The proposed research addresses three important objectives (1) complete a scoping review to map determinants of teacher implementation of evidence-based practices for child behavior in preschool , (2) identify strategies to improve teacher implementation through a series of casual pathway diagrams, and (3) conceptualize and operationalize strategies with stakeholders to increase feasibility.
Geographic Area: National
Using deep learning to diagnose Alzheimer’s disease and predict its progression
Alzheimer’s Disease (AD) is a degenerative condition that affected 5.8 million seniors in 2020 and is the sixth leading cause of death in the United States. Detecting mild cognitive impairment, often a precursor to AD, and predicting its advance to AD dementia are key clinical diagnostic problems. Early diagnosis can motivate early intervention with lifestyle changes that build cognitive reserve or reduce comorbidity and thus prolong functional independence. MRI scans and specialized tests for AD-related proteins in spinal fluid or on PET brain scans are available, but it is not known how best to deploy these expensive tests or combine the information from them. New computer-based “machine learning” software tools may provide a solution to these problems.
This project will explore the use of a machine learning technology called deep learning to diagnose the stage of AD and to predict its progression. We will use the data available from the scientifically open Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which contains MRI, PET, risk genes, cerebrospinal fluid and other data. We will develop a deep learning model that performs its predictions using MRI data alone, and can also augment the MRI data with the other datatypes for improved performance at some expense. Our modern machine-learning methods are designed to be rationally factored in with other individualized clinical information to aid clinicians in these vital diagnostic decisions.
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.
Quantifying socio-cognitive deficits to optimize schizophrenia treatment
Schizophrenia is a debilitating mental health condition with high societal and personal costs, due largely to chronic difficulties with social and occupational functioning. While classical symptoms of schizophrenia – such as hearing voices – are often responsive to medication, people with schizophrenia also experience difficulties in social cognition, or understanding and interpreting the intentions and emotions of others. Social cognition affects the ability to function in society, and is a key determinant of real-world outcomes in schizophrenia.
Despite its importance, we lack objective and easy-to-deploy instruments to assess social cognition. This measurement gap presents a critical stumbling block for development of interventions to improve social cognition, because the effects of potential treatments cannot be assessed efficiently and at high resolution. Better measurements are also needed to identify individuals likely to benefit from such treatments and monitor treatment effects over time.
This project will develop innovative automated methods to measure a key component of social cognition – the ability to recognize the intentions and emotions of others. The underlying idea is to present a participant with a cue – such as a short video clip intended to be amusing – and then apply computational methods to their spoken response to see if it aligns with the intention behind the cue. The result will be a set of validated measurement tools to facilitate objective, repeatable, and scalable assessment of social cognition. These tools will accelerate our ability to rigorously test new treatments targeting these key deficits impacting people living with schizophrenia.
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.
Opportunity Based Probation
Opportunity-Based Probation (OBP) is a new juvenile probation model that expands on adolescent development research by leveraging adolescents’ drive towards independence as well as their heightened receptivity to rewards. In collaboration with their probation officers, youth create meaningful goals and incentives that reward the development of prosocial behavior. Probation officers scaffold prosocial behavior by reinforcing success and constructively addressing probation violations and problem behaviors. OBP was originally developed through a collaboration between UW CoLab and juvenile court leadership in Pierce County, Washington with funding from the Annie E. Casey Foundation and is now being implemented, refined, and tested for acceptability, implementation, and preliminary effectiveness. In 2021, a second OBP site was started in Hartford County, Connecticut and is currently undergoing co-design and implementation efforts with an eye for eventual testing and expansion statewide.
Accelerating research use in courts
Measuring the use of research evidence within organizations and systems is a rapidly growing area of study in the social sciences as researchers, policymakers, and practitioners in a variety of systems try to bridge the research-to-practice gap. With growing calls for justice systems, especially juvenile justice systems, to integrate developmental and behavioral health science within all aspects of the justice process, it is critical to develop a standardized measure of how individuals use research evidence within these systems. This will allow researchers to examine how research is used across studies, sites, and points in time, as well as to refine and compare new interventions aimed at increasing the use of research. Toward this end, the UW CoLab research team with the help of the William T. Grant Foundation, is developing and validating a measure of research use with collaborators nationwide.
Applying Critical Race Theory to investigate the impact of COVID-19-related policy changes on racial/ethnic disparities in medication treatment for opioid use disorder
With the rise in opioid use disorder (OUD) and overdose, racialized disparities in buprenorphine access and use are a significant concern nationally—studies estimate that Black patients with OUD are 50-60% less likely to access buprenorphine compared to White patients, and similar disparities have also been observed for Hispanic/Latinx patients. COVID-19-related policy changes increased flexibility in the provision of buprenorphine and other effective medications for OUD over telehealth and present an unprecedented opportunity to examine impacts of a structural intervention—relaxed MOUD restrictions—on disparities that result from structural racism and discrimination (SRD). The proposed study, guided by Public Health Critical Race Praxis, will use data from the nation’s largest provider of substance use care and quantitative and qualitative methods to examine the impact of these policy changes on racialized disparities for Black and Hispanic/Latinx patients to inform future policy and interventions to improve equitable care for OUD.
Evaluating the National Implementation of Virtual Interdisciplinary Pain Care Teams – TelePain
Chronic pain is one of the most prevalent and disabling conditions affecting Veterans. One of the Veterans Health Administration’s (VHA’s) most pressing national clinical priorities is to increase access to non- pharmacological pain management and improve the safety of opioid prescribing. The National Pain Management and Opioid Safety Program (PMOP) is implementing virtual interdisciplinary pain management teams, TelePain, to improve access to evidence-based pain care among rural Veterans and those served by smaller VA facilities. The proposed evaluation, developed closely with PMOP, uses a rigorous prospective design to evaluate TelePain’s impact on clinical outcomes for Veterans and costs to VHA, while also evaluating TelePain’s impact on access to care and other implementation outcomes. These findings will provide actionable information to improving ongoing TelePain implementation efforts and inform VHA of the potential sustainability of TelePain as a model of care.
Consumer perspectives of online & in-person suicide prevention strategies
This study will explore which interventions people with lived experience of suicide find acceptable (e.g., different types of in-person and telehealth care, web-based, text message, app, etc.), who should be the agent to deliver the intervention, and what concerns would they have in having social media and search data used for risk identification and then intervention. These findings have the potential to impact how suicide prevention strategies are brought to scale in a way that is seen as acceptable and appropriate to patients at risk for suicide
