Evaluating the Use of Online Self-Report Questionnaires as Clinically Valid Mental Health Monitoring Tools in the Clinical Whitespace.

Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the "clinical whitespace" between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.

Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls.

This study investigates clinically valid signals about psychiatric symptoms in social media data, by rating severity of psychiatric symptoms in donated, de-identified Facebook posts and comparing to in-person clinical assessments. Participants with schizophrenia (N=8), depression (N=7), or who were healthy controls (N=8) also consented to the collection of their Facebook activity from three months before the in-person assessments to six weeks after this evaluation. Depressive symptoms were assessed in- person using the Montgomery-Åsberg Depression Rating Scale (MADRS), psychotic symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS), and global functioning was assessed using the Community Assessment of Psychotic Experiences (CAPE-42). Independent raters (psychiatrists, non-psychiatrist mental health clinicians, and two staff members) rated depression, psychosis, and global functioning symptoms from the social media activity of deidentified participants. The correlations between in-person clinical ratings and blinded ratings based on social media data were evaluated. Significant correlations (and trends for significance in the mixed model controlling for multiple raters) were found for psychotic symptoms, global symptom ratings and depressive symptoms. Results like these, indicating the presence of clinically valid signal in social media, are an important step toward developing computational tools that could assist clinicians by providing additional data outside the context of clinical encounters.

Implementation Determinants and Outcomes of a Technology-Enabled Service Targeting Suicide Risk in High Schools: Mixed Methods Study.

BACKGROUND: Technology-enabled services (TESs), which integrate human service and digital components, are popular strategies to increase the reach and impact of mental health interventions, but large-scale implementation of TESs has lagged behind their potential. OBJECTIVE: This study applied a mixed qualitative and quantitative approach to gather input from multiple key user groups (students and educators) and to understand the factors that support successful implementation (implementation determinants) and implementation outcomes of a TES for universal screening, ongoing monitoring, and support for suicide risk management in the school setting. METHODS: A total of 111 students in the 9th to 12th grade completed measures regarding implementation outcomes (acceptability, feasibility, and appropriateness) via an open-ended survey. A total of 9 school personnel (school-based mental health clinicians, nurses, and administrators) completed laboratory-based usability testing of a dashboard tracking the suicide risk of students, quantitative measures, and qualitative interviews to understand key implementation outcomes and determinants. School personnel were presented with a series of scenarios and common tasks focused on the basic features and functions of the dashboard. Directed content analysis based on the Consolidated Framework for Implementation Research was used to extract multilevel determinants (ie, the barriers or facilitators at the levels of the outer setting, inner setting, individuals, intervention, and implementation process) related to positive implementation outcomes of the TES. RESULTS: Overarching themes related to implementation determinants and outcomes suggest that both student and school personnel users view TESs for suicide prevention as moderately feasible and acceptable based on the Acceptability of Intervention Measure and Feasibility of Intervention Measure and as needing improvements in usability based on the System Usability Scale. Qualitative results suggest that students and school personnel view passive data collection based on social media data as a relative advantage to the current system; however, the findings indicate that the TES and the school setting need to address issues of privacy, integration into existing workflows and communication patterns, and options for individualization for student-centered care. CONCLUSIONS: Innovative suicide prevention strategies that rely on passive data collection in the school context are a promising and appealing idea. Usability testing identified key issues for revision to facilitate widespread implementation.

Natural Language Processing of Social Media as Screening for Suicide Risk.

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media-and the near-ubiquity of mobile devices used to access social media networks-offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have "opted in" for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention-we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?

Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study.

BACKGROUND: With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States. OBJECTIVE: The aim of this study, building on previous work qualitatively analyzing depression-related Twitter data, was to describe the development of a comprehensive annotation scheme (ie, coding scheme) for manually annotating Twitter data with Diagnostic and Statistical Manual of Mental Disorders, Edition 5 (DSM 5) major depressive symptoms (eg, depressed mood, weight change, psychomotor agitation, or retardation) and Diagnostic and Statistical Manual of Mental Disorders, Edition IV (DSM-IV) psychosocial stressors (eg, educational problems, problems with primary support group, housing problems). METHODS: Using this annotation scheme, we developed an annotated corpus, Depressive Symptom and Psychosocial Stressors Acquired Depression, the SAD corpus, consisting of 9300 tweets randomly sampled from the Twitter application programming interface (API) using depression-related keywords (eg, depressed, gloomy, grief). An analysis of our annotated corpus yielded several key results. RESULTS: First, 72.09% (6829/9473) of tweets containing relevant keywords were nonindicative of depressive symptoms (eg, "we're in for a new economic depression"). Second, the most prevalent symptoms in our dataset were depressed mood and fatigue or loss of energy. Third, less than 2% of tweets contained more than one depression related category (eg, diminished ability to think or concentrate, depressed mood). Finally, we found very high positive correlations between some depression-related symptoms in our annotated dataset (eg, fatigue or loss of energy and educational problems; educational problems and diminished ability to think). CONCLUSIONS: We successfully developed an annotation scheme and an annotated corpus, the SAD corpus, consisting of 9300 tweets randomly-selected from the Twitter application programming interface using depression-related keywords. Our analyses suggest that keyword queries alone might not be suitable for public health monitoring because context can change the meaning of keyword in a statement. However, postprocessing approaches could be useful for reducing the noise and improving the signal needed to detect depression symptoms using social media.

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media.

History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.

Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides.

The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.

The effect of increased serotonergic neurotransmission on aggression: a critical meta-analytical review of preclinical studies.

RATIONALE: The role of serotonin (5-HT) on aggression has been extensively studied; nonetheless, the role of this neurotransmitter in aggression is still inconclusive. OBJECTIVES: The current meta-analytical review investigated the role of increased 5-HT neurotransmission in aggression. METHODS: Preclinical studies using serotonin reuptake inhibitors, 5-hydroxytryptophan, L-tryptophan, or serotonin (5-HT) to increase 5-HT levels were included in this meta-analysis. An overall effect of serotonin on aggression was calculated, and the role of several moderator variables was analyzed. RESULTS: A total of 218 effect sizes revealed that increased 5-HT had an overall significant inhibitory effect on aggression (r = 0.3). The results showed that increased 5-HT had the strongest inhibitory effect on aggression when (1) a specific strain or species (e.g., Long Evans) was used; (2) aggression was offensive or predatory and/or induced by administration of 5,7-dihydroxytryptamine or p-chlorophenylalanine; (3) zimelidine, sertraline, L-tryptophan, citalopram, or 5-HT were used to increase 5-HT; (4) treatment was acute; (5) long chronic treatment durations were used; and (6) time between last injection and behavior testing was within 8 h before or after peak plasma concentration of drug. In contrast, the results revealed that increased-5-HT-facilitated aggression could be predicted when (1) Wistar rats, (2) social isolation or stress to induce aggression, and/or (3) animals treated for less than 3 weeks were used. CONCLUSIONS: Although 5-HT has an overall inhibitory effect on aggression, the animal's genetic background, drug, treatment time, aggression inducing paradigm, and aggression type are critical variables that influence and modify this effect.