurrence of posttraumatic stress disorder (PTSD) and hazardous drinking (HD) can be particularly devastating; though evidence-based treatments exist, many individuals with this co-occurrence drop out from or do not or cannot access specialty care. Text-messaging is a mode of intervention delivery that is low-cost, low-burden, and accessible to most people; development and testing of self-directed text message interventions that use evidence- and theoretically-based strategies to reduce PTSD and HD symptom burden is highly needed. Such interventions have the potential for great clinical significance via providing additional, novel treatment options that are readily scalable and have wide reach and thus can have a large impact on individual and public health.
Enhancing engagement with digital mental health care
Although several randomized clinical trials have demonstrated that digital mental health (DMH) tools are highly effective, most consumers do not sustain their use of these tools. The field currently lacks an understanding of DMH tool engagement, how engagement is associated with well-being, and what practices are effective at sustaining engagement. In this partnership between Mental Health America, Talkspace, and the University of Washington (UW), we propose a naturalistic and experimental, theory driven program of research, with the aim of understanding 1) how consumer engagement in self-help and clinician assisted DMH varies and what engagement patterns exist, 2) the association between patterns of engagement and important consumer outcomes, and 3) the effectiveness of personalized strategies for optimal engagement with DMH treatment.
