Faculty Bibliography

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.

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.

Mental illness often emerges during the formative years of adolescence and young adult development and interferes with the establishment of healthy educational, vocational, and social foundations. Despite the severity of symptoms and decline in functioning, the time between illness onset and receiving appropriate care can be lengthy. A method by which to objectively identify early signs of emerging psychiatric symptoms could improve early intervention strategies. We analyzed a total of 405,523 search queries from 105 individuals with schizophrenia spectrum disorders (SSD, N = 36), non-psychotic mood disorders (MD, N = 38) and healthy volunteers (HV, N = 31) utilizing one year's worth of data prior to the first psychiatric hospitalization. Across 52 weeks, we found significant differences in the timing (p<0.05) and frequency (p<0.001) of searches between individuals with SSD and MD compared to HV up to a year in advance of the first psychiatric hospitalization. We additionally identified significant linguistic differences in search content among the three groups including use of words related to sadness and perception, use of first and second person pronouns, and use of punctuation (all p<0.05). In the weeks before hospitalization, both participants with SSD and MD displayed significant shifts in search timing (p<0.05), and participants with SSD displayed significant shifts in search content (p<0.05). Our findings demonstrate promise for utilizing personal patterns of online search activity to inform clinical care.

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.

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.

Introduction: Although attenuated psychotic symptoms often occur for the first time during adolescence, studies focusing on adolescents are scarce. Attenuated psychotic symptoms form the criteria to identify individuals at increased clinical risk of developing psychosis. The study of individuals with these symptoms has led to the release of the DSM-5 diagnosis of Attenuated Psychosis Syndrome (APS) as a condition for further research. We aimed to characterize and compare hospitalized adolescents with DSM-5-APS diagnosis vs. hospitalized adolescents without a DSM-5-APS diagnosis. Methods: Interviewing help-seeking, hospitalized adolescents (aged 12-18 years) and their caregivers independently with established research instruments, we (1) evaluated the presence of APS among non-psychotic adolescents, (2) characterized and compared APS and non-APS individuals regarding sociodemographic, illness and intervention characteristics, (3) correlated psychopathology with levels of functioning and severity of illness and (4) investigated the influence of individual clinical, functional and comorbidity variables on the likelihood of participants to be diagnosed with APS. Results: Among 248 consecutively recruited adolescents (age=15.4 ± 1.5 years, females = 69.6%) with non-psychotic psychiatric disorders, 65 (26.2%) fulfilled APS criteria and 183 (73.8%) did not fulfill them. Adolescents with APS had higher number of psychiatric disorders than non-APS adolescents (3.5 vs. 2.4, p < 0.001; Cohen's d = 0.77), particularly, disruptive behavior disorders (Cramer's V = 0.16), personality disorder traits (Cramer's V = 0.26), anxiety disorders (Cramer's V = 0.15), and eating disorders (Cramer's V = 0.16). Adolescents with APS scored higher on positive (Cohen's d = 1.5), negative (Cohen's d = 0.55), disorganized (Cohen's d = 0.51), and general symptoms (Cohen's d = 0.84), and were more severely ill (Cohen's d = 1.0) and functionally impaired (Cohen's d = 0.31). Negative symptoms were associated with lower functional levels (Pearson ρ = -0.17 to -0.20; p = 0.014 to 0.031). Global illness severity was associated with higher positive, negative, and general symptoms (Pearson ρ = 0.22 to 0.46; p = 0.04 to p < 0.001). APS status was independently associated with perceptual abnormalities (OR = 2.0; 95% CI = 1.6-2.5, p < 0.001), number of psychiatric diagnoses (OR = 1.5; 95% CI = 1.2-2.0, p = 0.002), and impaired stress tolerance (OR = 1.4; 95% CI = 1.1-1.7, p = 0.002) (r² = 0.315, p < 0.001). Conclusions: A considerable number of adolescents hospitalized with non-psychotic psychiatric disorders meet DSM-5-APS criteria. These help-seeking adolescents have more comorbid disorders and more severe symptoms, functional impairment, and severity of illness than non-APS adolescents. Thus, they warrant high intensity clinical care.