The Washington State Behavioral Health Student Assistance Program (BH-SAP) is a research-based, statewide model that places trained Student Assistance Professionals (SAPs) in schools to deliver prevention, early intervention, and referral support within Washington’s Multi-Tiered System of Support (MTSS) framework. Through our collaboration with the Assocation for Educational Service Districts, the UW SMART Center’s Training and Technical Assistance Core supports this project through program evaluation efforts including overseeing collection of student, school, and district outcome data, analyzing data, and producing reports to inform continuous improvement.
Funding Type: State/UW
Was it all a dream? Understanding sleep’s role in mediating psychiatric management in a rural outpatient setting
Individuals with posttraumatic stress disorder (PTSD) commonly experience sleep disturbance. Such disturbances can have a birdrectional effect in those with PTSD –interrupted sleep due to hypervigilance and nightmares are common symptoms of PTSD, and poor sleep is associated with a worse course of PTSD. Sleep disturbance can potentially be a prognostic factor in overall outcome in those seeking treatment for PTSD.
Our proposed project will explore the effect of sleep quality on PTSD outcomes and assess the
effect of treatment of PTSD on patient-reported sleep quality.
Development of a task to measure the impact of PTSD symptoms on cognitive control and physiologic endpoints in response to interpersonal stressors
Posttraumatic stress disorder (PTSD) is linked to altered physiologic functioning, including increased blood pressure and heart rate, especially in response to stressors. Increased cardiovascular reactivity to trauma-related stressors may link PTSD to poorer cardiovascular health and thus an increased risk of cardiovascular disease. Prior work by our lab suggests that these effects of trauma may be due to persistent changes in the central and peripheral nervous systems’ stress-response system. While there is an urgent need to address these effects, limitations in our ability to effectively measure these changes have led to a paucity of data regarding the impact of current PTSD treatments on these important endpoints.
We propose to develop a novel emotional Stroop task that will quantify physiologic reactivity to interpersonal and affective cues, and quantify the impact of affective and physiologic reactivity on cognitive control. In a Veteran sample, we will characterize how performance on this task relates to PTSD symptom burden and physiologic reactivity to tilt-table testing, and gather pilot data assessing its response to pharmacologic treatment.
A scalable psychoeducational intervention to improve sexual quality of life in women by addressing reproductive anxiety
This project aims to understand how concerns about pregnancy, whether planning for a child or trying to avoid an unintended one, affect women’s sexual well-being. Despite public health goals to reduce unintended pregnancies and STDs (Higgins et al., 2009), and the importance of sexual health to overall quality of life (Flynn et al., 2016), little research has explored the specific impact of pregnancy-related worries on sexual satisfaction and experience in women who are not currently pregnant (Bond et al., 2023a; Higgins et al., 2009). We know that sexual function issues are prevalent even among women planning pregnancy. We propose to explore pregnancy concerns and sexual quality of life among women (Bond et al., 2023b). We will then provide targeted education (depending on the responses in the survey and can differ for each individual) addressing their specific concerns and measure their sexual quality of life again to see if this information helps improve their sexual experience. The findings will highlight the sexual health needs related to pregnancy concerns and inform future educational programs.
Engagement patterns in pediatric integrated behavioral health: investigating service utilization and attrition among children with mental health and neurodevelopmental disorders
Children with comorbid mental health and neurodevelopmental disorders (NDDs) often require more intensive and coordinated care than those with mental health disorders alone. These two categories of disorders often present with comorbidity and engagement disparities in integrated behavioral health programs among this population remain poorly understood. This study examines differences in service utilization and attrition rates between these two populations using retrospective electronic health record (EHR) data from the SCCN study population (ages 6-18) across multiple primary care sites.
We will analyze visit frequency over a 6-12 month period to assess whether children with comorbid NDDs have higher service utilization. Additionally, early dropout rates will be examined using Kaplan-Meier survival analysis and Cox proportional hazards models to identify risk factors for attrition. Findings will provide critical insights into engagement patterns, informing strategies to enhance retention, reduce access disparities, and improve care coordination for children with complex behavioral needs. This study will contribute to investigating further integrated care model improvements in order to ensure more equitable and sustained mental health treatment for vulnerable pediatric populations.
Discovering how a task-shifted Care Manager workforce of community health workers can address geriatric mental health
Older adults are less likely to receive the recommended standard of care for preventative services, chronic diseases and geriatric concerns such as complex care navigation. Late-life depression is a common chronic disease, and older adults face multiple barriers obtaining depression care from healthcare settings, especially if things like fragility, social needs, and transportation limit access to primary care. Offering depression care in non-traditional healthcare settings is one way to increase access. Community health workers (CHWs) are trusted community members who increase the health of communities through care coordination, health education and outreach. One approach is to task-shift the Care Manager (CM) role of a Collaborative Care framework to CHWs in the community. Global health work has demonstrated that non-clinicians can conduct low-intensity psychosocial interventions for depression. However, task-shifting the Care Manager role in a non-clinical setting requires additional skills and poses added challenges. We have gathered prior formative work among CHWs on what they think about being trained and supported in the skills of CM. We now seek to understand Collaborative Care stakeholders’ perspectives on this proposed role expansion of CHWs to CHW Care Managers (CHW-CMs) to understand how to design this role.
Subtyping the neurobiology of PTSD through novel methods for the alpha- and beta-adrenergic autonomic nervous system components: a pilot study
Trauma and posttraumatic stress disorder (PTSD) are common in veteran and civilian populations. Meanwhile, observationally estimated half of people cared for at UW autonomic nervous system (ANS) disorder clinic have a history of trauma. Increasingly, research explains this observation through an evolving understanding of the complex interplay of peripheral and central catecholamine signaling that appear to underlie much of the persistent impact of trauma. An improved understanding of ANS changes in trauma and their relationship to the complex symptoms people experience is an important research frontiers to improve a) our understanding of PTSD, b) our ability to predict effective treatment for a given person, and c) the development of new treatments for PTSD. We propose implementing a novel analysis method for non-invasive autonomic testing in context of several clinical studies at VA Puget Sound. The proposed research aims to 1) make use of ANS testing results already available to provide quantitative data for adrenergic signaling, 2) test associations of extracted biomarkers with symptoms of PTSD, and 3) collect prospective data to test the relationship of peripheral autonomic signaling to quantitative measures of central nervous system (CNS) catecholamine signaling measured by event-related potential (ERP).
Developing person-specific signatures of momentary risk for alcohol use
Alcohol use disorder remains a major public health concern, with persistent disparities in treatment outcomes. Traditional interventions often fail to account for the heterogeneity of drinking triggers, limiting their effectiveness. This study aims to develop and evaluate an idiographic, mobile-based clinical tool to identify personalized triggers for alcohol use. Idiographic methods allow for individualized assessments of momentary risk factors, providing tailored insights into when a person is most vulnerable to drinking.
The study will allow for customization of a given participant’s data collection process, such that individuals can track what is clinically meaningful to them (e.g. one individual may track feelings of loneliness following divorce, while another may track experiences of racial microaggressions) through diverse data collection techniques including ecological momentary assessment (EMA), audio diaries, and GPS. We will evaluate the feasibility and acceptability of this approach by pilot testing the data collection process, analyzing each participant’s data, and providing personalized feedback on the momentary conditions that influence one’s drinking.
This approach leverages current advances in mobile monitoring and precision idiographic machine learning analysis to pilot a novel clinical tool. If successful, this tool could enhance treatment equity and effectiveness by empowering individuals to recognize their unique drinking triggers.
How fentanyl changes the brain: assessing mood, cognition, and withdrawal using animal models of addiction and brain-wide neural activity markers
Fentanyl overdose is responsible for nearly 75,000 deaths each year in the U.S. and causes severe psychological, physical, financial, and social harm. Despite existing treatments, fentanyl addiction remains difficult to overcome due to the chronic and complex nature of fentanyl addiction which contributes to patterns of chronic use and high relapse rates. This is partly due to fentanyl’s ability to rewire the brain’s reward and executive cognitive system to cause lasting changes in mood and cognition while also triggering intense withdrawal symptoms that drive continued use. To better understand the widespread impact of fentanyl on the brain, this project uses mouse models of addiction to explore the effects of fentanyl on various brain regions and neural populations involved in reward, motivation, mood, and cognition. Using artificial intelligence-guided behavioral and cellular analyses, we then correlate these neural signatures to mouse behaviors during withdrawal and a cognitive working memory task. We will then test whether two promising emerging treatments, semaglutide and ketamine, can improve cognition, withdrawal symptoms, or mood in mice exposed to fentanyl. Through this, we will contribute to our understanding of how fentanyl exerts its negative effects which can inform the development of more effective therapies for its devastating impact.
From symptom relief to subtype identification: exploring patterns of cannabis use in PTSD
Post-traumatic stress disorder (PTSD) and substance use often go hand in hand, with many people using substances like cannabis to manage their symptoms. This concept, known as the self-medication hypothesis, suggests that people might use cannabis differently depending on the nature of their symptoms. Symptoms are classically split into domains – hyperarousal, emotional numbing, re-experiencing, and avoidance. However, it remains unclear whether different patterns of cannabis use might correspond to specific symptom domains, which could reveal distinct clinical phenotypes of PTSD.
By analyzing data from the PREDICT clinical trial, this study will apply advanced statistical methods to identify unobservable (“latent”) factors that characterize cannabis use in individuals with PTSD and examine their relationship with symptom presentation. In statistics and psychometrics, latent refers to a variable that cannot be directly observed—such as internal motivations, behavioral tendencies, or physiological dependence—but which can be inferred from patterns in observed data (e.g., questionnaire responses). These patterns could offer insights into subgroups of people with PTSD who experience different symptom profiles, also known as phenotypes, and may respond to treatments in unique ways. Ultimately, this research could contribute to more personalized, targeted interventions for individuals living with PTSD.
