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.
Practice Type: Online/remote/apps/social media
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.
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.
AMPERE (Augmented Momentary Personal Ecological Risk Evaluation)
Death by suicide is the 2nd leading cause of death among young adults in the United States. While most patients who die by suicide have had recent contact with their health care providers, the medical delivery system is poorly equipped to address this preventable issue. EMA (ecological momentary assessment) systems show promise as indicators of suicide risk and as a means of enhancing existing resources. However, little is known about how to apply these methods in the context of clinical care. The AMPERE study leverages existing work on EMAs and human-centered design principles to develop and pilot a prototype suicide risk monitoring system to support suicide risk management for adolescents and young adults (ages 16-30) within the UW Medicine Primary Care system.
Developing an artificial intelligence digital navigator system to support patients’ use of technology-based interventions
The objective of this project is to leverage Artificial Intelligence (AI) to create COACH: an on-device AI-driven digital navigator system that will support patients’ effective use of Digital Mental Health Technologies. We aim to: 1. Develop a prototype chatbot-based digital navigator; 2. Conduct preliminary evaluation of the system including lab-based usability testing with healthy participants and “red-team” stress testing with project confederates.
Making generative AI safe for people with mental health conditions
Hundreds of millions of people are already using Large Language Models (LLMs), including for mental health purposes, which has led to inadvertent harms. Critically, people with mental health conditions may be especially vulnerable to such harms.
In this project, we will develop the first computational framework to systematically quantify and benchmark the risks that LLMs present to people with mental health conditions. Our approach will simulate interactions of hundreds of users and LLMs to evaluate safety across a variety of mental health conditions, demographics, and AI failure modes.
Optimizing telemental health with live artificial intelligence clinical scaffolding and feedback
This project aims to develop a clinical scaffolding system to enhance telemental health care by providing real-time coaching and actionable suggestions during video-based sessions. Modeled after live supervision methodologies, it supports clinicians by identifying intervention targets and offering text-based coaching prompts to guide care. Unlike automated chatbots, this approach enables clinicians to adapt suggestions to patient needs, balancing automation with oversight for safer AI-supported mental healthcare. The proposed in-session support will facilitate efficient implementation of strategies and clinician skill development. This project seeks to enhance data privacy by processing all data on-device and avoiding external data transfers.
Project RELATE
The present study seeks to significantly expand our understanding of alcohol and cannabis co-use behaviors in the context of young adult romantic relationships through collecting daily dyadic quantitative data and qualitative interviews, and using this information to develop and pilot an integrated brief intervention to decrease alcohol and cannabis misuse and increase healthy relationships skills among this understudied, high-risk group.
Development of an mHealth support specialist for early psychosis caregivers in Washington State
Early intervention can significantly improve the trajectory of a young adult at risk for psychosis. Specialized treatment programs for youth at risk are associated with reduced symptoms and relapse risk and increased functioning. Family caregivers play a critical role in facilitating treatment engagement and recovery, but too often they lack the support they need. Specialty psychosis services providing psychoeducation for family members are expanding but still difficult to access. Caregivers face many barriers to care: limited providers and session time availability, long travel times, or patient ambivalence about treatment. As a result, a minority of youth with early psychosis have caregivers that have accessed standard-of-care family interventions.
To address these gaps, our team developed Bolster, a mobile health (mHealth) app designed to provide psychoeducation, communication coaching, and self-care support to caregivers to youth at risk for psychosis. In preliminary work, Bolster was feasible to deliver, acceptable to caregivers, and showed promising efficacy. However, mHealth interventions that are supplemented by a human clinical support have higher engagement and effectiveness than those that are purely self-guided. To optimally implement mHealth for early psychosis caregivers, there is a need for development of this clinical workforce.
We propose to develop and pilot an emerging clinical role – the mHealth support specialist (mHSS) – equipped specifically to support caregivers to youth with early psychosis. Specifically, we will (1) develop a training and supervision framework supporting the mHSS for caregivers, (2) test this framework through training and supervising one mHSS, and (3) evaluate this approach as the mHSS provides support to caregivers to young adults with early psychosis throughout Washington State. Delivering this intervention has the potential to greatly expand population access to evidence-based strategies for psychosis. Developing the mHealth support specialist model would make Washington a national leader in scalable digital interventions for caregivers. This study takes a critical step toward realizing that vision.
A pilot trial on EMA habit formation behavioral strategies for improving engagement of digital mindfulness interventions among non-suicidal self-injury engagers
Non-suicidal self-injury (NSSI), the purposeful, direct damage of one’s body without the intent to die, is a pervasive public health concern with clinically significant long-term consequences. Mindfulness – a core skill in DBT, an evidence-based treatment for NSSI, is designed to target emotion dysregulation and rumination and may be particularly relevant due to the proliferation of digital mindfulness interventions in recent years. To this end, the goal of this study is to expand the use of ecological momentary assessment (EMA) and to develop and evaluate a program of habit-formation strategies (e.g., SMART-goal setting, reinforcement scheduling) to boost user engagement and treatment effects of DMI. Following a 1-week EMA baseline period, participants (N=40) will be randomized to either TAU (Mindfulness only) or Experimental (Mindfulness + Behavioral Prompts) conditions for a 4-week intervention EMA period.
