Faculty Bibliography

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.

Prior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.

The aim of this study is to examine the familial aggregation of Attention-deficit/hyperactivity disorder (ADHD) and its cross-transmission with bipolar disorder (BD) in a community-based family study of mood spectrum disorders. A clinically-enriched community sample of 562 probands recruited from the greater Washington, DC metropolitan area and their 698 directly interviewed relatives were included in analyses. Inclusion criteria were English speaking and consent to contact at least two first-degree relatives. Standard family study methodology was used and DSM-IV classified mental disorders were ascertained through a best-estimate procedure based on direct semi-structured interviews and multiple family history reports. There was specificity of familial aggregation of both bipolar I disorder (BD I) and bipolar II disorder (BD II) (i.e., BD I OR = 6.08 [1.66, 22.3]; BD II OR = 2.98 [1.11, 7.96]) and ADHD (ADHD OR = 2.13 [1.16, 3.95]). However, there was no evidence for cross-transmission of BD and ADHD in first degree relatives (i.e., did not observe increased rates of BD in relatives of those with ADHD and vice versa; all ps > 0.05). The specificity of familial aggregation of ADHD and BD alongside the absence of shared familial risk are consistent with the notion that the comorbidity between ADHD and BD may be attributable to diagnostic artifact, could represent a distinct BD suptype characterized by childhood-onset symptoms, or the possibility that attention problems serve as a precursor or consequence of BD.

BACKGROUND:Sleep is vital to youth well-being and when it becomes disturbed - whether due to environmental or individual factors - mental and physical health suffer. Sleep problems can also be a symptom of underlying mental health disorders. Assessing different components of sleep, including quality and hygiene, can be useful both for identifying mental health problems and for measuring changes in well-being over time. However, there are dozens of sleep-related measures for youth and it can be difficult to determine which to select for a specific research or clinical purpose. The goal of this review was to identify sleep-related measures for clinical and/or research use in youth mental health settings, and to update the evidence base on this topic. METHOD:We generated a list of candidate measures based on other reviews and searched in PubMed and PsycINFO using the terms "sleep" AND (measure OR assessment OR questionnaire) AND (psychometric OR reliability OR validity). Search results were limited to studies about children and adolescents (aged 2-17) published in English. Additional criteria for inclusion were that there had to be at least three publications reporting on the measure psychometrics in community or mental health populations. Sleep measures meeting these criteria were evaluated using the criteria set by De Los Reyes and Langer (2018). RESULTS:Twenty-six measures, across four domains of sleep - insomnia, sleep hygiene, sleepiness, sleep quality - met inclusion criteria. Each measure had at least adequate clinical utility. No measure(s) emerged as superior across psychometric domains. CONCLUSION:Clinicians and researchers must evaluate sleep measures for each use case, as the intended purpose will dictate which measure is best. Future research is necessary to evaluate measure performance in transdiagnostic mental health populations, including youth with serious mental illness.

BACKGROUND:Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. OBJECTIVE:We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. METHODS:We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. RESULTS:Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. CONCLUSIONS:Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

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.