Though the focus of most research on dementia is the pathogenesis of cognitive deficits, neuropsychiatric symptoms (NPS) are identified in >90% of those afflicted, resulting in hastened cognitive decline, worsened general health, reduced patient and caregiver quality of life, sooner institutionalization, and increased mortality. Affective symptoms, including depression, are the most common NPS in Alzheimer’s Disease (AD), and are present in over half of patients. Using the in-depth clinical phenotyping of participants in the National Alzheimer’s Coordinating Center (NACC) with matched plasma samples, we propose to determine the correlation between select cytokines/chemokines and T-cell differentiation with depression in dementia.
Targeted Condition: Depression
State Implementation and Scaling-up of Evidence-based Practices (SISEP) Center
The State Implementation and Scaling-up of Evidence-based Practices (SISEP) Center is a national technical assistance center funded by the U.S. Department of Education’s Office of Special Education Programs. Through this subcontract, the UW SMART Center has a subcontract to develop a micro-credentialing program on implementation science (IS) for educators, and to develop and convene a national community of practice of educators focused on application of IS.
Spanish-language lay-delivered Behavioral Activation in senior centers
This supplement seeks to expand the Collaborative R01 on Lay-delivered Behavioral Activation in Senior Centers for clients whose preferred language is Spanish. The aims are to translate DMFB intervention materials and and test the effect of Spanish DMFB in comparison to professionally-delivered BA (Clinician BA) among older senior center clients on increased activity level and decreased depressive symptoms.
Developing a pediatric telebehavioral health consultation model for emergency departments
As rates of pediatric mental health emergencies have skyrocketed over the last decade – and even more so since the Covid-19 pandemic – the number of youth staying in emergency departments (EDs) and medical units while awaiting inpatient psychiatric care or stabilization (i.e., “boarding”) has reached unprecedented levels. The massive surges in patient volume, coupled with widespread staff shortages and lack of staff expertise in treating mental health, are overwhelming ED and hospital resources. This causes dangerous or even life-threatening delays in care for youth populations in greatest need of medical and psychiatric treatment. Prolonged ED stays not only delay necessary mental health care, but they can cause additional trauma and distress for youth already in crisis. While the boarding crisis affects all hospitals and EDs, it poses an even greater challenge to community EDs that lack on-site mental health specialists and/or pediatric providers.
To address the boarding crisis, this project will pilot a model in which a multidisciplinary team of mental health clinicians at Seattle Children’s Hospital provides telebehavioral health consultation to community EDs in Western Washington to guide care for youth who are boarding. The primary goals of this model are (1) to improve timeliness of mental health care and reduce length of stay for youth boarding in community EDs, and (2) to support ED staff in providing more developmentally appropriate and evidence-informed mental healthcare. The Seattle Children’s team will provide case consultation to ED providers and staff, including support with decisions about hospitalization, medication treatment, behavioral interventions and case management services. The team will also deliver practical trainings to community ED staff to build their internal capacity to care for boarding youth. If this initiative is successful, additional funding could expand ED telebehavioral health consultation services statewide, with a focus on rural communities.
Expanding access to adolescent depression care by non-specialists with a digital intervention
Adolescent depression is one of the most common mental health concerns during adolescence and can be a cause of significant impairment across the lifespan, particularly if untreated. Access to evidence-based psychotherapy is poor and pandemic-related increased demand for services has greatly worsened access issues, leaving many adolescents without effective and critically needed treatment. Adolescent depression is often first identified in primary care, making it the ideal setting for improving early access to treatment. While treatment by mental health specialists within primary care is effective, the workforce is not adequate to meet the high demand for services. Online depression treatment has been shown to be effective and has the added potential to expand access, particularly given adolescent’s comfort with digital technology. Importantly, the addition of human coaching alongside online treatments has been shown to boost engagement and treatment outcomes.
The goal of this project is to increase early access to evidence-based depression treatment in primary care settings. The project team will work with adolescents, caregivers and providers to develop an accessible and engaging online treatment for delivery in primary care. To support integration and enhance engagement, the project will also develop a coaching toolkit that can be utilized by a range of non-mental health specialists, including nurses, medical providers, social workers and bachelor’s level staff.
Optimizing mental health first-aid programming for sport coaches
Many sport organizations are increasingly vocal about the importance of athlete mental health. Helping organizations move beyond rhetoric to improved athlete wellbeing and safety requires evidence-based resources that are setting-appropriate and feasibly implemented.
This project will develop and obtain feasibility and acceptability data on “Time Out for Mental Health”—a mental health first aid training for sport coaches. This will be accomplished by adapting an existing evidence-based mental health first aid resource to the coach role and sport setting, working closely with a small group of coach partners. The team will focus on ensuring the training is considered useful and feasible by coaches who work in resource deprived school and community-settings given the heightened needs and challenges of youth in such settings, and will train coaches to deliver “Time Out for Mental Health”—to build organizational capacity. “Time Out for Mental Health”—has the potential to strengthen connections between sports organizations and school- and community-based mental health services for millions of adolescents as more than half of high school students play at least one organized school or community sport.
Monitoring mood symptoms in young adults at-risk for bipolar disorder
The ages of 18-25 years are ‘peak onset’ times of major depression and bipolar disorder. These disorders have different courses and treatments, but diagnosing bipolar disorder is difficult because manic symptoms occur less often than depressive symptoms and many individuals do not recall manic symptoms. A ‘misdiagnosis lag’ of 8-10 years can contribute to prolonged periods of potentially ineffective treatments and suboptimal outcomes such as high symptom burden, relationship problems, educational attainment and occupational functioning.
This project will use remote prospective assessment and monitoring of depressive and manic symptoms in at-risk patients in-between patient visits to increase the ‘data points’ clinicians have when assessing a bipolar disorder diagnosis. This is especially important for people at risk for bipolar disorder (for example those with a family history of bipolar disorder) because manic symptoms can be provoked by first-line medication treatments for major depression. The project will use a new manic symptom measure (the Patient Mania Questionnaire-9) and a commonly used depressive symptom measure (the Patient Health Questionnaire-9) to monitor symptoms, and learn how clinicians and patients use this information clinically.
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.
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.
