Faculty Bibliography

Emotion regulation and executive function are associated: adaptive regulatory strategies are linked to better executive functioning while maladaptive strategies correspond with worse executive functioning. However, if - and how - these two processes affect one another has not previously been explored; most studies have employed a correlational approach, leaving the direction of influence unknown. We aim to address this gap by using an experimental design to explore the impact of emotion regulation on executive functioning. Adult participants (N=31) completed an executive functioning task (Computerized Task-Switching Test) under four induced emotion regulation conditions (1) neutral/baseline, (2) positive mood-maintain, (3) negative mood-maintain, (4) negative mood-reduce (conditions 2-4 were randomized). Relative to baseline, participants demonstrated better set-shifting performance across regulation conditions. In contrast, inhibitory control performance was slower, despite anticipated improvement due to practice effects. This suggests that inhibitory control may be more involved in the emotion regulation process than set-shifting when participants have a specific emotion regulation goal to achieve. The present study provides preliminary evidence that individuals' ability to perform executive function tasks may be affected by concurrent emotion regulation demands; additional experiments are necessary to further probe the complexity of the association between these two processes.

Emotion regulation and executive function are associated: adaptive regulatory strategies are linked to better executive functioning while maladaptive strategies correspond with worse executive functioning. However, if - and how - these two processes affect one another has not previously been explored; most studies have employed a correlational approach, leaving the direction of influence unknown. We aim to address this gap by using an experimental design to explore the impact of emotion regulation on executive functioning. Adult participants (N=31) completed an executive functioning task (Computerized Task-Switching Test) under four induced emotion regulation conditions (1) neutral/baseline, (2) positive mood-maintain, (3) negative mood-maintain, (4) negative mood-reduce (conditions 2-4 were randomized). Relative to baseline, participants demonstrated better set-shifting performance across regulation conditions. In contrast, inhibitory control performance was slower, despite anticipated improvement due to practice effects. This suggests that inhibitory control may be more involved in the emotion regulation process than set-shifting when participants have a specific emotion regulation goal to achieve. The present study provides preliminary evidence that individuals' ability to perform executive function tasks may be affected by concurrent emotion regulation demands; additional experiments are necessary to further probe the complexity of the association between these two processes.

BACKGROUND:Digital technology has the potential to transform psychiatry, but its adoption has been limited. The proliferation of telepsychiatry during the COVID-19 pandemic has increased the urgency of optimizing technology for clinical practice. Understanding clinician attitudes and preferences is crucial to effective implementation and patient benefit. OBJECTIVE:Our objective was to elicit clinician perspectives on emerging digital technology. METHODS:Clinicians in a large psychiatry department (inpatient and outpatient) were invited to complete a web-based survey about their attitudes toward digital technology in practice, focusing on implementation, clinical benefits, and expectations about patients' attitudes. The survey consisted of 23 questions that could be answered on either a 3-point or 5-point Likert scale. We report the frequencies and percentages of responses. RESULTS:In total, 139 clinicians completed the survey-they represent a variety of years of experience, credentials, and diagnostic subspecialties (response rate 69.5%). Overall, 83.4% (n=116) of them stated that digital data could improve their practice, and 23.0% (n=32) of responders reported that they had viewed patients' profiles on social media. Among anticipated benefits, clinicians rated symptom self-tracking (n=101, 72.7%) as well as clinical intervention support (n=90, 64.7%) as most promising. Among anticipated challenges, clinicians mostly expressed concerns over greater time demand (n=123, 88.5%) and whether digital data would be actionable (n=107, 77%). Furthermore, 95.0% (n=132) of clinicians expected their patients to share digital data. CONCLUSIONS:Overall, clinicians reported a positive attitude toward the use of digital data to not only improve patient outcomes but also highlight significant barriers that implementation would need to overcome. Although clinicians' self-reported attitudes about digital technology may not necessarily translate into behavior, our results suggest that technologies that reduce clinician burden and are easily interpretable have the greatest likelihood of uptake.

OBJECTIVE/UNASSIGNED:Intensive residential treatment (IRT) for obsessive-compulsive disorder (OCD) includes frequent meetings with a cognitive-behavioral therapist. We examined whether this therapeutic working alliance relates to IRT outcomes. METHOD/UNASSIGNED:= 124) who received IRT at a specialty OCD clinic. Patients completed measures of OCD severity and well-being at admission and discharge. Both the patient and treating psychologist completed the Working Alliance Inventory-Short Form (WAI-SF). Alliance ratings were tested as predictors in models predicting outcomes (discharge scores adjusting for baseline and treatment duration) as well as logistic regression predicting treatment response (≥35% symptom reduction in OCD symptoms). RESULTS/UNASSIGNED:Patient and clinician ratings of the quality of the alliance were weakly yet significantly correlated. Patient ratings of the alliance predicted outcomes, while therapist ratings did not. Moreover, greater discrepancy between patient and client ratings predicted worse outcomes. Patient ratings of the task dimension of the alliance uniquely related to responder status. CONCLUSIONS/UNASSIGNED:Patient perceptions of the working alliance, particularly as pertaining to agreement on therapeutic tasks, related to success with IRT for OCD. Further study is needed test interventions to improve task alliance as a strategy to enhance treatment.

Previous research has examined how mood affects individuals"™ judgment. Our study aims to extend this research to evaluate the influence of mood on judgment in vivo. Using a smartphone-based design, we prompted participants (N = 103) three times a day to rate their mood and perform one of three judgment tasks three times a day for one week: (1) evaluate their self-efficacy on a word unscrambling task, (2) identify face emotions, or (3) judge risk by pumping virtual balloons. Our results showed that, contrary to our hypotheses, mood did not have a significant effect on the judgments individuals made. Prior task performance and task trial were significant predictors of self-efficacy; sex and task trial were associated with face emotion recognition; and balloons popped the day prior, and task trial influenced how individuals evaluated risk. The in vivo design of this study is a novel and more ecologically valid than some earlier work, but it is not without limitations, including the self-reported nature of mood, and potential for unmeasured third variable effects. This research raises questions about the validity of lab-based studies of the relation between mood and judgment, and shows the capacity of in vivo research and technology to challenge and enhance our understanding of how mood influences behavior.

BACKGROUND:Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored. OBJECTIVE:The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients' social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations. METHODS:We developed a prototype that can analyze consented patients' Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient's social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis. RESULTS:Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients' verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients' social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings. CONCLUSIONS:Our findings support the touted potential of computational mental health insights from patients' social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology.

BACKGROUND:Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. OBJECTIVE:We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health-related content. METHODS:Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. RESULTS:A majority (27/43, 63%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44% (19/43) of participants. Queries related to traumatic experiences were present among 33% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). CONCLUSIONS:Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients.

OBJECTIVE:Risk calculators (RC) to predict clinical outcomes are gaining interest. An RC to estimate risk of bipolar spectrum disorders (BPSD) could help reduce the duration of undiagnosed BPSD and improve outcomes. Our objective was to adapt an RC previously validated in the Pittsburgh Bipolar Offspring Study (BIOS) sample to achieve adequate predictive ability in both familial high-risk and clinical high-risk youths. METHOD/METHODS:Participants (aged 6-12 years at baseline) from the Longitudinal Assessment of Manic Symptoms (LAMS) study (N = 473) were evaluated semi-annually. Evaluations included a Kiddie Schedule for Affective Disorders (K-SADS) interview. After testing an RC that closely approximated the original, we made modifications to improve model prediction. Models were trained in the BIOS data, which included biennial K-SADS assessments, and tested in LAMS. The final model was then trained in LAMS participants, including family history of BPSD as a predictor, and tested in the familial high-risk sample. RESULTS:Over follow-up, 65 youths newly met criteria for BPSD. The original RC identified youths who developed BPSD only moderately well (area under the curve [AUC] = 0.67). Eliminating predictors other than the K-SADS screening items for mania and depression improved accuracy (AUC = 0.73) and generalizability. The model trained in LAMS, including family history as a predictor, performed well in the BIOS sample (AUC = 0.74). CONCLUSION/CONCLUSIONS:The clinical circumstances under which the assessment of symptoms occurs affects RC accuracy; focusing on symptoms related to the onset of BPSD improved generalizability. Validation of the RC under clinically realistic circumstances will be an important next step.

BACKGROUND:Adults with bipolar disorder (BD) often experience neurocognitive impairment that negatively impacts functioning and quality of life. Previous trials have found that dopamine agonist agents improve cognition in healthy volunteers and that adults with BD who have stable mood and mild cognitive deficits may also benefit. We hypothesized that pramipexole, a dopamine agonist, would improve neurocognitive function in patients with BD. METHODS:We recruited 60 adults (aged 18-65 years) with a diagnosis of BD I or II for an 8-week, double-blind, placebo-controlled trial (NCT02397837). All had stable mood and clinically significant neurocognitive impairment at baseline. Participants were randomized to receive pramipexole (n = 31) or a placebo (n = 29), dose was initiated at 0.125 mg 2 times a day and increased to a target of 4.5 mg/d. RESULTS:At trial end, the primary outcome, MATRICS Consensus Cognitive Battery composite score, had not improved more in the pramipexole group (mean [SD] = 1.15 [5.4]) than in the placebo group (mean [SD] = 4.12 [5.2], Cohen's d = 0.56, P = 0.049), and mixed models, controlling for symptoms, showed no association between treatment group and MATRICS Consensus Cognitive Battery scores. No serious adverse events were reported. CONCLUSIONS:These results suggest that pramipexole is not an efficacious cognitive enhancement agent in BD, even in a sample enriched for characteristics that were associated with a beneficial response in prior work. There are distinct cognitive subgroups among adults with BD and may be related differences in neurobiology that affect response to pramipexole. Additional research to better understand the onset and nature of the cognitive deficits in people with BD will be an important step toward a more personalized approach to treatment.