Faculty Bibliography

Assessment of psychiatric symptoms relies on subjective self-report, which can be unreliable. Digital phenotyping collects data from smartphones to provide near-continuous behavioral monitoring. It can be used to provide objective information about an individual's mental state to improve clinical decision-making for both diagnosis and prognostication. The goal of this study was to evaluate the feasibility and acceptability of smartphone-based digital phenotyping for long-term mental health monitoring in adolescents with bipolar disorder and typically developing peers. Participants (aged 14-19) with bipolar disorder (BD) or with no mental health diagnoses were recruited for an 18-month observational study. Participants installed the Beiwe digital phenotyping app on their phones to collect passive data from their smartphone sensors and thrice-weekly surveys. Participants and caregivers were interviewed monthly to assess changes in the participant's mental health. Analyses focused on 48 participants who had completed participation. Average age at baseline was 15.85 years old (SD = 1.37). Approximately half (54%) identified as female, and 54% identified with a minoritized racial/ethnic background. Completion rates across data types were high, with 99% (826/835) of clinical interviews completed, 89% of passive data collected (22,233/25,029), and 47% (4,945/10,448) of thrice-weekly surveys submitted. The proportion of days passive data were collected was consistent over time for both groups; the clinical interview and active survey completion decreased over the study course. Results of this study suggest digital phenotyping has significant potential as a method of long-term mental health monitoring in adolescents. In contrast to traditional methods, including interview and self-report, it is lower burden and provides more complete data over time. A necessary next step is to determine how well the digital data capture changes in mental health to determine the clinical utility of this approach.

Generative artificial intelligence (genAI) has potential to improve healthcare by reducing clinician burden and expanding services, among other uses. There is a significant gap between the need for mental health care and available clinicians in the United States-this makes it an attractive target for improved efficiency through genAI. Among the most sensitive mental health topics is suicide, and demand for crisis intervention has grown in recent years. We aimed to evaluate the quality of genAI tool responses to suicide-related queries. We entered 10 suicide-related queries into five genAI tools-ChatGPT 3.5, GPT-4, a version of GPT-4 safe for protected health information, Gemini, and Bing Copilot. The response to each query was coded on seven metrics including presence of a suicide hotline number, content related to evidence-based suicide interventions, supportive content, harmful content. Pooling across tools, most of the responses (79%) were supportive. Only 24% of responses included a crisis hotline number and only 4% included content consistent with evidence-based suicide prevention interventions. Harmful content was rare (5%); all such instances were delivered by Bing Copilot. Our results suggest that genAI developers have taken a very conservative approach to suicide-related content and constrained their models' responses to suggest support-seeking, but little else. Finding balance between providing much needed evidence-based mental health information without introducing excessive risk is within the capabilities of genAI developers. At this nascent stage of integrating genAI tools into healthcare systems, ensuring mental health parity should be the goal of genAI developers and healthcare organizations.

Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should store, how to engage users in data collection, and how outcomes data can improve care. Given these challenges, we conducted interviews with 30 U.S.-based mental health clinicians to explore the design space of health information technologies that support outcomes data specification, collection, and use in value-based mental healthcare. Our findings center clinicians' perspectives on aligning outcomes data for payment programs and care; opportunities for health technologies and personal devices to improve data collection; and considerations for using outcomes data to hold stakeholders including clinicians, health insurers, and social services financially accountable in value-based mental healthcare. We conclude with implications for future research designing and developing technologies supporting value-based care across stakeholders involved with mental health service delivery.

Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.

OBJECTIVE/UNASSIGNED:Patients' race and age have each been identified as risk factors for experiencing restraint events during psychiatric hospitalization. Restraint duration is also an important variable in determining disparities in treatment. To the authors' knowledge, no studies to date have examined the effect of the interaction of race and age on restraint use and duration in inpatient psychiatric settings. This retrospective chart review of electronic medical records of patients admitted between 2012 and 2019 sought to examine whether race and age interacted in predicting differences in the use and duration of restraints in a psychiatric inpatient setting. METHODS/UNASSIGNED:Logistic and hierarchical regression analyses were conducted on data from a sample of 29,739 adolescent (ages 12-17 years) and adult (ages ≥18 years) inpatients to determine whether the interaction of race and age group (adolescent or adult) significantly predicted a restraint event or differences in restraint duration. RESULTS/UNASSIGNED:Black (adjusted OR [AOR]=1.85) and multiracial (AOR=1.36) patients were more likely to experience a restraint event than were their White peers. Black race was also significantly (p=0.001) associated with longer restraint duration. No significant interaction was detected between race and age in predicting restraint events or duration. CONCLUSIONS/UNASSIGNED:Although the interaction between race and age did not predict restraint events or duration, the findings indicate racial disparities in the frequency and duration of restraint events among Black and multiracial individuals and may inform efforts to reduce these events.

The course of childhood-onset attention deficit hyperactivity disorder (ADHD) varies across individuals; some will experience persistent symptoms while others' symptoms fluctuate or remit. We describe the longitudinal course of ADHD symptoms and associated clinical characteristics in adolescents with childhood-onset ADHD. Participants (aged 6-12 at baseline) from the Longitudinal Assessment of Manic Symptoms (LAMS) study who met DSM criteria for ADHD prior to age 12 were evaluated annually with the Kiddie Schedule for Affective Disorders and Schizophrenia for eight years. At each timepoint, participants were categorized as meeting ADHD criteria, subthreshold criteria, or not having ADHD. Stability of course was defined by whether participants experienced consistent ADHD symptoms, fluctuating symptoms, or remission. The persistence of the symptoms was defined by symptom status at the final two follow-ups (stable ADHD, stable remission, stable partial remission, unstable). Of 685 baseline participants, 431 had childhood-onset ADHD and at least two follow-ups. Half had a consistent course of ADHD, nearly 40% had a remitting course, and the remaining participants had a fluctuating course. More than half of participants met criteria for ADHD at the end of their participation; about 30% demonstrated stable full remission, 15% had unstable symptoms, and one had stable partial remission. Participants with a persistent course and stable ADHD outcome reported the highest number of symptoms and were most impaired. This work builds on earlier studies that describe fluctuating symptoms in young people with childhood-onset ADHD. Results emphasize the importance of ongoing monitoring and detailed assessment of factors likely to influence course and outcome to help young people with childhood-onset ADHD.