Patients on long-term opioid therapy are aging and now face magnified risk of harm with continued high-dose opioid use. These increased risks are due to age-related changes in drug metabolism, multi-morbidity, and polypharmacy. The dominant approach to mitigate these risks is to screen for aberrant patient opioid behaviors, with assessments like the Current Opioid Misuse Measure (COMM), so that clinicians can pre-empt misuse early through review of contractual opioid agreements or by lowering patient dosages. By focusing on opioid misuse alone, this strategy encourages forced opioid tapering that is associated with opioid overdose and mental health crisis. Many persons have mental health, trauma-related or polysubstance use disorders that need to be addressed. Directing clinician attention to the comorbid conditions associated with opioid misuse may promote safer and more effective care. Such an approach provides a broader understanding of the pain experience and may help address the reasons why patients use or misuse opioids.
The specific aims are: (1) to develop a simple experimental approach for the collection of clinical pain data and for assessing, preventing, and managing pain in later life through the electronic health record; and (2) to assess the comparative effectiveness of PainTracker, a set of questions that targets a broad range of problems associated with pain, in a randomized controlled trial.
In an effort to address the significant challenges in access to and engagement with evidence-based psychosocial interventions for adolescent depression, the proposed research is piloting the use of Asynchronous Remote Communities (ARC) supported behavioral activation (BA) to treat adolescent depression. We aim to 1) build and conduct usability testing on a functional and robust ActivaTeen platform that will satisfy the needs of mental health clinicians and adolescent patients and 2) test the feasibility, usability, and change in proposed target mechanisms (therapist alliance, timeliness of intervention, social belongingness, and engagement) and outcomes of BA+ActivaTeen compared to BA treatment only within a moderately-sized randomized control trial conducted within Seattle Children’s Hospital outpatient psychiatry clinic.
The primary objective of this study is to verify the clinical benefit of monthly doses of aducanumab in slowing cognitive and functional impairment as measured by changes in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score as compared with placebo in participants with early Alzheimer’s disease.
The reason for this study is to see how safe and effective the study drug donanemab is in participants with early Alzheimer’s disease.
Long Covid includes symptoms of fatigue, sleep changes, anxiety and depression lasting at least three months following infection with COVID-19 and occurs in 10-20% of individuals following infection. Approximately 16% of children experience persistent mood symptoms as part of their Long Covid symptoms. This amounts to over two million children with new symptoms of anxiety and depression after COVID-19 in the US since the onset of the pandemic. Studies show that gradual increases in exercise targets or “pacing” improves symptoms in adults with Long Covid, but this treatment remains to be evaluated in children. Most pacing programs require weekly in-person physical therapy visits and therefore, are difficult to access. Our group has demonstrated improvement in mental health outcomes for adolescents following concussion using a virtual paced exercise program, Mobile Subthreshold Exercise Program (MSTEP). This project aims to evaluate MSTEP as an intervention to improve function and improve mental health symptoms in adolescents and young adults with Long Covid.
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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.
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.
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.
