Faculty Bibliography

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.

BACKGROUND:Bipolar disorder (BD) is associated with emotion interpretation biases that can exacerbate depressed mood. Interpretation bias training (IBT) may help; according to the "virtuous cycle" hypothesis, interpreting others' emotions as positive can lead to interactions that improve mood. Our goals were to determine whether IBT can shift emotion interpretation biases and demonstrate clinical benefits (lower depressed mood, improved social function) in people with BD. METHOD:Young adults with BD were recruited for three sessions of computer-based IBT. Active IBT targets negative emotion bias by training judgments of ambiguous face emotions towards happy judgments. Participants were randomized to active or sham IBT. Participants reported on mood and functioning at baseline, intervention end (week two), and week 10. RESULTS:Fifty participants (average age 22, 72% female) enrolled, 38 completed the week 10 follow-up. IBT shifted emotion interpretations (Hedges g = 1.63). There was a group-by-time effect (B = -13.88, p < .0001) on self-reported depression; the IBT group had a larger decrease in depressed mood. The IBT group also had a larger increase in perceived familial support (B = 3.88, p < .0001). Baseline learning rate (i.e., how quickly emotion judgments were updated) was associated with reduced clinician- (B = -54.70, p < 0.001) and self-reported depression (B = -58.20, p = 0.009). CONCLUSION:Our results converge with prior work demonstrating that IBT may reduce depressed mood. Additionally, our results provide support for role of operant conditioning in the treatment of depression. People with BD spend more time depressed than manic; IBT, an easily disseminated intervention, could augment traditional forms of treatment without significant expense or side effects.

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.

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.

To develop short forms of parent-rated mania and depression scales, evaluating their reliability, content coverage, criterion validity, and diagnostic accuracy. Caregivers completed the Parent General Behavior Inventory about their youth 5-18 years of age seeking outpatient mental health services at either an academic medical clinic (n = 617) or urban community mental health center (n = 530), along with other rating scales. Families also completed a semistructured Kiddie Schedule for Affective Disorders and Schizophrenia interview, with the rating scales masked during diagnosis. Ten-item short forms and projections of their psychometrics (vs. the full-length 46-item Depression and 28-item Hypomanic/Biphasic scales) were built in the academic sample and then externally cross-validated in the community sample. The mania and two depression short forms maintained high reliability (αs > .87 across both samples); high correlations with the full-length scales (rs> .93); excellent convergent and discriminant validity with mood, behavior, and demographic criteria; and diagnostic accuracy undiminished compared to using the full-length scales. Present analyses developed and externally cross-validated 10-item short forms that maintain high reliability and content coverage and show strong criterion validity and diagnostic accuracy-even when used in an independent sample with markedly different demographics and referral patterns. The short forms appear useful in clinical applications, including screening and initial evaluation, as well as in research settings, where they offer an inexpensive quantitative score. Future work should further evaluate sensitivity to treatment effects. The short forms are available in more than a dozen translations.

BACKGROUND:Recent research has emphasized the need for accessing information about patients to augment mental health patients' verbal reports in clinical settings. Although it has not been introduced in clinical settings, computational linguistic analysis on social media has proved it can infer mental health attributes, implying a potential use as collateral information at the point of care. To realize this potential and make social media insights actionable to clinical decision making, the gaps between computational linguistic analysis on social media and the current work practices of mental health clinicians must be bridged. OBJECTIVE:This study aimed to identify information derived from patients' social media data that can benefit clinicians and to develop a set of design implications, via a series of low-fidelity (lo-fi) prototypes, on how to deliver the information at the point of care. METHODS:A team of clinical researchers and human-computer interaction (HCI) researchers conducted a long-term co-design activity for over 6 months. The needs-affordances analysis framework was used to refine the clinicians' potential needs, which can be supported by patients' social media data. On the basis of those identified needs, the HCI researchers iteratively created 3 different lo-fi prototypes. The prototypes were shared with both groups of researchers via a videoconferencing software for discussion and feedback. During the remote meetings, potential clinical utility, potential use of the different prototypes in a treatment setting, and areas of improvement were discussed. RESULTS:Our first prototype was a card-type interface that supported treatment goal tracking. Each card included attribute levels: depression, anxiety, social activities, alcohol, and drug use. This version confirmed what types of information are helpful but revealed the need for a glanceable dashboard that highlights the trends of these information. As a result, we then developed the second prototype, an interface that shows the clinical state and trend. We found that focusing more on the changes since the last visit without visual representation can be more compatible with clinicians' work practices. In addition, the second phase of needs-affordances analysis identified 3 categories of information relevant to patients with schizophrenia: symptoms related to psychosis, symptoms related to mood and anxiety, and social functioning. Finally, we developed the third prototype, a clinical summary dashboard that showed changes from the last visit in plain texts and contrasting colors. CONCLUSIONS:This exploratory co-design research confirmed that mental health attributes inferred from patients' social media data can be useful for clinicians, although it also revealed a gap between computational social media analyses and clinicians' expectations and conceptualizations of patients' mental health states. In summary, the iterative co-design process crystallized design directions for the future interface, including how we can organize and provide symptom-related information in a way that minimizes the clinicians' workloads.

BACKGROUND:Research suggests that people with bipolar disorder (BD), like individuals with autism spectrum disorders or schizophrenia (among other forms of psychopathology), often have social cognition deficits that negatively impact relationships and quality of life. Studies of social cognition largely focus on face emotion recognition. However, relying solely on faces is not ecologically valid - other cues are available outside of a lab environment. If the ability to correctly interpret other emotion cues is intact, people with face emotion recognition deficits could learn to rely on other cues in order to make inferences about peoples' emotional states. This study explored whether both facial emotion and emotional body language (EBL) recognition are impaired in people with BD. METHOD:We measured the performance of individuals with BD relative to community controls on a computer-based emotion recognition task that isolated participants' ability to interpret emotions in faces, bodies without faces, and in bodies with faces. RESULTS:Results indicated that the BD group was significantly less accurate on face emotion recognition (Cohen's d = -0.87, p = .023), and was more likely to misidentify neutral stimuli as sad (Cohen's d = -0.58, p = .030). Emotion identification accuracy was equivalent across groups when the body (not just face) was visible. CONCLUSION:People with BD experience deficits in face emotion recognition, and their emotional state may influence their interpretation of others' emotions. However, recognition of EBL seems largely intact in this population. Paying attention to EBL may help people with BD to compensate for face emotion processing deficits and improve social functioning.