Faculty Bibliography

OBJECTIVE:Achieving condition-related autonomy is an important developmental milestone for youth with spina bifida (SB). However, the transfer of condition-related responsibility to these youth can be delayed due to parent factors. This study aimed to investigate two potential pathways by which maternal factors may be associated with condition-related responsibility among youth with SB: (a) Maternal adjustment → perception of child vulnerability (PPCV) → youth condition-related responsibility; and (b) Maternal PPCV → overprotection → youth condition-related responsibility. METHODS:Participating youth with SB (N = 140; Mage=11.4 years, range = 8-15 years) were recruited as part of a longitudinal study; data from three time points (each spaced 2 years apart) from the larger study were used. Mothers reported on personal adjustment factors, PPCV, and overprotection. An observational measure of overprotection was also included. Mothers, fathers, and youth with SB reported on youths' degree of responsibility for condition-related tasks. Analyses included age, lesion level, IQ, and the dependent variables at the prior wave as covariates. RESULTS:Bootstrapped mediation analyses revealed that PPCV significantly mediated the relationship between maternal distress and youth responsibility for medical tasks such that higher levels of distress at Time 1 predicted higher levels of PPCV at Time 2 and lower youth medical responsibility at Time 3. Furthermore, self-reported maternal overprotection significantly mediated the relationship between maternal PPCV and youth responsibility for medical tasks. CONCLUSIONS:Maternal personal distress, PPCV, and self-reported overprotection are interrelated and affect youth's condition-related responsibility. Interventions for mothers of youth with SB that target these factors may improve both maternal and youth outcomes.

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.

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.

PURPOSE:The majority of behavioral intervention technologies (BITs) have been designed and targeted towards the general population (i.e., typically-developing individuals); thus, little is known about the use of BITs to aid those with special needs, such as youth with disabilities. The current study assessed adolescents and young adults with spina bifida (AYA-SB) for: 1) their technology usage, and 2) anticipated barriers to using technology to help manage their health. METHODS:AYA-SB completed a survey of their media and technology usage. A card sorting task that ranked and grouped anticipated barriers to using a mobile app to manage health was also completed. Ranked means, standard deviations, and the number of times a barrier was discarded were used to interpret sample rankings. RESULTS:AYA-SB reported less frequent technology and media use than the general population. However, differences emerged by age, with young adults endorsing higher usage than their younger counterparts. Top concerns focused on usability, accessibility, safety, personal barriers due to lack of engagement, technological functioning, privacy, and efficacy. CONCLUSIONS:AYA-SB appear to be selective users of technology. It is therefore critical that the design of BITs address their concerns, specifically aiming to have high usability, accessibility, and engagement.

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.

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.

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.

PURPOSE OF REVIEW:This paper aims to acquaint child and adolescent psychiatrists with the field of pharmacogenomics (PGX) and review the most up-to-date evidence-based practices to guide the application of this field in clinical care. RECENT FINDINGS:Despite much research being done in this area, the field of PGX continues to yield controversial findings. In the adult world, studies have focused on the impact of combinatorial gene panels that guide medication selection by providing reports that estimate the impact of multiple pharmacodynamic and pharmacokinetic genes, but to date, these have not been directly examined in younger patient populations. Pharmacokinetic genes, CYP2D6 and CYP2C19, and hypersensitivity genes, HLA-A and HLA-B, have the strongest evidence base for application to pharmacotherapy in children. Although the field is evolving, and the evidence is mixed, there may be a role for PGX testing in children to help guide dosing and monitoring strategies. However, evidence-based medicine, rather than PGX testing, continues to play the lead role in guiding medication selection in pediatric psychopharmacology.